Module Owner: Mark Rivers: University of Chicago
This page is the home of areaDetector, an application for controlling area (2-D) detectors, including CCDs, pixel array detectors, and online imaging plates.
NOTE: This module replaces the ccd and pilatusROI modules. These older modules will no longer be supported, and users are encouraged to convert to this new areaDetector software.
Devices supported in areaDetector include:
From ADSC
From Allied Vision Technologies (formerly Prosilica)
From Andor Technology
From Axis Communications and many other manufacturers.
From Bruker
From Dectris
From Marresearch GmbH
From Perkin Elmer
From Pixirad
From Photonic Science
From Point Grey Research and many other manufacturers
From Rayonix (formely Mar-USA)
From Roper
Please email any comments and bug reports to Mark Rivers who is responsible for coordinating development and releases.
Some areaDetector support have been developed by others. These are not distributed with the areaDetector releases (source or binary) and are not directly supported by the areaDetector working group, but may be useful for users:
From ImXPAD
Beginning with release R2-0 (March 2014) the areaDetector module is in the areaDetector project on Github. This project is organized as a top level module and a set of submodules, e.g. ADCore, ADProsilica, ADPilatus, etc.
The following table provides links to the github repository, the documentation, and pre-built binaries.
| Github repository | Description | Documentation | Release Notes | Pre-built binaries |
|---|---|---|---|---|
| areaDetector | Top-level module; ADCore, ADSupport, ADProsilica, etc. go under this | areaDetectorDoc | Release Notes | N.A. |
| ADCore | Base classes, plugins, simulation detector | areaDetectorDoc | Release Notes | Pre-built binaries |
| ADSupport | Source code for support libraries (TIFF, JPEG, NETCDF, HDF5, etc.) | N.A. | Release Notes | N.A. |
| ADADSC | Driver for ADSC detectors | adscDoc | Release Notes | Pre-built binaries |
| ADAndor | Driver for Andor CCD detectors | andorDoc | Release Notes | Pre-built binaries |
| ADAndor3 | Driver for Andor sCMOS detectors | andor3Doc | Release Notes | Pre-built binaries |
| ADBruker | Driver for Bruker detectors using the Bruker Instrument Server (BIS) | BrukerDoc | Release Notes | Pre-built binaries |
| ADCameraLink | Drivers for Silicon Software and Dalsa/Coreco frame grabbers | ADCameraLinkDriver | Release Notes | N.A. |
| ADCSimDetector | Driver for ADC simulation | ADCSimDetectorDoc | Release Notes | Pre-built binaries |
| ADDexela | Driver for Perkin Elmer Dexela detectors | DexelaDoc | Release Notes | Pre-built binaries |
| ADFastCCD | Driver for APS/LBL Fast CCD detector | N.A. | Release Notes | N.A. |
| ADFireWireWin | Driver for Firewire DCAM detectors on Windows using the Carnegie Mellon Firewire driver | FirewireWinDoc | Release Notes | Pre-built binaries |
| ADLambda | Driver for Lambda detectors | N.A. | Release Notes | N.A. |
| ADLightField | Driver for Princeton Instruments detectors using their LightField application | LightFieldDoc | Release Notes | N.A. |
| ADmar345 | Driver for the mar345 image plate detector | Mar345Doc | Release Notes | Pre-built binaries |
| ADmarCCD | Driver for CCD detectors from Rayonix (formerly Mar-USA) | MarCCDDoc | Release Notes | Pre-built binaries |
| ADMerlin | Driver for Merlin detectors from Quantum Detectors | N.A. | Release Notes | N.A. |
| ADMythen | Driver for Mythen detectors from Dectris | N.A. | Release Notes | N.A. |
| ADnED | Driver for neutron event data | N.A. | Release Notes | N.A. |
| ADPCO | Driver for PCO detectors | PCODriver | Release Notes | N.A. |
| ADPerkinElmer | Driver for Perkin Elmer flat-panel detectors | PerkinElmerDoc | Release Notes | Pre-built binaries |
| ADPICam | Driver for Princeton Instruments detectors using the PICam library | PICamDoc | Release Notes | N.A. |
| ADPilatus | Driver for Pilatus pixel-array detectors | PilatusDoc | Release Notes | Pre-built binaries |
| ADPixirad | Driver for Pixirad pixel-array detectors | PixiradDoc | Release Notes | Pre-built binaries |
| ADPointGrey | Driver for Point Grey Research cameras | PointGreyDoc | Release Notes | Pre-built binaries |
| ADProsilica | Driver for Allied Vision Technologies (formerly Prosilica) cameras | prosilicaDoc | Release Notes | Pre-built binaries |
| ADPSL | Driver for Photonic Science detectors | PSLDoc | Release Notes | Pre-built binaries |
| ADPvCam | Driver for Photometics and Princeton Instruments detectors using the PvCam library | pvcamDoc | Release Notes | Pre-built binaries |
| ADQImaging | Driver for QImaging detectors | QImagingDoc | Release Notes | Pre-built binaries |
| ADRoper | Driver for Princeton Instruments and Photometics detectors using the WinView/WinSpec programs | RoperDoc | Release Notes | Pre-built binaries |
| ADSimDetector | Driver for simulation detector | simDetectorDoc | Release Notes | Pre-built binaries |
| ADURL | Driver for reading images from any URL using the GraphicsMagick library | URLDriverDoc | Release Notes | Pre-built binaries |
| aravisGigE | Driver using the GNOME Aravis library for Genicam GigE cameras | README | Release Notes | N.A. |
| ffmpegViewer | A stand-alone Qt4 application to display a stream of ffmpeg compressed images | README | N.A | N.A. |
| ffmpegServer | Plugin that use the ffmpeg libraries to compress a stream of images to files or via an html service | ffmpegServer | Release Notes | N.A. |
| firewireDCAM | Driver for Firewire DCAM detectors on Linux | README | Release Notes | N.A. |
| NDDriverStdArrays | Driver that allows EPICS Channel Access clients to create NDArrays in an IOC | NDDriverStdArraysDoc | Release Notes | Pre-built binaries |
| pvaDriver | Driver that receives EPICS V4 NTNDArrays and converts them to NDArrays in an IOC | pvaDriverDoc | Release Notes | Pre-built binaries |
Prior to release R2-0 the areaDetector module is in the synApps Subversion repository at the APS. The "trunk" directory contains the most recent unreleased code, while the "tags" directory contains the released versions.
For releases prior to R2-0 the software can be downloaded from the links in the table below. The software is available both in source code form, and in pre-built form so that it can be used without an EPICS build system. There are separate pre-built binary packages for linux-x86, linux-x86_64, win32-x86, windows-x64, and cygwin-x86.
| Module Version | Requires module | Release needed | Required for |
|---|---|---|---|
| 1-9, 1-9-1 | EPICS base | <IP_ADDRESS> | Base support |
| asyn | 4-21 | Socket and interface support | |
| busy | 1-4 | busy record | |
| calc | 3-0 | scalcout and sseq records, needed by sscan database and useful for other databases | |
| sscan | 2-8-1 | sscan record | |
| autosave | 5-0 | Save/restore | |
| 1-8 | EPICS base | <IP_ADDRESS> (with all current patches) | Base support |
| asyn | 4-20 | Socket and interface support | |
| busy | 1-4 | busy record. This was formerly included in sscan, but now has its own support module. | |
| calc | 3-0 | scalcout and sseq records, needed by sscan database and useful for other databases | |
| sscan | 2-8-1 | sscan record | |
| autosave | 5-0 | Save/restore | |
| 1-7 | EPICS base | <IP_ADDRESS> | Base support |
| asyn | 4-17 | Socket and interface support | |
| busy | 1-3 | busy record. This was formerly included in sscan, but now has its own support module. | |
| calc | 2-8 | scalcout record, needed by sscan database and useful for other databases | |
| sscan | 2-6-6 | sscan record | |
| mca | 7-0 | mca record for getting time sequence of Total or Net counts from statistics plugin | |
| autosave | 4-7 | Save/restore | |
| 1-6 | EPICS base | 3.14.11 | Base support |
| asyn | 4-13-1 | Socket and interface support | |
| busy | 1-3 | busy record. This was formerly included in sscan, but now has its own support module. | |
| calc | 2-8 | scalcout record, needed by sscan database and useful for other databases | |
| sscan | 2-6-6 | sscan record | |
| mca | 6-12-1 | mca record for getting time sequence of Total or Net counts from statistics plugin | |
| autosave | 4-7 | Save/restore | |
| 1-5 | EPICS base | 3.14.10 | Base support |
| asyn | 4-12 | Socket and interface support | |
| busy | 1-2 | busy record. This was formerly included in sscan, but now has its own support module. | |
| calc | 2-7 | scalcout record, needed by sscan database and useful for other databases | |
| sscan | 2-6-3 | sscan record | |
| mca | 6-11 | mca record for getting time sequence of ROI counts | |
| autosave | 4-5 | Save/restore | |
| 1-4 | EPICS base | 3.14.10 | Base support |
| asyn | 4-10 | Socket and interface support | |
| busy | 1-1 | busy record. This was formerly included in sscan, but now has its own support module. | |
| calc | 2-7 | scalcout record, needed by sscan database and useful for other databases | |
| sscan | 2-6-3 | sscan record | |
| mca | 6-10 | mca record for getting time sequence of ROI counts | |
| autosave | 4-5 | Save/restore | |
| 1-3 | EPICS base | 3.14.10 | Base support. <IP_ADDRESS> also works, but the bug in epicsRingPointer can be a problem on multi-processor Linux systems. |
| asyn | 4-10 | Socket and interface support | |
| calc | 2-6-7 | scalcout record, needed by sscan database and useful for other databases | |
| sscan | 2-6-2 | sscan and busy records | |
| mca | 6-10 | mca record for getting time sequence of ROI counts | |
| autosave | 4-4 | Save/restore | |
| 1-2 | EPICS base | 3.14.10 | Base support. <IP_ADDRESS> also works, but the bug in epicsRingPointer can be a problem on multi-processor Linux systems. |
| asyn | 4-10 | Socket and interface support | |
| calc | 2-6-5 | scalcout record, needed by sscan database and useful for other databases | |
| sscan | 2-5-6 | Busy record | |
| mca | 6-10 | mca record for getting time sequence of ROI counts | |
| autosave | 4-3 | Save/restore | |
| 1-1 | EPICS base | <IP_ADDRESS> | Base support |
| asyn | 4-10 | Socket and interface support | |
| sscan | 2-5-6 | Busy record | |
| autosave | 4-3 | Save/restore | |
| 1-0 | EPICS base | <IP_ADDRESS> | Base support |
| asyn | 4-10 | Socket and interface support | |
| sscan | 2-5-6 | Busy record | |
| autosave | 4-3 | Save/restore |
For R2-0 and later this is described in the INSTALL_GUIDE.md on Github.
For releases prior to R2-0 this is described in detail in the installation section of the areaDetector documentation for that particular release.
Please email Mark Rivers so that a record can be kept of which sites are using this software.
This software was originally developed by Mark Rivers.


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Learn how to build an interactive mapping application with ASP.NET Core, JavaScript and Mapbox
Many books and courses are available to teach you ASP.NET Core. Most of them show you how to build a CRUD application which displays items in a grid on the screen and allow you to add, edit or delete items.
But I wanted to do something a little different. And more fun!
And I also wanted to teach developers how to do something other than insert data in a database. Yes, that is an important skill, but it is only part of the picture.
So I set out to write a simple app which will teach you some other skills, such as talking to external APIs, working with CSV files, displaying data on a map and much more!
This book will take you through the process of building a simple application which allows users to explore all the airports of the world on a map.
Here's what you will learn in this book:
Building a very simple, one-page ASP.NET Razor Pages application.
Using AJAX requests in an ASP.NET Razor Pages application.
Using Mapbox to display a map and add markers to the map
Handling a large number of markers on a map using clusters.
Reading data from a CSV file.
Using GeoJSON to represent geographical information.
Using 3rd party APIs such as Google Places to augment airport information.
Using Algolia Places to add a location search autocomplete
Determining the current location of a user from their IP address.
Using the Tailwind CSS framework to style HTML.


Given that functional programs avoid mutations and side effects, that means the programs change nothing and are useless. Simon Peyton Jones brought up this point in his Haskell is Useless video.
Image your functional program takes input a chocolate cream pie, performs no mutations, and outputs the same chocolate cream pie with no changes. The cookie monster on the other end gets the pie, eats it, and declares functional programming is great! Those nasty imperative programs eat the whole pie but functional programs do nothing.
If functional programming has no side effects, how can the program perform any useful work?
I faced the problem of tracking two variables that change state in ProjectEuler Problem 40. The initial solution uses Scala vars that changed state.
Most non-trivial need to mutate code. The sequence is something like:
def workerTask(var state) { for ( i <- 1 to 100 ) { state = new State(doSomeWork) }
How do we stop the mutation of the state var? We can stop the mutation by adding a new immutable variable. We also need to remove the for-loop.
def workerTask(var state) { val stateNew = new State(doSomeWork) }
This works but the loop is gone so only one state transition can take place. We need to do just one of these state transitions in the function but we also need to repeat the function to make more state transition.
How?
Let's look at some code I wrote that solves a math problem.
object P040_ChampernownesConstant { def main(args: Array[String]) = champernownesConstant private def champernownesConstant = { var product = 1 var number = 1 val targetDigits = Array( 1, 10, 100, 1000, 10000, 100000, 10000000 )
Notice the two vars. After studying how factorials are implemented with tail recursion, how to to eliminate the mutations became clear.
My mutation free solution contains a tuple with two scalars and essentially forms a vector. Another alternative would be to create a case class to hold the state variables. Whenever the state needs to change, a new tuple generated to absorb the changes.
I view the tuple as a state token. I am calling it a state token because the item is passed from one function to another. That is what a token represents, something passed from one entity to another. The state tokens are always modified by creating a new state token from the old so state tokens are never mutated. If you look at the function call, the old tuple plus the changes are passed to another function that constructs a new tuple with a new state.
That's why functional programs perform useful work. Mutations occur, but the compiler tracks the mutation not the developer.
If you use a language that does not support tuples, you can still use this pattern. Just use a class, structure, or list to contain multiple values. In Scala, the case class is perfect since the value in the class are immutable. Many languages support classes and those that do not usually support some kind of aggregation using arrays or lists.
Three basic techniques to avoid mutations when using a state token are:
Here are examples of each.
The string sequence in the code below mutates but the compiler does the mutation. The mutation occurs in the last line that is the recursive call to makeSequenceImpl. The same goes for the position variable that is just a count concatenated to the end of the sequence string. The two variables involved, sequence and position, so not mutate.
private def makeSequence(startNum:Int):String = { val sequence = makeSequenceImpl("", startNum, startNum + SEQUENCESIZE) sequence } @tailrec private def makeSequenceImpl(sequence:String, position:Int, seqLimit: Int): String = { if ( position >= seqLimit ) return sequence makeSequenceImpl(sequence + position.toString, position + 1, seqLimit) }
To make this easier to see, here is the same code with the mutations assigned to state token variables.
private def makeSequence(startNum:Int):String = { val sequence = makeSequenceImpl("", startNum, startNum + SEQUENCESIZE) sequence } @tailrec private def makeSequenceImpl(sequence:String, position:Int, seqLimit: Int): String = { if ( position >= seqLimit ) return sequence val sequenceStateToken = sequence + position.toString val positionStateToken = position + 1 makeSequenceImpl(sequenceStateToken, positionStateToken, seqLimit) }
The state tokens are easier to see but I had to create two extra variables. In any case, recursion is what makes this possible. Recursion gives us the repetition we need with the nastiness of mutable variables.
The following code needs to do two things when a target digit is found:
private def calcProduct( targetDigitPos: Int, sequence: String, totalSeqLength: Int, product: Int) = { if ( isDigitInSequence( targetDigitPos, sequence, totalSeqLength ) ) { val offset = totalSeqLength - targetDigitPos val digit = sequence charAt ( sequence.length - offset - 1 ) // The tuple that contains the product and the target digit acts as a state token // to track the machine state without modifying any variables... (product * ( digit.toInt - 48 ), targetDigitPos * 10) } else (product, targetDigitPos) }
If the target digit is not found then the product and target digit remain unaltered. Since the function only returns one value, the tuple is what I would call the state token.
The technique can be used with Java if there is not too much tail recursion. This will cause a stack overflow if the recursion is too deep. Eliminating mutations in Scala seems to be quite straight forward. Java 8 and previous versions are more problematic because they do not support tail recursion optimization.
I left the state tokens hidden in the calcProduct. If you know Scala, as an exercise, you can easily make the tokens explicit adding a new variable.
Another thing to try is solving problem 5 in the S-99: Ninety-Nine Scala Problems list. After that, try reversing strings instead of lists. There are at least three different ways to reverse lists without using mutations.
So we have the world divided in two, language with tail recursion optimization and those without optimization. Haskell, Scala, and F# have it. Java and C# do not. There is talk of changing the JVM to support tail call optimization. That would be awesome because languages that run on the JVM could fully implement this feature.
If you want more examples of mutation free Scala code, please visit my GitHub site


--- layout: post title: The Frontrunner date: '2011-09-05 15:59:48 -0700' mt_id: 1461 blog_id: 1 post_id: 1461 basename: the_frontrunner categories: - politics - current-events ---
I am very excited by Gary Johnson—he's the best candidate I've come across since Steve Forbes. Moreover, he has all the pedigree you'd want in a presidential candidate: two-term popular governor, successful businessman, outdoorsman, and no hint of scandal.
Everything I've heard from suggests that he's the best of Ron Paul without all the crazy. He wants a self-interested foreign policy; supports abortion rights; wants to abolish HUD, the TSA, and the Department of Education; and thinks the government has no business in traditional social conservative issues.
Unfortunately, he's been routinely ignored by the media. That doesn't bode well: it seems like the media wants the Tea Party portrayed as being represented by the likes of Michele Bachmann, Ron Paul, and Sarah Palin. The thinking seems to be that independents will recoil (rightly so) at those candidates and end up selecting Mitt Romney during the primary.
That would be a travesty, as Johnson is the best choice for Tea Party types. He doesn't want to enforce any religion or morality, and he seems to understand the role of individual rights in government. Also, he has an actual plan for balancing the budget and a track record in New Mexico for cutting spending.


THE CIDER HOUSE RULES A film review by Steve Rhodes Copyright 1999 Steve Rhodes RATING (0 TO ****): **
Yawn. THE CIDER HOUSE RULES, written by John Irving, based on his book, and directed by Lasse Hallström (MY LIFE AS A DOG), is an exceedingly handsome picture. But like a TV movie featuring John-Boy Walton, the film's slow pacing and sappy storyline is sleep inducing.
The acting is close to being inert, and each of the excessively large number of characters is drastically underwritten. At least the messages, which the film provides in abundance, are good ones.
The film opens at the world's kindest orphanage. Run by a wise and generous doctor, Dr. Wilbur Larch (Michael Caine), the institution features fun snowball fights, nightly book readings and free movies. Still, as we are poignantly reminded, the occupants would much rather be adopted.
Aiding the good doctor is his unschooled assistant, Homer Wells (Tobey Maguire), who was "twice adopted and twice returned" when little. Although Homer lives in Maine, he has never seen an ocean or even a lobster. He does, however, know how to be almost as good a physician as Dr. Lynch due to his extensive, on-the-job training.
Following the lure of the open road, Homer leaves his orphanage home to go off with a beautiful woman, Candy Kendall (Charlize Theron), and her serviceman boyfriend, Wally Worthington (Paul Rudd). They take him to work in the apple orchard owned by Wally's family. (They had come to the orphanage in order for Dr. Lynch to perform an abortion on Candy.)
A pivotal scene in the movie has Homer and Dr. Lynch disagreeing about abortions with Homer dead-set against them and Dr. Lynch, who performs them regularly, in favor of them. In this predictable plot, as soon as you know that the young man can perform abortions, even though he is against them, you know what a major scene later will undoubtedly be. And when Wally inevitably rejoins his unit -- the movie is set during 1943 -- you can guess what will happen back at the home front.
Every time you get on the verge of nodding off, Hallström pulls the camera back, revealing a sweeping vista of the Maine mountains in all their autumn splendor of golds, reds and greens. Keep those gorgeous visuals in your mind. Enjoying them almost makes up for the lameness of the script.
THE CIDER HOUSE RULES runs a long 2:11. It is rated PG-13 for mature thematic elements, sexuality, nudity, substance abuse and some violence and would be fine for teenagers.
Email<EMAIL_ADDRESS>Web: http://www.InternetReviews.com
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rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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Related links: index of all rec.arts.movies.reviews reviews


The Reset Password tool allows you to request a new system password by simply entering your email address.
This feature may only be available to you if you have a guest account on the system. If you normally login with a username other than your email address, you may not be able to change your password using this tool.


Romain Perraud lifts lid on Celtic transfer KO as he claims Nice played hardball over Hoops move
The 22-year-old plumped for a switch to Brest but admits a move to Glasgow became a non-starter.
by Graeme Young, https://www.dailyrecord.co.uk/authors/graeme-young/Former Celtic target Romain Perraud has claimed Nice blocked his chance to move to Glasgow this summer.
The left-back previously stated it was the Premiership champions who called time on their pursuit but has now hinted Nice were never keen to do a deal with the Hoops.
And the 22-year-old plumped for a switch to Brest after also receiving an offer from Dijon once it became apparent his time on the French Riviera was coming to an end.
Celtic moved to address their need for a left-back reinforcements after landing Boli Bolingoli in a £3m deal from Rapid Vienna. They further bolstered their options by signing Greg Taylor on deadline day.
The Hoops' deal for Scotland international Taylor came after star turn Kieran Tierney joined Arsenal for £25m.
But Perraud insists the Hoops were in the mix before becoming aware of Nice's hardline stance.
He told Goal: "Celtic and Dijon made me concrete offers.
"With Celtic, the discussions started at the beginning of the transfer window, but Nice did not want to hear anything, so they did not come back.
"There remained Dijon and Brest. In the end, I chose Brest. I was seduced by the project and the speech I received."


-
Tweet Shot
You can now initialize a tweet in your favorite Twitter client when you post a shot. Enable this feature in the Twitter tab of the Preferences. Twitter.app is currently the only supported client.
-
Fixed Dribbble Bugs
Fixed a bug that blocked shooting to Dribbble if the default tags were blank, and fixed a small bug that would enable the Dribbble button even if the username or password were blank.
-
Fixed Court Shot Bug
Fixed a bug that could cause the screenshot to include the court view.


Disclaimer
The postings on this site are my own and do not necessarily represent the postings, strategies or opinions of any employer I work for, have worked for, or will work for. Furthermore, these postings are thoughts at a point-in-time and do not necessarily represent or influence current or future thoughts on the subject matter which may be materially different from what have been posted.


The Personal Chauffeur
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Component: tos.chips.atm128.i2c.Atm128I2CMasterImplP
generic module Atm128I2CMasterImplP()
The configuration that takes the underlying I2C driver on the
Atmega128 and turns it into a shared abstraction.
- Date:
-
May 28 2006
- Author:
-
Philip Levis
Provides
Uses
interface
I2CPacket<TI2CBasicAddr> as
SubPacketinterface
Resource as
SubResource[uint8_t]


Welcome to Pig Dice Game, A ONE on ONE game
Kindly, Fill In your names in the Form on your right to begin the game
Gamer A
Total Round:
Gamer B
Round Total:


Psalms 69:16
Hear me, O LORD; for thy lovingkindness is good: turn unto me according to the multitude of thy tender mercies.


4 writes to _description
System.Management.Automation (4)
singleshell\config\MshSnapinInfo.cs (4)
172
_description
= description;
357
_description
= resourceReader.GetResourceStringIndirect(
372
_description
=
390
_description
= _descriptionFallback;
4 references to _description
System.Management.Automation (4)
singleshell\config\MshSnapinInfo.cs (4)
275
if (
_description
== null)
280
return
_description
;
388
if (string.IsNullOrEmpty(
_description
))
411
_description
,


{% extends base.html %} {% block header %}
Your open, curated classification of neuroimaging literature.
Sync With Github.
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This is my favorite week so far! I had been waiting to make my own designed clock for so long.
My design surrounds a girl holding an umbrella background. It was combined with lots of different pictures. Like flower and other symbols.
I learned to used trace bitmap to outline image and use trim to cut out extra line and combine all the images into one piece. The idea behind this is nature and human thoughts on it.
After it is cut by laser printer, I colored it with both spray paint and water color on it. It is a masterpiece :)
I am very satisfied with the result :D
Secondly, we made a press fit kit. I made the outline from sketching in Fusion 360 because it is easier to determine the kit's teeth's dimension.
Then I turn it into jpg file and using bitmap and hairline, I turned the image into an outline of my press fit kit.
Thrid, I made a bookmark using laser printing. I found out something really cool that laser cutting aluminum will produce a shiny white surface, it looks so cool :)
Bookmark original fileI added texts and picture online, using the right perimeter and align the different piece into one bookmark. The theme is "Life's good". (Very Positive image here)
Lastly, I design the outer surface of a water bottle. It is called rotary attachment in the same laser printer.
I wanted a nature style, so I put in a moon and a pretty wave image. Simple and lovely :)


Informant Details
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Address Details
Address
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CITY
STATE
DISTRICT
Identity Details
AADHAAR NO./PASSPORT NO.
Additional Details
REASONS FOR DELAY OF FIR
enter text
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TYPE OF CRIME
MURDER
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DACOITY
DACOITY WITH FIREARMS
ROBBERY
ROBBERY WITH FIREARMS
BURGLARY
THEFT
RAPE
MOLESTATION
HURT
RIOTS
COMMUNAL RIOTS
ASSAULT ON GOVT. SERVANT ACT
GAMBLING
MONEY LENDERS ACT
ESSENTIAL COMMODITIES ACT
OTHERS
DATE OF lODGING FIR
TIME OF LODGING FIR
Incident Details
PLACE OF INCIDENCE
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CITY
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SOURCE STATION
DESTINATION STATION
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MAXIMUM 8000 CHARACTERS
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Enter detailed description of Item
Details Of Suspect
Suspect's Name
Relative's Name
Contact Number
GENDER
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§271-23 Bills of lading, shipping documents. The public utilities commission may prescribe for motor carriers the form and content of all bills of lading, freight bills, receipts, or other shipping documents governing the movement of traffic by motor carriers regulated under this chapter, and may prescribe the length of time the same shall be preserved. [L 1961, c 121, pt of §2; Supp, §106C-21; HRS §271-23]
Cross References
Uniform commercial code, see chapter 490, Art. 7.


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WITNESS LIST Elections Committee April 23, 2003-2:00P HB 310 Registering, but not testifying: For: Mitchell, Seth (Bexar County Judge Nelson Wolff) Smith, Tom (Public Citizen) Woodford, Suzy (Common Cause Texas) Against: Carlson, Pat (Tarrant County Republican Party) Lewis, Jesse (The Republican Party of Texas) On: Lundquist, Karen (Texas Ethics Commission) HB 382 For: DeBeauvoir, Dana (County and District Clerks Association of Texas) Valentine, David (First Baptist Church of Huntsville, TX) On: Nickless, Melinda (Secretary of State) Reynolds, Carl (Texas Department of Criminal Justice) Registering, but not testifying: For: Barber, Anthony (Self and NAACP National Voters Fund, H.O.W. Foundation Drug/Alcohol Treatment Center) Castanon, Ricardo (Southwest Voter Registration Education Project) Farahkhan, Ethea (NAACP- Houston Branch, Voter Empowerment Program) Foster, Claude (NAACP National Voter Fund) Hankins, Barbara (League of Women Voters of Texas) Lanez-Correa, Ana (Texas LULAC) Longbottom, Stephen (Self) Martin Rountree, Meredith (ACLU of Texas, Prison & Jail Accountability Project) Ortiz, Hector Luis (William C. Velasquez Institute) Owens, Eva (Texas Criminal Justice Reform Coalition) Romero, Jesse (Center for Policy Alternatives) Ruiz, Roseanna (Self) Sanchez, Joe (MALDEF) Solomon, Emmett (Restorative Justice Ministry Network) Against: Lewis, Jesse (The Republican Party of Texas) HB 482 For: Webb, Justin (National Youth Rights Association) On: Hanshaw Winn, Elizabeth (Secretary of State) Registering, but not testifying: For: Castanon, Ricardo (Southwest Voter Registration Education Project) Ortiz, Hector (William C. Velasquez Institute) Sanchez, Joe (MALDEF) Against: Carlson, Pat (Tarrant County Republican Party) Lewis, Jesse (The Republican Party of Texas) HB 1362 For: Agan, Steve (Self) Hankins, Barbara (Self) Howard, Robert (Americans for Approval Voting) Kunkee, Randy (Self) McBride, Micah (Self) Riddering, Hannah (Self and Texas National Organization for Women) St. Denis, John (Self) Against: Sherbet, Bruce (Dallas County, Texas Association of Elections Administrators) Registering, but not testifying: For: Cantu, Rebecca (Students for Universal Living Wage-UT Austin) Friedley, Tom (Self) Hill, Claire (Self) Lanez-Correa, Ana (Texas LULAC) Romero, Jesse (Center for Policy Alternatives) Smith, Tom (Public Citizen) Smith, Tom (Self) Vidheecharoen-Glatz, Joy (Self) Webb, Justin (Self) Woodford, Suzy (Common Cause Texas) Against: Lewis, Jesse (The Republican Party of Texas) On: Hanshaw Winn, Elizabeth (Secretary of State) HB 2085 Registering, but not testifying: For: Castanon, Ricardo (Southwest Voter Registration Education Project) Lanez-Correa, Ana (Texas LULAC) Ortiz, Hector (William C. Velazquez Institute) On: Hanshaw Winn, Elizabeth (Secretary of State) Lewis, Jesse (The Republican Party of Texas) HB 2496 For: Maxey, Glen (Self) On: Nickless, Melinda (Secretary of State) Registering, but not testifying: For: Carlson, Pat (Tarrant County Republican Party) On: Lewis, Jesse (The Republican Party of Texas) HB 2756 For: DeBeauvoir, Dana (County and District Clerks Association of Texas) Peacock, J.R. (Self) Ruiz, Philip (Texas Democratic County Chair Association) On: Hanshaw Winn, Elizabeth (Secretary of State) Registering, but not testifying: For: Cantu, Rebecca (Self) Carlson, Pat (Tarrant County Republican Party) Malcolm, Molly Beth (Texas Democratic Party) Mendez, Mark (Tarrant County Commissioners Court) Smith, Tom (Public Citizen) Webb, Justin (Self) On: Lewis, Jesse (The Republican Party of Texas) HB 2871 Registering, but not testifying: For: Castanon, Ricardo (Southwest Voter Registration Education Project) Lanez-Correa, Ana (Texas LULAC) Martin Rountree, Meredith (ACLU-TX) Ortiz, Hector (William C. Velazquez Institute) Romero, Jesse (Center for Policy Alternatives) Sanchez, Joe (MALDEF) Against: Lewis, Jesse (The Republican Party of Texas) On: Hanshaw Winn, Elizabeth (Secretary of State) HB 3119 For: Acevedo, Fidel (Self) Registering, but not testifying: For: Castanon, Ricardo (Southwest Voter Registration Education Project) Lanez-Correa, Ana (Texas LULAC) Martin Rountree, Meredith (ACLU-TX) Ortiz, Hector (William C. Velasquez Institute) Romero, Jesse (Center for Policy Alternatives) Woodford, Suzy (Common Cause Texas) Against: DeBeauvoir, Dana (County and District Clerks Association of Texas) Lewis, Jesse (The Republican Party of Texas) On: Nickless, Melinda (Secretary of State) HB 3166 On: Hanshaw Winn, Elizabeth (Secretary of State) Registering, but not testifying: On: Lewis, Jesse (The Republican Party of Texas) HB 3222 Registering, but not testifying: On: Lundquist, Karen (Texas Ethics Commission) HJR 29 Registering, but not testifying: For: Castanon, Ricardo (Southwest Voter Registration Education Project) Ortiz, Hector (William C. Velasquez Institute) Sanchez, Joe (MALDEF) Against: Lewis, Jesse (The Republican Party of Texas) On: Hanshaw Winn, Elizabeth (Secretary of State) HJR 41 For: Howard, Robert (Americans for Approval Voting) Riddering, Hannah (Self and Texas National Organization for Women) Registering, but not testifying: For: Cantu, Rebecca (Self and Students for Universal Living Wage- UT Austin) Neavel, Nancy (Self) Ramsey, Lesley (National Organization for Women in Texas) Romero, Jesse (Center for Policy Alternatives) Against: Lewis, Jesse (The Republican Party of Texas) On: Hanshaw Winn, Elizabeth (Secretary of State)


MBTBreedDetailViewController
class MBTBreedDetailViewController : UIViewController
A view controller for showing the breed details with some example images.
-
Reference to the embedded breed info view controller.
Declaration
Swift
var breedInfoViewController: MBTBreedInfoViewController?
-
Constant string for identifing the imageID data when passing a SetCat notification.
See also
MBTCatsRatingViewController.SetCatDeclaration
Swift
static let ImageIDKey: String
-
The represented breed shown in the view. Setting this will refresh the UI.
Declaration
Swift
var representedBreed: MBTCatAPIBreedModel? { get set }
-
Undocumented
Declaration
Swift
override func viewDidLoad()
-
Refreshes the UI of the controlled view with the represented breed data.
Declaration
Swift
func populateUI()
-
Shows an spinning activity indicator, hiding all the rest of the UI.
Declaration
Swift
func showLoadingUI()
-
Hides the spinning activity indicator, showing the informational UI.
Declaration
Swift
func hideLoadingUI()
-
Objective-C compilant function that listens when the user taps one of the thumbnails.
Note
This function uses the UIImageView accesibilityIdentifier property to retrieve the image ID.Declaration
Swift
@objc func breedImageTapped(tapGestureRecognizer: UITapGestureRecognizer)
Parameters
tapGestureRecognizer
The sender of the event.
-
Performs an Cat API request for 9 or less cat images.
See also
MBTBreedImageModelDeclaration
Swift
func getThumbnails(with breedID:String, completion: (([MBTBreedImageModel])->())? = nil)
Parameters
breedID
Cat API breed ID to search.
completion
Closure capturing an array of MBTBreedImageModel with the URLs of the images. Called upon request success.
-
Undocumented
Declaration
Swift
override func prepare(for segue: UIStoryboardSegue, sender: Any?)
-
Switches to the Cats tab and shows an specific image there.
Declaration
Swift
func goToCatRating(with imageID: String)
Parameters
imageID
Cat API image ID to show in the Cats tab.


rnpn
is an R client for interacting with the USA National Phenology Network data web services. These services include access to a rich set of observer-contributed, point-based phenology records as well as geospatial data products including gridded phenological model and climatological data.
Documentation is available for the National Phenology Network API documentation, which describes the full set of REST services this package wraps.
There is no need for an API key to grab data from the National Phenology Network but users are required to self identify, on an honor system, against requests that may draw upon larger datasets. For functions that require it, simply populate the request_source parameter with your name or the name of your institution.
CRAN version
install.packages("rnpn")
Development version:
install.packages("devtools")
library('devtools')
devtools::install_github("usa-npn/rnpn")
This package has dependencies on both curl and gdal. Some Linux based systems may require additional system dependencies for those required packages, and accordingly this package, to install correctly. For example, on Ubuntu:
Many of the functions to search for data require knowing the internal unique identifiers of some of the database entities to filter the data down efficiently. For example, if you want to search by species, then you must know the internal identifier of the species. To get a list of all available species use the following:
species_list <- npn_species()
Similarly, for phenophases:
phenophases <- npn_phenophases()
There are four main functions for accessing observational data, at various levels of aggregation. At the most basic level you can download the raw status and intensity data.
some_data <- npn_download_status_data(request_source='Your Name or Org Here',years=c(2015),species_id=c(35),states=c('AZ','IL'))
Note that through this API, data can only be filtered chronologically by full calendar years. You can specify any number of years in each API call. Also note that request_source is a required parameter and should be populated with your name or the name of the organization you represent. All other parameters are optional but it is highly recommended that you filter your data search further.
This package wraps around standard WCS endpoints to facilitate the transfer of raster data. Generally, this package does not focus on interacting with WMS services, although they are available. To get a list of all available data layers, use the following:
layers <- npn_get_layer_details()
You can then use the name of the layers to select and download geospatial data as a raster.
npn_download_geospatial(coverage_id = 'si-x:lilac_leaf_ncep_historic',date='2016-12-31',format='geotiff',output_path='./six-test-raster.tiff')
If you’re looking for a grid value at a specific latitude/longitude, that is also possible.
point_value <- npn_get_point_data('si-x:lilac_leaf_ncep_historic',date='2016-12-31',lat=38.5,long=-110.7)
Please read and review the vignettes for this package to get further information about the full scope of functionality available.
This code was developed, in part, as part of the integrated Pheno-Synthesis Software Suite (PS3). The authors acknowledge funding for this work through NASA’s AIST program (80NSSC17K0582, 80NSSC17K0435, 80NSSC17K0538, and 80GSFC18T0003). The University of Arizona and the USA National Phenology Network’s efforts with this package are supported in part by US Geological Survey (G14AC00405, G18AC00135) and the US Fish and Wildlife Service (F16AC01075 and F19AC00168).
rnpn
in R doing citation(package = 'rnpn')


2:15
Mike Doe
I would like to meet you to discuss the latest news about
the arrival of the new theme. They say it is going to be one the
best themes on the market
ملحقات:
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5:15
Jane Doe
I would like to meet you to discuss the latest news about
the arrival of the new theme. They say it is going to be one the
best themes on the market
5:30
Susan Doe
I would like to meet you to discuss the latest news about
the arrival of the new theme. They say it is going to be one the
best themes on the market


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NEWS.md
2020-11-11
read_cell_seg_data
determines column types to prevent errors in some edge cases.make_ppp
to work correctly when given compound phenotypes such as “CD8+/PD-L1+”2020-08-22
make_ppp
function to create marked point patterns.parse_phenotypes
makes nicer names for expression-based phenotypes.phenotype_columns
in support of akoyabio/phenoptrReports#36.read_field_info
and get_map_path
functions.2020-02-28
read_cell_data
more robust against data with comma as the decimal separator.count_touching_cells_fast
.2019-10-22
count_touching_cells_fast
for phenoptrReports spatial map viewer.select_rows()
; return FALSE
instead.2019-06-11
phenoptrReports
to avoid installation pain. phenoptrReports
is still needed to build the vignettes and to run a few of the examples.parse_phenotypes
and validate_phenotype_definitions
support expressions in phenotype definitions.2019-05-31
Bug fixes:
density_bands
to work with cell seg data in microns with slide origin (#10).2019-05-10
New feature (and breaking change):
compute_all_nearest_distance
and find_nearest_distance
now create columns containing the Cell ID of the nearest cell, as well as the columns with the actual distance. This column can be used to find the locations of nearest cells and to find mutual nearest neighbors. The “Computing inter-cell distances” tutorial shows some uses of this data.Bug fixes:
read_cell_seg_data
recognizes and correctly reads inForm data which uses comma as the decimal separator (#8).count_touching_cells
ignores phenotype pairs of self to self with a warning because the touching algorithm does not handle this case (#7).2019-04-22
Failed with error: ‘there is no package called ‘rtree’’
message that requireNamespace('rtree')
generates (#5).2019-04-12
New features:
count_within_many
and count_within_batch
using akoyabio/rtree
package.count_within_detail
to give per-cell counts.Bug fixes:
get_field_info
to work with the standard (CRAN) tiff
package.count_within
and related functions return NA
for within_mean
when there are no from
cells (rather than a mean of 0).spatial_distribution_report
vignette and example to use phenoptrExamples
, which contains the required component data file (#3).count_touching_cells
when there is exactly one touching cell pair (#4)2019-03-03
count_within_many
in support of phenoptrReports.count_within
does not count self when ‘from’ and ‘to’ phenotypes are the same.field_column
(moved from phenoptrReports) and validate_phenotypes
.compute_all_nearest_distance
to recognize “Annotation ID” as a field name.2019-02-08
find_nearest_distance
, compute_all_nearest_distance
, density_at_distance
, density_bands
, count_within
and spatial_distribution_report
to work with consolidated data from phenoptrReports
(#1).Bug fixes
sq microns
and sq mm
to square microns
and square mm
to match inForm output.2018-12-27
spatial_distribution_report
works correctly with cell seg data in microns. To do so, it requires a component data file for the target field. Image dimensions are taken from that file.count_touching_cells
works correctly when cell seg data is in microns.read_cell_seg_data
#9
count_within
won’t complain if the data doesn’t have a Sample Name
column.2018-09-28
2018-09-28
parse_phenotypes
function simplifies creation of selectors for select_rows
.select_rows
recognizes the column format of phenotype-per-marker data.select_rows
treats a NA
selector as “select all”. This is helpful in lists of selectors.read_cell_seg_data
attempts to skip microns conversion if it was already done.2018-07-11
count_touching_cells
to work with two-pixel-wide membrane map. #82018-04-24
count_within_batch
to work with cell seg files which don’t have Slide ID
fields.density_at_distance
and density_bands
to estimate cell density at a distance from a boundary.pixels_per_micron="auto"
option to read_cell_seg_data()
.informr
to phenoptr
.subset_distance_matrix
in a backwards-incompatible way. This was done to put the csd
parameter first, matching other functions with a csd
parameter.spatial_distribution_report
creates an HTML report showing the location and nearest-neighbor relations of cells in a single field.count_touching_cells
uses morphological analysis of nuclear and membrane segmentation maps to find touching cells of paired phenotypes.read_components
reads component image files.count_within
and count_within_batch
count the number of from
cells having a to
cell within a given radius.list_cell_seg_files
lists all cell seg data files in a folder.


public interface MonetaryConversionsSingletonSpi
MonetaryConversions
singleton accessor. It should be registered as a
service using the JDK ServiceLoader
. Hereby only one instance can be
registered at a time.
This interface is designed to support also contextual behaviour, e.g. in Java
EE containers each application may provide its own
ExchangeRateProvider
instances, e.g. by registering them as CDI
beans. An EE container can register an according
MonetaryConversionsSingletonSpi
that manages the different application
contexts transparently. In a SE environment this class is expected to behave
like an ordinary singleton, loading its SPIs from the ServiceLoader
.
Instances of this class must be thread safe. It is not a requirement that they are serializable.
Only one instance can be registered using the ServiceLoader
. When
registering multiple instances the MonetaryConversions
accessor will
not work.
| Modifier and Type | Method and Description |
|---|---|
default CurrencyConversion |
getConversion(ConversionQuery conversionQuery)
Access an instance of
CurrencyConversion . |
default CurrencyConversion |
getConversion(CurrencyUnit termCurrency,
String... providers)
Access an instance of
CurrencyConversion . |
List<String> |
getDefaultProviderChain()
Get the default provider chain used.
|
ExchangeRateProvider |
getExchangeRateProvider(ConversionQuery conversionQuery)
Access an instance of
ExchangeRateProvider . |
default ExchangeRateProvider |
getExchangeRateProvider(String... providers)
Access a compound instance of an
ExchangeRateProvider based on the given provider chain. |
default List<ExchangeRateProvider> |
getExchangeRateProviders(String... providers)
Access the current registered
ExchangeRateProvider instances. |
Collection<String> |
getProviderNames()
Get all currently registered provider names.
|
default boolean |
isConversionAvailable(ConversionQuery conversionQuery)
Allows to quickly check, if a
CurrencyConversion is accessible for the given
ConversionQuery . |
default boolean |
isConversionAvailable(CurrencyUnit termCurrency,
String... providers)
Allows to quickly check, if a
CurrencyConversion is accessible for the given
ConversionQuery . |
default boolean |
isExchangeRateProviderAvailable(ConversionQuery conversionQuery)
Allows to quickly check, if a
ExchangeRateProvider is accessible for the given
ConversionQuery . |
Collection<String> getProviderNames()
AbstractContext.getProviderName()
List<String> getDefaultProviderChain()
null
and not empty.ExchangeRateProvider getExchangeRateProvider(ConversionQuery conversionQuery)
ExchangeRateProvider
. By setting .ConversionQuery#getCurrencyProviderNames()
multiple providers can be selected,
that will be included into a compound instance, with the same order as returned by the .money.convert.ConversionQuery
.conversionQuery
- the ConversionQuery
determining the type of conversion
required, not null.ExchangeRateProvider
built up with the given sub
providers, never null
MonetaryException
- if a provider could not be found.isExchangeRateProviderAvailable(javax.money.convert.ConversionQuery)
default boolean isExchangeRateProviderAvailable(ConversionQuery conversionQuery)
ExchangeRateProvider
is accessible for the given
ConversionQuery
.conversionQuery
- the ConversionQuery
determining the type of conversion
required, not null.true
, if such a conversion is supported, meaning an according
ExchangeRateProvider
can be
accessed.getExchangeRateProvider(ConversionQuery)
,
getExchangeRateProvider(String...)}
default boolean isConversionAvailable(ConversionQuery conversionQuery)
CurrencyConversion
is accessible for the given
ConversionQuery
.conversionQuery
- the ConversionQuery
determining the type of conversion
required, not null.true
, if such a conversion is supported, meaning an according
CurrencyConversion
can be
accessed.getConversion(javax.money.convert.ConversionQuery)
,
getConversion(CurrencyUnit, String...)}
default boolean isConversionAvailable(CurrencyUnit termCurrency, String... providers)
CurrencyConversion
is accessible for the given
ConversionQuery
.termCurrency
- the terminating/target currency unit, not null.providers
- the provider names defines a corresponding
provider chain that must be encapsulated by the resulting .money.convert.CurrencyConversion
. By default the provider
chain as defined by #getDefaultCurrencyProviderChain will be used.true
, if such a conversion is supported, meaning an according
CurrencyConversion
can be
accessed.getConversion(javax.money.convert.ConversionQuery)
,
getConversion(CurrencyUnit, String...)}
default List<ExchangeRateProvider> getExchangeRateProviders(String... providers)
ExchangeRateProvider
instances. If no provider
names are passed ALL current registered providers are returned in undefined order.providers
- the provider names of hte providers to be accessedMonetaryException
- if a provider could not be resolved.default ExchangeRateProvider getExchangeRateProvider(String... providers)
ExchangeRateProvider
based on the given provider chain.providers
- the ConversionQuery
provider names defines a corresponding
provider chain that must be
encapsulated by the resulting ExchangeRateProvider
. By default
the default
provider changes as defined in #getDefaultCurrencyProviderChain will be used.ExchangeRateProvider
built up with the given sub
providers, never null
.MonetaryException
- if a provider listed could not be found.getProviderNames()
,
isExchangeRateProviderAvailable(javax.money.convert.ConversionQuery)
default CurrencyConversion getConversion(ConversionQuery conversionQuery)
CurrencyConversion
.conversionQuery
- the ConversionQuery
determining the type of conversion
required, not null.MonetaryException
- if no matching conversion could be found.isConversionAvailable(javax.money.convert.ConversionQuery)
default CurrencyConversion getConversion(CurrencyUnit termCurrency, String... providers)
CurrencyConversion
.termCurrency
- the terminating/target currency unit, not null.providers
- the ConversionQuery
provider names defines a corresponding
provider chain that must be encapsulated by the resulting .money.convert.CurrencyConversion
. By default the default
provider chain as defined by #getDefaultCurrencyProviderChain will be used.MonetaryException
- if no matching conversion could be found.isConversionAvailable(javax.money.convert.ConversionQuery)
Copyright © 2012–2016 JSR 354 - Expert Group. All rights reserved.


ally.element.disabled
Example
There are three buttons, the "disable element" button allows you to toggle the disabled state of the other two. One of the two example buttons is a real button, the other is a <div role="button">


Using finite integer sets
[Programming models]
Modules | |
| Set variables | |
| Projector constraints | |
| Range and value iterators for set variables | |
| Argument arrays | |
| Variable arrays | |
| Domain constraints | |
| Relation constraints | |
| Set operation/relation constraints | |
| Convexity constraints | |
| Sequence constraints | |
| Distinctness constraints | |
| Connection constraints to finite domain variables | |
| Element constraints | |
| Branching | |
Enumerations | |
| enum | Gecode::SetRelType { Gecode::SRT_EQ, Gecode::SRT_NQ, Gecode::SRT_SUB, Gecode::SRT_SUP, Gecode::SRT_DISJ, Gecode::SRT_CMPL } |
| Common relation types for sets. More... | |
| enum | Gecode::SetOpType { Gecode::SOT_UNION, Gecode::SOT_DUNION, Gecode::SOT_INTER, Gecode::SOT_MINUS } |
| Common operations for sets. More... |
Enumeration Type Documentation
| enum Gecode::SetRelType |
| enum Gecode::SetOpType |


"""
The :mod:`sklearn.kernel_approximation` module implements several
approximate kernel feature maps base on Fourier transforms.
"""
# Author: Andreas Mueller<EMAIL_ADDRESS>#
# License: BSD 3 clause
import warnings
import numpy as np
import scipy.sparse as sp
from scipy.linalg import svd
from .base import BaseEstimator
from .base import TransformerMixin
from .utils import check_array, check_random_state, as_float_array
from .utils.extmath import safe_sparse_dot
from .utils.validation import check_is_fitted
from .metrics.pairwise import pairwise_kernels, KERNEL_PARAMS
class RBFSampler(BaseEstimator, TransformerMixin):
"""Approximates feature map of an RBF kernel by Monte Carlo approximation
of its Fourier transform.
It implements a variant of Random Kitchen Sinks.[1]
Read more in the :ref:`User Guide <rbf_kernel_approx>`.
Parameters
----------
gamma : float
Parameter of RBF kernel: exp(-gamma * x^2)
n_components : int
Number of Monte Carlo samples per original feature.
Equals the dimensionality of the computed feature space.
random_state : int, RandomState instance or None, optional (default=None)
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`.
Examples
--------
>>> from sklearn.kernel_approximation import RBFSampler
>>> from sklearn.linear_model import SGDClassifier
>>> X = [[0, 0], [1, 1], [1, 0], [0, 1]]
>>> y = [0, 0, 1, 1]
>>> rbf_feature = RBFSampler(gamma=1, random_state=1)
>>> X_features = rbf_feature.fit_transform(X)
>>> clf = SGDClassifier(max_iter=5, tol=1e-3)
>>> clf.fit(X_features, y)
... # doctest: +NORMALIZE_WHITESPACE
SGDClassifier(alpha=0.0001, average=False, class_weight=None,
early_stopping=False, epsilon=0.1, eta0=0.0, fit_intercept=True,
l1_ratio=0.15, learning_rate='optimal', loss='hinge', max_iter=5,
n_iter_no_change=5, n_jobs=None, penalty='l2', power_t=0.5,
random_state=None, shuffle=True, tol=0.001, validation_fraction=0.1,
verbose=0, warm_start=False)
>>> clf.score(X_features, y)
1.0
Notes
-----
See "Random Features for Large-Scale Kernel Machines" by A. Rahimi and
Benjamin Recht.
[1] "Weighted Sums of Random Kitchen Sinks: Replacing
minimization with randomization in learning" by A. Rahimi and
Benjamin Recht.
(https://people.eecs.berkeley.edu/~brecht/papers/08.rah.rec.nips.pdf)
"""
def __init__(self, gamma=1., n_components=100, random_state=None):
self.gamma = gamma
self.n_components = n_components
self.random_state = random_state
def fit(self, X, y=None):
"""Fit the model with X.
Samples random projection according to n_features.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Training data, where n_samples in the number of samples
and n_features is the number of features.
Returns
-------
self : object
Returns the transformer.
"""
X = check_array(X, accept_sparse='csr')
random_state = check_random_state(self.random_state)
n_features = X.shape[1]
self.random_weights_ = (np.sqrt(2 * self.gamma) * random_state.normal(
size=(n_features, self.n_components)))
self.random_offset_ = random_state.uniform(0, 2 * np.pi,
size=self.n_components)
return self
def transform(self, X):
"""Apply the approximate feature map to X.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
New data, where n_samples in the number of samples
and n_features is the number of features.
Returns
-------
X_new : array-like, shape (n_samples, n_components)
"""
check_is_fitted(self, 'random_weights_')
X = check_array(X, accept_sparse='csr')
projection = safe_sparse_dot(X, self.random_weights_)
projection += self.random_offset_
np.cos(projection, projection)
projection *= np.sqrt(2.) / np.sqrt(self.n_components)
return projection
class SkewedChi2Sampler(BaseEstimator, TransformerMixin):
"""Approximates feature map of the "skewed chi-squared" kernel by Monte
Carlo approximation of its Fourier transform.
Read more in the :ref:`User Guide <skewed_chi_kernel_approx>`.
Parameters
----------
skewedness : float
"skewedness" parameter of the kernel. Needs to be cross-validated.
n_components : int
number of Monte Carlo samples per original feature.
Equals the dimensionality of the computed feature space.
random_state : int, RandomState instance or None, optional (default=None)
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`.
Examples
--------
>>> from sklearn.kernel_approximation import SkewedChi2Sampler
>>> from sklearn.linear_model import SGDClassifier
>>> X = [[0, 0], [1, 1], [1, 0], [0, 1]]
>>> y = [0, 0, 1, 1]
>>> chi2_feature = SkewedChi2Sampler(skewedness=.01,
... n_components=10,
... random_state=0)
>>> X_features = chi2_feature.fit_transform(X, y)
>>> clf = SGDClassifier(max_iter=10, tol=1e-3)
>>> clf.fit(X_features, y) # doctest: +NORMALIZE_WHITESPACE
SGDClassifier(alpha=0.0001, average=False, class_weight=None,
early_stopping=False, epsilon=0.1, eta0=0.0, fit_intercept=True,
l1_ratio=0.15, learning_rate='optimal', loss='hinge', max_iter=10,
n_iter_no_change=5, n_jobs=None, penalty='l2', power_t=0.5,
random_state=None, shuffle=True, tol=0.001, validation_fraction=0.1,
verbose=0, warm_start=False)
>>> clf.score(X_features, y)
1.0
References
----------
See "Random Fourier Approximations for Skewed Multiplicative Histogram
Kernels" by Fuxin Li, Catalin Ionescu and Cristian Sminchisescu.
See also
--------
AdditiveChi2Sampler : A different approach for approximating an additive
variant of the chi squared kernel.
sklearn.metrics.pairwise.chi2_kernel : The exact chi squared kernel.
"""
def __init__(self, skewedness=1., n_components=100, random_state=None):
self.skewedness = skewedness
self.n_components = n_components
self.random_state = random_state
def fit(self, X, y=None):
"""Fit the model with X.
Samples random projection according to n_features.
Parameters
----------
X : array-like, shape (n_samples, n_features)
Training data, where n_samples in the number of samples
and n_features is the number of features.
Returns
-------
self : object
Returns the transformer.
"""
X = check_array(X)
random_state = check_random_state(self.random_state)
n_features = X.shape[1]
uniform = random_state.uniform(size=(n_features, self.n_components))
# transform by inverse CDF of sech
self.random_weights_ = (1. / np.pi
* np.log(np.tan(np.pi / 2. * uniform)))
self.random_offset_ = random_state.uniform(0, 2 * np.pi,
size=self.n_components)
return self
def transform(self, X):
"""Apply the approximate feature map to X.
Parameters
----------
X : array-like, shape (n_samples, n_features)
New data, where n_samples in the number of samples
and n_features is the number of features. All values of X must be
strictly greater than "-skewedness".
Returns
-------
X_new : array-like, shape (n_samples, n_components)
"""
check_is_fitted(self, 'random_weights_')
X = as_float_array(X, copy=True)
X = check_array(X, copy=False)
if (X <= -self.skewedness).any():
raise ValueError("X may not contain entries smaller than"
" -skewedness.")
X += self.skewedness
np.log(X, X)
projection = safe_sparse_dot(X, self.random_weights_)
projection += self.random_offset_
np.cos(projection, projection)
projection *= np.sqrt(2.) / np.sqrt(self.n_components)
return projection
class AdditiveChi2Sampler(BaseEstimator, TransformerMixin):
"""Approximate feature map for additive chi2 kernel.
Uses sampling the fourier transform of the kernel characteristic
at regular intervals.
Since the kernel that is to be approximated is additive, the components of
the input vectors can be treated separately. Each entry in the original
space is transformed into 2*sample_steps+1 features, where sample_steps is
a parameter of the method. Typical values of sample_steps include 1, 2 and
3.
Optimal choices for the sampling interval for certain data ranges can be
computed (see the reference). The default values should be reasonable.
Read more in the :ref:`User Guide <additive_chi_kernel_approx>`.
Parameters
----------
sample_steps : int, optional
Gives the number of (complex) sampling points.
sample_interval : float, optional
Sampling interval. Must be specified when sample_steps not in {1,2,3}.
Examples
--------
>>> from sklearn.datasets import load_digits
>>> from sklearn.linear_model import SGDClassifier
>>> from sklearn.kernel_approximation import AdditiveChi2Sampler
>>> X, y = load_digits(return_X_y=True)
>>> chi2sampler = AdditiveChi2Sampler(sample_steps=2)
>>> X_transformed = chi2sampler.fit_transform(X, y)
>>> clf = SGDClassifier(max_iter=5, random_state=0, tol=1e-3)
>>> clf.fit(X_transformed, y) # doctest: +NORMALIZE_WHITESPACE
SGDClassifier(alpha=0.0001, average=False, class_weight=None,
early_stopping=False, epsilon=0.1, eta0=0.0, fit_intercept=True,
l1_ratio=0.15, learning_rate='optimal', loss='hinge', max_iter=5,
n_iter_no_change=5, n_jobs=None, penalty='l2', power_t=0.5,
random_state=0, shuffle=True, tol=0.001, validation_fraction=0.1,
verbose=0, warm_start=False)
>>> clf.score(X_transformed, y) # doctest: +ELLIPSIS
0.9499...
Notes
-----
This estimator approximates a slightly different version of the additive
chi squared kernel then ``metric.additive_chi2`` computes.
See also
--------
SkewedChi2Sampler : A Fourier-approximation to a non-additive variant of
the chi squared kernel.
sklearn.metrics.pairwise.chi2_kernel : The exact chi squared kernel.
sklearn.metrics.pairwise.additive_chi2_kernel : The exact additive chi
squared kernel.
References
----------
See `"Efficient additive kernels via explicit feature maps"
<http://www.robots.ox.ac.uk/~vedaldi/assets/pubs/vedaldi11efficient.pdf>`_
A. Vedaldi and A. Zisserman, Pattern Analysis and Machine Intelligence,
2011
"""
def __init__(self, sample_steps=2, sample_interval=None):
self.sample_steps = sample_steps
self.sample_interval = sample_interval
def fit(self, X, y=None):
"""Set the parameters
Parameters
----------
X : array-like, shape (n_samples, n_features)
Training data, where n_samples in the number of samples
and n_features is the number of features.
Returns
-------
self : object
Returns the transformer.
"""
check_array(X, accept_sparse='csr')
if self.sample_interval is None:
# See reference, figure 2 c)
if self.sample_steps == 1:
self.sample_interval_ = 0.8
elif self.sample_steps == 2:
self.sample_interval_ = 0.5
elif self.sample_steps == 3:
self.sample_interval_ = 0.4
else:
raise ValueError("If sample_steps is not in [1, 2, 3],"
" you need to provide sample_interval")
else:
self.sample_interval_ = self.sample_interval
return self
def _transform_dense(self, X):
non_zero = (X != 0.0)
X_nz = X[non_zero]
X_step = np.zeros_like(X)
X_step[non_zero] = np.sqrt(X_nz * self.sample_interval_)
X_new = [X_step]
log_step_nz = self.sample_interval_ * np.log(X_nz)
step_nz = 2 * X_nz * self.sample_interval_
for j in range(1, self.sample_steps):
factor_nz = np.sqrt(step_nz /
np.cosh(np.pi * j * self.sample_interval_))
X_step = np.zeros_like(X)
X_step[non_zero] = factor_nz * np.cos(j * log_step_nz)
X_new.append(X_step)
X_step = np.zeros_like(X)
X_step[non_zero] = factor_nz * np.sin(j * log_step_nz)
X_new.append(X_step)
return np.hstack(X_new)
def _transform_sparse(self, X):
indices = X.indices.copy()
indptr = X.indptr.copy()
data_step = np.sqrt(X.data * self.sample_interval_)
X_step = sp.csr_matrix((data_step, indices, indptr),
shape=X.shape, dtype=X.dtype, copy=False)
X_new = [X_step]
log_step_nz = self.sample_interval_ * np.log(X.data)
step_nz = 2 * X.data * self.sample_interval_
for j in range(1, self.sample_steps):
factor_nz = np.sqrt(step_nz /
np.cosh(np.pi * j * self.sample_interval_))
data_step = factor_nz * np.cos(j * log_step_nz)
X_step = sp.csr_matrix((data_step, indices, indptr),
shape=X.shape, dtype=X.dtype, copy=False)
X_new.append(X_step)
data_step = factor_nz * np.sin(j * log_step_nz)
X_step = sp.csr_matrix((data_step, indices, indptr),
shape=X.shape, dtype=X.dtype, copy=False)
X_new.append(X_step)
return sp.hstack(X_new)
def _more_tags(self):
return {'stateless': True}
class Nystroem(BaseEstimator, TransformerMixin):
"""Approximate a kernel map using a subset of the training data.
Constructs an approximate feature map for an arbitrary kernel
using a subset of the data as basis.
Read more in the :ref:`User Guide <nystroem_kernel_approx>`.
Parameters
----------
kernel : string or callable, default="rbf"
Kernel map to be approximated. A callable should accept two arguments
and the keyword arguments passed to this object as kernel_params, and
should return a floating point number.
gamma : float, default=None
Gamma parameter for the RBF, laplacian, polynomial, exponential chi2
and sigmoid kernels. Interpretation of the default value is left to
the kernel; see the documentation for sklearn.metrics.pairwise.
Ignored by other kernels.
coef0 : float, default=None
Zero coefficient for polynomial and sigmoid kernels.
Ignored by other kernels.
degree : float, default=None
Degree of the polynomial kernel. Ignored by other kernels.
kernel_params : mapping of string to any, optional
Additional parameters (keyword arguments) for kernel function passed
as callable object.
n_components : int
Number of features to construct.
How many data points will be used to construct the mapping.
random_state : int, RandomState instance or None, optional (default=None)
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`.
Attributes
----------
components_ : array, shape (n_components, n_features)
Subset of training points used to construct the feature map.
component_indices_ : array, shape (n_components)
Indices of ``components_`` in the training set.
normalization_ : array, shape (n_components, n_components)
Normalization matrix needed for embedding.
Square root of the kernel matrix on ``components_``.
Examples
--------
>>> from sklearn import datasets, svm
>>> from sklearn.kernel_approximation import Nystroem
>>> digits = datasets.load_digits(n_class=9)
>>> data = digits.data / 16.
>>> clf = svm.LinearSVC()
>>> feature_map_nystroem = Nystroem(gamma=.2,
... random_state=1,
... n_components=300)
>>> data_transformed = feature_map_nystroem.fit_transform(data)
>>> clf.fit(data_transformed, digits.target)
... # doctest: +NORMALIZE_WHITESPACE
LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,
intercept_scaling=1, loss='squared_hinge', max_iter=1000,
multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,
verbose=0)
>>> clf.score(data_transformed, digits.target) # doctest: +ELLIPSIS
0.9987...
References
----------
* Williams, C.K.I. and Seeger, M.
"Using the Nystroem method to speed up kernel machines",
Advances in neural information processing systems 2001
* T. Yang, Y. Li, M. Mahdavi, R. Jin and Z. Zhou
"Nystroem Method vs Random Fourier Features: A Theoretical and Empirical
Comparison",
Advances in Neural Information Processing Systems 2012
See also
--------
RBFSampler : An approximation to the RBF kernel using random Fourier
features.
sklearn.metrics.pairwise.kernel_metrics : List of built-in kernels.
"""
def __init__(self, kernel="rbf", gamma=None, coef0=None, degree=None,
kernel_params=None, n_components=100, random_state=None):
self.kernel = kernel
self.gamma = gamma
self.coef0 = coef0
self.degree = degree
self.kernel_params = kernel_params
self.n_components = n_components
self.random_state = random_state
def fit(self, X, y=None):
"""Fit estimator to data.
Samples a subset of training points, computes kernel
on these and computes normalization matrix.
Parameters
----------
X : array-like, shape=(n_samples, n_feature)
Training data.
"""
X = check_array(X, accept_sparse='csr')
rnd = check_random_state(self.random_state)
n_samples = X.shape[0]
# get basis vectors
if self.n_components > n_samples:
# XXX should we just bail?
n_components = n_samples
warnings.warn("n_components > n_samples. This is not possible.\n"
"n_components was set to n_samples, which results"
" in inefficient evaluation of the full kernel.")
else:
n_components = self.n_components
n_components = min(n_samples, n_components)
inds = rnd.permutation(n_samples)
basis_inds = inds[:n_components]
basis = X[basis_inds]
basis_kernel = pairwise_kernels(basis, metric=self.kernel,
filter_params=True,
**self._get_kernel_params())
# sqrt of kernel matrix on basis vectors
U, S, V = svd(basis_kernel)
S = np.maximum(S, 1e-12)
self.normalization_ = np.dot(U / np.sqrt(S), V)
self.components_ = basis
self.component_indices_ = inds
return self
def transform(self, X):
"""Apply feature map to X.
Computes an approximate feature map using the kernel
between some training points and X.
Parameters
----------
X : array-like, shape=(n_samples, n_features)
Data to transform.
Returns
-------
X_transformed : array, shape=(n_samples, n_components)
Transformed data.
"""
check_is_fitted(self, 'components_')
X = check_array(X, accept_sparse='csr')
kernel_params = self._get_kernel_params()
embedded = pairwise_kernels(X, self.components_,
metric=self.kernel,
filter_params=True,
**kernel_params)
return np.dot(embedded, self.normalization_.T)
def _get_kernel_params(self):
params = self.kernel_params
if params is None:
params = {}
if not callable(self.kernel):
for param in (KERNEL_PARAMS[self.kernel]):
if getattr(self, param) is not None:
params[param] = getattr(self, param)
else:
if (self.gamma is not None or
self.coef0 is not None or
self.degree is not None):
raise ValueError("Don't pass gamma, coef0 or degree to "
"Nystroem if using a callable kernel.")
return params


--- layout: post esbma_id: unknown title: MongoDB and WTFs and Anger date: 2015-07-20 12:00:00 ---
Recently, Sven Slootweg (joepie91) published a blog entry entitled Why you should never, ever, ever use MongoDB. It starts out with the words "MongoDB is evil" and proceeds to give a list of negative statements about same.
I am not here to respond to each of his statements. He labels them as "facts", and some (or perhaps all) of them surely are. In fact, for now, let's assume that everything he wrote is correct. My point here is not to say that the author is wrong.
Rather, my point here is that this kind of blog entry tells me very little about MongoDB while it tells me a great deal about the emotions of the person who wrote it.
Like I said, it may be true that every WTF the author listed is correct. It is also true that some software has more WTFs than others.
I'm not a MongoDB expert, but I've been digging into it quite a bit, and I could certainly make my own list of its WTFs. And I would also admit that my own exploration of Couchbase has yielded fewer of those moments. Therefore, every single person on the planet who chooses MongoDB instead of Couchbase is making a terrible mistake, right?
Let me briefly shift to a similar situation where I personally have a lot more knowledge: Microsoft SQL Server vs PostgreSQL. For me, it is hard to study SQL Server without several WTF moments. And while PostgreSQL is not perfect, I have found that a careful study there tends to produce more admiration than WTFs.
So, after I discovered that (for example) SQL Server has no support for deferred foreign keys, why didn't I write a blog entry entitled "Why you should never, ever, ever use SQL Server"?
Because I calmed down and looked at the bigger picture.
I think I could make an entirely correct list of negative things about SQL Server that is several pages long. And I suppose if I wanted to do that, and if I were really angry while I was writing it, I would include only the facts that support my feelings, omitting anything positive. For example, my rant blog entry would have no reason to acknowledge that SQL Server is the mostly widely used relational database server in the world. These kinds of facts merely distract people from my point.
But what would happen if I stopped writing my rant and spent some time thinking about the fact I just omitted?
I just convinced myself that this piece of software is truly horrible, and yet, millions of people are using it every day. How do I explain this?
If I tried to make a complete list of theories that might fit the facts, today's blog entry would get too long. Suffice it to say this: Some of those theories might support an anti-Microsoft rant (for example, maybe Microsot's field sales team is really good at swindling people), but I'm NOT going to be able to prove that every single person who chose SQL Server has made a horrible mistake. There is no way I can credibly claim that PostgreSQL is the better choice for every single company simply because I admire it. Even though I think (for example) that SQL Server handles NULL and UNIQUE in a broken way, there is some very large group of people for whom SQL Server is a valid and smart choice.
 I wouldn't. Unless I was really angry.
MongoDB is undisputably the top NoSQL vendor. It is used by thousands of companies who serve millions of users every day. Like all young software serving a large user base, it has bugs and flaws, some of which are WTF-worthy. But it is steadily getting better. Any discussion of its technical deficiences which does not address these things is mostly just somebody venting emotion.


Classes | |
| interface | IITArtwork |
| Defines a single piece of artwork. More... | |
| interface | IITArtworkCollection |
| Represents a collection of iTunes artwork objects. More... | |
Enumerations | |
| enum | ITArtworkFormat { ITArtworkFormatUnknown = 0, ITArtworkFormatJPEG, ITArtworkFormatPNG, ITArtworkFormatBMP } |
| Specifies the artwork format. More... |
|
|
Specifies the artwork format.
|


Eight people have died so far at our firm, not 60 – Shaanxi mining
Shaanxi Mining Company has refuted claims that over 60 people have died as a result of its operations in the Upper East region.
According to the company, only 8 of its workers have lost their lives in the line of duty.
There was a volcanic outpouring of rage Monday in Talensi, a district in the Region, over a fresh disaster that has claimed the lives of two Ghanaian employees of the mining company.
Clarifying the issue on the Morning Starr Maxwell Wooma, PRO for the company said “safety is key when it comes to our mines. We expect that workers do not even lose their fingernails when they come to work. We are creating jobs and not setting a death trap for people. We do not plan accidents for our staff.”
He stressed “we have a workforce we account for. Within the mines, we have about 8 workers who have lost their lives.”
He entreated “we need to be careful in determining which death is ours and which aren’t ours because some people who have lost their lives around our mines are illegal miners and we do not account for them.”
He however assured that whatever recommendations will be brought up to ensure safety will be taken very seriously.
“Because our members died strictly in their line of duty, the work-man compensation law will be used to compensate the family. We will do the needful,” he added.


?<0>
IMAGED
NEW
YORK
BOTANICAL
GARDEN
01263895
yo^
BOTANICAL
GARDEN
PLANTS
OF
BAHAMA
ISLANDS
Capraria
biflora
LĒ
Great
Inagua:
in
vacant
lots
about
Matthew
Town.
Flowers
white.
July
21,
1976
D.
S.
Correli
47434
Herbarium
FAIRCHILD
TROPICAL
GARDEN
01263895
Legend -
Level of confidence that token is an accurately-transcribed word
extremely low
very low
low
undetermined
medium
high
very high


Joshua Philip Miller
Unafffiliated
-Of Counsel; HLBS Law -Founder; ESQ Analytics -Reserve Support Officer; U.S. Marine Corps
-JD; University of Denver-2012 -BS Economics; Arizona State University-2012 -Certificate in Data Science; Galvanize 2020
80209
-I donated approximately $5 to the President Trump's campaign, that is it.
I am a member of the Colorado Bar Association YLD, Military Officers' Association
I have advocated on behalf of the Marine Corps Recruiting Station Denver before school boards and other organizations.
I understand the importance that the demographic makeup of a district can have upon the success of an area and the ability for an official to effectively represent that community.
I believe that I can calm a situation and create an environment where all commissioners will hear out the others and come to a logical decision as opposed to an emotional decision. I pride myself on being fair and impartial, to the point that when I answered questions truthfully, I was selected to sit on a jury, in a courtroom where I clerked for the judge, and where I answered that I has practiced recently as a criminal lawyer. I ended up serving as the foreman in that case and I believe we reached the correct result, which was a split verdict, since we were able to be fair and impartial.
My most relevant analytical skills in this context were gained through a data science program where we analyzed data of all types to create effective models. I founded a company where we create similar models for law firms to analyze their data. Additionally, while serving as the the Executive Officer for Recruiting Station Denver, it was my responsibility to divide up the areas of responsibility of the states of Colorado, Nebraska, Wyoming, and South Dakota into equitable sectors of population, by zip code, to allow for each recruiter to have a fair chance of writing their monthly quota of contracts. This directly translates into the districting problem set, because you have to come up with the most equitable division of population amongst areas, to include things that make an area a community and groping with large geographical areas.
Male


This documentation is for version {{ version }}, which is not released yet.
You can use it from the Git repo or look for released versions in the Python Package Index.
{% else %}Current version: {{ version }}
Get Pygments from the Python Package Index, or install it with:
pip install Pygments{% endif %}
Clone at GitHub.
You can also open an issue at the tracker.


Kickstart of my work career. Not in any terms related to IT, but a Media industry. Worked here as part of C&D (Conversion & Delivery) team. One of the most interesting fun jobs which includes, final converting English and language subtitles for major Hollywood Studios into formats like TIFF, BMP, PNG, DFXP, SRT, SCC etc. and then posting them to client sites, respecting tight client deadlines.
An Introduction to the IT world. Entering such a giant company definitely had its perks. These 2 years, lucky enough to work for a well-renowned/brilliant client named Mckinsey&Company, I got huge hands on exposure to various tech-stacks like RoR, Angular, UI/UX, Full Stack Development, DBs in such a short span of time. It helped me mould my skills and choose a stream that I henceforth passionately followed -- UI.
After I chose UI and managed to get good with it, this could simple be described as one of my favourite playgrounds. Xebia was brilliant in terms of giving me the freedom to expand my creativity, innovation taking my skills to the next level. Working on fun POCs, numerous excellent pet projects, attending conferences, winning hackathons, writing blogs, building their Official website along with other internal critical applications gave me the ammunitions I needed. Fortunately after all this I got the chance to work for Mckinsey&Company again, this time for a huge involvement, redesigning the Mckinsey&Company Official Website
My responsibilities include solely maintaining the Frontend of white label Insurance products - Motor Insurance & Home Insurance. Also responsible in development of the current OceanFinance blog website. Followed Agile methodology using TFS as version control system & tracking tool, and C#.NET as the backend.
As a Full Stack developer, my primary goal was to build GDS (Government Digital Service) compliant applications following GDS design principles like security, accessibility, performance, responsiveness and agile methodologies.
Apart from writing quality code, my typical work involves :
My daily responsibilities involve working with TNLCF developers to migrate a legacy application to a Progressive Web Application using modern tech-stacks, standards, methodologies and constantly improve it. Making it available to customers in various regions of the UK namely Scotland, Wales, Northern Ireland and England by deploying the code into multiple AWS EC2 instances strategically using feature toggles to make sure flexibility is maintained. I personally also suggested & successfully implemented a mechanism to drastically lower the time taken to run anti-virus checks on the AWS server. Reduced the same from the earlier 30 seconds to less than 1 second using ClamAV. The quality of the code is of the utmost importance, maintaining it by running various code quality checks using Travis CI, Percy (visual review platform), Cypress tests on git feature branches in addition to the usual code quality standards like Code Review, Linting etc.
Worked on an Angular 8 powered web application which consumed APIs built on PHP backend. My responsibilities was to single handedly build all the complicated components of the Angular application referring to an Adobe XD built design within 20 days’ time. Some of those components included building:
Successfully drove the development of an Angular 8 application following GDS standards right upto deployment stage.


Crater Lake (Klamath: giiwas)[1] is a crater lake in south-central Oregon in the western United States. It is the main feature of Crater Lake National Park and is famous for its deep blue color and water clarity. The lake partly fills a nearly 2,148-foot-deep (655 m) caldera[2] that was formed around 7,700 (± 150) years ago[3] by the collapse of the volcano Mount Mazama. There are no rivers flowing into or out of the lake; the evaporation is compensated for by rain and snowfall at a rate such that the total amount of water is replaced every 250 years. With a depth of 1,949 feet (594 m),[4] the lake is the deepest in the United States. In the world, it ranks ninth for maximum depth, and third for mean (average) depth.[5]
Crater Lake features two small islands. Wizard Island, located near the western shore of the lake, is a cinder cone approximately 316 acres (128 ha) in size. Phantom Ship, a natural rock pillar, is located near the southern shore.


{% extends 'base.html' %} {% block body %}
{{ project.description }}
Organize your teams schedule and collaborate!


Copyright © 2009 Mark Feldman. All Rights Reserved.
Kopi Lua is a C# port of the Lua v.5.1.4 virtual machine, parser, libraries and command-line utilities. KL is not simply an interface to a compiled DLL, all original code has been ported over to managed C#.
Please note that this is an alpha release only. I've tested it against numerous test samples and it seems to mostly hold up as well as the x86 C implementation upon which it was based. That said, I'm sure there are many bugs lurking within, so if you encounter any then please let me know. Bear in mind that managed environments are notoriously restrictive, so I'm sure the VM can be forcibly crashed without too much trouble. The code has not been subjected to rigorous scrutiny and is by no means meant to be regarded as secure in its current state.
The latest version of Kopi Lua can be downloaded from the Kopi Lua download page: http://www.ppl-pilot.com/KopiLua/download.htm
Kopi Lua provides a project file for Microsoft Visual C# 2008 Express Edition containing three solutions:
KopiLua is a class library containing the port of the core Lua VM and parser.
Lua is a Windows console application port of the Lua 5.1.4 interpreter.
Luac is a Windows console application port of the Lua 5.1.4 compiler.
Note that although the KopiLua solution compiles into a DLL, the source files have also been added to Lua and Luac so that they produce standalone executables. At this time all projects containing the Lua core files must be compiled with the following compilation flags, changing them will break the compile:
LUA_CORE;_WIN32;LUA_COMPAT_VARARG;LUA_COMPAT_MOD;LUA_COMPAT_GFIND;CATCH_EXCEPTIONS
The only new flag is CATCH_EXCEPTIONS and this is the one flag that can be safely disabled. When the Lua VM encounters an error it typically breaks out of execution with a long jump, although this can be changed to throw an exception in C++ builds. The C# port also throws exceptions and catches them higher up the execution heirarchy, but the catch is only done when CATCH_EXCEPTIONS is specified during the build. In general, this is the desired behavior, but turning it off can make it easier to track down exceptions in the code while debugging the VM code.
There is currently no support for locale.
There are several differences in the behaviour of calls to operating system functions, particularly with undefined operations e.g. renaming an open file works in C but throws an exception in C#.
The strftime() function has not yet been ported, so os_date() currently has an incomplete implementation.
The handling of lua_str2number() errors is a bit of a hack job at the moment. If the number being converted is outside the allowable range then the function is supposed to return either positive or negative infinity. The current code will return the correct value in most non-pathological cases but it needs additional work to make it more robust.
Memory allocation and tracking has not been properly implemented with user data objects so the behaviour of the garbage collector won't be 100% identical to the C version when user data objects are in use. This is currently high on the list of things to be fixed.
The primary goal of KL was for the port to match the behaviour of the C code as closely as possible. A secondary goal was to maintain a code base that is as close to the original C version as possible in order to assist with future version upgrades and patches.
The C code has been modified to remove all structs with the exception of Value; all other objects are passed by reference. This was done to assist in a future port to Java and/or any other languages that don't support passing objects via the stack. At some point the KL code will be modified so that Value is implemented with a class as well.
The project currently does not employ the use of any pre-processor, so all function #defines etc have been implemented by expanding them out to full-bodied functions. This will be changed in future as it obviously has a negative effect on performance.
The original source is littered with C-style conditionals of the form “if (val)” where val is a pointer or integer variable. These have all been expanded out to their explicit forms i.e. “if (val != 0)” etc.
There is a significant amount of mixing of 16-bit/32-bit and signed/unsigned arithmetic; in most cases a simple cast was enough to fix this. It will, however, need to be cleaned up at some point as it may be having an effect on performance.
The C code makes judicious use of pointer arithmetic, which presented a particularly challenging technical difficulty. When KL creates or resizes an array it loops through every element and stores a pointer to the array as well as it's position index within it. Overloaded operators are then used to reference neighbouring elements. Similarly, statements that use indirection such as “*s” etc have been converted to array look-ups of the form “s[0]”. In certain cases the code works with pointer pointers (e.g. GCObject **), this was solved by creating an intermediate class to represent a pointer-to-array (i.e. CGObjectRef).
The handling of strings is a very specific example of arrays that needed to be solved with a custom object (CharPtr) which stores a pointer to the char array and an offset into that array. Operator overloading then allows for the code to work with the string similar to the way in which the original code works with the char *. One notable difference here is the fact that in the original C code a char * passed to a function resides on the stack, so the function can change it at will. In KL the CharPtr object is a reference, so if any changes to the index itself are made then a local copy of the CharPtr has to be made first so that the object passed in by the calling function isn't corrupted. Chars are stored internally as Unicode but are treated as ASCII when converting to and from their byte representations i.e. the source code uses Unicode chars but from the perspective of the Lua scripts all strings appear as ASCII.
Several variables in the C code use C# reserved words, notably “base” and “ref”. These have been post-fixed with an underscore i.e. “base_” and “ref_”.
In order for KL to behave exactly in the same way as the original code the Lua garbage collector needed to be ported as well. This is particularly important for the behaviour of things such as weak tables etc. This presented a few problems, however, since the garbage collector relies on the total size of allocated objects, and generally speaking the “size” of a class is a meaningless concept in managed environments like C#. In order to get around this the memory allocation routines have been hard-coded with the size of all objects in the original C code. This is used to ensure that the value of the totalbytes member in global_State behaves identically to its C counterpart, which in turn ensures that the garbage collector behaves the same as well (an obvious downside is that this value is no longer an accurate representation of the true number of bytes allocated). When the Lua garbage collector frees an object it simply releases all handles to it and allows the C# garbage collector to then truly free the object as and when it chooses.
The Lua source code calls a lot of standard C functions (memcpy, gets, ungetc, fopen etc, to name a few). C# implementations for all of these have been developed and added to the bottom of the luaconf.cs file.
The most challenging problem by far was overcoming the C code's use of unions. Unions of the type used by the Lua code are not possible in managed code so a variety of tricks were employed to remove them. In cases where unions are used to save memory (e.g. Value) the union itself was unecessary and the members were simply added to a regular class. In some cases, e.g. the Closure class, a common header was added to the members CClosure and LClosure and casting was used depending on the closure type; this has been implemented by seperating the header out into a seperate class (ClosureHeader), adding a pointer to it to both the CClosure and LClosure members and using inherited operator overloading so that they can be “cast” to the ClosureHeader type.
There were many language-specific features in the code that caused problems such as using typed enums with ints, using fall-through in switch statements, failing to break from default handlers in switch statements, using commas to seperate statements, using “0” to represent NULL etc. In general though, the KL code follows the original C code very closely and shouldn't be too difficult to work with if you're familiar with the original code base.
Kopi Lua is licensed under the terms of the MIT license, the same terms under which Lua is licensed. This means that like Lua, Kopi Lua is free software and can be used for both academic and commercial purposes at absolutely no cost. The terms of the MIT license state:
"Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software."
The full text of the license can be found on the Lua site at http://www.lua.org/license.html, a copy of the original Lua license is also distributed with Kopi Lua. Additional details can be found at http://en.wikipedia.org/wiki/MIT_License.
The C# implementaion of printf that Kopi Lua uses was developed by Richard Prinz and is bundled as-is and unmodified. Its use is subject to the terms of The Code Project Open License (CPOL) 1.02 which state that both source code and executable "can be used in commercial applications", "can be redistributed" and "can be modified to create derivative works".
Thanks to Niki Bowe, Nick McVeity and Pete Henry at Infinite Interactive for introducing me to Lua and engaging in so many discussions about it. Richard Prinz saved me a considerable amount of work by making his C# implementation of printf available to the public. And it of course thanks to the creators of Lua and the members of the Lua mailing list for all their ongoing work.
Mark Feldman, November 2009.
mailto:lua NOTTHISBIT @ppl-pilot.com


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