The Secret Garden
by Frances Hodgson Burnett
Chapter 18: “‘Tha’ Munnot Waste No Time’”
Additional Information
- Year Published: 1909
- Language: English
- Country of Origin: England
- Source: Burnett, F. H. (1909). The Secret Garden. London, England: F. H. Burnett.
-
Readability:
- Flesch–Kincaid Level: 4.0
- Word Count: 2,604
- Genre: Romance
- Keywords: 20th century literature, british literature, children's stories
- ✎ Cite This
Downloads
Of course Mary did not waken early the next morning. She slept late because she was tired, and when Martha brought her breakfast she told her that though Colin was quite quiet he was ill and feverish as he always was after he had worn himself out with a fit of crying. Mary ate her breakfast slowly as she listened.
“He says he wishes tha’ would please go and see him as soon as tha’ can,” Martha said.  Tha’ did give it him last night for sure—didn’t tha? Nobody else would have dared to do it. Eh! Poor lad! He’s been spoiled till salt won’t save him. Mother says as th’ two worst things as can happen to a child is never to have his own way—or always to have it. She doesn’t know which is th’ worst. Tha’ was in a fine temper tha’self, too. But he says to me when I went into his room, ‘Please ask Miss Mary if she’ll please come an, talk to me?’ Think o’ him saying please! Will you go, Miss?”
“I’ll run and see Dickon first,” said Mary. “No, I’ll go and see Colin first and tell him—I know what I’ll tell him,” with a sudden inspiration.
She had her hat on when she appeared in Colin’s room and for a second he looked disappointed. He was in bed. His face was pitifully white and there were dark circles round his eyes.
“I’m glad you came,” he said. “My head aches and I ache all over because I’m so tired. Are you going somewhere?”
Mary went and leaned against his bed.
“I won’t be long,” she said. “I’m going to Dickon, but I’ll come back. Colin, it’s—it’s something about the garden.”
His whole face brightened and a little color came into it.
“Oh! Is it?” he cried out. “I dreamed about it all night I heard you say something about gray changing into green, and I dreamed I was standing in a place all filled with trembling little green leaves—and there were birds on nests everywhere and they looked so soft and still. I’ll lie and think about it until you come back.”
In five minutes Mary was with Dickon in their garden. The fox and the crow were with him again and this time he had brought two tame squirrels. “I came over on the pony this mornin’,” he said.  I brought these two in my pockets. This here one he’s called Nut an’ this here other one’s called Shell.”
When he said “Nut” one squirrel leaped on to his right shoulder and when he said “Shell” the other one leaped on to his left shoulder.
When they sat down on the grass with Captain curled at their feet, Soot solemnly listening on a tree and Nut and Shell nosing about close to them, it seemed to Mary that it would be scarcely bearable to leave such delightfulness, but when she began to tell her story somehow the look in Dickon’s funny face gradually changed her mind. She could see he felt sorrier for Colin than she did. He looked up at the sky and all about him.
 “Look at ‘em dartin’ about, an’ hearken at ‘em callin’ to each other. Come springtime seems like as if all th’ world’s callin’. The leaves is uncurlin’ so you can see ‘em—an’, my word, th’ nice smells there is about!” Sniffing with his happy turned-up nose.  Eh! My! We mun get him out here—we mun get him watchin’ an listenin’ an’ sniffin’ up th’ air an’ get him just soaked through wi’ sunshine. An’ we munnot lose no time about it.”
When he was very much interested he often spoke quite broad Yorkshire though at other times he tried to modify his dialect so that Mary could better understand. But she loved his broad Yorkshire and had in fact been trying to learn to speak it herself. So she spoke a little now.
“Aye, that we mun,” she said (which meant “Yes, indeed, we must”). “I’ll tell thee what us’ll do first,” she proceeded, and Dickon grinned, because when the little wench tried to twist her tongue into speaking Yorkshire it amused him very much. “He’s took a graidely fancy to thee. He wants to see thee and he wants to see Soot an’ Captain.
When she stopped she was quite proud of herself. She had never made a long speech in Yorkshire before and she had remembered very well.
“Tha’ mun talk a bit o’ Yorkshire like that to Mester Colin,” Dickon chuckled. “Tha’ll make him laugh an’ there’s nowt as good for ill folk as laughin’ is. Mother says she believes as half a hour’s good laugh every mornin’ ‘ud cure a chap as was makin’ ready for typhus fever.”
“I’m going to talk Yorkshire to him this very day,” said Mary, chuckling herself.
The garden had reached the time when every day and every night it seemed as if Magicians were passing through it drawing loveliness out of the earth and the boughs with wands. It was hard to go away and leave it all, particularly as Nut had actually crept on to her dress and Shell had scrambled down the trunk of the apple-tree they sat under and stayed there looking at her with inquiring eyes. But she went back to the house and when she sat down close to Colin’s bed he began to sniff as Dickon did though not in such an experienced way.
“You smell like flowers and—and fresh things,” he cried out quite joyously.  It’s cool and warm and sweet all at the same time.”
“It’s th’ wind from th’ moor,” said Mary. “It comes o’ sittin’ on th’ grass under a tree wi’ Dickon an’ wi’ Captain an’ Soot an’ Nut an’ Shell. It’s th’ springtime an’ out o’ doors an’ sunshine as smells so graidely.”
She said it as broadly as she could, and you do not know how broadly Yorkshire sounds until you have heard some one speak it. Colin began to laugh.
“What are you doing?” he said. “I never heard you talk like that before. How funny it sounds.”
“I’m givin’ thee a bit o’ Yorkshire,” answered Mary triumphantly. “I canna’ talk as graidely as Dickon an’ Martha can but tha’ sees I can shape a bit. Doesn’t tha’ understand a bit o’ Yorkshire when tha’ hears it? An’ tha’ a Yorkshire lad thysel’ bred an’ born! Eh! I wonder tha’rt not ashamed o’ thy face.”
And then she began to laugh too and they both laughed until they could not stop themselves and they laughed until the room echoed and Mrs. Medlock opening the door to come in drew back into the corridor and stood listening amazed.
“Well, upon my word!” she said, speaking rather broad Yorkshire herself because there was no one to hear her and she was so astonished. “Whoever heard th’ like! Whoever on earth would ha’ thought it!”
There was so much to talk about. It seemed as if Colin could never hear enough of Dickon and Captain and Soot and Nut and Shell and the pony whose name was Jump. Mary had run round into the wood with Dickon to see Jump.  He was rather thin with living on moor grass but he was as tough and wiry as if the muscle in his little legs had been made of steel springs.
“Does he really understand everything Dickon says?” Colin asked.
“It seems as if he does,” answered Mary. “Dickon says anything will understand if you’re friends with it for sure, but you have to be friends for sure.”
Colin lay quiet a little while and his strange gray eyes seemed to be staring at the wall, but Mary saw he was thinking.
“I wish I was friends with things,” he said at last, “but I’m not. I never had anything to be friends with, and I can’t bear people.”
“Can’t you bear me?” asked Mary.
“Yes, I can,” he answered. “It’s funny but I even like you.”
“Ben Weatherstaff said I was like him,” said Mary. “He said he’d warrant we’d both got the same nasty tempers. I think you are like him too. We are all three alike—you and I and Ben Weatherstaff. He said we were neither of us much to look at and we were as sour as we looked. But I don’t feel as sour as I used to before I knew the robin and Dickon.”
“Did you feel as if you hated people?”
“Yes,” answered Mary without any affectation. “I should have detested you if I had seen you before I saw the robin and Dickon.”
Colin put out his thin hand and touched her.
“Mary,” he said, “I wish I hadn’t said what I did about sending Dickon away. I hated you when you said he was like an angel and I laughed at you but—but perhaps he is.”
“Well, it was rather funny to say it,” she admitted frankly, “because his nose does turn up and he has a big mouth and his clothes have patches all over them and he talks broad Yorkshire, but—but if an angel did come to Yorkshire and live on the moor—if there was a Yorkshire angel—I believe he’d understand the green things and know how to make them grow and he would know how to talk to the wild creatures as Dickon does and they’d know he was friends for sure.”
“I shouldn’t mind Dickon looking at me,” said Colin; “I want to see him.”
“I’m glad you said that,” answered Mary, “because—because—”
Quite suddenly it came into her mind that this was the minute to tell him. Colin knew something new was coming.
“Because what?” he cried eagerly.
Mary was so anxious that she got up from her stool and came to him and caught hold of both his hands.
“Can I trust you? I trusted Dickon because birds trusted him. Can I trust you—for sure—for sure?” she implored.
Her face was so solemn that he almost whispered his answer.
“Yes—yes!”
“Well, Dickon will come to see you tomorrow morning, and he’ll bring his creatures with him.”
“Oh! Oh!” Colin cried out in delight.
“But that’s not all,” Mary went on, almost pale with solemn excitement. “The rest is better. There is a door into the garden. I found it. It is under the ivy on the wall.”
If he had been a strong healthy boy Colin would probably have shouted “Hooray! Hooray! Hooray!” but he was weak and rather hysterical; his eyes grew bigger and bigger and he gasped for breath.
“Oh! Mary!” he cried out with a half sob. “Shall I see it? Shall I get into it? Shall I live to get into it?” and he clutched her hands and dragged her toward him.
“Of course you’ll see it!” snapped Mary indignantly. “Of course you’ll live to get into it! Don’t be silly!”
And she was so un-hysterical and natural and childish that she brought him to his senses and he began to laugh at himself and a few minutes afterward she was sitting on her stool again telling him not what she imagined the secret garden to be like but what it really was, and Colin’s aches and tiredness were forgotten and he was listening enraptured.
“It is just what you thought it would be,” he said at last. “It sounds just as if you had really seen it. You know I said that when you told me first.”
Mary hesitated about two minutes and then boldly spoke the truth.
“I had seen it—and I had been in,” she said. “I found the key and got in weeks ago. But I daren’t tell you—I daren’t because I was so afraid I couldn’t trust you—for sure!”


Simulation and Inference for SDEs and Other Stochastic Processes.
| Version: | 1.8.1 |
| Depends: | R (≥ 2.10.0), methods, zoo, stats4, utils, expm, cubature, mvtnorm |
| Imports: | Rcpp (≥ 0.12.1), boot (≥ 1.3-2) |
| LinkingTo: | Rcpp, RcppArmadillo |
| Published: | 2018-05-21 |
| Author: | YUIMA Project Team yuima author details |
| Maintainer: | Stefano M. Iacus <stefano.iacus at unimi.it> |
| License: | GPL-2 |
| Copyright: | see file COPYRIGHTS |
| URL: | http://www.yuima-project.com |
| NeedsCompilation: | yes |
| Citation: | yuima citation info |
| Materials: | NEWS |
| In views: | TimeSeries |
| CRAN checks: | yuima results |
| Reference manual: | yuima.pdf |
| Package source: | yuima_1.8.1.tar.gz |
| Windows binaries: | r-devel: yuima_1.8.1.zip, r-release: yuima_1.8.1.zip, r-oldrel: yuima_1.8.1.zip |
| OS X binaries: | r-release: yuima_1.8.1.tgz, r-oldrel: yuima_1.8.1.tgz |
| Old sources: | yuima archive |
| Reverse imports: | valuer, yuimaGUI |
Please use the canonical form https://CRAN.R-project.org/package=yuima to link to this page.


About
Renge is designed by Junchao (www.junchaowang.com), inspired by the amination called “のんのんびより” and named by one of the Characters, Renge Miyauchi (宮内 れんげ). If you have any questions, you can reach me via Renge on GitHub by submitting an issue or via email billyfish152[at]gmail.com.


navigationPreload
Optional Boolean
, defaulting to false
Whether or not to enable navigation preload in the generated service worker.
When set to true
, you must also use
runtimeCaching
to
set up an appropriate reesponse strategy that will match navigation
requests, and make use of the preloaded response.
Example:
navigationPreload: true, runtimeCaching: [{ urlPattern: ({event}) => event.request.mode === 'navigate', handler: 'NetworkOnly', }]


Early years
- 1992: Turning professional at the age of 17
- 1995: Winning 3 ranking titles, the Grand Prix, the British Open, and the International Open
- 1996: Becoming number 2 in the world of snooker.
- 1998: World Champion at 23 years old and becoming number 1 of the world.
After the first world title
- 1998: Higgins won the UK Championship and the Master, becoming the third player to hold all Triple Crown titles simultaneously.
- 1999: He was replaced as the number one, despite winning the UK championship and the Grand Prix.
- 2000: As the first player ever, Higgins won the three opening tournaments of a season.
- 2005: Higgins scored 4 consecutive centuries in the Grand Prix final.
- 2006: In a final against Ronnie O'Sullivan, Higgins won his second Masters.
Second and third world titles
- 2007: Higgins won his second World Championship in a final against Mark Selby. He regained his number one position in the World Rankings.
- 2008: Fourth title at the Grand Prix.
- 2009: Higgins could win his third World Championship with a 18-9 win against Shaun Murphy.
- With a victory at the Welsh Open, Higgins could remain the world number one.
Match-fixing allegations and fourth world title
- 2010: Suspension due to match-fixing allegations. Allegiations were later dropped.
- 2010: Higgins won the UK Championship for the third time.
- 2011: Another title at the Welsh Open for John Higgins.
- 2011: After beating Ronnie O'Sullivan, Mark Williams, and Judd Trump, John Higgins won his fourth World Championship.
Struggle with form
- 2012: During the 2011/2012 season Higgins only reached two quarter finals.
- 2013: After winning his 25th ranking title at the Shanghai Open, he struggled for the rest of the year and did not win another tournament.
- 2014: During the 2013/2014 season Higgins changed his cue frequently, but he never found his form and didn't win any major tournaments.
- 2015: After almost dropping out of the Top 16 in the world during the 2014/2015 season, Higgins improved his form despite not winning any titles.
Return to form
- 2016: Higgins returned to his good form and could win a fourth Welsh Open title. He reached the finals of the World Championship but lost.
- 2017: In 2017 Higgins played well all year long and could win multiple titles. Again, he reached the finals in the World Championship, but lost.
- 2018: Higgins could reach another World championship final, but lost again, for the third year in a row.
- 2019: In this year, Higgins played the 800th century of his career, surpassing the great Stephen Hendry.
First ranking title in three years
- 2021: After multiple years without a title, Higgins could win the Players Championship, after "the best week of [his] snooker career".
If you want to learn more about John Higgins you can visit his Wikipedia page.


Nokia TV will be listed as early as December - Gizchina.com
by Efe UdinRate this post
About a week ago, Nokia and Flipkart announced that they are collaborating to bring Nokia-branded smart TVs to the Indian market. At the time, there was no information about the Nokia TV. A few days back, there were reports that this TV has gotten the certification of the Bureau of Indian Standards (BIS). This certification shows that the Nokia TV will feature a 55-inch 4K UHD panel. According to a BIS certification, the Nokia smart TV comes with model number 55CAUHDN. Now, the latest report suggests that this TV will arrive in India in December this year.
After Xiaomi, Jiajia, and Motorola, Nokia is the fourth mobile phone manufacturer to enter the smart TV market. Flipkart has established a partnership with Nokia, and in a statement, Flipkart will manufacture and sell Nokia smart TVs in India as part of the “Made in India” program. In addition, the company will create thousands of new jobs.
What we know for now is that it will have a 55-inch display with 4K resolution. Furthermore, it uses JBL speakers and the Android 9 Pie operating system. In addition, this TV will also use “smart dimming technology”, which can provide better color display and restoration.


Open the interactive manual of AngularFaces 2.1.
BootsFaces combines rock-solid JSF with the fancy Bootstrap look and feel. It can be used stand-alone, but it's also a natural companion to AngularFaces and PrimeFaces. For instance, the page you're viewing is a JSF page running on BootsFaces, AngularFaces and OmniFaces.
Check out the BootsFaces showcase!


This option allows the user to specify the function of the Multi-Purpose Input (MPI) signal (pin 15 on the Main I/O harness).
Note: This input is 48v (60v max) tolerant. Do not apply a negative voltage to this input
Available Functions:
Ready Power Status: Using the MPI as this function allows the BMS to sense the presence of "Ready" power (ie: the application is in operation mode as opposed to charging mode). This can be useful if an application requires the presence of "Ready" power before activating the Discharge Enable output.
Clear Fault Codes: When this function is selected the MPI operates as a "clear error codes" input button. When raised high it will cause the BMS fault codes to be cleared.
| < Multi Purpose Inputs/Outputs | Multi-Purpose Output Function > |


Write a functio for creating persons. Each person must have firstname, lastname, age and gender (true is female, false is male) Generate an array with ten person with different names, ages and genders
| First name | Last name | Age | Gender |
|---|
Write a function that checks if an array of person contains only people of age (with age 18 or greater) Use only array methods and no regular loops (for, while)
Result:
Write a function that prints all underaged persons of an array of person Use Array#filter and Array#forEach Use only array methods and no regular loops (for, while)
Result:
Write a function that calculates the average age of all females, extracted from an array of persons Use Array#filter Use only array methods and no regular loops (for, while)
result:
Write a function that finds the youngest male person in a given array of people and prints his full name Use only array methods and no regular loops (for, while) Use Array#find
Result:
Write a function that groups an array of persons by first letter of first name and returns the groups as a JavaScript Object Use Array#reduce Use only array methods and no regular loops (for, while)
Result:


This is a K-task experiment created with Flask and Heroku.
Flask is a lightweight web framework for python and Heroku is a free web deploymennt service.
The code for the experiment can be found here.
In this experiment, your goal is to determine if a square changed color or not.
An array of colored squares will appear on the screen, then go away. One square from the array will pop back up.
Press 'F' if the square stayed the same color and 'J' if it changed color.
A participant ID is required to participate in this experiment. Please enter yours in the box below.
(For demonstration purposes, feel free to put any ID greater than zero.)


Equalizer
This application helps People from different cultural background to communicate and converse with each other without any difficulty of the language
Read to find out what more this application can offer.
Scroll down to contact us and know more about us.
Any issues? Drop a note!
NITK, SurathkalKarnataka, 575025
<EMAIL_ADDRESS>+1 5589 55488 55s


Class HttpResponseMessageExtensions
Extensions for the HttpResponseMessage class
Inheritance
System.Object
HttpResponseMessageExtensions
Namespace:Dapplo.HttpExtensions
Assembly:Dapplo.HttpExtensions.dll
Syntax
public class HttpResponseMessageExtensions
Methods
| Improve this Doc View SourceGetAsAsync<TResult>(HttpResponseMessage, CancellationToken)
Extension method reading the HttpResponseMessage to a Type object Currently we support Json objects which are annotated with the DataContract/DataMember attributes We might support other object, e.g MemoryStream, Bitmap etc soon
Declaration
public static Task<TResult> GetAsAsync<TResult>(HttpResponseMessage httpResponseMessage, CancellationToken token = null)where TResult : class
Parameters
| Type | Name | Description |
|---|---|---|
| System.Net.Http.HttpResponseMessage | httpResponseMessage | HttpResponseMessage |
| System.Threading.CancellationToken | token | CancellationToken |
Returns
| Type | Description |
|---|---|
| System.Threading.Tasks.Task<TResult> | the deserialized object of type T or default(T) |
HandleErrorAsync(HttpResponseMessage, CancellationToken)
Simplified error handling, this makes sure the uri and response are logged
Declaration
public static Task<string> HandleErrorAsync(HttpResponseMessage httpResponseMessage, CancellationToken token = null)
Parameters
| Type | Name | Description |
|---|---|---|
| System.Net.Http.HttpResponseMessage | httpResponseMessage | HttpResponseMessage |
| System.Threading.CancellationToken | token | CancellationToken |
Returns
| Type | Description |
|---|---|
| System.Threading.Tasks.Task<System.String> | string with the error content if HttpBehaviour.ThrowErrorOnNonSuccess = false |


Searching for an item in an array is a common need. Software developers should know the possible algorithms for searching and which is best for different use cases.
Agari used Radix trees to optimize their IP address search engine - their efficiency went from 0(n) (searching an array) to O(log n).
Participants will be able to:
What is an algorithm?
Think about a shuffled deck of cards. Let’s say you’re looking for the 6 of hearts. How do you find it? You could just start grabbing random cards until you find it. If you grab a card and say “this isn’t it” and put it back, you really could be looking for a long time. Starting at the top of the deck and looking at each card one by one, you’re guaranteed to find it. This is the basis of a linear search.
If you have a phone book, and you’re looking for Jessica Smith, do you start on page 1 and read every name until you find her? Probably not. By starting in the middle, flipping larger chunks of pages until you get to the Ss, then eventually finding Smith, then finding Jessica eliminates a TON of time. This is the idea behind a binary search.
All of these algorithms are pretty common and have standard implementations in most programming languages. But understanding how they work and when to use one over another will help you write better software.
Watch the videos to learn more details about linear and binary search algorithms!
Slides: Searching algorithm (slides)
Video:Searching algorithm (video walkthrough of slides)
Write a linear search to find a given item in an array. The function should take in an array of strings and a string to search for, and return whether that string is in the array
Write a linear search to find the largest item in an array. The function should take in an array of integers and return the largest integer.
Say you have an array and want to find the number closest to a given number n. Write a linear search to find the closest number. E.g. if the array is [4, 1, 6, 8] and n=3, your function should return 4.
How long does linear search take for large arrays vs binary search? Draw a graph with values 1 to 100 on the x-axis. Graph a linear function (y=x) and a log function (y=log2(n))
Make a cheat sheet answering the following questions for linear search and binary search:


Mini Line Chart
Mouse over the chart to see the tooltip
26,68,35,38,53,95,114,87,114
9,43,50,36,95,41,69,113,50
5,43,66,73,50,102,40,92,59
14,89,16,35,63,63,64,124,100
38,13,36,62,98,48,93,60,75
Loading...
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Composite Inline
8,4,0,0,0,0,1,4,4,10,10,10,10,0,0,0,4,6,5,9,10
Mini Bar Chart
Negative values
25, -10, 15, 35, -15, -5, 10, 20, 15
Stacked
2:4, 5:7, 2:9, 4:8, 3:10, 4:10, 3:6, 5:8, 2:3
Composite Bar
4,6,7,7,4,3,2,1,4
Mini Pie Chart
30, 15, 55
65, 25, 10
70, 30
Simple Stat
Visits
1,363
Loading...
Unique Visitors
1,221
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Page Views
2,300
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Pages/Visit
1.19
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Avg. Visit Duration
00:00:30
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% New Visits
28.35%
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260
New Orders
3%
340, 837, 645, 393, 412, 324, 465, 643, 987
LIKES
81,450
SUBSCRIBERS
211,530
CUSTOMERS
64,957


The WSO2 WSF/PHP extension supports reliable messaging (RM) protocol versions 1.0 and 1.1. It also supports one way as well as two way reliable messaging.
To implement reliable messaging, WSO2 WSF/PHP uses SQLite database system to store state information related to messaging. This is packed with the binary or source.
If you using WSF/PHP on windows, please make sure to add the php.ini entry
wsf.rm_db_dir="<path to wsf/php extract directory>\wsf_c\"
Since WS-Addressing is required for WS-RM to work, you need to specify the necessary WS-Addressing options along with RM Options. Let's implement a simple RM client with WSO2 WSF/PHP.
First create a suitable payload to be sent to the echo service.
$req_payload_string = <<<XML <ns1:echoString xmlns="http://www.wsf.org/echo/echoString"><text>Hello RM!</text></ns1:echoString>XML;
try {
$msg = new WSMessage($reqPayloadString, array("action" => "http://wso2.org/wsfphp/samples/echoString") );
$client = new WSClient(array( "to"=>"http://localhost/echo_service_rm.php", "reliable"=>TRUE));
$result = $client->request($msg);
echo $result->str;
} catch (Exception $e) {
if ($e instanceof WSFault) {
printf("Soap Fault: %s\n", $e->code);
} else {
printf("Message = %s\n",$e->getMessage()); }
}
Note that we set the addressing action in WSMessage options and "reliable"=>TRUE option in WSClient options to enable RM. Since the WS-Addressing action is present, addressing will be enabled with "reliable"=>TRUE option. This client will create an RM Sequence, send its application message and terminate the sequence.
If you wish to send multiple application messages reliably to the receiving RM endpoint, you can configure the above Web service client as follows.
$msg0 = new WSMessage($reqPayloadString, array("action" => "http://wso2.org/wsfphp/samples/echoString") );
$msg1 = new WSMessage($reqPayloadString, array("action"=>"http://wso2.org/wsfphp/samples/echoString"));
$msg2 = new WSMessage($reqPayloadString, array("action"=>"http://wso2.org/wsfphp/samples/echoString", "lastMessage"=>TRUE));
$client = new WSClient(array( "to"=>"http://localhost:8080/echo_service_rm.php",
"reliable"=>TRUE,
"willContinueSequence"=>TRUE));
$result = $client->request($msg0);
$client->request($msg1);
$client->request($msg2);
Since we specified "willContinueSequence"=>TRUE option in WSClient , it will not terminate the sequence after sending the first message. It keeps the sequence open till the default sequenceExpiryTime expires. You can configure this value using the "sequenceExpiryTime" option in WSClient. So we send the $msg1 using the currently opened sequence. On $msg2 we specify that this will be the last message that will be sent using the current sequence using the option "lastMessage"=>TRUE. Now WSClient will terminate the current sequence after sending $msg2.
The following code demonstrates how to write a RM Web service. This echo_service_rm.php was used as the above client's receiving endpoint.
function echoFunction($inMessage){
return new WSMessage($inMessage->str);
}
$operations = array("echoString" => "echoFunction");
$actions = array("http://wso2.org/wsfphp/samples/echoString"=>"echoString");
$service = new WSService(array("operations"=>$operations,
"reliable"=>TRUE));
$service->reply();
Note how we map "echoString" operation to "http://wso2.org/wsfphp/samples/echoString" which is the same action URI used in the above example client.
On WSService options, "reliable"=>TRUE option is used to tell the service to use the RM protocol.


Welcome to the Entera.ai styleguide.
Colors
Typography
Font face
Roboto
Font size
14px
Font color
#5a5a5a
Font icons
FontAwesome
Buttons
Primary
Secondary
Link
Cards
Example content.
Logo
Animated Logo


4.0
New Popup
code
featureLayer = new FeatureLayer({ ... // add a custom action popupTemplate: { title: '{Cmn_Name}', content: '{*}', actions: [{ id: 'tree-photos', className: 'esri-icon-media', title: 'Tree Photos' }] } }); view.popup.on('trigger-action', function (event) { var actionId = event.action.id; var feature = view.popup.viewModel.selectedFeature; var attributes = feature.attributes; // Search on Flickr with the name of the tree if (actionId === 'tree-photos') { var search = 'https://www.flickr.com/search/?text=' + attributes.Cmn_Name + ' tree'; window.open(search); } });


This is a template project created to understand web app development using technologies like Angular 8, Bootstrap 4 & SpringBoot2. In addition to exploring these technologies, we will also try to understand the basic CS concepts of data structure & algorithms.
A data structure is a data organization, management, and storage format that enables efficient access and modification. More precisely, a data structure is a collection of data values, the relationships among them, and the functions or operations that can be applied to the data.
An algorithm is a well-defined procedure that allows a computer to solve a problem. Another way to describe an algorithm is a sequence of unambiguous instructions.
Some quick example text to build on the card title and make up the bulk of the card's content.


A primer on decision tree
Decision tree is the crux of many popular algorithms in data science: xgboost, lightgbm, catboost, etc. In this post I aim to explain how decision tree works in both classification and regression setting. This post is meant as a learning notes also for myself.
Classification decision trees
Given series of features \(X\) and a categorical label \(Y\), decision tree works by finding which feature in our instances to split and further divide the instances so that we have a leaf that represent the final prediction. For classification trees, we usually do the split based on a technique called gini impurity (the most popular technique). In practice there are several ways to calculate the split for a leaf. Deciding a split based on gini impurity involves several steps:
- Calculate gini impurity for a leaf
- Calculate total gini impurity for a split
- Decide the split based on feature that has the lowest total gini impurity.
- Repeat step 1-3 until convergence / stopping criteria
Figure 1: A toy dataset that is used to illustrate the classification section.
Gini impurity for a leaf
Gini impurity for a leaf is calculated as:
\[1 - (\sum_{c=1}^C p_{c}^2)\]where \(p_{c}\) is the class probability and \(C\) is the total number of class. For binary classification, the equation would become:
\[1 - y_{1}^2 - y_{0}^2\]where \(y_1\) is the yes class probability and \(y_0\) is the no class probability.
For example, if we split the dataset on the “loves sugar” column, we would have:
Figure 2: A split example on the “loves sugar” column.
where the gini impurity for the left leaf is given by:
\[1 - (\frac{1}{1+3})^2 - (\frac{3}{1+3})^2 = 0.375\]and the right gini impurity:
\[1 - (\frac{2}{2+1})^2 - (\frac{1}{2+1})^2 = 0.444\]Total gini impurity for a split
Once we have the gini impurity for each leaf, we need to calculate the gini impurity for a split. Note that these two are different quantities. The gini impurity for a split is the quantity that we use to decide which feature we want to split our instance first. To assess the total gini impurity for a split, we do a weighted average of gini impurities for each leaves:
\[(\frac{4}{4+3}) 0.375 - (\frac{3}{4+3}) 0.444\]The idea behind gini impurity is that the more homogeneous sample that we can divide based on a split, then the more pure the split is, hence the lower gini impurity score (0 being the most pure).
Gini impurity for multi categorical variable and numeric continuous variables
For categorical variable with category greater than 2, we calculate gini impurity for every possible split. For example, if we have a categorical variable with value 1,2, and 3, we split:
- based on 1 and not 1 (2 and 3)
- based on 2 and not 2 (1 and 3)
- based on 3 and not 3 (1 and 2)
and then we follow the exact same steps as above. The idea is to binarize the split so that we can calculate the leaf gini impurity and split total gini impurity. The same thing also applies to continuous variable. Coming back to our example, “age” column is a numeric continuous variable. To make a split on that variable, we do something like this:
Figure 3: Gini impurity for continuous variables.
Note that we do this on a sorted column because we want the split to be on the sorted instances. The idea is that we decide a split criteria for the continuous variable and we do the exact same steps as the above: treating the split as a binary choice. In the above case, the split criteria is computed based on the mean of every adjacent instances. After we get the mean we then split the instances and calculate the gini impurity like in Figure 2. In practice, there are many ways to decide the split criteria.
Choosing the split
Based on the example above, we have multiple split criterias and their gini impurity scores. We choose the split that has the lowest gini impurity and made it our first split.
Adding branches
We do the same thing as above but now we only consider samples that are included in the node. As we split more, the instances that end up in the leaf will be smaller and smaller.
Deciding when to stop (adding leaves)
We stop when we consider a leaf contains only homogeneous instances. We do not need to split more if all instances on a leaf already belong to one label. We can also stop based on some stopping criteria, for example, by maximum depth. Know when to stop is related to overfitting issue. Splitting perfectly on a training set lead to overfitting whereas splitting fewer lead to underfitting. If by stopping criteria we end up with impure leaf (3 yes label and 1 no label) then we take the majority as the decision.
Do classification decision trees have probabilities?
It is easy to fall into thinking that a classifier always have a probability interpretation. But decision tree is a non-parametric model. It does not have a probabilistic interpretation. One might get a pseudo-probability based on an impure node.
Regression decision trees
For regression trees, the leaf produces a continuous value, which is usually an average \(y\) over the splitted samples. Therefore, to evaluate the split, the sum of squared residuals (SSR) of every splitted samples are usually used. These are the steps:
- Calculate the average predicted value of a leaf
- Calculate sum of squared residuals (average predicted value - sample value) of every samples in both sides of the split
- Decide the split based on a feature that has the lowest sum of squared residuals
- Repeat step 1 -3 until convergence / stopping criteria
Figure 1: A toy dataset to illustrate this regression section.
Deciding the values to split our feature/column
In regression trees, we do this by sorting the column of our features and then we take the average of the adjacent value. This average value will be our threshold to split our feature into two groups. After the split, we calculate the average predicted value of the leaf.
Sum of squared residuals
After we get the average predicted value of the leaf, we then calculate the sum of squared residuals, which is given by:
\[\sum_i (y - f(x_{i}))^2\]where \(y\) is our label and \(f(x_{i})\) is our prediction value. Here the prediction depends on the average predicted value of the leaf/ where the sample ends up after being splitted.
Refer to the two figures below for an example of how to do calculate sum of squared residuals for a split:
Figure 2a: Example of splitting at a threshold value.
Figure 2a shows how we can calculate a sum of squared residuals for one threshold split, which is at 100 mg. We do this for all possible threshold value for this column:
Figure 2b: Another example of splitting at a threshold value.
Choosing the root for both single variable and multiple variables
If we have multiple variables, we do the same as above for every variable possible and we choose the split that has the lowest sum of squared residuals. If we have a binary column, we are just doing one split. Remember that the idea of a split is that we split the instances into two groups and then we move further from there.
Figure 3: Illustration to get the smallest SSR in multiple variables. The values are all made up.
Adding branches
We do the same thing as in the classification decision tree. Further split only consider samples that are included in the node. As we split more, the instances that end up in the leaf will be smaller and smaller.
Deciding when to stop (adding leaves)
There are multiple ways to do this. The most common one is that we stop when there are less than \(k\) instances in the leaf. \(k\) is usually set at 20. We can also stop based on some stopping criteria, for example, by maximum depth. Know when to stop is related to overfitting issue. Splitting perfectly on a training set lead to overfitting whereas splitting fewer lead to underfitting. With stopping criteria we then take the average \(y\) values of every instances in the leaf and use that as our prediction.
Closing
Decision tree is a non-parametric model that powers several most popular models in data science. In practice, there are many ways to decide which features to split. This post highlights one of the most popular technique for splitting: gini impurity for classification and sum of squared residuals for regression.


Created: 02/04/2015
By: Code This Lab S.r.l.
Email<EMAIL_ADDRESS>Thank you for purchasing our game. If you have any questions that are beyond the scope of this help file, please feel free to email via user page contact form here. Thanks so much!
3 Cards Monte is a HTML5 Gambling Game. Enjoy this stylish version of the 3 cards monte gambling game!
The ZIP package contains the game with 1920x768 resolution that scales to fit the whole screen device
Just warning that for very wide screens, the game may not be perfectly full screen.
The game is fully compatible with all most common mobile devices.
Sounds are enabled for mobile but we can't grant full audio compatibility on all mobile devices due to some well-know issue between some mobile-browser and HTML5.
So if you want to avoid sound loading, please read Disable Sound section).
WARNING: Sounds can't be enabled for Windows Phone as this kind of device have unsolved issues with 'audio' and 'video' tag.
To install the game just upload on your server the game folder game1136x640.
$(document).ready(function(){ var oMain = new CMain(); $(oMain).on("save_score", function(evt,iScore) { //alert("iScore: "+iScore); }); });
TEXT_GAMEOVER = "WRONG CHOICE, YOU'RE OUT!"; TEXT_SCORE = "SCORE"; TEXT_BEST_SCORE = "BEST SCORE"; TEXT_CUR_SCORE = "CURRENT SCORE"; TEXT_PLAY = "PLAY"; TEXT_RESTART = "RESTART"; TEXT_CHOOSE = "WHERE IS THE ACE OF HEARTS?"; TEXT_INFO = "FIND THE ACE OF HEARTS!"; TEXT_SHUFFLE = "SHUFFLING..."; TEXT_WIN = "WELL DONE!";  TEXT_CREDITS_DEVELOPED = "DEVELOPED BY"; var TEXT_PRELOADER_CONTINUE = "START";
var oMain = new CMain({ points_to_win: 1, //Number of win points start_num_shuffle: 5, //Starting number of shuffle in a level num_level_to_increase_num_shuffle: 2, //Levels to play to increase by 1 the number of shuffle start_timespeed_shuffle: 800, //Starting time (in ms) to shuffle 2 cards decrease_timespeed_shuffle: -50, //Decrease starting timespeed to shuffle 2 cards every level (in ms) minimum_timespeed: 200, //Minimum time speed limit audio_enable_on_startup:false, //ENABLE/DISABLE AUDIO WHEN GAME STARTS show_credits:true, fullscreen:true, //SET THIS TO FALSE IF YOU DON'T WANT TO SHOW FULLSCREEN BUTTON check_orientation:true //SET TO FALSE IF YOU DON'T WANT TO SHOW ORIENTATION ALERT ON MOBILE DEVICES });
This game have the canvas tag in the body. The ready event into the body calls the main function of the game: CMain().
The head section declares all the javascript functions of the game. The whole project uses a typical object-oriented approach.
In the init function there are 6 mapped events that can be useful eventually for stats
The game use two CSS files. The first one is a generic reset file. Many browser interpret the default behavior of html elements differently. By using a general reset CSS file, we can work round this. Keep in mind, that these values might be overridden somewhere else in the file.
The second file contains all of the specific stylings for the canvas and some hack to be fully compatible with all most popular mobile devices
This game contains:
Resuming, the complete game flow is the following:
The game contains the card images that you can replace if you want to change cards.
Currently each card has following dimensions: 217x330. So if you change card size, be sure to change also card size settings in the settings.js file.
If you want to disable all the sounds for mobile devices, you have to change the following value in settings.js file:
var DISABLE_SOUND_MOBILE = true;
CTL Arcade will allow you to add a real arcade on your worpress website, in this way your users will be more involved and will stay connected longer.
It's possible to add Ads banner at the beginning of each game and at the end of each level. This will give you a new tool to increase your revenues.
Your own users will promote your website sharing their scores on the main Social Networks, with no extra costs for you.
You'll get by default the score-sharing on Twitter. To add Facebook just follow the guideline below.
3 widgets can be added in your pages through a shortcode.
Once again, thank you so much for purchasing this game. Fell free to contact us if you have any questions or issue relating to this game. No guarantees, but we'll do our best to assist.


Problem 1. Format with placeholders Write a function that formats a string. The function should have placeholders, as shown in the example Add the function to the String.prototype Problem 2. HTML binding Write a function that checks if an array of person contains only people of age (with age 18 or greater) Use only array methods and no regular loops (for, while)
See the results in the console -> F12 + F5


Creative | Persistant | Logical
I am a young professional, eager to take action in the tech industry. With great communication and leadership skills, I make a great asset to any team.
Creative and reliable, with attention to detail, I will make our workplace a happier, smoother, and smarter place to be.
Would you like to get in contact? Click here to send an email and I will be in touch shortly!


[1]
this phone easy to use .
[2]
this phone is really a great device , great , really a device and awesome .


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Before Change
f = None
if structure == UPPER_TRIANGULAR and A.dtype == B.dtype:
if not isspmatrix(A) and not isspmatrix(B):
if A.dtype == np.float32:
f = blas.strmm
elif A.dtype == np.float64:
f = blas.dtrmm
elif A.dtype == np.complex64:
f = blas.ctrmm
elif A.dtype == np.complex128:
f = blas.ztrmm
if f is not None:
if alpha is None:
alpha = 1.
out = B.copy()
After Change
f = None
if structure == UPPER_TRIANGULAR:
if not isspmatrix(A) and not isspmatrix(B):
f, = scipy.linalg.get_blas_funcs(("trmm",), (A, B))
if f is not None:
if alpha is None:
alpha = 1.
out = B.copy()


--- layout: post date: 2007-09-25 time: "09:00:00" authors: ["Greg Wilson"] title: "Another Sighting of Software Carpentry" tags: ["Bernstein Center - Berlin"] ---
Tiziano Zito is teaching Software Carpentry in Berlin. I admit I'm biased, but I think that's pretty cool ;-) Traffic on the site is holding steady as well — wish I had time to go back and update the notes...


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Family of as_*()
functions should be used to convert existing distribution functions into desired class (“p”, “d”, “q”, or “r”). Roughly, this is a new_*()
family but with function as an input.
There are two main use cases:
Converting existing pdqr-function to desired type is done straightforwardly by changing function’s class without touching the underlying distribution (“x_tbl” metadata is the same):
d_fin <- new_d(1:4, "discrete")
meta_x_tbl(d_fin)
#> x prob cumprob
#> 1 1 0.25 0.25
#> 2 2 0.25 0.50
#> 3 3 0.25 0.75
#> 4 4 0.25 1.00
# This is equivalent to `new_p(1:4, "discrete")`
(p_fin <- as_p(d_fin))
#> Cumulative distribution function of discrete type
#> Support: [1, 4] (4 elements)
meta_x_tbl(p_fin)
#> x prob cumprob
#> 1 1 0.25 0.25
#> 2 2 0.25 0.50
#> 3 3 0.25 0.75
#> 4 4 0.25 1.00
Another important use case for as_*()
functions is to convert some other distribution functions to be pdqr-functions. Except small number of special cases, output of as_*()
function will have “continuous” type. The reason is because identifying exact values of distribution in discrete case is very hard in this setup (when almost nothing is known about the input function). It is assumed that if user knows those values, some new_*()
function with data frame input can be used to create arbitrary “discrete” pdqr-function.
General conversion algorithm is as follows:
new_*()
function is created which approximates input function. Approximation precision can be tweaked with n_grid
(and n_sample
for as_r()
) argument: bigger values lead to better approximation precision, but worse memory usage and evaluation speed (direct and of summ_*()
functions).For input distribution function to be recognized as “honored”, it should be supplied directly with its original name:
# "Honored" distributions
as_d(dnorm)
#> Density function of continuous type
#> Support: ~[-4.75342, 4.75342] (10000 intervals)
# Underlying distribution doesn't depend on class ("p", "d", "q", "r").
# Following code has the same effect as `as_r(as_d(dnorm))`
as_r(rnorm)
#> Random generation function of continuous type
#> Support: ~[-4.75342, 4.75342] (10000 intervals)
# Different picewise-linear approximation precision is achieved with different
# `n_grid` argument value
as_d(dnorm, n_grid = 101)
#> Density function of continuous type
#> Support: ~[-4.75342, 4.75342] (100 intervals)
# Different extra arguments for input
as_d(dnorm, mean = 10, sd = 0.1)
#> Density function of continuous type
#> Support: ~[9.52466, 10.47534] (10000 intervals)
# Currently only five distributions result into "discrete" output of `as_*()`
as_d(dbinom, size = 10, prob = 0.3)
#> Probability mass function of discrete type
#> Support: [0, 10] (11 elements)
as_d(dgeom, prob = 0.3)
#> Probability mass function of discrete type
#> Support: [0, 38] (39 elements)
as_d(dhyper, m = 10, n = 10, k = 7)
#> Probability mass function of discrete type
#> Support: [0, 7] (8 elements)
as_d(dnbinom, size = 10, prob = 0.3)
#> Probability mass function of discrete type
#> Support: [0, 87] (88 elements)
as_d(dpois, lambda = 1)
#> Probability mass function of discrete type
#> Support: [0, 9] (10 elements)
# This isn't recognized as "honored", but output is very close to "honored"
as_d(function(x) {dnorm(x)})
#> Density function of continuous type
#> Support: ~[-38.41642, 38.41642] (10000 intervals)
Support detection is implemented for more smooth user experience. For more details on algorithms behind it, see section “Support detection” in as_p()
documentation. Generally, if you know exactly what support should be, it is better to provide it.
my_d <- function(x) {ifelse(x >= -1 & x <= 1, 0.75 * (1 - x^2), 0)}
# With default support detection
as_d(my_d)
#> Density function of continuous type
#> Support: ~[-1.00018, 1.00019] (7588 intervals)
# Providing custom, maybe only partially known, support
as_d(my_d, support = c(-1, NA))
#> Density function of continuous type
#> Support: ~[-1, 1.00007] (9327 intervals)
as_d(my_d, support = c(NA, 1))
#> Density function of continuous type
#> Support: ~[-1.0002, 1] (8027 intervals)
as_d(my_d, support = c(-1, 1))
#> Density function of continuous type
#> Support: [-1, 1] (10000 intervals)
Here is a comparison of support detection performance. One important note here is that algorithm has random nature in as_r()
(which is reasonable because the only information available about distribution is its random generation function).
(p_norm <- as_p(function(x) {pnorm(x)}))
#> Cumulative distribution function of continuous type
#> Support: ~[-4.75343, 4.75342] (10000 intervals)
(d_norm <- as_d(function(x) {dnorm(x)}))
#> Density function of continuous type
#> Support: ~[-38.41642, 38.41642] (10000 intervals)
(q_norm <- as_q(function(x) {qnorm(x)}))
#> Quantile function of continuous type
#> Support: ~[-3.89971, 3.89971] (8204 intervals)
(r_norm <- as_r(function(x) {rnorm(x)}))
#> Random generation function of continuous type
#> Support: ~[-3.91006, 3.82021] (10000 intervals)
plot(
as_d(p_norm), col = "black",
main = "Comparison of `as_*()` functions support detection"
)
lines(d_norm, col = "blue")
lines(as_d(q_norm), col = "red")
lines(as_d(r_norm), col = "green")
If for some point density function goes to infinity, it is imputed linearly from its neighborhood. For not “honored” distribution functions, it can be more robust to use as_p()
for initial conversion.
Note that output distribution is usually an approximation (albeit a reasonably good one) of input due to the following facts:
‘pdqr’ provides a diagnostic function pdqr_approx_error()
to look at the precision of approximation. It accepts a pdqr-function and original reference distribution function with its possible extra arguments. It constructs a grid that is more dense than “x” column in pdqr-function’s “x_tbl” metadata (to actually test the precision of piecewise-linear nature). Output is a data frame with rows corresponding to that grid elements and columns with two kinds of errors: “error” (with direct, signed error as difference between values of reference function and pdqr-function) and “abserror” (with absolute error):
approx_err <- pdqr_approx_error(as_d(dnorm, sd = 2), dnorm, sd = 2)
head(approx_err)
#> grid error abserror
#> 1 -9.506849 -4.948351e-12 4.948351e-12
#> 2 -9.506658 -7.126907e-12 7.126907e-12
#> 3 -9.506468 -8.822366e-12 8.822366e-12
#> 4 -9.506278 -1.003453e-11 1.003453e-11
#> 5 -9.506088 -1.076320e-11 1.076320e-11
#> 6 -9.505898 -1.100818e-11 1.100818e-11
summary(approx_err)
#> grid error abserror
#> Min. :-9.507 Min. :-3.989e-07 Min. :4.900e-12
#> 1st Qu.:-4.753 1st Qu.:-2.000e-07 1st Qu.:9.874e-10
#> Median : 0.000 Median :-2.776e-08 Median :2.776e-08
#> Mean : 0.000 Mean :-1.052e-07 Mean :1.052e-07
#> 3rd Qu.: 4.753 3rd Qu.:-9.874e-10 3rd Qu.:2.000e-07
#> Max. : 9.507 Max. :-4.900e-12 Max. :3.989e-07
Here are estimation of median and maximum errors for most common “honored” distributions using default n_grid
value (tested for d-functions, but can be used also for p- and q-functions):
abserror_stat <- function(f, ref_f, ...) {
approx_err <- pdqr_approx_error(f, ref_f, ...)
c(
median_abserror = median(approx_err[["abserror"]]),
max_abserror = max(approx_err[["abserror"]])
)
}
abserror_stat_fin <- function(f, ref_f, grid, ...) {
abserror <- abs(f(grid) - ref_f(grid, ...))
c(median_abserror = median(abserror), max_abserror = max(abserror))
}
# Normal
abserror_stat(as_d(dnorm), dnorm)
#> median_abserror max_abserror
#> 5.551817e-08 7.978876e-07
# Beta
abserror_stat(
as_d(dbeta, shape1 = 10, shape2 = 20), dbeta, shape1 = 10, shape2 = 20
)
#> median_abserror max_abserror
#> 1.270044e-06 9.263838e-06
# By default, `pdqr_approx_error()` removes infinity errors. As one can see,
# when density goes to infinity, error can be quite big
abserror_stat(
as_d(dbeta, shape1 = 0.1, shape2 = 0.2), dbeta, shape1 = 0.1, shape2 = 0.2
)
#> median_abserror max_abserror
#> 0.108998 1630.863871
# Exponential
abserror_stat(as_d(dexp, rate = 10), dexp, rate = 10)
#> median_abserror max_abserror
#> 9.953929e-09 1.078784e-05
# Student
abserror_stat(as_d(dt, df = 5), dt, df = 5)
#> median_abserror max_abserror
#> 2.984622e-11 7.626976e-07
# Cauchy. Heavy tails also affect approximation error
abserror_stat(as_d(dcauchy), dcauchy)
#> median_abserror max_abserror
#> 2.518228e-08 6.378956e-04
# Poisson. Pdqr-function isn't exact because of tail trimming.
abserror_stat_fin(as_d(dpois, lambda = 10), dpois, grid = 0:30, lambda = 10)
#> median_abserror max_abserror
#> 9.757657e-09 5.134715e-07
# For some distributions functions are exact
# Uniform
abserror_stat(as_d(dunif), dunif)
#> median_abserror max_abserror
#> 0 0
# Binomial
abserror_stat_fin(
as_d(dbinom, size = 10, prob = 0.1), dbinom, grid = 0:10,
size = 10, prob = 0.1
)
#> median_abserror max_abserror
#> 0 0


4.6. Microdata Application¶
This application is the direct interface between devices and the InfluxDB database. It is responsible for exposing the REST API endpoints.
A microdata.models.Device
is what links to a SEAD Light out in the world. When a new SEAD connects to the system, it will check to see
if it has been registered on the system by querying /api/device-api/{serial}/. If the response is 404, the device will then register
on the system as new with no owner. It is then the user’s responsibility to pair the device via the web application in order to access
the data.
Refer to the Getting Started guide for more information on interfacing the devices with the server.
4.6.1. Subpackages¶
4.6.2. Submodules¶
4.6.3. microdata.admin module¶
Models registered to the administrative interface are listed below. These are the interfaces provided to an administrator that may have control over the system for a group of users.
-
class
microdata.admin.
ApplianceAdmin
(model, admin_site)¶ Bases:
django.contrib.admin.options.ModelAdmin
Class that allows administrator access to the Appliance models.
This class was included to give the administartor the ability to add new appliances as the algorithms detect them. This is exposed so that an administrator can extend the functionality of the Appliances without having to touch the source code.
-
list_display
= ('name', 'pk', 'serial', 'chart_color')¶
-
media
¶
-
-
class
microdata.admin.
CircuitAdmin
(model, admin_site)¶ Bases:
django.contrib.admin.options.ModelAdmin
-
list_display
= ('name', 'circuittype', 'pk')¶
-
media
¶
-
-
class
microdata.admin.
CircuitTypeAdmin
(model, admin_site)¶ Bases:
django.contrib.admin.options.ModelAdmin
-
list_display
= ('name', 'pk')¶
-
media
¶
-
-
class
microdata.admin.
DeviceAdmin
(model, admin_site)¶ Bases:
django.contrib.admin.options.ModelAdmin
This class gives an administrator direct access to the device model.
Most of the fields of the device model can be modified through the custom settings interface on the web application, but this interface gives the administrator direct control.
-
inlines
= (<class 'microdata.admin.DeviceWebSettingsInline'>, <class 'farmer.admin.DeviceSettingsInline'>)¶
-
list_display
= ('name', 'owner', 'serial', 'position', 'secret_key', 'registered', 'fanout_query_registered')¶
-
media
¶
-
readonly_fields
= ('secret_key',)¶
-
search_fields
= ('name', 'serial')¶
-
4.6.4. microdata.models module¶
-
class
microdata.models.
Appliance
(*args, **kwargs)¶ Bases:
django.db.models.base.Model
Describes a single Appliance. In the future, this model will have describing attributes that give the user helpful information when visualizing.
-
exception
DoesNotExist
¶ Bases:
django.core.exceptions.ObjectDoesNotExist
-
exception
Appliance.
MultipleObjectsReturned
¶ Bases:
django.core.exceptions.MultipleObjectsReturned
-
Appliance.
chart_color
= None¶ This field defines what color the chart will assign the appliance when it is being displayed. Modifiable via the admin interface.
-
Appliance.
circuittype_set
¶
-
Appliance.
eventnotification_set
¶
-
Appliance.
objects
= <django.db.models.manager.Manager object>¶
-
exception
-
class
microdata.models.
Circuit
(*args, **kwargs)¶ Bases:
django.db.models.base.Model
Most likely deprecated.
-
exception
DoesNotExist
¶ Bases:
django.core.exceptions.ObjectDoesNotExist
-
exception
Circuit.
MultipleObjectsReturned
¶ Bases:
django.core.exceptions.MultipleObjectsReturned
-
Circuit.
circuittype
¶
-
Circuit.
objects
= <django.db.models.manager.Manager object>¶
-
exception
-
class
microdata.models.
CircuitType
(*args, **kwargs)¶ Bases:
django.db.models.base.Model
Describes a Circuit Type. A Circuit Type is related to a list of
microdata.models.Appliance
and acts as a set of objects to discover within a circuit.-
exception
DoesNotExist
¶ Bases:
django.core.exceptions.ObjectDoesNotExist
-
exception
CircuitType.
MultipleObjectsReturned
¶ Bases:
django.core.exceptions.MultipleObjectsReturned
-
CircuitType.
appliances
¶
-
CircuitType.
chart_color
= None¶ This field defines what color the chart will assign the circuit when it is being displayed. Modifiable via the admin interface.
-
CircuitType.
circuit_set
¶
-
CircuitType.
objects
= <django.db.models.manager.Manager object>¶
-
exception
-
class
microdata.models.
Device
(*args, **kwargs)¶ Bases:
django.db.models.base.Model
Describes a single Device owned by
settings.AUTH_USER_MODEL
.-
exception
DoesNotExist
¶ Bases:
django.core.exceptions.ObjectDoesNotExist
-
exception
Device.
MultipleObjectsReturned
¶ Bases:
django.core.exceptions.MultipleObjectsReturned
-
Device.
channel_1
¶ The first of three channels of the SEAD Light. This is a design flaw and should be instead a ManyToManyField.
-
Device.
channel_2
¶ The second of three channels of the SEAD Light. This is a design flaw and should be instead a ManyToManyField.
-
Device.
channel_3
¶ The third of three channels of the SEAD Light. This is a design flaw and should be instead a ManyToManyField.
-
Device.
cost_daily
= None¶ The total cost calculated by the server today. Much cheaper to keep this in the django database than the InfluxDB.
-
Device.
data_retention_policy
= None¶ The amount of time the data from this device can live in the database. Anything older will be archived to Amazon Glacier.
-
Device.
delete
(*args, **kwargs)¶ Custom delete method.
Drop the series from the influxdb database.
-
Device.
devicesettings
¶
-
Device.
devicewebsettings
¶
-
Device.
event_set
¶
-
Device.
fanout_query_registered
= None¶ A true/false that is set when a device is created indicating the continuous queries in the database have been registered.
-
Device.
ip_address
= None¶ This field is no longer actively used. This was a proof of concept for early device communication. Deprecated.
-
Device.
kilowatt_hours_daily
= None¶ A counter that is reset by a cron job once a day that keeps an accumulation of the total kwh this device has measured over the course of a day.
-
Device.
kilowatt_hours_monthly
= None¶ A counter that is reset by a cron job once a month that keeps an accumulation of the total kwh this device has measured over the course of a month.
-
Device.
name
= None¶ A non-unique name field for a device. This field is solely for user experience, it has no functional purpose.
-
Device.
objects
= <django.db.models.manager.Manager object>¶
-
Device.
owner
¶ A Foreign key relation. We use this relation to pair a device to a user on the web application.
-
Device.
position
¶ A position field that essentially translates to coordinates on a map. Automatically generates a map on the admin interface.
-
Device.
registered
= None¶ Synonym for paired. This protects againts users trying to pair an already paired device.
-
Device.
save
(**kwargs)¶ Custom save method.
This function will do several things if it has not done so already:
- Create a secret key. 3 digits followed by 4 letters. Not currently in use (could be used for pairing devices)
- Register fanout queries in database.
- Device name cannot be None, default is “Device <serial>”.
- Create a
farmer.models.DeviceSettings
object. - Create a
webapp.models.DeviceWebSettings
object. - Give default values to the channels.
-
Device.
secret_key
= None¶ This field is not currently in use, but the functionality exists. This field was intended to be used to pair a device to a user.
-
Device.
serial
= None¶ The primary key of the model. This is what the device uses to interface to the API and how users currently pair a device.
This field acts much like an owner, however share_with users cannot alter device settings.
-
Device.
testevent_set
¶
-
exception
-
class
microdata.models.
Event
(*args, **kwargs)¶ Bases:
django.db.models.base.Model
Generic class to catch the Event REST Packets from devices. Relates to a
microdata.models.Device
.These models are not stored on the Django database since they are converted to InfluxDB.
-
exception
DoesNotExist
¶ Bases:
django.core.exceptions.ObjectDoesNotExist
-
exception
Event.
MultipleObjectsReturned
¶ Bases:
django.core.exceptions.MultipleObjectsReturned
-
Event.
dataPoints
= None¶ An array of undefined size containing measured values for the server. The data points are numbered 0 ... n ... j where nth data point has a timestamp of start_time + n * period and j is undefined.
-
Event.
device
¶ This is the relation between the device and its data. This is established when the device specifies its hyperlinked model via the REST call.
-
Event.
frequency
= None¶ The frequency, in Hertz, of the packet’s data points. Used to calculate the offset of all points.
-
Event.
objects
= <django.db.models.manager.Manager object>¶
-
Event.
query
= None¶ Deprecated. This field is useful for debugging the REST requests, but since the model is not saved, this is a volatile field.
-
Event.
save
(**kwargs)¶ Custom save method.
This method is the powerhouse of the API. It can take an array of data points from a device and convert them into database entries in InfluxDB.
The method will also keep a running count of how many kwh have been consumed this day and this month. If it exceeds the allotted kwh for the device’s tier, advance the tier a level.
If the data coming in is sufficiently in the past such that the database will not calculate its mean value, refresh the query to trigger a backfill of the data.
When a model is being saved, it has already been created by
microdata.views.EventViewSet
.The Event is parsed as follows:
start = self.start frequency = self.frequency count = 0 for point in dataPoints: time = start + count * (1/frequency) db.write_points(time, wattage)
-
Event.
start
= None¶ The start time, in milliseconds, of the first data point in the packet. Used to calculate offset of all proceeding points.
-
exception
4.6.5. microdata.serializers module¶
-
class
microdata.serializers.
ApplianceSerializer
(instance=None, data=<class rest_framework.fields.empty>, **kwargs)¶ Bases:
rest_framework.serializers.HyperlinkedModelSerializer
-
class
microdata.serializers.
CircuitSerializer
(instance=None, data=<class rest_framework.fields.empty>, **kwargs)¶ Bases:
rest_framework.serializers.HyperlinkedModelSerializer
-
class
microdata.serializers.
DeviceSerializer
(instance=None, data=<class rest_framework.fields.empty>, **kwargs)¶ Bases:
rest_framework.serializers.HyperlinkedModelSerializer
4.6.6. microdata.tests module¶
4.6.7. microdata.views module¶
-
class
microdata.views.
ApplianceViewSet
(**kwargs)¶ Bases:
rest_framework.viewsets.ModelViewSet
API endpoint that allows appliances to be viewed or edited.
-
queryset
¶
-
serializer_class
¶ alias of
ApplianceSerializer
-
-
class
microdata.views.
CircuitViewSet
(**kwargs)¶ Bases:
rest_framework.viewsets.ModelViewSet
API endpoint that allows circuits to be viewed or edited.
-
queryset
¶
-
serializer_class
¶ alias of
CircuitSerializer
-
-
class
microdata.views.
DeviceViewSet
(**kwargs)¶ Bases:
rest_framework.viewsets.ModelViewSet
API endpoint that allows devices to be viewed or edited.
-
create
(request)¶
-
queryset
¶
-
serializer_class
¶ alias of
DeviceSerializer
-
-
class
microdata.views.
EventViewSet
(**kwargs)¶ Bases:
rest_framework.viewsets.ModelViewSet
API endpoint that allows events to be viewed or edited.
-
create
(request)¶ Custom create method.
Used to parse the packets sent via the REST API. Since the packets are in a JSON array, the Django REST Framework has no native way of handling these, so we do it ourselves.
-
queryset
¶
-
serializer_class
¶ alias of
EventSerializer
-
-
class
microdata.views.
KeyForm
(data=None, files=None, auto_id=u'id_%s', prefix=None, initial=None, error_class=<class 'django.forms.utils.ErrorList'>, label_suffix=None, empty_permitted=False)¶ Bases:
django.forms.forms.Form
Form class used to generate a simple key form. Currently used when a user intends to add a new device via the webapp interface.
-
base_fields
= OrderedDict([('serial', <django.forms.fields.IntegerField object at 0x2b3a39557cd0>)])¶
-
declared_fields
= OrderedDict([('serial', <django.forms.fields.IntegerField object at 0x2b3a39557cd0>)])¶
-
media
¶
-
-
microdata.views.
initiate_job_to_glacier
(request, requester, end_time)¶ Experimental class to demonstrate the possibility to archive old data to Amazon Glacier.
Requires an Amazon AWS key to be set in the environment variables.
-
microdata.views.
new_device
(request)¶ Function used to service a user’s request to add a new
microdata.models.Device
.Context
form
microdata.views.KeyForm
object if user requests the formerror
string - error description if presentcreated
true/false if device is createdDevice
serializedmicrodata.models.Device
object ifmicrodata.models.Device
is createdTemplates:
base/new_device/key.html
base/new_device/help.html
base/new_device/first.html
base/new_device/result.html
-
microdata.views.
timestamp
(request)¶ Function to return the server’s time in milliseconds.
This function is possibly deprecated. Devices should now get the server time from
farmer.DeviceSettingsViewSet
.


{% extends 'layouts/layout-cookie-banner.html' %} {% extends 'layouts/layout.html' %} {% from 'button/macro.njk' import button %} {% from 'back-link/macro.njk' import backLink %} {% block pageTitle %} Phone number changed {% endblock %} {% block content %}
Your phone number will stay the same as it is now.
If you think you need to secure your NHS login you can reset your password. To do this, go back to the website or app you were trying to access.


My first project, and probably yours too. Uses only HTML.
A basic project on how to bake chocolate chip cookies. Includes HTML and Markdown.
Simple project promoting a resort. Includes HTML, Markdown, and CSS.
Project containing information, pictures, and tour dates of a band. Uses HTML, Markdown, and CSS.
Project promoting a fantasy animal shelter. Uses HTML, Markdown, and CSS (specifically Bootstrap).
A review of the first week of Epicodus, showcasing things learned and highlights. Uses HTML, Markdown, and Bootstrap/CSS.
I was born and raised in Overland Park, KS (suburb of Kansas City) and have always been very interested in computers and tech in general. However, the extent of my computer knowledge was limited to building computers and playing videogames until very recently. Through school I studied kinesiology and physical therapy, though I have always had an interest in computers and technology. I have had a variety of jobs, including dental assistant, barista, white water rafting and backpacking guide, and physical therapist assistant. I see coding as the future of the tech industry and recognize that without at least a baseline knowledge of it, I will forever be behind the times in most businesses.This coding bootcamp through Epicodus is my attempt at breaking more into tech and potentially making it my career. Through it, I hope to learn:


AID Austin invites you to a talk by Jayaram on "Ideas for governance, democracy and environmentalism"
Hello there! Our special guest will be Jayaram Venkatesan, co-founder of Arappor Iyakkam (Tamil for “Non-Violent Movement for Justice”). Jayaram‘s interest in social justice and livelihood generation was deepened when he began volunteering for AID-US in 2002.
He moved back to India in 2007 to work full time on social justice issues.
Jayaram founded Arappor Iyakkam in 2015 to build a society where citizens are informed and empowered to ensure government officials are attending to the needs of and are accountable to ordinary citizens.
In the few years of its existence, Arappor has grown to engage thousands of volunteers to expose some of the most corrupt political officials of the Tamil Nadu government using creative campaigns and mobilization efforts.
The movement has also thrown light on environmental issues such as water body (lakes, ponds etc) maintenance, water works contract work etc. Citizens are now more aware then ever before about the importance of water and water bodies due to Arappor Iyakkam's efforts.
Today, this is one of the foremost movements against corruption in India. Jayaram will share Arappor volunteers’ persistent efforts to be changemakers pushing for transparent governance.
Please save the date and do join us. We look forward to hosting you.


Description goes here, but basically it was all because no one bought the milk, the cookies then disappeared and Santa Claus never showed up, this really saddened all of us and we decided to buy a cat and we called him MILKO!
function nodeschool(items) {
var i;
for (i = 0; i < items.length; i++) {
alert("It works in the browser" + items[i]);
}
}


Super Simple Static Site Generator
Does the world need another static site generator? Yes, yes it does. 😄
Features
As many
layouts
as you want
Build websites with
components
Your
pages
get built and voila, you have a website
🤓✨


[gecode-users] inter_r with views of vars in different spaces
Denys Duchier
denys.duchier at univ-orleans.fr
Thu Dec 13 17:32:51 CET 2007
is it possible to use inter_r with IntViews of 2 vars each in a
different space?
Cheers,
--Denys
More information about the gecode-users
mailing list


"""Fully continuous unit operations.
Unit operations that accept and provide
constant or box-shaped flow rate profile.
Box-shaped flow rate profile is a profile with constant value with
optional trailing zeros at front or at end.
"""
__all__ = ['Dilution', 'Concentration', 'BufferExchange',
'FlowThrough', 'FlowThroughWithSwitching']
__version__ = '0.7.2'
__author__ = 'Jure Sencar'
import typing as _typing
import numpy as _np
import bio_rtd.core as _core
import bio_rtd.utils as _utils
[docs]class Dilution(_core.UnitOperation):
"""Process fluid stream dilution by constant ratio.
Parameters
----------
t
Simulation time vector.
Starts with 0 and has a constant time step.
dilution_ratio
Dilution ratio. Must be >= 1.
uo_id
Unique identifier.
gui_title
Readable title for GUI. Default = "Dilution".
Attributes
----------
c_add_buffer
Concentration of species in dilution buffer.
"""
def __init__(self, t: _np.ndarray,
dilution_ratio: float,
uo_id: str,
gui_title: str = "Dilution"):
super().__init__(t, uo_id, gui_title)
assert dilution_ratio >= 1
self.dilution_ratio: float = dilution_ratio
"""Dilution ratio. Must be >= 1.
Dilution ratio of 1.2 means adding 20 % of dilution buffer.
"""
self.c_add_buffer: _np.ndarray = _np.array([])
"""Concentration of species in dilution buffer.
Default = empty array (= all components are 0).
If defined, it must have a value for each process fluid specie.
"""
def _calc_c_add_buffer(self):
"""Get concentration of species in dilution buffer."""
assert self.c_add_buffer.size == 0 \
or self.c_add_buffer.size == self._n_species
if self.c_add_buffer.size == 0:
self._c_add_buffer = _np.zeros([self._n_species, 1])
else:
self._c_add_buffer = \
self.c_add_buffer.reshape(self._n_species, 1)
def _calculate(self):
self._calc_c_add_buffer()
assert hasattr(self, "_c_add_buffer")
assert self.dilution_ratio >= 1
if self.dilution_ratio == 1:
self.log.w("Dilution ratio is set to 1.")
# Apply dilution.
self._f = self._f * self.dilution_ratio
self._c = (self._c + (self.dilution_ratio - 1) * self._c_add_buffer) \
/ self.dilution_ratio
[docs]class Concentration(_core.UnitOperation):
"""Concentrate process fluid stream.
Parameters
----------
t
Simulation time vector.
Starts with 0 and has a constant time step.
flow_reduction
Flow reduction (== volume reduction). Must be > 1.
uo_id
Unique identifier.
gui_title
Readable title for GUI. Default = "Concentration".
Attributes
----------
non_retained_species
List of indexes of non-retained species. Indexing starts with 0.
relative_losses
Relative losses of retained species.
"""
def __init__(self, t: _np.ndarray,
flow_reduction: float, # f_out = f_in / flow_reduction
uo_id: str, gui_title: str = "Concentration"):
super().__init__(t, uo_id, gui_title)
assert flow_reduction > 1
self.flow_reduction: float = flow_reduction
"""Flow reduction (== volume reduction). Must be > 1.
`outlet flow rate` = `inlet flow rate` / `flow_reduction`.
"""
self.non_retained_species: _typing.Sequence[int] = []
"""Indexes of non-retained species. Indexing starts with 0."""
self.relative_losses: float = 0.
"""Relative losses of retained species."""
# noinspection DuplicatedCode
def _calculate(self):
if len(self.non_retained_species) > 0:
assert len(self.non_retained_species) < self._n_species, \
f"All species cannot be `non_retained_species`."
assert list(self.non_retained_species) \
== list(set(self.non_retained_species)), \
f"Indexes must be unique and in order."
assert min(self.non_retained_species) >= 0
assert max(self.non_retained_species) < self._n_species, \
f"Index for species starts with 0."
assert 1 >= self.relative_losses >= 0
assert self.flow_reduction >= 1
retained_species = [i for i in range(self._n_species)
if i not in self.non_retained_species]
self._f = self._f / self.flow_reduction
self._c[retained_species] *= \
self.flow_reduction * (1 - self.relative_losses)
[docs]class BufferExchange(_core.UnitOperation):
"""Buffer exchange.
Parameters
----------
t
Simulation time vector.
Starts with 0 and has a constant time step.
exchange_ratio
Exchange ratio (== efficiency). Must be > 0 and <= 1.
uo_id
Unique identifier.
gui_title
Readable title for GUI. Default = "BufferExchange".
Attributes
----------
non_retained_species
List of indexes of non-retained species. Indexing starts with 0.
c_exchange_buffer
Exchange buffer composition.
relative_losses
Relative losses of retained species.
Notes
-----
Can be combined with `Concentration` and one of the `FlowThrough`
or `FlowThroughWithSwitching` steps
in order to simulate unit operations such as SPTFF or UFDF
"""
def __init__(self, t: _np.ndarray,
exchange_ratio: float,
uo_id: str, gui_title: str = "BufferExchange"):
super().__init__(t, uo_id, gui_title)
assert 1 >= exchange_ratio > 0
self.exchange_ratio: float = exchange_ratio
"""Exchange ratio (== efficiency). Must be > 0 and <= 1.
Share of exchange buffer in outlet buffer.
"""
self.non_retained_species: _typing.Sequence[int] = []
"""Indexes of non-retained species. Indexing starts with 0."""
self.c_exchange_buffer = _np.array([]) # dilution buffer composition
"""Concentration of species in exchange buffer.
Default = empty array (= all components are 0).
If defined, it must have a value for each process fluid specie.
"""
self.relative_losses: float = 0.
"""Relative losses of retained species during dilution."""
def _calc_c_exchange_buffer(self):
"""Calc concentration of species in exchange buffer."""
assert self.c_exchange_buffer.size == 0 \
or self.c_exchange_buffer.size == self._n_species
if self.c_exchange_buffer.size == 0:
self._c_exchange_buffer = _np.zeros([self._n_species, 1])
else:
self._c_exchange_buffer = \
self.c_exchange_buffer.reshape(self._n_species, 1)
# noinspection DuplicatedCode
def _calculate(self):
if len(self.non_retained_species) > 0:
assert len(self.non_retained_species) < self._n_species, \
f"All species cannot be `non_retained_species`."
assert list(self.non_retained_species) \
== list(set(self.non_retained_species)), \
f"Indexes must be unique and in order."
assert min(self.non_retained_species) >= 0
assert max(self.non_retained_species) < self._n_species, \
f"Index for species starts with 0."
assert 1 >= self.exchange_ratio >= 0
assert 1 >= self.relative_losses >= 0
self._calc_c_exchange_buffer()
if self.exchange_ratio == 0:
self.log.w(f"Exchange ratio is set to 0")
retentate_mask = _np.ones(self._n_species, dtype=bool)
retentate_mask[self.non_retained_species] = False
# Apply buffer exchange.
self._c[retentate_mask] *= 1 - self.relative_losses
self._c[~retentate_mask] *= 1 - self.exchange_ratio
self._c += self._c_exchange_buffer * self.exchange_ratio
[docs]class FlowThrough(_core.UnitOperation):
"""Fully continuous unit operation without life cycle.
`FlowThrough` has a constant PDF, which depends on `flow rate` and
`void volume`.
If `initial volume` < `void volume`,
then the unit operation is first filled up.
During the fill-up an ideal mixing is assumed.
Parameters
----------
t
Simulation time vector.
Starts with 0 and has a constant time step.
pdf
PDF that described propagation of process fluid through the
unit operation.
`pdf` is updated with `void volume` and `flow rate` at runtime.
uo_id
Unique identifier.
gui_title
Readable title for GUI. Default = "FlowThrough".
Notes
-----
**Attributes with short description:**
Void volume (one should be defined):
* :attr:`v_void`
* :attr:`rt_target` - `void volume` = `rt_target` * `flow rate`
Initial fill volume (one can be defined, optional):
* :attr:`v_init`
* :attr:`v_init_ratio` - `init volume` = `v_init_ratio` *
`void volume`
* If none are defined, then `init volume` = `void volume`
Init fill volume concentration (optional):
* :attr:`c_init` - must have values for each specie
* In undefined, then all species are 0.
Losses (optional):
* :attr:`losses_share` - 0.1 == 10 % losses
Losses species list (required if :attr:`losses_share` is defined):
* :attr:`losses_species_list` - Indexes of species affected by
losses. Indexing starts at 0.
Examples
--------
A vessel is half-empty at the beginning of the process,
then :attr:`v_init_ratio = 0.5`.
During simulation, the vessel gets fully filled in first part.
Ideal mixing is assumed during the first part.
Fully filled vessel serves then as an initial state for
the rest of the simulation.
"""
def __init__(self, t: _np.ndarray,
pdf: _core.PDF,
uo_id: str, gui_title: str = "FlowThrough"):
super().__init__(t, uo_id, gui_title)
self.pdf: _core.PDF = pdf
"""Probability distribution function for describing RTD.
PDF that described propagation of process fluid through the
unit operation.
`pdf` is updated with `void volume` and `flow rate` at runtime.
"""
# void volume definition (one of those should be positive)
self.v_void: float = -1
"""Void volume. This is effective void volume.
Either `v_void` or :attr:`rt_target` should be defined.
"""
self.rt_target: float = -1
"""Target residence time.
Used to determine (effective) void volume of the unit operation.
Either `rt_target` or :attr:`v_void` should be defined.
"""
self.v_init: float = -1
"""Initial fill volume.
One of `v_init` or :attr:`v_init_ratio` may be defined,
but not both. If both are undefined, then the init volume is
the same as void volume.
"""
self.v_init_ratio: float = -1
"""Initial fill volume relative to void volume.
One of `v_init` or :attr:`v_init_ratio` may be defined,
but not both. If both are undefined, then the init volume is
the same as void volume.
"""
self.c_init: _np.ndarray = _np.array([])
"""Buffer composition in initial fill volume.
E.g. equilibration buffer composition.
If undefined (default) then all components are set to 0.
If defined then it has to have a value for each specie.
"""
self.losses_share: float = 0
"""Relative losses."""
self.losses_species_list: _typing.Sequence[int] = []
"""Indexes of process fluid components affected by losses.
Must be defined if :attr:`losses_share` > 0.
"""
@_core.UnitOperation.log.setter
def log(self, logger: _core._logger.RtdLogger):
self._logger = logger
# propagate logger across other elements with logging
self._logger.set_data_tree(self._log_entity_id, self._log_tree)
self.pdf.set_logger_from_parent(self.uo_id, logger)
def _calc_v_void(self):
"""Void volume of the unit operation.
This is so-called "effective" void volume
(dead zones are excluded).
"""
assert self.rt_target > 0 or self.v_void > 0, \
"Void volume must be defined."
if self.rt_target > 0 and self.v_void > 0:
self.log.w(f"Void volume is defined in two ways:"
f" By `rt_target` and `v_void`. `v_void` is used.")
self._v_void = self.v_void if self.v_void > 0 \
else self._f.max() * self.rt_target
# Log.
self.log.i_data(self._log_tree, 'v_void', self._v_void)
def _calc_v_init(self):
"""Initial fill level in unit operation."""
if self.v_init >= 0:
self._v_init = self.v_init
if self.v_init_ratio >= 0:
self.log.w(f"Initial volume is already defined by `v_init`"
f" (`v_init_ratio` is ignored).")
elif self.v_init_ratio >= 0:
assert hasattr(self, '_v_void') and self._v_void > 0
self._v_init = self.v_init_ratio * self._v_void
else:
assert hasattr(self, '_v_void') and self._v_void > 0
self._v_init = self._v_void
# Log.
self.log.i_data(self._log_tree, 'v_init', self._v_init)
def _calc_c_init(self):
"""Calc composition of equilibration buffer."""
assert self.c_init.size == 0 or self.c_init.size == self._n_species
if self.c_init.size == 0:
self._c_init = _np.zeros([self._n_species, 1])
else:
self._c_init = self.c_init.reshape(self._n_species, 1)
# Log.
self.log.i_data(self._log_tree, 'c_init', self._c_init)
def _calc_p(self):
"""Updates and evaluates flow-through PDF."""
assert hasattr(self, '_v_void')
self.pdf.update_pdf(v_void=self._v_void,
f=self._f.max(),
rt_mean=self._v_void / self._f.max())
self._p = self.pdf.get_p()
# Log.
self.log.d_data(self._log_tree, 'p', self._p)
def _pre_calc(self):
"""Prepare for fill-up and convolution."""
self._calc_v_void()
self._calc_v_init()
self._calc_c_init()
self._calc_p()
# affects `_c_init`
def _sim_init_fill_up(self):
"""Initial fill-up of the unit operation.
This step is applicable is `v_init < v_void`.
It changes :attr:`_c_init`, thus one needs to be careful not to
define it after this step.
"""
assert hasattr(self, '_v_void')
assert hasattr(self, '_v_init')
assert hasattr(self, '_c_init')
# Fill up the unit operation.
if self._v_void > self._v_init:
fill_up_phase_i = _utils.vectors.true_start(
_np.cumsum(self._f) * self._dt >= self._v_void - self._v_init
)
fill_up_volume = _np.sum(self._f[:fill_up_phase_i]) * self._dt
fill_up_amount = _np.sum(self._f[:fill_up_phase_i]
* self._c[:, :fill_up_phase_i],
1) * self._dt
self._c_init = \
(self._c_init * self._v_init + fill_up_amount[:, _np.newaxis])\
/ (self._v_init + fill_up_volume)
self._c[:, :fill_up_phase_i] = 0
self._f[:fill_up_phase_i] = 0
# Log.
self.log.i_data(self._log_tree, 'c_init_after_fill_up', self._c_init)
self.log.d_data(self._log_tree, 'c_after_fill_up', self._c)
def _sim_convolution(self):
assert self._is_flow_box_shaped(), \
f"Inlet flow rate must be constant (or box shaped)"
assert hasattr(self, '_c_init')
assert hasattr(self, '_p')
# Convolution with initial concentration.
self._c[:, self._f > 0] = _utils.convolution.time_conv(
dt=self._dt,
c_in=self._c[:, self._f > 0],
rtd=self._p,
c_equilibration=self._c_init,
logger=self.log
)
self._c[:, self._f <= 0] = 0
def _sim_losses(self):
if len(self.losses_species_list) == 0 or self.losses_share == 0:
return
assert 1 >= self.losses_share > 0
assert min(self.losses_species_list) >= 0
assert max(self.losses_species_list) < self._n_species
assert list(self.losses_species_list) \
== list(set(self.losses_species_list))
# Apply losses.
self._c[self.losses_species_list] *= 1 - self.losses_share
def _calculate(self):
# Prepare.
self._pre_calc()
# Simulate.
self._sim_init_fill_up()
self._sim_convolution()
self._sim_losses()
[docs]class FlowThroughWithSwitching(FlowThrough):
"""Fully continuous unit operation with inline switching.
`FlowThroughWithSwitching` has a constant PDF, which depends on
`flow rate` and `void volume`.
First cycle starts when the inlet flow rate is turned on.
Cycle duration might represent a flow-through column lifetime.
If `initial volume` < `void volume`,
then the unit operation is first filled up.
During the fill-up an ideal mixing is assumed.
Parameters
----------
t
Simulation time vector.
Starts with 0 and has a constant time step.
pdf
PDF that described propagation of process fluid through the
unit operation.
`pdf` is updated with `void volume` and `flow rate` at runtime.
uo_id
Unique identifier.
gui_title
Readable title for GUI. Default = "FlowThroughWithSwitching".
Notes
-----
Cycle duration (e.g. column lifetime) should be defined by one of
the following attributes:
* :attr:`t_cycle`
* :attr:`v_cycle`
* :attr:`v_cycle_relative`
"""
def __init__(self, t: _np.ndarray,
pdf: _core.PDF,
uo_id: str,
gui_title: str = "FlowThroughWithSwitching"):
super().__init__(t, pdf, uo_id, gui_title)
self.t_cycle: float = -1
"""Cycle duration (time).
E.g. lifecycle of flow-through column.
Only one cycle duration definition is expected. Available:
* :attr:`t_cycle` (this one)
* :attr:`v_cycle`
* :attr:`v_cycle_relative`
"""
self.v_cycle: float = -1
"""Cycle duration (volume).
Cycle duration (time) = `v_cycle` / `inlet flow rate`.
E.g. lifecycle of flow-through column.
Only one cycle duration definition is expected. Available:
* :attr:`t_cycle`
* :attr:`v_cycle` (this one)
* :attr:`v_cycle_relative`
"""
self.v_cycle_relative: float = -1
"""Cycle duration (relative volume).
Cycle duration (time) = `v_cycle_relative` * `void volume`
/ `inlet flow rate`.
E.g. lifecycle of flow-through column.
Only one cycle duration definition is expected. Available:
* :attr:`t_cycle`
* :attr:`v_cycle`
* :attr:`v_cycle_relative` (this one)
"""
def _calc_t_cycle(self):
assert hasattr(self, '_v_void')
# Get cycle duration.
if self.t_cycle > 0:
self._t_cycle = self.t_cycle
if self.v_cycle > 0:
self.log.w(f"Cycle duration defined in more than one way."
f" `v_cycle` is ignored.")
if self.v_cycle_relative > 0:
self.log.w(f"Cycle duration defined in more than one way."
f" `v_cycle_relative` is ignored.")
elif self.v_cycle > 0:
self._t_cycle = self.v_cycle / self._f.max()
if self.v_cycle_relative > 0:
self.log.w(f"Cycle duration defined in more than one way."
f" `v_cycle_relative` is ignored.")
elif self.v_cycle_relative > 0:
self._t_cycle = \
self.v_cycle_relative * self._v_void / self._f.max()
else:
raise AssertionError("Cycle duration must be defined")
# Log.
self.log.i_data(self._log_tree, 't_cycle', self._t_cycle)
def _sim_piece_wise_time_convolution(self):
assert self._is_flow_box_shaped(), \
"Inlet flow rate must be constant (or box shaped)."
assert hasattr(self, '_v_void')
assert hasattr(self, '_c_init')
assert hasattr(self, '_p')
assert hasattr(self, '_t_cycle')
# Convolution.
self._c = _utils.convolution.piece_wise_time_conv(
dt=self._dt, f_in=self._f, c_in=self._c,
t_cycle=self._t_cycle, rt_mean=self._v_void / self._f.max(),
rtd=self._p, c_wash=self._c_init, logger=self.log
)
def _calculate(self):
# Prepare.
self._pre_calc()
self._calc_t_cycle()
# Simulate.
self._sim_init_fill_up()
self._sim_piece_wise_time_convolution()
self._sim_losses()


Defining R1 and R2Most of the stemmers make use of at least one of the region definitions R1 and R2. They are defined as follows:R1 is the region after the first non-vowel following a vowel, or is the null region at the end of the word if there is no such non-vowel. R2 is the region after the first non-vowel following a vowel in R1, or is the null region at the end of the word if there is no such non-vowel. The definition of vowel varies from language to language. In French, for example, é is a vowel, and in Italian i between two other vowels is not a vowel. The class of letters that constitute vowels is made clear in each stemmer. Below, R1 and R2 are shown for a number of English words, b e a u t i f u l |<------------->| R1 |<----->| R2Letter t is the first non-vowel following a vowel in beautiful, so R1 is iful. In iful, the letter f is the first non-vowel following a vowel, so R2 is ul. b e a u t y |<->| R1 ->|<- R2In beauty, the last letter y is classed as a vowel. Again, letter t is the first non-vowel following a vowel, so R1 is just the last letter, y. R1 contains no non-vowel, so R2 is the null region at the end of the word. b e a u ->|<- R1 ->|<- R2In beau, R1 and R2 are both null.


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Runs the extensions which mut be used for the checkout:
(It is needed in case of multi repo in workspace, because one is controlled by jenkins, the other shall not)
- isDefaultBranch tell if checkout must be controlled by Jenkins
- relativeTargetDir is the overridden path where to extract the repo


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The SQLs listed on the results pane gives the top statements accumulated in the package cache as
measured by various metrics.
The database administrator views the list of statements and finds that most of
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Manager admits he’s struggling to keep hold of Liverpool and Arsenal transfer target
by Mark BrusBournemouth manager Eddie Howe has provided an update on the future of star player Ryan Fraser amid transfer links with the likes of Liverpool and Arsenal.
As noted by the Daily Mirror, both the Reds and the Gunners have been mentioned as admirers of Fraser in recent times, with the Scotland international showing himself to be a top creative talent in recent times.
Although Fraser has suffered a bit of a dip in form this season, he was an assist machine last term, setting up 14 goals for the Cherries in the Premier League.
MORE: Jurgen Klopp delivers hugely positive Liverpool injury update ahead of Napoli clash
The 25-year-old’s current contract with Bournemouth is due to expire at the end of this season, meaning he could soon be set for a free transfer away, while his club could also do well to cash in on him for a reduced fee while they still can this January.
Discussing Fraser’s future, Howe said the club have held talks over trying to persuade him to stay, but they have proven unsuccessful for now.
“We would love him to stay and commit to us but, if not, we need to get the best out of him between now and the end of the season,” Howe is quoted by the Mirror.
“We’ve had conversations with Ryan to try and persuade him to stay but, at the moment, he’s keeping his options open.”
This will be music to the ears of Liverpool, who could do with more depth in attack as the likes of Xherdan Shaqiri perhaps don’t look the best backup options.
Arsenal, meanwhile, could also really do with improving their chance creation after some dire recent performances, with Fraser looking like a player who could help give strikers like Pierre-Emerick Aubameyang and Alexandre Lacazette more to work with in the final third.


others
A brief review of additional projects I have worked on.
Presentations and Posters
- Presentation slides for invited talk on "Introduction to Causal Inference" for COSC 5931/4931 Machine Learning Principles, Spring 2021 at Marquette University
- Poster and recorded talk for "A Causally Formulated Hazard Ratio Estimation through Backdoor Adjustment on Structural Causal Model" in MLHC 2020
- Presentation slides for "Analyzing Happiness: Investigation on Happy Moments using a Bag-of-words Approach and Related Ethical Discussions" in Compsac 2019
- Presentation slides for "Towards Predicting Risky Behavior among Veterans with PTSD by Analyzing Gesture Patterns" in Compsac 2019
- Poster for "“Is Haloperidol effective in Delirium treatment?”: Answering Treatment and Policy Controversies in Healthcare through Prior Knowledge-driven Structural Causal Model Generation" in Forward Thinking Poster Session 2020
- Poster for "Causal Exploration on Large Healthcare Datasets through Emulation of Randomized Controlled Trials: An Application on Delirium Patients in the ICU" in Forward Thinking Poster Session 2019
- Poster for "MARQUECare: Information Technology Tools and a System for Palliative Care for Everyone" in Healthy People on a Healthy Planet Conference
- Poster for "Non-invasive Human Identification using Wi-fi Signals" in Forward Thinking Poster Session 2017
Open Source Projects
- BottomNavBar (~200 Stars) - Android library to add bottom navigation bar to application
- FormMaster (~150 Stars) - Android library to easily build and use larger forms
Live Applications
- Harriken (Restaurant discovery android app based in Dhaka, Bangladesh)
- Stay Focused (Goal-focused to-do list android app with reminders)
- Meghdut Fieldforce (Field-level task distribution and management app, for android)
- APPothecary (Android app for healthcare: medicine, doctor, reminder management)
- QRF Mentor and Veteran (Android client for collaboration and communication between peer mentors and veterans)
- World-in-360 (Web application to showcase 360 degree panaromic images)


The Portland CocoaHeads group is devoted to discussion of Apple's Cocoa Framework for programming on Mac OS X and iOS devices. During our regularly scheduled meetings (Discussion Night) members of all experience levels have a moderated group discussion on a given topic. When we have a presenter we will have an additional meeting for the month where a presenter (a PDX-Cocoaheads member or out-of-towner) will give a presentation on their topic of choice lecture/tutorial style. Developers using any Cocoa-related language (Swift, Objective-C, Python, Ruby, javascript, etc.) are welcome.
Discussion Night meetings are held on the 2nd Wednesday of the month. Presentation Night meetings are held on a month-to-month basis. Head to our meetup.com page to sign up for notifications of new meetings and last minute updates.