|
LCDGFX LCD display driver
1.0.3
This library is developed to control SSD1306/SSD1325/SSD1327/SSD1331/SSD1351/IL9163/PCD8554 RGB i2c/spi LED displays
|
#include <tiler.h>
Public Member Functions | |
| virtual void | draw ()=0 |
| virtual void | update ()=0 |
| virtual void | refresh ()=0 |
| void | focus () |
| void | defocus () |
| bool | isFocused () |
| bool | hasTiler () |
| T & | getTiler () |
Protected Member Functions | |
| void | setTiler (T *tiler) |
Protected Attributes | |
| T * | m_tiler = nullptr |
| Active tiler, assigned to the NanoEngineObject. | |
| NanoEngineObject< T > * | m_next = nullptr |
| Next NanoEngineObject in the list. | |
Friends | |
| template<class C , class D > | |
| class | NanoEngineTiler |
Template class for all NanoEngine objects
|
inline |
Clears logic focus from NanoEngineObject
|
pure virtual |
draw() method is called by NanoEngine, when it needs to refresh object graphics.
Implemented in NanoObjectList< T >, NanoFixedWidthMenu< T >, NanoListMenu< T >, NanoTextMenuItem< T >, NanoObject< T >, and NanoSprite< T >.
|
inline |
Sets logic focus on NanoEngineObject
|
inline |
Returns reference to NanoEngine. Before call to this function, please check that NanoEngine is valid via hasTiler() function.
|
inline |
Returns true if NanoEngineObject is bound to NanoEngine instance
|
inline |
Returns true if logic focus points to the object. At present NanoEngine allows many objects to be in focus at once
|
pure virtual |
update() method is called by NanoEngine, when it needs object to mark occupied place for refreshing.
Implemented in NanoObjectList< T >, and NanoObject< T >.
|
inlineprotected |
Bind NanoEngineObject to specific NanoEngine
| tiler | pointer to NanoEngine object |
|
pure virtual |
update() method is called by NanoEngine, when it needs all objects to update their logic state.
Implemented in NanoObjectList< T >, NanoObject< T >, and NanoTextMenuItem< T >.


Filesystem
2.0
Logs:
Info
This application presents a small fraction of what FileSystem API can do.
Show/hide info


|
Figure 1
|
(a) Overlap in occurrence of hotspots based on the historica ... |
|
Figure 2
|
Relative deviation of the decadal‐averaged surface current s ... |
|
Figure 3
|
Annual mean SST (°C) for the period 1990–2099 averaged over ... |
|
Figure 4
|
Number of years in a decade 2010–2019 (a) and 2020–2029 (b) ... |
|
Figure 5
|
Same as Fig. 3 for maximum UML depth (m). |
|
Figure 6
|
Same as Fig. 3 for annual primary production (g C m −2 yr − ... |


--- title: I love Göteborg excerpt: A few days ago, I came back from a trip to Sweden. After a couple of days in Helsingborg (a nice town with beautiful seafront!), I continued to Gothenburg. I still have a very special relation to that city; after all, it's the city where I lived in the nineties (as exchange student). date: '2007-11-11 20:34:08 +0100' date_gmt: '2007-11-11 18:34:08 +0100' categories: - Traveling - Music tags: - Göteborg - Gothenburg - Sweden ---
A few days ago, I came back from a trip to Sweden. After a couple of days in Helsingborg (a nice town with beautiful seafront!), I continued to Gothenburg. I still have a very special relation to that city; after all, it's the city where I lived in the nineties (as exchange student).
Gothenburg is one of those places that always make you feel good; people are very friendly, funny and laid back. I had such good fun with many of my friends who are still living there.
Gothenburg is full of talented musicians and personalities. I find the atmosphere there particularly positive and encouraging. It's a place that has made a lasting impression on me, so I try to visit the city whenever I can. In fact, every time I go there, it feels like I never left!
Belgium is where I live and work -- and I love Brussels too --, but Gothenburg has a special place in my heart. You can see my photos on Flickr, taken in Gothenburg and Helsingborg during my trip.


Functions | |
| template<typename Geometry1 , typename Geometry2 , typename Strategy > | |
| comparable_distance_result < Geometry1, Geometry2, Strategy >::type | boost::geometry::comparable_distance (Geometry1 const &geometry1, Geometry2 const &geometry2, Strategy const &strategy) |
| Calculate the comparable distance measurement of two geometries using the specified strategy. More... | |
| template<typename Geometry1 , typename Geometry2 > | |
| default_comparable_distance_result < Geometry1, Geometry2 >::type | boost::geometry::comparable_distance (Geometry1 const &geometry1, Geometry2 const &geometry2) |
| Calculate the comparable distance measurement of two geometries. More... | |
| template<typename Geometry1 , typename Geometry2 , typename Strategy > | |
| distance_result< Geometry1, Geometry2, Strategy >::type | boost::geometry::distance (Geometry1 const &geometry1, Geometry2 const &geometry2, Strategy const &strategy) |
| Calculate the distance of two geometries using the specified strategy. More... | |
| template<typename Geometry1 , typename Geometry2 > | |
| default_distance_result < Geometry1, Geometry2 >::type | boost::geometry::distance (Geometry1 const &geometry1, Geometry2 const &geometry2) |
| Calculate the distance of two geometries. More... | |
| comparable_distance_result<Geometry1, Geometry2, Strategy>::type boost::geometry::comparable_distance | ( | Geometry1 const & | geometry1, |
| Geometry2 const & | geometry2, | ||
| Strategy const & | strategy | ||
| ) |
Calculate the comparable distance measurement of two geometries using the specified strategy.
The free function comparable_distance does not necessarily calculate the distance, but it calculates a distance measure such that two distances are comparable to each other. For example: for the Cartesian coordinate system, Pythagoras is used but the square root is not taken, which makes it faster and the results of two point pairs can still be compared to each other.
| Geometry1 | first geometry type |
| Geometry2 | second geometry type |
| Strategy | Any type fulfilling a Distance Strategy Concept |
| geometry1 | A model of the specified concept |
| geometry2 | A model of the specified concept |
| strategy | The strategy which will be used for distance calculations |
| default_comparable_distance_result<Geometry1, Geometry2>::type boost::geometry::comparable_distance | ( | Geometry1 const & | geometry1, |
| Geometry2 const & | geometry2 | ||
| ) |
Calculate the comparable distance measurement of two geometries.
The free function comparable_distance does not necessarily calculate the distance, but it calculates a distance measure such that two distances are comparable to each other. For example: for the Cartesian coordinate system, Pythagoras is used but the square root is not taken, which makes it faster and the results of two point pairs can still be compared to each other.
| Geometry1 | first geometry type |
| Geometry2 | second geometry type |
| geometry1 | A model of the specified concept |
| geometry2 | A model of the specified concept |
| distance_result<Geometry1, Geometry2, Strategy>::type boost::geometry::distance | ( | Geometry1 const & | geometry1, |
| Geometry2 const & | geometry2, | ||
| Strategy const & | strategy | ||
| ) |
Calculate the distance of two geometries using the specified strategy.
The free function area calculates the area of a geometry. using the specified strategy. Reasons to specify a strategy include: use another coordinate system for calculations; construct the strategy beforehand (e.g. with the radius of the Earth); select a strategy when there are more than one available for a calculation.
| Geometry1 | Any type fulfilling a Geometry Concept |
| Geometry2 | Any type fulfilling a Geometry Concept |
| Strategy | Any type fulfilling a Distance Strategy Concept |
| geometry1 | A model of the specified concept |
| geometry2 | A model of the specified concept |
| strategy | The strategy which will be used for distance calculations |
| default_distance_result<Geometry1, Geometry2>::type boost::geometry::distance | ( | Geometry1 const & | geometry1, |
| Geometry2 const & | geometry2 | ||
| ) |
Calculate the distance of two geometries.
The default strategy is used, corresponding to the coordinate system of the geometries
| Geometry1 | Any type fulfilling a Geometry Concept |
| Geometry2 | Any type fulfilling a Geometry Concept |
| geometry1 | A model of the specified concept |
| geometry2 | A model of the specified concept |
|
April 2, 2011 |
Copyright © 2007-2011 Barend Gehrels, Amsterdam, the Netherlands Copyright © 2008-2011 Bruno Lalande, Paris, France Copyright © 2009-2010 Mateusz Loskot, London, UK |


Welcome to OpenDST.
Mission
Open Data Science Training is a repository of freely available resources for self-directed learning in Data Science. The resources include lessons, curated datasets and exercises with a focus on practical and immediate applicability.
The goal is to enable sufficiently motivated and technically capable learners to use data science techniques in their applications without having to go get a degree in data science.
The target audience is anyone who is motivated to learn data science and is not afraid of a small amount of math, which is introduced gently.
The expectation is that the user is open to expanding and enhancing their math skills as a result of using these materials. Supplementary math learning resources are either provided or pointed too.
The approach is breadth first, so that a user may learn at a level comfortable to them or can dig deeper, but useful results and techniques are available with a small amount of learning, and not postponed for a semester or after a degree program.
The long term goal is to evolve towards a self-sustaining self-organizing community around Data Science learning, with ongoing knowledge creation via forums and community driven curation.
The Content
The current state of the content is beta. It is based on IPython Notebook and Python based Data Science libraries.
Background
If you are unfamiliar with IPython Notebook you can start with http://ipython.org/notebook
Prerequisites
One of the following distributions is needed.
- EPD Free Enthought Python Distribution from http://enthought.com
- Anaconda Python from http://continuum.io
Development has been done on v 1.5 of Anaconda distribution but EPD Free should work just as well.
Please note that even if you have Python installed it is important to have one of these distributions installed and the binary for this installation in your path. This is because these distributions come packaged with all the supplementary libraries needed and these have been historically difficult to install separately.
Installation
These steps assume you have installed one of the distributions mentioned in prerequisites.
From the zip for tar file
- download and unpack the file to a directory called opendst
- cd to the 'notebooks' subdirectory
- start IPython Notebook 'ipython notebook --pylab=inline'
From the git repo
- clone the repo
- cd to 'notebooks'
- start IPython Notebook 'ipython notebook --pylab=inline'


Jose Mourinho explains why he was "upset" after Liverpool's win over Man City
Former Manchester United boss Jose Mourinho hit out at the VAR decision from the fifth minute of Liverpool's Premier League win over Manchester City
by Jake Polden, https://www.mirror.co.uk/authors/jake-polden/Jose Mourinho said the inconsistency of VAR left him feeling upset following Liverpool's Premier League win over Manchester City.
VAR was called into action in just the sixth minute of Sunday's title crunch match at Anfield after Liverpool opened the scoring through Fabinho.
Trent Alexander-Arnold had handled the ball in his own area in the build-up to the goal, but a VAR check ruled his arm wasn't in an unnatural position and no penalty was awarded.
Manchester City boss Pep Guardiola was incensed by the decision, and sarcastically thanked referee Mike Riley after the final whistle.
Mourinho discussed VAR in the Sky Sports studio following the game and sympathised with the Spaniard, admitting the technology's inconsistency upsets him.
"I think we've spoken enough about it and I think the main question is just consistency," said the former Manchester United boss.
"That's what really upsets me in the game, in this moment, that one weekend it's not a penalty and next week it's going to be a penalty. For me that's the crucial point of it.
"The game was so good and there is so much to speak about the game that I think enough is enough.
On Liverpool's impressive counter-attack move, which directly lead to the goal, the Portuguese added: "Yeah of course [City could have prevented the goal], there's no doubt about it.
"And I think everybody knows how strong Liverpool is in these situations.
"Everybody knows how they play and the way they manage to only leave [Mohamed] Salah and [Sadio] Mane in privileged positions for these transitions.

"City is too good to be now crying for a week about an incident, they should focus also on the game, what they could do better."


Alfred-no
Watch Rangers star Alfredo Morelos miss golden chance in Colombia’s win over Ecuador
by Ewan PatonRANGERS star Alfredo Morelos missed a golden opportunity as Colombia defeated Ecuador.
The Light Blues striker played the first 45 minutes as Carlos Queiroz's men secured back-to-back 1-0 wins.
Mateus Uribe scored the games only goal early in the second half - but Morelos should have found the net in the 26 minute.
The hitman capitalised on a defensive error that sent him one-on-one with Ecuador goalie Johan Padilla.
The 23-year-old tried to give the keeper the eyes as he sent his effort near post, but the stopper was able to get down and turn his effort around the post.
Colombia had several opportunities to extend their advantage in the second period but were denied by the Ecuador No1.
(You can watch his big chance from 00:54 -1:21 in the video below)
Morelos broke his international duck on Friday night against Peru and it was a typical striker's finish, in what was a proud moment for the 23-year-old.
It was his 23rd goal of the season in all competitions as his hot-red club form continued on the international stage.
Speaking of his pride at netting his first Colombia goal, he said: "The goal was just intuition on my part, but it feels great to score my first ever goal for my country especially so late in the game.
"It's always a huge honour to play for my country and I feel very proud I was able to score too.
"It wasn't the best game in the world, but the important thing was to get the win.
"I have always said it was important for me to score my country and it's another step on what has been a great season for me so far at Rangers.
"And now I have scored my first goal, I hope to go on and score many, many more."
He will now return to club duty with Steven Gerrard's men travelling to Hamilton Accies on Sunday as they look to keep the pressure on Old Firm rivals Celtic.
The Hoops currently lead the Premiership table by one goal.


Annotation Query Format
An annotation is an event that is overlaid on top of graphs. The query can have up to three columns per row, the time column is mandatory. Annotation rendering is expensive so it is important to limit the number of rows returned. - column with alias: time for the annotation event time. Use epoch time or any native date data type. - column with alias: text for the annotation text - column with alias: tags for annotation tags. This is a comma separated string of tags e.g. 'tag1,tag2' Macros: - $__timeFilter(column) -> column BETWEEN '2017-04-21T05:01:17Z' AND '2017-04-21T05:01:17Z'


Public libraries provide opportunities for information access and knowledge acquisition, especially to the socioeconomically disadvantaged in society who may not have the financial means to afford it.


A group of human beings with their own uniqee way of living and agricultural practices makes a civilisation.
Civis in Latin means citizen or law abiding city man.


Introduction to CONSTELLATION
This is a quick-start guide to CONSTELLATION.
- Select File → New Graph.
This will open a new window containing an empty graph.
-
Select Experimental → Build graph → Sphere to build a random spherical graph in the window.
-
Select OK in the dialog box to accept the default values for a small graph. The graph will be built in the active window.
-
Select Views → Attribute Editor. This displays the attribute values of the current selection on the graph.
-
Go here
to find out how navigate around the graph.
-
Go here to learn about the graph structure.
What's New
New features and announcements will be displayed here so keep an eye out for them!


They are offering a complimentary box of pastries to veterans who drop by any of their locations Monday, Veterans Day.
The idea came about after an employee was inspired to honor men and women who have served in the armed forces.
The offer is good until 8:30 p.m. or while supplies last. Porto's has several locations throughout Southern California.


HI everyone !
I had some problems with binary files and I created a topic and Disch did a great help and I thought it's better for that post not to be just in that topic . (link to topic : bottom of the article)
This article is a background to this one :
Disch's tutorial to good binary files
In this article instead of "HOW TO write data to binary files" you will see "WHY shouldn't we write variables and data larger than 1 byte in binary files .
Here we go :
When you do a raw write of a block of memory, write() will look at the pointer you give it and blindly start copying X bytes to the file. This sort of works for POD (plain old data) types... but it utterly fails for complex types (like strings).
Let's take a look at why.
****Why you should not read/write complex non-POD structs/classes****
Reason #1: Complex types may contain dynamically allocated memory or other pointers
here's a simplistic example:
1 2 3 4 5 6 7 8 9
|
class Foo
{
private:
int* data;
public:
Foo() { data = new int[10]; }
~Foo() { delete[] data; }
};
| |
Here... our Foo class conceptually contains information for 10 ints (~40 bytes). Yet if you do sizeof(Foo)... it'll probably give you the size of one pointer (~4 bytes).
This is because the Foo class does not contain the data it's referring to... it merely contains a pointer to it. Therefore... a naive write to a file would simply write the pointer and not the actual data.
Attempting to read that data later would just result in having a pointer that points to random memory.
This is similar to what is happening with strings. The string data is actually not in the string class... but rather it is allocated dynamically.
#2: Non POD types may contain VTables and other "hidden" data that you absolutely must not touch
Trivial example:
1 2 3 4 5 6
|
class Foo
{
public:
virtual ~Foo() { }
int x;
};
| |
sizeof(Foo) is likely going to be larger than sizeof(int) because Foo is now polymorphic... meaning it has a VTable. VTables are black magic and you absolutely must not tinker with them or you risk destroying your program.
But again... a naive read/write doesn't acknowledge that... and will simply try to read/write the full object... vtable and all. Resulting in massive screw ups.
So yeah. Naive reads/writes do not work with complex types unless they are POD.
But if you notice before I said POD types only "sort of" work. What do I mean by that?
****Why you should not read/write POD structs/classes****
Well let's take a look at another trivial example:
1 2 3 4 5 6
|
struct Foo
{
char a; // 1 byte
int b; // 4 bytes
char c; // 1 byte
};
| |
Here we have a POD struct. It would not suffer from any of the problems previously mentioned. I added comments to show how many bytes each individual var might take (technically this may vary, but it's typical).
So if a struct is just a collection of all these vars... you would expect the size of the struct to be equal to the sum of all of them... right? so sizeof(Foo) would be 6?
Well... on my machine sizeof(Foo) is 12. SURPRISE!
What's happening is that the compiler is adding padding to the struct so that variables are aligned on certain memory boundaries. This makes accessing them faster.
So when you do a naive, raw write to a file, it will also write the padding bytes. Of course when you read it... you'll read the padding bytes and it'll work as you'd expect.
So why did I say it only sorta works?
Well consider the following situation.

- You port your program to another platform and/or change or update your compiler
- This new compiler happens to assign different padding to the struct
- You run the newly compiled program and try to load the files you saved in the old version of your program
Since the padding changed, the data is read differently (more or less data is read, or the padding is in different spots) - so the read fails and you get garbage.
There are ways you can tell the compiler to leave off the padding. But that raises other problems I won't get into now. Let's just say that memory alignment is important.
So okay... simply put... it's not a great idea to read/write structs in full. So just reading/writing individual vars works... right?
Well.....
****Why you should not read/write any variable larger than 1 byte****
There are 2 things you have to watch out for.
#1: ill-defined size of variables.
int
might be 4 bytes depending on your platform/compiler... or it might be 2 bytes or it might be 8 bytes.
So reading/writing a full
int
suffers the same problems as the 'padding' scenario above. If you have a file saved with version X of your program, then rebuild in version Y where int suddenly changed size on you.... your file will not load any more.
This can be solved by using the
<cstdint>
types like uint8_t, uint16_t, etc which all are guaranteed to have a certain byte size.
#2: endianness. Memory consists of a series of bytes. How an int is stored in memory is how it is stored in the file when you do a raw write. But how the int is stored in memory varies depending on the machine you're running on.
x86/x64 machines are little endian. So if you have
int foo = 1;
, foo will look like this in memory:
01 00 00 00
So if you save 'foo' to a file on your x86 machine.. then hand that file off to your buddy who is running a big endian machine... he'll read it back the same way.

This is why I make it a point to never read/write anything larger than a single byte to a binary file. Do so ensures that your file will always work.
With that in mind.... I wrote an article that explains how to do all your binary file IO with just reading/writing individual bytes. This includes how to read/write strings.
The article is here:
http://www.cplusplus.com/articles/DzywvCM9/
And this is the original forum post made by Disch :
http://www.cplusplus.com/forum/beginner/108114/#msg587223


Kirtivardhan Singh
Deep Learning | Natural Language Processing
Email<EMAIL_ADDRESS>GitHub: ikirtivardhansingh
linkedIn: kirtivardhansingh
Home
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@import uk.gov.hmrc.govukfrontend.views.html.components._
@this(govukErrorSummary : GovukErrorSummary)
@()
@govukErrorSummary(ErrorSummary(
errorList = Seq(
ErrorLink(
href = Some("#passport-issued-error"),
content = Text("The date your passport was issued must be in the past")
),
ErrorLink(
href = Some("#postcode-error"),
content = Text("Enter a postcode, like AA1 1AA")
)
),
title = Text("There is a problem")
))


Take a look at the index.html and css/style.css for an example.
The only required files are jQuery and js/jmpress.js to use.
Create a root element:
Fill it up with steps:
Slide 1
Slide 2
Tell jQuery to run it:
$(function() {
$('#jmpress').jmpress();
});
Configure jmpress
Don't want to use `.step` as a selector? Okay:
$('#jmpress').jmpress({
stepSelector: 'li'
});
Customize the hash id of each slide
The id of the step will appear as the URI hash to recall the slide later. If you
don't give your steps ids then the id `step-N` will be used.
Slide 1
Load slides dynamically
You can load a slide dynamically by setting the `data-src` or `href` attribute
on the slide. The slide will only be loaded when an adjacent slide or the slide
itself is selected.
Loading...


Cacti - T. bridgesii
Preparation-I purchased an 18 inch trichocereus bridgesii, also known as achuma cactus from a local nursery. The catus is a deep turqouise color and looks very healthy. I remove the spines and cut the cactus into thin circles. then I boil the material in water and lemon juice for 1 hour. The resulting brew is by far the nastiest, most bitter thing I’ ve ever tasted. Down the hatch!
Setting-my house, the upstairs apartment to be precise. Its always served as a good place for parties, and a very mellow place for me to smoke ganja and trip. I lie on the bed and watch Adult Swim, waiting for the effects.
After about 1 hour, I feel myself slipping from sanity. The ties that bind my rational personality are slowly melted away. I try to be positive and control the trip, but the trip controls me, increasingly pulling me from reality. I begin to get increasingly paranoid, and glance around the room . I look over to the kitchen and see something moving on the linoleum. A hand slowly pushes its way through the tiles, and an arm and head follow.  At this point I knew I’ d taken too much. I look away from the man and close my eyes. I blast from the bed with such force that the vertigo makes me vomit. After a short vomiting session I lay back down. This is where things get seriously intense. At this point im incapacitated, barely clinging to conciousness.
I feel a presence enter the room. The room darkens and trembles. Its emmensly large and male. With a thundering voice It declares itself The Beast. ‘ Why do you seek me?’ the beast demands. I say ‘ I come seeking inner wisdom. ‘ The Beast says something that I’ ll always remember.  Hapiness can never be found through materials(such as the cactus) , True hapiness lies within’ .
Then he leaves. The paranoia and anxiety leave. He leaves me with a smile on my face. I see the ceiling above me crack open and crumble away and universes and galaxies rush by. The secrets of life are exposed to me. my life is just an insant on the infinite span of time. The universe expands forever within me and without. Races and civilizations are born and die immeasurable times. I cry from the beauty of the things im exposed to. And 12 hours later, still tripping very hard, im left with the knowledge that sometime, somewhere, I have lived before, and will live again. And again. and again. . . . . . .
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


The CSS margin properties are used to create space around elements, outside of any defined borders.
##Helper mixins:
| Mixin | Resultant CSS | $value |
|---|---|---|
| @include sap-set-margin-all($value) | margin: $value; | defined by the user |
| @include sap-set-margin-top($value) | margin-top: $value; | defined by the user |
| @include sap-set-margin-bottom($value) | margin-bottom: $value; | defined by the user |
| @include sap-set-margin-begin($value) | margin-left: $value; | defined by the user |
| @include sap-set-margin-end($value) | margin-right: $value; | defined by the user |
| @include sap-set-margin-x-equal($value) | margin-right: $value; margin-left: $value; |
defined by the user |
| @include sap-set-margin-y-equal($value) | margin-top: $value; margin-bottom: $value; |
defined by the user |
| @include sap-set-margin-x($valueLeft, $valueRight) | margin-left: $valueLeft; margin-right: $valueRight; |
defined by the user |
| @include sap-set-margin-y($valueTop, $valueBottom) | margin-top: $valueTop; margin-bottom: $valueBottom; |
defined by the user |
| @include sap-margin($value, $side) | based on parameters | $value:tiny,small, medium,large or defined by the user $side: top, bottom, begin, end, x, y |


Connecting R to an enterprise data warehouse? Do it properly and do not hard-code your passwords! Here is how you can do it in R with rMarkdown and RStudio version 1.0+
The Problem
Working as a data scientist in a large organisation, chances are you will have to get data out of an Enterprise Data Warehouse (EDW) and into your Data Manipulation Environment (DME, usually R, Python, Julia, or SAS). Of course, you could create a manual extract, save it as .csv and read it from disk. However, this approach has a number of downsides:
- the manual workflow may be hard to reproduce later on
- files use up additional disk space
- csv files do not store data types
Generally, I prefer to connect my DME directly to the database, as do many other Data Scientists. What I have repeatedly come across in this context is people hard-coding their passwords and access tokens into their analysis code. In my opinion, this is a dangerous practice! It is most likely in violation of the security regulations of your organisation, and for good reason. It is far too easy for your code to accidentally end up on an unrestricted access github repository, an unprotected S3 bucket, or similar. With GDPR just around the corner, a mistake like that could soon cost your organisation up to 3% of their global annual revenue in fines!
The Solution
So what’s the “proper” way to do this? Well, RStudio (v1.0+) offers some great new features in this context. If you are using Windows (like many big corporations do) and you are connecting to your EDW using the Windows ODBC Data Source Administrator, you can read your connection details directly from there using the “odbc” package.
Note: each code block below should be a chunk in an rMarkdown
# Unfortunately, odbc is not on CRAN yet.
# So if you do not have it yet we will need devtools
install.packages(devtools)
# Using devtools, we can now install the odbc package
devtools::install_github('rstats-db/odbc')
# Get connection info from Windows ODBC Data Source Administrator
# Using the name you set manually
con <- dbConnect(odbc::odbc(), 'EDW_name')
Using this connection object, we can now write and run SQL code snippets in rMarkdowns, rNotebooks, and shiny apps. Just pass the connection as property to the snippet and specify an “output.var” that will capture the output. This “output.var” will be available in your R workspace afterwards.
-- This should be a chunk with the following header:
-- {SQL, connection = con, output.var = result}
-- As a result, this turns into sql code.
-- comments need to be marked accordingly
SELECT TOP 10 * FROM EDW_database.EDW_table
# result will be available in the next chunk!
result
This code has syntax highlighting, runs start to finish without any manual steps, does not rely on “hacky” string queries, does not have hard-coded passwords, and your data updates as and when new data becomes available in your EDW!
As always, hope this is useful for someone. Please leave a comment below!


3. Tutorial¶
In this section, code snippets showing how to use SURF are presented for quick start. A more detailed example can be found in Verification and Validation, in which there is a jupyter notebook that can run in Google Colab.
3.1. Neural network¶
The example below shows how to train a neural network
on a data set, and use it to predict.
from surf.NN import SpatialNeuralNet
#---------------------------------------
# 1. Prepare your data
#---------------------------------------
data = load your data here
# data is a numpy matrix with columns: [x,y,value]
# ... see SURF.ET-AI.py for an example:
# https://github.com/NHERI-SimCenter/SURF/blob/master/examples/demo-NN.py
#---------------------------------------
# 2. Train the neural network
#---------------------------------------
# define a spatial neural network
# numNei is the number of nearest neighbors to be considered
nn = SpatialNeuralNet(rawData = data, numNei = 20)
# create a neural network that can take information of numNei points as input
nn.build_model()
# this trains the neural network on rawData
nn.train()
#---------------------------------------
# 3. Predict
#---------------------------------------
# define a point located at (x,y)
unkown_point = [x, y]
# predict a value at the unkown_point
predicted = nn.predict(unkown_point)
3.2. Random field¶
The example below shows how to define a spatial model for a data set, and insert it into random field to predict.
from surf.rf import SK
import surf.spatialModel as m
#---------------------------------------
# 1. Prepare your data
#---------------------------------------
data = load your data here
# data is a numpy matrix with columns: [x,y,value]
# ... see SURF.ET.py for an example:
# https://github.com/NHERI-SimCenter/SURF/blob/master/examples/demo-NN.py
#---------------------------------------
# 2. Define spatial model
#---------------------------------------
# correlation length
colLength = 0.1
# sill of the semivariogram
sill = np.var( data[:,2] )
# cov is covariance function;
# exponential means using exponential as the covariance function
cov = m.cov( m.exponential, ( colLength, sill ) )
#---------------------------------------
# 3. Predict
#---------------------------------------
# define a point located at (x,y)
unkown_point = [x, y]
# predict a value at the unkown_point
# N is the number of nearest neighbors to depend on
predicted_mu, predicted_std = SK( data, cov, unkown_point, N=100 )


Your browser doesn't seem to support generic sensors. If you are running Chrome, please enable the Generic Sensor API in about:flags to run this demo.
Video stream not available.


|
CPUTemp
1
Analyzes the core temperature of a four core system and does a peice-wise linear interpolation and global least squares approximation.
|
#include <myMatrix.h>
Public Member Functions | |
| myMatrix () | |
| myMatrix (int x, int y) | |
| virtual | ~myMatrix () |
| int | getRows () const |
| int | getCols () const |
| double | getC0 () const |
| double | getC1 () const |
| double | getXk (int n, int m) const |
| double | getYi () |
| void | setRows (int x) |
| void | setCols (int y) |
| void | addContent (int i, std::list< double > data) |
| void | transform (myMatrix M) |
| void | multiply (myMatrix M, myMatrix N) |
| void | createAugmented (myMatrix M, myMatrix N) |
| void | sortRow () |
| void | scale (int x) |
| void | reduce () |
| void | backSolve () |
| void | Display (std::ostream &outs) const |
matrix solver class
Definition at line 11 of file myMatrix.h.
| myMatrix::myMatrix | ( | ) |
| myMatrix::myMatrix | ( | int | x, |
| int | y | ||
| ) |
Alternate constructor
| x | is the number of rows. |
| y | is the number of columns. |
Alternative constructor with two arguments.
| x | the number of rows. |
| y | the number of columns. |
Definition at line 24 of file myMatrix.cpp.
|
virtual |
Destructor
Definition at line 42 of file myMatrix.cpp.
| void myMatrix::addContent | ( | int | j, |
| std::list< double > | data | ||
| ) |
| i | is the value for the length of the data to be added. |
| data | are the values to be added to the matrix. |
Add data to the j column.
| j | is the the column number. |
| data | is the list of values to be added. |
Definition at line 52 of file myMatrix.cpp.
| void myMatrix::backSolve | ( | ) |
Preforms the backsolve operation to create the 0 upper diagonal in the matrix.
Backsolve for the C0 value.
Definition at line 180 of file myMatrix.cpp.
appends one matrix to the other.
Append two matrices together.
| M | the left matrix. |
| N | the rigth matrix. |
Definition at line 105 of file myMatrix.cpp.
| void myMatrix::Display | ( | std::ostream & | outs | ) | const |
Display for the myMatrix class.
Display the matrix.
| &outs | a constant output variable for use in output stream, cout, or cerr. |
Definition at line 199 of file myMatrix.cpp.
|
inline |
Definition at line 45 of file myMatrix.h.
|
inline |
Definition at line 50 of file myMatrix.h.
|
inline |
Definition at line 40 of file myMatrix.h.
|
inline |
Definition at line 35 of file myMatrix.h.
|
inline |
| n | is a value for a row in the matrix. |
| m | is a value for a column in the matrix. |
Definition at line 57 of file myMatrix.h.
|
inline |
Definition at line 62 of file myMatrix.h.
Performs matrix multiplication between two matrices (order matters.)
| M | is the first matrix. |
| N | is the matrix M will be multiplied with. |
Dot product of two matrices (order matters.)
| M | the first matrix. |
| N | the matirx to be multiplied with M. |
Definition at line 84 of file myMatrix.cpp.
| void myMatrix::reduce | ( | ) |
Performs the reduce operation to create the 0 lower diagonal in the matrix.
Reduce the row so the first number is 0.
Definition at line 161 of file myMatrix.cpp.
| void myMatrix::scale | ( | int | x | ) |
Scales the row so the first value is 1.
| x | is the row to be scaled. |
Scale the first row so the first number is 1.
| x | is the row to be scaled. |
Definition at line 142 of file myMatrix.cpp.
|
inline |
| y | values to set the number of columns. |
Definition at line 72 of file myMatrix.h.
|
inline |
| x | value to set the number of rows. |
Definition at line 67 of file myMatrix.h.
| void myMatrix::sortRow | ( | ) |
Reorders a matrix from least to greatest.
Sort rows from least to greatest in the first column.
Definition at line 122 of file myMatrix.cpp.
| void myMatrix::transform | ( | myMatrix | M | ) |
Performs the transform operation on the matrix.
| M | is the switching of values from rows to columns in the matrix. |
Perform a matrix transform.
| M | is the original matrix. |
Definition at line 68 of file myMatrix.cpp.


 Wouldn't it be nice to have a yard!? Chickens come in all sorts of sizes, colors, and feather stylings. They have various waddles and combs, some have more than 4 toes, short legs and long legs. Some like the cold, some are pampered in palaces. Chickens are found in nearly every corner of the world. (I wonder if there are chickens in Antarctica?)
Gallery
Appenzeller
A Swiss bird with two different varieties. The Spitzhauben is named for a woman's hat and the Barthuhner is named for bearded hens with a rose comb and no crest.
Chantecler
This practical chicken hails from Canada around the turn of the 20th century. They are a hardy breed with small cushion combs and wattles.
Houdan
These French birds are descended from ancient 5-toed fowl common in the area. They are good for both meat and egg-laying, plus looks! This bird has it all!
Naked Neck
Also known as the "Translyvanian Naked Neck", these birds are named for their featherless necks. This makes them easier to pluck if raised for meat, but they also are good egg-layers.
Scots Dumpy
An ancient Sccotish breed of chicken that dates back 700 years ago. They have short little legs which make them excellent for those who want to keep manicured lawns.
Sultan
This cuddly cutie is native to Turkey–their Turkish name is "Serai Taook" which translates to "Fowl of the Sultan's Palace." If you're looking for a unique bird, this one is for you!


?1
The
New
York
copyright
reserved
Botanical
Garden
PLANTS
OF
HAITI
.i»»
I
'J/uss
-
totnlty
of
Geos
Morne,
Departement
de
I’Artlboutte
tude
about
385
meters
New
York
Botanical
Garden
E.
C.
LEONARD,
Collector
1623516
Legend -
Level of confidence that token is an accurately-transcribed word
extremely low
very low
low
undetermined
medium
high
very high


Following are some basic elastic search queries that may be helpful to maintain the Kylo repository.
Refer to the documentation for additional information.
Additional information relating to Kylo's use of search engines can be found in the following pages.
Refer to the Kylo documentation for information relating to Kylo search configuration.
Check that you are referencing the latest version of the documentation by following this link: latest Kylo docs
Then scroll down to the "HowTo guides" section and click on the "Kylo & Global Search" sub heading.
To query the repository to return any record that contains the word "roll" use the following:
curl http://localhost:9200/_search?q=roll |python -m json.tool
To search for records that contain words starting with roll (e.g. will match "rolling") use the following:
curl http://localhost:9200/_search?q=roll* |python -m json.tool
To search for words starting with "stone" and limit the search to data ingested by Kylo (i.e. exclude metadata from the search), add "kylo-data"to the URL:
curl http://localhost:9200/kylo-data/_search?q=stone* |python -m json.tool
Assuming one of the above "query" requests returned a hit that looked like the following
{ "_id": "a846b37f-adeb-46dc-bb1c-96b56646af81", "_index": "kylo-data", "_score": 1.0, "_source": { "eventid": 8151, "eventname": "Rolling Stones", "kylo_schema": "concerts", "kylo_table": "events", "starttime": "4/15/08 19:00", "venueid": 115 }, "_type": "hive-data" }
This specific record may be deleted using a the following request:
curl -XDELETE http://localhost:9200/kylo-data/hive-data/a846b37f-adeb-46dc-bb1c-96b56646af81 |python -m json.tool
Note that the record ID (a846b37f-adeb-46dc-bb1c-96b56646af81) has been transcribed into the request URL
This will output a confirmation of the form:
{ "_id": "a846b37f-adeb-46dc-bb1c-96b56646af81", "_index": "kylo-data", "_shards": { "failed": 0, "successful": 1, "total": 2 }, "_type": "hive-data", "_version": 2, "found": true, "result": "deleted" }
The following request will delete all data contained in the Kylo any (and all) tables named 'stones' without regards as to which database the table(s) resides.
Note that this is a multi-line request. As such, proper use of the quotation marks is important.
curl -XPOST 'localhost:9200/kylo-data/_delete_by_query' -H 'Content-Type: application/json' -d'
{
"query": {
"match" : {
"kylo_table" : "stones"
}
}
}
' |python -m json.tool
This will output a confirmation of the form:
{ "batches": 1, "deleted": 8, "failures": [], "noops": 0, "requests_per_second": -1.0, "retries": { "bulk": 0, "search": 0 }, "throttled_millis": 0, "throttled_until_millis": 0, "timed_out": false, "took": 33, "total": 8, "version_conflicts": 0 }
To delete on multiple fields:
curl -XPOST 'localhost:9200/kylo-data/_delete_by_query' -H 'Content-Type: application/json' -d' { "query": { "bool" : { "must" : [ { "match" : {"kylo_schema" : "testing" }}, { "match" : {"kylo_table" : "stones"}} ] } } }' | python -m json.tool
This will delete all indexed records that contain both the "kylo_schema" being equal to "testing" and the "kylo_table" being equal to "stones"
If the user searches for text that happens to align with a schema and/or table name, then all records in that schema and/or table will be returned. This will occur regardless of whether or not the entered text matches the content of the table. It has the potential to generate large quantities of results.
For example, entering "test" as the search string in the demo VM will result in every record in the test schema being returned. This occurs despite the fact that though only one or two records have the word "test" in them. The problem is that each and every one of these records contains a field "kylo-schema" that has the value test.
The following queries provide examples of how to avoid searching the kylo_schema (and by extension the kylo_table) field(s).
# This query works, but has the potential to miss some records.
# If any of the the content includes the word test, then the record is not selected.
curl -XGET 'localhost:9200/kylo-data/_search?pretty=true' -d '{
"query" : {
"query_string": {
"query": "test AND -kylo_schema:test"
}
}
}'
# This query works better, but could become difficult to formulate as the number of indexed columns grows.
# Also, it appears to be limited to just the text fields. If any non text fields are included (e.g. id, amt, concertdate) an
# exception is thrown. The exception is of the form:
# "caused_by" : {
# "type" : "number_format_exception",
# "reason" : "For input string: \"test\""
# }
curl -XGET 'localhost:9200/kylo-data/_search?pretty=true' -d '{
"query" : {
"query_string": {
"fields": [ "addr", "eventname",
"name", "productdescription",
"productname", "productvendor", "suburb", "venuecity",
"venuename", "venuestate" ],
"query": "test"
}
}
}'
To see the structure of an index:
curl -XGET 'localhost:9200/kylo-data/_mapping' |python -m json.tool
To store stuff into the kylo-data index.
NB: The 9999 is the record id and should be unique. You may also "update" an existing record by supplying it's id in place of the 9999.
curl -XPUT 'localhost:9200/kylo-data/9999' -d '{
"kylo_schema": "other",
"kylo_table": "stones",
"name": "Lisa passes test for high school"
}' |python -m json.tool
Search the field called "name" for the value "bart". Return an explanation of the method used to calculate the score and return up to 100 hits.
curl "http://localhost:9200/kylo-data/_search?q=name:Bart&explain&size=100" |python -m json.tool
Retrieve records by specified id's.
curl -XGET 'localhost:9200/_search?pretty' -H 'Content-Type: application/json' -d'
{
"query": {
"ids" : {
/* "type" : "hive-data", */
"values" : ["9990", "9999"]
}
}
}
'
More information about elastic search queries can be found in the official documentation.
For full information the Elastic Search Reference manual may also be useful.


--- layout: post title: New Blog Address date: 2011-05-21 16:42:00.000000000 -05:00 type: post published: true status: publish categories: [] tags: [] meta: _wpas_done_fb: '1' _edit_last: '15714693' _wpas_done_twitter: '1' author: login: broberts65 email<EMAIL_ADDRESS>display_name: Bob Roberts first_name: '' last_name: '' ---
I have changed the address for my blog; bob-roberts.net. I have done this as it seems as though I am branching out into a lot more than Python.
The bobspython.com link will continue to work until next year.


Before Change
Returns premises where the predicates have exactly the same
arguments as in the conclusion.
conclusion_terms = conclusion.split()
candidate_premises = []
for premise in premises:
premise_terms = premise.split()[2:]
logging.debug(premise_terms)
After Change
Returns premises where the predicates have at least one argument
in common with the conclusion.
conclusion_terms = [c.strip(")(") for c in conclusion.split()]
conclusion_args = set(conclusion_terms[1:])
candidate_premises = []
for premise in premises:
premise_terms = [p.strip(")(") for p in premise.split()[2:]]
premise_args = set(premise_terms[1:])
logging.debug("Conclusion args: " + str(conclusion_args) + \
"\nPremise args: " + str(premise_args))


|
||||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object com.primus.searchstudio.SpellingTerm
Spelling term value object.
| Field Summary | |
protected int |
length
|
protected int |
offset
|
protected java.lang.String |
suggestion
|
protected java.lang.String |
text
|
| Constructor Summary | |
protected |
SpellingTerm(java.lang.String suggestion,
int offset,
int length)
Constructs new term with specified parameters. |
protected |
SpellingTerm(java.lang.String text,
java.lang.String suggestion,
int offset,
int length)
Constructs new term with specified parameters. |
| Method Summary | |
int |
getLength()
Get term length used in the initial query. |
int |
getOffset()
Get term offset in the initial query. |
java.lang.String |
getSuggestion()
Get spell checker suggested text. |
java.lang.String |
getText()
Get spell checker initial text. |
boolean |
isUnknown()
Indicates whether this term is "unknown". |
boolean |
isUnsearchable()
Indicates whether this term is "unsearchable". |
| Methods inherited from class java.lang.Object |
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Field Detail |
protected int offset
protected int length
protected java.lang.String suggestion
protected java.lang.String text
| Constructor Detail |
protected SpellingTerm(java.lang.String suggestion, int offset, int length)
suggestion
- text suggested by spell checkeroffset
- text offsetlength
- text lengthprotected SpellingTerm(java.lang.String text, java.lang.String suggestion, int offset, int length)
text
- current term textsuggestion
- text suggested by spell checkeroffset
- text offsetlength
- text length| Method Detail |
public java.lang.String getSuggestion()
public java.lang.String getText()
public int getOffset()
public int getLength()
public boolean isUnknown()
true
if term is "unknown" or false
otherwise.public boolean isUnsearchable()
true
if term is "unsearchable" or false
otherwise.
|
||||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |


Next we want to group up similar job posts into clusters. To do this, we use a K-Means algorithm. We do this for
two reasons. First, the result will be more practical if the clusters are similar in size. Second, we want
to rescale the features using the weights in the regression model, so we need to maintain the relative importance of
each direction. To choose the number of clusters we graph two measures of quality against the number of clusters.
- The second moment measures the (negative squared) distance of the points in each cluster to the cluster's center
- Purity measures the (weighted) average of what fraction of each job title is in a single cluster
The purity falls nearly linearly, and so isn't helpful for choosing the number of clusters. Instead we
apply the elbow heuristic to the moment graph. In particular, we see k=60 is approximately the point where the graph
starts to flatten out (marked with a vertical line below).


I am a Software Developer with MSc degree at University of Technology Wrocław. Mostly interested in .net platform. TDD evangelist and SOLID principles believer. I enjoy writing posts on a dev.to for some time. I am still learning, I want to catch an opportunity to get involve into open source projects.
Love board games, used to train swimming, like football both watching and playing.
Simple ASP.NET app to search through github api.
Project for a training conduction purpose.
Proof of concept which shows a forbidden headers concept.
Feel free to contact me in any topic


The tele-TASK platform is an online archive managing a heavily increasing number of tele lectures and video recordings of scientific conferences. These resources are freely available for every internet user. The organization and preparation of content – video data in this case – is essential for the success of a web portal and for the usability. These tasks should be as convenient as possible for those who enter content into the platform.
The goal of the project was to extend and enhance the tele-TASK platform with respect to the integration of semantic web technologies into the system and thus giving the user further possibilities for the purposeful search in multimedia data. The semantic data for the videos were generated with the help of OCR data and the extracted voice of the speaker. Moreover the project developed a tool to create time-related notes for the recorded lectures.


--- title: Badges ---
Badges display the approximation of the given value.
Badges hide when value is falsey unless specified otherwise.
Alerts
Messages
Alerts Alerts Messages


Skip to main content
Sorry - something has gone wrong.
Please try again soon.
Or go back to
beta.parliament.uk


Tags
-
console
blender
tutorial
compositing
console
blender
- How to make a Flare in Blender
- How to make a Android robot in Blender Part 2
- How to make a Android Robot in Blender Part 1
- How to make a Sun Beams in Blender
tutorial
- How to make a Flare in Blender
- How to make a Android robot in Blender Part 2
- How to make a Android Robot in Blender Part 1
- How to make a Sun Beams in Blender


appletviewer - The Java Applet Viewer.
The f3appletviewer command allows you to run applets outside of a web browser.
f4appletviewer f2[ f2options f2] f2urls ...
The f3appletviewer command connects to the documents or resources designated by f2urls and displays each applet referenced by the documents in its own window. Note: if the documents referred to by f2urls do not reference any applets with the f2OBJECT, f2EMBED, or f2APPLET tag, then f3appletviewer does nothing. For details on the HTML tags that f3appletviewer supports, see
f2AppletViewer Tags @
http://docs.oracle.com/javase/7/docs/technotes/tools/appletviewertags.html.
f3Note: The f3appletviewer is intended for development purposes only. See About Sample / Test Applications and Code for more information.
f3Note: The f3appletviewer requires encoded URLs according to the escaping mechanism defined in RFC 2396. Only encoded URLs are supported. However, file names must be unencoded, as specified in RFC 2396.
-debug Starts the applet viewer in the Java debugger, jdb(1), thus allowing you to debug the applets in the document.
-encoding encoding name Specify the input HTML file encoding name.
-Jjavaoption Passes through the string f2javaoption as a single argument to the Java interpreter which runs the appletviewer. The argument should not contain spaces. Multiple argument words must all begin with the prefix f3-J, which is stripped. This is useful for adjusting the compiler's execution environment or memory usage.


Icon navigation bar is known for its simplicity and functionality for mobile devices.
The classic bar can be used in multiple cases and adapted to be responsive for desktop and mobile.
Alternative to the top navigation bar, its nice design will make your website look sleek.
With the input navigation bar you will learn how to add a search, username or password field.


¿What is TROLLEYES?
- TROLLEYES is an educative application that was created for students to learn how to develop web applications.
- It was designed for Spanish Ciclos Formativos de Grado Superior students, more specifically, the Web Application Development students.
- The structure of TROLLEYES was thought to be
-
- maintanable
- easy to undestand
- easy to expand in a workgroup
Who is driving this project?
- The author of the application is Rafael Aznar (rafaaznar {at} gmail {dot} com). Professor of the Higher Level Formative Cycle called Web Application Development.
Objectives
- Learn how to structure the design of a web project in the client environment
- Provide the student with the experience of working on a structured project
- Provide an environment for students to develop in a team
- Simulate the creation of a complex project in a working environment
- Participate in the creation of a complex AJAX web application


Main Page
Letter Counting
TGV Matching
Ordered Verse Search
Analysis
Letter selection panel
Arabic Letters - Grouped by Shape
{% for letter_group in letters %}
{% for tuple in letter_group %}
{{ tuple[0] }}
{% endfor %}
{% endfor %}
Calculate letter count and TGV
Result =
{{ result }}
Sura selection panel
{% for number_group in sura_numbers %}
{% for number in number_group %}
{{ number }}
{% endfor %}
{% endfor %}


About
This is the UK Web Archive's collaborative web archiving annotation and curation tool, W3ACT.
For more information, see the W3ACT Wiki
Version: @ApplicationController.getVersion
@(title: String, user: User) @main(title, user){
This is the UK Web Archive's collaborative web archiving annotation and curation tool, W3ACT.
For more information, see the W3ACT Wiki
Version: @ApplicationController.getVersion


How can I customize the way selections are displayed?
Nothing is being displayed when I select an option
{% include options/not-written.html %}
I am using HTML in my selection template but it isn't displaying it
{% include options/not-written.html %}
How can I access the container where the selection is displayed?
{% include options/not-written.html %}


Implementing algorithms for mechanism reduction
Abstract
Reaction mechanisms that describe the oxidation of a fuel are huge with several thousands of individual steps. While using them in computations, compact representation of these schemes are essential. This project initially involves developing a black box tool to reduce reaction mechanisms based on existing ideas. Improvements will be suggested to existing methods, their pros and cons will be evaulated, and the perfect order in which they must be applied to achieve best results will be investigated.
Pre-requisite
Penchant for writing stand-alone codes, working knowledge in C++ (inclusive of pointers, classes, inheritance, memory handling), and interest in combustion
Additional details
Position: Project assistant
Duration: 1 year
Salary: As per institute norms
Selection procedure: Personal interview.
If interested, write to me with a subject line that includes the title of the topic and your CV. If your application is found suitable, you will receive an intimation for a personal interview.


Wastes from agriculture, horticulture, aquaculture, forestry, hunting and fishing, food preparation and processing Oil wastes and wastes of liquid fuels (except edible oils and those in chapters 05, 12 and 19) Waste packaging; absorbents, wiping cloths, filter materials and protective clothing not otherwise specified Wastes from industrial processes Construction and demolition waste Healthcare waste Minerals from waste and water treatment Municipal waste and similar materials from commerce and industry Another type of waste {% endblock %}


⍷
)⍷ 𝕩
: DeduplicateUnique major cells of 𝕩
.
⍷ 4‿5‿6‿6‿4‿7‿5 ⟨ 4 5 6 7 ⟩ a ← 3‿3 ⥊ ↕6 ⍷ a ┌─ ╵ 0 1 2 3 4 5 ┘
𝕨 ⍷ 𝕩
: FindMark the top left location of the occurrences of 𝕨
in 𝕩
with a 1, and other locations with 0.
Result is the same shape as (≢𝕨)↕x
.
"string" ⍷ "substring" ⟨ 0 0 0 1 ⟩ "loooooong" ⍷ "short" ⟨⟩ b ← 7 (4|⋆˜)⌜○↕ 9 c ← (0‿3‿0≍0‿1‿0) c ⍷ b ┌─ ╵ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 ┘


{{ script_tags|safe }} {{ style_tags|safe }}
Wiki-Labels is a human computation service for Wikipedia. In order perform difficult analyses and train intelligent wiki-tools (e.g. for detecting vandalism and assessing the quality of articles), we need labeled data and lots of it. Wiki-Labels is a tool that makes it easy to collaboratively label wiki artifacts (like revisions) quickly and easily.


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My name is Adam Stankiewicz.
I am professional Ruby developer currently working at Monterail.com
I love Open Source. I'm actively developing following software:
My articles:


Cygnet GSP simplifies the GST returns by providing comprehensive and end to end automation consisting of data extraction, data validation, returns filing and reporting.
Carver is a Cybersecurity Compliance Application created by a top ranked global cybersecurity audit and advisory firm SeraBrynn to make the Cybersecurity Compliance process 10x faster and easier.
And – it’s backed by Blockchain technology.
Idox Transport’s Icarus – RTI is a real time vehicle tracking and management solution that enables public transport operators to keep track of vehicles’ movements within the network. Core competencies of Icarus enables it to populate key road network information from UTMC systems, capture data from desperate sources, interpret data irrespective of their format and present it in the most simplest and uncluttered format for the operators to attain strategic control.
The purpose of Gatwick's internal transportation was to provide bus service from terminal to car parking and vice versa
Idox Transport’s Gatwick – RTI is a real time vehicle tracking and management solution that enables Gatwick's internal fleet management system to keep track of vehicles’ movements within the network. It provides real time information of Gatwick buses which consist of Automatic Passenger count, Zone managemnet and Stops Arrival/Departure
The purpose of Ticketing system was to simplify the process of resolving and troubleshooting the IT support and HR department issues raised within company infrastructure.
Completed my B.E with CGPA 8.02
Completed HSC with 75.84 %
Completed SSC with 82.6 %


nips nips2008 nips2008-126 nips2008-126-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Qiang Wu, Sayan Mukherjee, Feng Liang
Abstract: We developed localized sliced inverse regression for supervised dimension reduction. It has the advantages of preventing degeneracy, increasing estimation accuracy, and automatic subclass discovery in classiﬁcation problems. A semisupervised version is proposed for the use of unlabeled data. The utility is illustrated on simulated as well as real data sets.
[1] M. Belkin and P. Niyogi. Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation, 15(6):1373–1396, 2003.
[2] R. Cook and L. Ni. Using intra-slice covariances for improved estimation of the central subspace in regression. Biometrika, 93(1):65–74, 2006.
[3] R. Cook and S. Weisberg. Disussion of li (1991). J. Amer. Statist. Assoc., 86:328–332, 1991.
[4] R. Cook and X. Yin. Dimension reduction and visualization in discriminant analysis (with discussion). Aust. N. Z. J. Stat., 43(2):147–199, 2001.
[5] D. Donoho and C. Grimes. Hessian eigenmaps: new locally linear embedding techniques for highdimensional data. PNAS, 100:5591–5596, 2003.
[6] K. Fukumizu, F. R. Bach, and M. I. Jordan. Kernel dimension reduction in regression. Annals of Statistics, to appear, 2008.
[7] A. Globerson and S. Roweis. Metric learning by collapsing classes. In Y. Weiss, B. Sch¨ lkopf, o and J. Platt, editors, Advances in Neural Information Processing Systems 18, pages 451–458. MIT Press, Cambridge, MA, 2006.
[8] T. Golub, D. Slonim, P. Tamayo, C. Huard, M. Gaasenbeek, J. Mesirov, H. Coller, M. Loh, J. Downing, M. Caligiuri, C. Bloomﬁeld, and E. Lander. Molecular classiﬁcation of cancer: class discovery and class prediction by gene expression monitoring. Science, 286:531–537, 1999.
[9] T. Hastie and R. Tibshirani. Discrminant adaptive nearest neighbor classiﬁcation. IEEE Transacations on Pattern Analysis and Machine Intelligence, 18(6):607–616, 1996.
[10] K. Li. Sliced inverse regression for dimension reduction (with discussion). J. Amer. Statist. Assoc., 86:316–342, 1991.
[11] K. C. Li. On principal hessian directions for data visulization and dimension reduction: another application of stein’s lemma. J. Amer. Statist. Assoc., 87:1025–1039, 1992.
[12] K. C. Li. High dimensional data analysis via the sir/phd approach, 2000.
[13] J. Nilsson, F. Sha, and M. I. Jordan. Regression on manifold using kernel dimension reduction. In Proc. of ICML 2007, 2007.
[14] S. Roweis and L. Saul. Nonlinear dimensionality reduction by locally linear embedding. Science, 290:2323–2326, 2000.
[15] M. Sugiyam. Dimension reduction of multimodal labeled data by local ﬁsher discriminatn analysis. Journal of Machine Learning Research, 8:1027–1061, 2007.
[16] J. Tenenbaum, V. de Silva, and J. Langford. A global geometric framework for nonlinear dimensionality reduction. Science, 290:2319–2323, 2000.
[17] Q. Wu, F. Liang, and S. Mukherjee. Regularized sliced inverse regression for kernel models. Technical report, ISDS Discussion Paper, Duke University, 2007.
[18] Y. Xia, H. Tong, W. Li, and L.-X. Zhu. An adaptive estimation of dimension reduction space. J. R. Statist. Soc. B, 64(3):363–410, 2002.
[19] G. Young. Maximum likelihood estimation and factor analysis. Psychometrika, 6:49–53, 1941.
[20] W. Zhong, P. Zeng, P. Ma, J. S. Liu, and Y. Zhu. RSIR: regularized sliced inverse regression for motif discovery. Bioinformatics, 21(22):4169–4175, 2005. 8


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This program is an example of writing some of the features of the XlsxWriter module. See the Working with Dates and Time section for more details on this example.
##############################################################################
#
# A simple program to write some dates and times to an Excel file
# using the XlsxWriter Python module.
#
# Copyright 2013, John McNamara<EMAIL_ADDRESS>#
from datetime import datetime
from xlsxwriter.workbook import Workbook
# Create a workbook and add a worksheet.
workbook = Workbook('datetimes.xlsx')
worksheet = workbook.add_worksheet()
bold = workbook.add_format({'bold': True})
# Expand the first columns so that the date is visible.
worksheet.set_column('A:B', 30)
# Write the column headers.
worksheet.write('A1', 'Formatted date', bold)
worksheet.write('B1', 'Format', bold)
# Create a datetime object to use in the examples.
date_time = datetime.strptime('2013-01-23 12:30:05.123',
'%Y-%m-%d %H:%M:%S.%f')
# Examples date and time formats. In the output file compare how changing
# the format codes change the appearance of the date.
date_formats = (
'dd/mm/yy',
'mm/dd/yy',
'dd m yy',
'd mm yy',
'd mmm yy',
'd mmmm yy',
'd mmmm yyy',
'd mmmm yyyy',
'dd/mm/yy hh:mm',
'dd/mm/yy hh:mm:ss',
'dd/mm/yy hh:mm:ss.000',
'hh:mm',
'hh:mm:ss',
'hh:mm:ss.000',
)
# Start from first row after headers.
row = 1
# Write the same date and time using each of the above formats.
for date_format_str in date_formats:
# Create a format for the date or time.
date_format = workbook.add_format({'num_format': date_format_str,
'align': 'left'})
# Write the same date using different formats.
worksheet.write_datetime(row, 0, date_time, date_format)
# Also write the format string for comparison.
worksheet.write_string(row, 1, date_format_str)
row += 1
workbook.close()