|
draw3D_gnuplot_api
0.7
|
Implementation drawing 3D class using gnuplot Class implements proxy between gnuplot and C++ code. More...
#include <Dr3D_gnuplot_api.hh>
Inherits drawNS::Draw3DAPI.
Public Member Functions | |
| APIGnuPlot3D ()=delete | |
| deleted non-parametric constructor to force using parametric. More... | |
| APIGnuPlot3D (double minX, double maxX, double minY, double maxY, double minZ, double maxZ, int ref_time_ms=0) | |
| Constructor. More... | |
| ~APIGnuPlot3D () | |
| destructor, also stops gnuplot proces. More... | |
| uint | draw_line (const Point3D &point1, const Point3D &point2, const std::string &color="black") override |
| draws line between 2 points More... | |
| uint | draw_polygonal_chain (const std::vector< Point3D > &points, const std::string &color="black") override |
| draws polygonal_chain between n points More... | |
| uint | draw_polyhedron (const std::vector< std::vector< Point3D > > &points_map, const std::string &color="black") override |
| draws polyhedron described by its apexs More... | |
| uint | draw_surface (const std::vector< std::vector< Point3D > > &points_map, const std::string &color="black") override |
| draws surface described by its points More... | |
| void | erase_shape (uint id) override |
| erase shape by id More... | |
| void | change_shape_color (uint id, const std::string &color) override |
| change color of shape More... | |
| void | change_ref_time_ms (int ref_time_ms) override |
| change mode for ploting and/or time between auto-replots More... | |
| void | redraw () override |
| replot all shapes Replots all shapes, works only in negative refresh_rate_ms value mode. More... | |
| Public Member Functions inherited from drawNS::Draw3DAPI | |
| Draw3DAPI (int ref_time_ms=0) | |
| Constructor. More... | |
| virtual | ~Draw3DAPI () |
Additional Inherited Members | |
| Protected Attributes inherited from drawNS::Draw3DAPI | |
| int | refresh_rate_ms |
| mode for ploting and time between auto-replots More... | |
Implementation drawing 3D class using gnuplot Class implements proxy between gnuplot and C++ code.
Colors are pre-defined or must be on form rgb:#rrggbb.
Pre defined colors: black, white, grey, blue, light-blue, red, green, yellow, orange, purple, as it was in default palette in Matlab 2014b.
Class have 3 modes, described in refresh_rate_ms variable.
|
delete |
deleted non-parametric constructor to force using parametric.
| APIGnuPlot3D::APIGnuPlot3D | ( | double | minX, |
| double | maxX, | ||
| double | minY, | ||
| double | maxY, | ||
| double | minZ, | ||
| double | maxZ, | ||
| int | ref_time_ms = 0 |
||
| ) |
Constructor.
| minX | - lower bounry in X axis, |
| maxX | - upper bounry in X axis, |
| minY | - lower bounry in Y axis, |
| maxY | - upper bounry in Y axis, |
| minZ | - lower bounry in Z axis, |
| maxZ | - upper bounry in Z axis, |
| ref_time_ms | - mode for plotting (negative, 0, positive, see refresh_rate_ms) and time between auto-replots (in miliseconds). |
|
inline |
destructor, also stops gnuplot proces.
|
overridevirtual |
change mode for ploting and/or time between auto-replots
| ref_time_ms | - mode for plotting (negative, 0, positive, see refresh_rate_ms) and time between auto-replots (in miliseconds). |
Implements drawNS::Draw3DAPI.
|
overridevirtual |
change color of shape
| id | - id of shape |
| color | - color of line (see class description) |
Implements drawNS::Draw3DAPI.
|
overridevirtual |
draws line between 2 points
| point1 | - first point |
| point2 | - second point |
| color | - color of line (see class description) |
Implements drawNS::Draw3DAPI.
|
overridevirtual |
draws polygonal_chain between n points
| points | - vector of points |
| color | - color of line (see class description) |
Implements drawNS::Draw3DAPI.
|
overridevirtual |
draws polyhedron described by its apexs
| points_map | - vector of points vectors. Every vector contains points describes one "layer" of polyhedron |
| color | - color of line (see class description) |
Implements drawNS::Draw3DAPI.
|
overridevirtual |
draws surface described by its points
| points_map | - vector of points vectors. Every vector contains points describes one "line" in surface |
| color | - color of line (see class description) |
Implements drawNS::Draw3DAPI.
|
overridevirtual |
|
overridevirtual |
replot all shapes Replots all shapes, works only in negative refresh_rate_ms value mode.
Implements drawNS::Draw3DAPI.


Depthkit.js
- Simple example using Three.js
Written by
juniorxsound
Press 1, 2, 3 to change meshScalar. q/w to rotate around X-axis, a/s to rotate around Y-axis, z/x to rotate around Z-axis
Press m to toggle mute/unmute


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Ploror688.github.io
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| Class | ActiveMerchant::Billing::PslCardGateway |
| In: |
lib/active_merchant/billing/gateways/psl_card.rb
|
| Parent: | Gateway |
ActiveMerchant PSL Card Gateway
Notes:
-To be able to use the capture function, the IP address of the machine must be registered with PSL -ESALE_KEYED should only be used in situations where the cardholder perceives the transaction to be Internet-based, such as purchasing from a web site/on-line store. If the Internet is used purely for the transport of information from the merchant directly to the gateway then the appropriate cardholder present or not present message type should be used rather than the ‘E’ equivalent. -The CV2 / AVS policies are set up with the account settings when signing up for an account
| URL | = | 'https://pslcard3.paymentsolutionsltd.com/secure/transact.asp?' | PslCard server URL - The url is the same whether testing or live - use the test account when testing… | |
| MESSAGE_TYPE | = | 'ESALE_KEYED' | eCommerce sale transaction, details keyed by merchant or cardholder | |
| RESPONSE_ACTION | = | 'HTML' | The type of response that we want to get from PSL, options are HTML, XML or REDIRECT | |
| CURRENCY_CODES | = | { 'AUD' => 036, 'GBP' => 826, 'USD' => 840 | Currency Codes | |
| EMV_TERMINAL_TYPE | = | 32 | The terminal used - only for swipe transactions, so hard coded to 32 for online | |
| DISPATCH_LATER | = | 'LATER' | Different Dispatch types | |
| DISPATCH_NOW | = | 'NOW' | ||
| APPROVED | = | '00' | Return codes | |
| NOMINAL_AMOUNT | = | 101 | Nominal amount to authorize for a ‘dispatch later’ type The nominal amount is held straight away, when the goods are ready to be dispatched, PSL is informed and the full amount is the taken. | |
| AVS_CODE | = | { "ALL MATCH" => 'Y', "SECURITY CODE MATCH ONLY" => 'N', "ADDRESS MATCH ONLY" => 'Y', "NO DATA MATCHES" => 'N', "DATA NOT CHECKED" => 'R', "SECURITY CHECKS NOT SUPPORTED" => 'X' | ||
| CVV_CODE | = | { "ALL MATCH" => 'M', "SECURITY CODE MATCH ONLY" => 'M', "ADDRESS MATCH ONLY" => 'N', "NO DATA MATCHES" => 'N', "DATA NOT CHECKED" => 'P', "SECURITY CHECKS NOT SUPPORTED" => 'X' |
Create a new PslCardGateway
The gateway requires that a valid :login be passed in the options hash
Paramaters:
-options: :login - the PslCard account login (required)
# File lib/active_merchant/billing/gateways/psl_card.rb, line 92 92: def initialize(options = {}) 93: requires!(options, :login) 94: 95: @options = options 96: super 97: end
Authorize the transaction
Reserves the funds on the customer‘s credit card, but does not charge the card.
This implementation does not authorize the full amount, rather it checks that the full amount is available and only ‘reserves’ the nominal amount (currently a pound and a penny)
Parameters:
-money: Money object for the total to be charged -authorization: the PSL cross reference from the previous authorization -options:
Returns:
-ActiveRecord::Billing::Response object
# File lib/active_merchant/billing/gateways/psl_card.rb, line 137 137: def authorize(money, credit_card, options = {}) 138: post = {} 139: 140: add_amount(post, money, DISPATCH_LATER, options) 141: add_credit_card(post, credit_card) 142: add_address(post, options) 143: add_invoice(post, options) 144: add_purchase_details(post) 145: 146: commit(post) 147: end
Post an authorization.
Captures the funds from an authorized transaction.
Parameters:
-money: Money object for the total to be charged -authorization: The PSL Cross Reference -options:
Returns:
-ActiveRecord::Billing::Response object
# File lib/active_merchant/billing/gateways/psl_card.rb, line 161 161: def capture(money, authorization, options = {}) 162: post = {} 163: 164: add_amount(post, money, DISPATCH_NOW, options) 165: add_reference(post, authorization) 166: 167: commit(post) 168: end
Purchase the item straight away
Parameters:
-money: Money object for the total to be charged -authorization: the PSL cross reference from the previous authorization -options:
Returns:
-ActiveRecord::Billing::Response object
# File lib/active_merchant/billing/gateways/psl_card.rb, line 109 109: def purchase(money, credit_card, options = {}) 110: post = {} 111: 112: add_amount(post, money, DISPATCH_NOW, options) 113: add_credit_card(post, credit_card) 114: add_address(post, options) 115: add_invoice(post, options) 116: add_purchase_details(post) 117: 118: commit(post) 119: end


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GrADS allows you use a single data descriptor file to aggregate multiple data files and handle them as if they were one individual file. The individual data files must be identical in the X, Y, and Z dimensions and have the same list of variables. The time range of each individual file must be indicated it its filename. Beginning with version 2.0, data files may also be aggregated in the ensemble dimension.
First, the DSET entry has a substitution template instead of
a filename. See below for a description of all the possible components of the
template. Second, the OPTIONS entry contains the template
keyword. Third, the TDEF entry describes the time range for the
entire set of data files.
Templating works on any GrADS data type for which you can write a descriptor file. If you specify any additional OPTIONS keywords in the data descriptor file, make sure the options apply equally to each file included in the template.
Valid components of the substitution template for the TIME axis are as follows:
%x1
1 digit decade
%x3
3 digit decade
%y2
2 digit year
%y4
4 digit year
%m1
1 or 2 digit month
%m2
2 digit month (leading zero if needed)
%mc
3 character month abbreviation
%d1
1 or 2 digit day
%d2
2 digit day (leading zero if needed)
%h1
1 or 2 digit hour
%h2
2 digit hour
%h3
3 digit hour (e.g., 120 or 012)
%n2
2 digit minute; leading zero if needed
%f2
2 digit forecast hour; leading zero if needed; more digits added for hours >99; hour values increase indefinitely
%f3
3 digit forecast hour; leading zeros if needed; more digits added for hours >999; hour values increase indefinitely
%fn2
2 digit forecast minute; leading zero if needed; more digits added for minutes > 99; minute values increase indefinitely (2.0.a9+)
%fhn
forecast time expressed in hours and minutes (hhnn
) where minute value (nn)
is always <=59
and hour value (hh
) increases indefinitely. If hh
or nn
are <=9, they are padded with a 0
so they are always at least 2 digits; more digits added for hours >99. (2.0.a9+)
%fdhn
forecast time expressed in days, hours, and minutes (ddhhnn
) where minute value (nn)
is always <=59,
hour value (hh
) is always <=23 and day value (dd
) increases indefinitely. If dd
, hh,
or nn
are <=9,
they are padded with a 0 so they are always at least 2 digits; more digits added for days >99. (2.0.a9+)
%j3
3 digit julian day (day of year) (2.0.a7+)
%t1
1 or 2 digit time index (file names contain number sequences that begin with 1 or 01) (2.0.a7+)
%t2
2 digit time index (file names contain number sequences that begin with 01) (2.0.a7+)
%t3
3 digit time index (file names contain number sequences that begin with 001) (2.0.a7+)
%t4
4 digit time index (file names contain number sequences that begin with 0001) (2.0.a8+)
%t5
5 digit time index (file names contain number sequences that begin with 00001) (2.0.a8+)
%t6
6 digit time index (file names contain number sequences that begin with 000001) (2.0.a8+)
%tm1
1 or 2 digit time index (file names contain number sequences that begin with 0 or 00) (2.0.a7+)
%tm2
2 digit time index (file names contain number sequences that begin with 00) (2.0.a7+)
%tm3
3 digit time index (file names contain number sequences that begin with 000) (2.0.a7+)
%tm4
4 digit time index (file names contain number sequences that begin with 0000) (2.0.a8+)
%tm5
5 digit time index (file names contain number sequences that begin with 00000) (2.0.a8+)
%tm6
6 digit time index (file names contain number sequences that begin with 000000) (2.0.a8+)
When specifying the initial time (e.g., NWP model output), use these substitutions:
%ix1
initial 1 digit decade %ix3
initial 3 digit decade %iy2
initial 2 digit year %iy4
initial 4 digit year %im1
initial 1 or 2 digit month %im2
initial 2 digit month (leading zero if
needed) %imc
initial 3 character month abbreviation
%id1
initial 1 or 2 digit day (leading zero
if needed) %id2
initial 2 digit day %ih1
initial 1 or 2 digit hour %ih2
initial 2 digit hour %ih3
initial 3 digit hour %in2
initial 2 digit minute (leading zero if needed)%ch
substitute string
%ch
template in your DSET entry, then you also need
to put additional CHSUB entries
in the descriptor file that contain two integers (t1 and t2) followed by a string
which will be substituted for %ch
in the data file names for the
time steps beginning withchsub t1 t2 string
In version 2.0, with the introduction of the extra grid dimension for ensembles, support was also added for file templating over E. The sole substitution template is %e
and the substitution string is the ensemble name, which is provided in the EDEF entry in the descriptor file. Note that the ensemble names are limited to 15 characters -- keep this limit in mind when designing your data directory structure and file naming conventions (or use symbolic links to create short aliases for longer filenames). If you are templating over the ensemble dimension, there can be only one ensemble member per file.
If your data set has an ensemble dimension, and you are using templating over T but not E (i.e., there is no %e in the DSET entry), then all ensemble members are presumed to have identical time axes, and all members must be contained in the data file for a given time. Templating over T but not E is not supported for data sets in flat binary or GRIB1 formats.
1may92.dat
2may92.dat
...
31may92.dat
DSET ^%d1may92.dat
OPTIONS template
TDEF 744 linear 0z1may1992 1hr
1jun92.dat
2jun92.dat
...
1jan93.dat
DSET ^%d1%mc%y2.dat
OPTIONS template
TDEF 6624 linear 0z1may1992 1hr
pr.1880_1889.nc
pr.1890_1899.nc
pr.1900_1909.nc
pr.1910_1919.nc
pr.1920_1929.nc
pr.1930_1939.nc
pr.1940_1949.nc
DSET ^pr.%x30_%x39.nc
OPTIONS template
DTYPE netcdf
TDEF 840 linear jan1880 1mo
pr.1851-1900.nc
pr.1901-2000.nc
DSET ^pr.%ch.nc
CHSUB 1 600 1851-1900
CHSUB 601 1800 1901-2000
OPTIONS template
DTYPE netcdf
TDEF 1800 linear jan1851 1mo
DSET %ch
CHSUB 1 600 /disk1/pr.1851-1900.nc
CHSUB 601 1800 /disk2/pr.1901-2000.nc
MMOUT_DOMAIN1_00
MMOUT_DOMAIN1_01
MMOUT_DOMAIN1_02
so your DSET enry will look like this: DSET ^MMOUT_DOMAIN1_%tm2


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Graduate programmes
Develop your passion for business
In the UK, Renault has successfully operated graduate programmes for a number of years to identify and develop leadership talent. Working within a highly competitive commercial industry we recognise the importance of creating the right environment for graduates to develop core skills and knowledge in the early years of their career. That is why all our programmes involve a comprehensive induction and personal development plans as well as hands on work experience in challenging business roles. Ideal for graduates interested in developing a career within retail or sales management, marketing or finance. Our bespoke programmes can vary in length from eighteen month to three year rotational programmes and offer graduates the opportunity to experience working in several disciplines within our UK operation.
Designed to fast track graduates into management, as a Renault graduate you can expect to:
• Experience a number of different business disciplines
• Gain a detailed understanding of our operation
• Cultivate an extensive network of contacts
• Develop the skills required for your first management role
It doesn’t stop there though, our training team are committed to supporting and developing you throughout your career.
Entrant Requirements:
To succeed on a Renault Graduate Programme you need a passion for business and to be able to adapt quickly and make an impression in placements across our key business areas. You don’t need to be a petrol
• Minimum Grade B (Maths and English) at GCSE Levels
• Attained a 2.1 University Degree – any discipline or final year students require 300 Min UCAS Points and must be on track for a 2.1 degree
•Experience of working in an office/sales environment is essential, ideally an industrial placement
•French Language skills are an advantage
I'd like to
Testimonials
Graduate Development Programme
Renault Graduate programmes are reviewed annually and tailored to meet business requirements. Each year graduates may experience changes to the programme. Renault UK graduates have provided testimonials so you can gain an insight into the culture at Renault.
Ife
Renault UK Graduate Trainee 2013
My name is Ife, I studied Economics at the University of Surrey and joined the Renault graduate scheme in October 2013. When I finished university there were many graduate roles available in the market but the number of choices can be overwhelming. I was attracted to Renault’s graduate scheme because there was the opportunity to explore all the different functions of the business and find out which career path I was interested in pursuing.
Read Ife's story
When I finished university there were many graduate roles available in the market but the number of choices can be overwhelming. I was attracted to Renault’s graduate scheme because there was
the opportunity to explore all the different functions of the business and find out which career path I was interested in pursuing.
My first rotation was in the Marketing Communications team where I worked on creating functionality videos for the Renault UK YouTube channel. This provided me with the opportunity to project manage and it was a great achievement to see the videos live on YouTube.
I recently finished my placement in the Electric Vehicle product marketing team and I found that in a newly emerging sector there is so much innovation and creativity. It definitely was a fun and exciting team to have worked in. I have just started my fourth rotation in the Renault Enfield dealership where I will be learning about what a typical customer requires when they walk into the Renault dealership.
I joined Renault without any automotive experience and during my time here at Renault, there have been many opportunities to grow and learn about the automotive industry. As a young graduate it has definitely been a great opportunity to challenge myself and push my career development further.
Charlotte
Renault UK Graduate Trainee 2013
Hello and welcome to Renault UK! When I first read about the Renault graduate scheme two years ago, I was excited to discover that the programme offered the opportunity to contribute to so many different areas of the business, whilst giving me a broad understanding of how a global organisation functions. Meeting some of the Renault team at the assessment centre sealed the deal for me.
Read Charlotte's story
Over the past 16
I then went to the Sales Operations team where I had two primary responsibilities. Firstly, I worked with the area team, our network partners and
In my dealership
Selling my first car is something I will never forget! I also had the opportunity to undertake technical training, learning about the different parts of a car and seeing engines being rebuilt.
Finally in Parts Marketing within our After-Sales team I launched new promotions for independent garages, produced a welcome pack for our 2015 loyalty club, and managed the Renault UK eBay store. I am now about to go in the field to be a link between Head Office and the dealerships, helping implement our Sales strategies and reach our objectives.
If you are looking for a graduate programme that will challenge you whilst working in a friendly and supportive work environment, then I would highly recommend the Renault UK graduate programme.
Luke
Renault UK Graduate Trainee 2012
I'm Luke and I joined Renault UK on the Graduate Programme in October 2012. I studied for my degree in Business and Enterprise Management at Sheffield Hallam university, which was a four-year course including a year's work placement in the automotive industry. I applied to do my placement at Renault, and my experience here led to me being offered a place on the graduate programme.
Read Luke's story
I'm Luke and I joined Renault UK on the Graduate Programme in October 2012. I studied for my degree in Business and Enterprise Management at Sheffield Hallam university, which was a four yearcourse including a year's work placement in the automotive industry. I applied to do my placement at Renault, and my experience here led to me being offered a place on the graduate programme.
Two and a bit years, six graduate rotations, and five company cars on, I have been in my first managerial role at Renault for 5 months now. My time with the business is flying by at an astonishing pace, and reflecting back it is hard to believe the diversity of projects, working groups and events I have been involved in. From organising the forecourt at our flagship dealership, to managing Renault’s attendance at the Goodwood Festival of Speed, then on to looking after 3 of my own dealerships as part of the field team.
I have been pushed, guided and coached at every stage and my learning curve has been extremely steep, and I continue to learn new things and develop myself on a daily basis.Having completed the graduate scheme, my first managerial role is with the Sales department where I am responsible for all sales communications and the infrastructure that supports that. The role is challenging with lots to do, develop and improve; but by the same token, it is also extremely rewarding and I could not have asked for a better team or role for my first step on the career ladder.I’m looking forward toI have been pushed, guided and coached at every stage and my learning curve has been extremely steep, and I continue to learn new things and develop myself on a daily basis.Having completed the graduate scheme, my first managerial role is with the Sales department where I am responsible for all sales communications and the infrastructure that supports that. The role is challenging with lots to do, develop and improve; but by the same token, it is also extremely rewarding and I could not have asked for a better team or role for my first step on the career ladder.
I’m looking forward toI’m looking forward to see where the future with Renault takes me, whether that be another managerial position within the business, anothercountry, or even another continent!
Adam Wood
Renault UK Graduate Trainee 2007
Deciding on a career path as I approached the end of my studies wasn't easy – so many avenues, so many companies promising great things, but would I enjoy their work day-to-day? I thought long and hard about what motivated me, and decided that I needed a career that would combine my personal interests and academic skills. As a lifelong car-lover with a BA in French from Bristol University and an MSc in International Business from Nottingham University, I chose Renault, since it appeared to fit the bill perfectly! 8 years on, and it's safe to say the company has delivered on its promises.
Read Adam's story
The first year of the Renault Graduate Scheme provided a rapid and enjoyable introduction to the Company's activities. Through three-month placements in Fleet, Customer Services and Distribution, I learnt just how diverse Renault's work is. The most enjoyable placement, though, was as a New Car Sales Executive at Renault Watford. Selling new cars to 'real people' proved a real challenge, yet also gave a great buzz and provided a valuable insight into the 'front end' of the business.
Following the graduate scheme, I secured my first permanent role in Marketing Planning as a Market Analyst. Responsible for providing the company's reports and market intelligence to the Board of Directors, I enjoyed real responsibility and exposure from the outset, whilst also developing new analytical skills.
My first role provided a vital foundation from which to progress to my next role, Clio & Renaultsport Product Manager, which provided the opportunity that I'd been hoping for: working closely with the product. Responsible for defining the product and price strategy for one of the UK's best-selling superminis and hot-hatch ranges, I enjoyed real hands-on experience right from the outset, as well as having the fantastic opportunity to test our Renaultsport range to its limits on several of our customer track day events!
My next role proved a real case of 'right place, right time', as I assumed responsibility for the UK launch of the Dacia brand, which was in its infancy at the time. I've been fortunate to have had a once-in-a-lifetime opportunity to define and oversee the launch of a new car brand into the UK market, which gave me the unique opportunity to work on a real live 'case study' you normally only read about in
After two years on the Dacia project, I progressed to my current role: Corporate Planning Manager. Together with the Managing Director and Board, I am responsible for defining and implementing the Company's business strategy, as well as managing cross-functional projects and liaising with our Alliance partner, Nissan. It's a fantastic role which gives great insight into all aspects of Renault UK's work, and one which involves many working relationships with people across the organisation, both in the UK and France.
So, after five roles in eight years, Renault has provided the ideal opportunity to learn and progress. Maple Cross has proved a relaxed and friendly, yet professional environment in which to work, and offers individuals with the desire to succeed the opportunity to take control of their career and tailor its path to suit their individual interests and goals.
FAQ's
How many graduates do you take on each year?
It varies each year, depending on business need, this year we are taking on five graduates.
What is the package?
Your starting salary will be £27,000 p.a., 24 days paid holiday plus a car (e.g Clio), private medical insurance, with the opportunity to join a pension scheme.
Can nationals from outside the EU apply to the Graduate Development Programme?
If you are not an EEA or Swiss national you will need a work permit in order to work in the UK. If you possess a valid work permit or you will be able to attain one before our October start date please go ahead and apply.
Please note as this is a training programme Renault would not offer sponsorship at this time.
Am I too old to apply to the Graduate Development Programme?
Absolutely not! We have never operated an age restriction on our Graduate Development Programme and welcome applications regardless of your age.
Do you accept paper applications?
Paper applications will always be read, but we only accept on-line applications so we can accurately track and record your details. If you have any problems applying this way please email us here.
What is the structure of the Graduate Development Programme?
The structure will depend on the graduate programme you are assigned to. At telephone
What sort of training is offered?
During your training period you will be immersed in the company, its products and services and have access to both formal and on the job training to ensure you are well equipped for your career ahead.
Where will I be based?
Depending upon where you start our programme, you could be based at Renault UK's Head Office in Maple Cross, RCI Financial Services Headquarters in Watford or any of the Renault Retail Group sites.
What type of role will I move into after my training has finished?
Once you have completed your scheme, we will place you in your first management role which will allow you to utilise the experience you have gained during your training programme. placements from day one.
Can I work abroad?
Although based in the UK, International mobility is a possibility but only for high achievers who wish to work abroad. It is unlikely to take place during your training period.
Will I get to meet other graduates on the Programme?
Yes, there will be plenty of opportunity to network with our current graduates before you join and you’ll also spend a fair amount of time getting to know the other new starters – both formally and socially – during the induction period.
I have previously submitted an application and was not successful, can I re-apply?
Yes, we will consider each application on its own merit, even if you have been unsuccessful in the past.
Recruitment calendar
We'll review your application as soon as we receive it so there's no hanging around! If we think we're a good match for each other, we'll be calling you to discuss your application in detail and to arrange the next steps. Successful candidates will be invited to an assessment centre in June, followed by job offers by the end of July.
26TH JUNE CLOSING DATE FOR APPLICATIONS
All online applications and CVs to be submitted by this date. Please note we can only accept one application per person.
JUNE TELEPHONE INTERVIEWS AND TESTING
If your application is successful, you will be invited to a brief telephone interview followed by some aptitude tests to assess your suitability to the programme.
JUNE / JULY ASSESSMENT CENTRE
Shortlisted candidates will be invited to an assessment centre to test their suitability to the programme.
JULY FINAL SELECTION


MusicTripper
Home
Playlists
Let's take a trip.
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System Architect Symbol details for "Authority To Authorise"
AAA/essential type (Generalization Node)
- Base Class:
"Authorised Agent Activity"
- Definitions Used:
-
Authorised Agent Activity (Class)
Authorised Activity Type (Class)
- Description:
"Activity Type" that an "Agent" has been authorised to perform by another "Agent".
- Comment:
-
This could be very broad: 'General Surgical Duties', or narrower: 'Cardiac Surgical Duties'.
It could be a hospital's authorisation to a clinician that, although generally authorised to perform
all surgical duties, the hospital only authorises the clinician to perform cardiac surgical
procedures.
The relationship to "Activity Type" via "Activity Type Permission" and its subtypes, allows for
selective inclusion and exclusion of Authorise Activities.
Authorised Agent Activity (Class)
- Description:
"Activity Type for Agent" that is subject to some type of authorisation or external control.
- Comment:
Such control can be interdictions, permissions and exceptions (including "Activity State Types") to
one or many Activity Types.
Authority To Authorise (Class)
- Description:
Authority held by one "Agent" to authorise other "Agents" to perform certain "Activity Types".
- Comment:
The General Medical Council has the "Authority to Authorise" medical staff to perform various kinds
of medical duties. It does not have the authority to perform these duties itself.


Hi! I'm Traci and I'm a graduate from General Assembly in Chicago.
Previously, I was a web manager for a mutual fund company where I was
responsible for the day to day operations of the company’s website
as well as various related projects such as marketing and email campaigns.
I was also the project manager for our website redesigns over the years
and served on the
company’s CRM implementation team.
As my career grew, I became more interested in the development and design side
which led me to enroll in General Assembly's bootcamp.
I am still learning to code and loving every minute of it!


Funding boost for Mayo regional sport projects
THE Minister for Rural & Community Development, Michael Ring, has confirmed approval of €405,123 to two regional sports projects in County Mayo under the 2019 Sports Capital Programme.
Mayo GMIT Campus is getting €225,000 for an all-weather rugby playing facilities in Castlebar while €180,120 is going to Lake District Athletic Club, Ballinrobe, for an athletics track and facilities.
Said Minister Ring: “The Sports Capital Programme is the most important way in which the government assists in the development and refurbishment of sporting facilities and in the purchase of non-personal sports equipment.”
“The regional projects in Mayo being funded will provide better equipment and facilities for athletes to train and compete to the best of their ability.
"The funding announced today follows on from the €1,143,493 approved to 36 local sports projects in Mayo last week.”


Please carefully review your information and check for any errors. Make sure to fill in any missing information, which can be found on your ticket. If you’re unsure what information to use, feel free to contact us or the court for help.
You will get a chance to review and edit your information at Check Out.


Reversable Causes: Hs and Ts
- Hypovolemia
- Hypoxemia
- Hydrogen ion (acidosis)
- Hyperkalemia (see CARD 5)
- Hypoglycemia
- Hypothermia
- Hypervagal
- malignant Hyperthermia
- Tension Pneumothorax (see CARD 10)
- Tamponade, cardiac
- Tamponade, pulmonary (breath stacking or excessive auto-PEEP)
- Thrombosis, pulmonary (if air embolus, see CARD 1)
- Thrombosis, coronary
- Toxins (anesthetic, β-blocker. If LAST, see CARD 7)
- Trauma (see CARD 9, massive hemorrhage)
- pulmonary hyperTension
- prolonged q-T


My key bindings for my dotfiles for tools such as vim, tmux, git, etc.
Git Customizations
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A much more useable git log |
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A list of branches with summary of last commit |
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A list of remotes with information |
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A` list of tags iwth information |
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(n)ew (b)ranch like git checkout -b |
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cherry pick Shows what was cherrypicked |
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A clean format for creating changelogs |
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If you forgot what you've been working on |
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Remove from index |
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Revert to the time prior to the last commit This is dangerous if already pushed |
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(g)it (d)elete (m)erged (b)ranches Deletes all branches already merged on current branch |
Navigation
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Go to previous buffer |
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Go to next buffer |
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To move up and down roughly by functions |
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Old cursor position |
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New cursor position |
Code Search/Navigation
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Instantly Find definition of class Must have exuberant ctags installed |
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Instantly Find definition of class and put in a vertical split |
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Same as vim normal gf (go to file), but in a vertical split |
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Search the current word under the cursor and show results in quickfix window |
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Grep the current word up to next exclamation point |
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Highlight all occurrences of a current word Similar to regular * except doesn't move |
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Toggle search highlight on and off |
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Grep command line, type between quotes Uses Ag Silver Searcher |
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Grep def when cursor is over the function name Greps for def [function name] |
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Grep current file to find references to the current file |
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Clear the search |
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Foward EasyMotion, a vimperator style tool that highlights jump points on the screen and lets you type there |
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Mark this word for MultiCursor. Ctrl-n (next), Ctrl-p (prev), Ctrl-x (skip) to add more cursors, then do normal vim things like edit the word |
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Opens the documentation for the word under the cursor |
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Global search, then do your normal %s/search/replace/g and follow up with :Greplace to replace across all files. When done use :wall to write all the files |


Using windows instead of Linux¶
Using bash¶
- o2
students please contribute
Using gitbash¶
- o2
students please contribute
Using conda¶
- o2
students please contribute
Using cmd¶
The following table lists commands that do the sam things between linux and windows.
Linux |
Windows |
Comment |
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??? |
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shows owner |
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see (1) |
sows read write |
powershell "dir | Get-Acl"
powershell "dir | Get-Acl | fl"
Do not use notepad, but if nothing else is available
notepad FILE
See also:
<https://access.redhat.com/documentation/en-US/Red_Hat_Enterprise_Linux/4 /html/Step_by_Step_Guide/ap-doslinux.html>
EASY INSTALL¶
Make sure you have python3.7.4 installed from python.org
Place the following in your folder that comes up when you start CMD.exe.
ENV3.bat¶
pip install pip -U --user
ENV3\Scripts\activate.bat
SETUP-ENV3.bat¶
rmdir ENV3 /s /q
python --version
python -m venv ENV3
echo "Start a new CMD.exe"
start cmd.exe /k "ENV3 & pip install pip -U --user"
exit
CMS-CLONE.bat¶
mkdir cm
cd cm
pip install cloudmesh-installer
cloudmesh-installer git clone cms
cloudmesh-installer git pull cms
cloudmesh-installer install cms -e
cms help
CMS-AA.bat¶
rmdir e-cloudmesh-command /s /q
mkdir e-cloudmesh-command
cd e-cloudmesh-command
cms sys command generate aa
cd cloudmesh-aa
pip install -e .
cms help
cms aa list
Using it¶
Do the following
start a cmd.exe in which you execute
SETUP-ENV3.bat
A new window will start in whcih you execute
CMS-CLONE.bat
..\CMS-AA.bat


It's night
I've flown thousands of miles
And I'm speechless
Still speechless
What could I say?
Tears steal most my words
And I don't have the poet's skill
But I will try
Because I need to.
In one side
I hold tears
And they are grey
Painted by the darkness of a wicked world
The merciless capital factory
Guaranteed to shake the body
It's not a tremble
It's a deadly earthquake
And the hands
The nearing anxiety
The smell of death.
But these
Are anxieties
Are fears
That won't come out.
These are grey tears
And they don't come out.
Because as I put my head on the pillow
I read her words
I feel her love
I feel her care
I feel our greatest dreams
Slowing fitting together
Building a home for our hearts.

Yes
I'm crying tonight
But these tears are light
Pure joy
Simple joy
Yellow
Brazilian
Blue-eyed
It's my little spider
She got a golden coat
Carefully, with her warmth
She put it on top of my heart
She protected it
And I will never give up
I will never succumb
Because I love her
I love her
I love her
I love her
I love my little spider.


In my previous post, I described a powerful optimization framework known as the Primal Dual Framework. The goal of this new post is to have a little bit of fun with the ROF model and the primal dual framework!
While the ROF model is efficient for denoising, its form is hand crafted, and one might wonder if there might be a more optimal formulation for this problem. Since the Primal Dual Framework works for a more general set of problems, i.e.:
$$ \begin{align} & \underset{x}{\text{minimize}} & & M(x) + R(Lx) \end{align} $$ with:
- \(M\) a lower semi continuous function
- \(R\) a lower semi continuous function
- \(L\) a continuous linear operator
we are going to explore in this post the possibility of learning these functions \(M\), \(R\)and \(L\) through a neural net with the observed images as input and the ground truth images as outputs. The layers will be custom layers consisting of the parametrized Primal Dual steps. The goal will be to optimize these parameters.
The idea here is a bit similar to the OptNet paper.
A more general formulation of the ROF model
But first, let's begin by giving a more general formulation of the ROF model: $$ \begin{align} & \underset{x}{\text{minimize}} & & \frac{1}{2} x^T H x + b^T x + \left\| Lx \right\|_1 \end{align} $$ with:
- \(H\) a symmetric invertible matrix of \(\mathcal{M}_n(\mathbb{R}^n\))
- \(b\) is a vector in \(\mathbb{R}^n\)
- \(L\) is a continuous linear operator
Let's now use the following caracterization of a norm : \(\left\| Lx \right\|_1 = \underset{y \in [-1, 1]^n}{\max}\langle L^T y, x \rangle\) in order to introduce a saddle problem formulation. The problem can now be written: $$ \begin{align} & \underset{x}{\min} \underset{y \in [-1, 1]^n }{\max}& & \frac{1}{2} x^T H x + \langle b + L^T y, x \rangle \end{align} $$ A simple gradient computation gives: \(x^* = -H^{-1} (b + L^T y) \). The dual problem is then: $$ \begin{align} & \underset{y \in [-1, 1]^n}{\text{maximize}} & & -\frac{1}{2} (b+L^T y )^T H^{-1} (b+L^Ty) \end{align} $$ We are going to note this function to maximize \(D(y; H, b, L)\).
The primal-dual gap is defined as : \(G(x, y ; H, b, L) = P(x; H, b, L) - D(y; H, b, L)\). Since this is a saddle point problem, we have that \(G(x^*, y^* ; H, b, L) = G(x^* ; H, b, L) = 0 \)
Now, we are going to consider this optimization problem through a computational graph view. It looks very similar to the one in the excellent paper Learning to learn by gradient descent by gradient descent
:If we note \(\theta_t = (H, b, L)_t\) the vector of parameters that we are trying to learn in the Primal Dual problem, \(\mathcal{PD}\) the recurrent neural net that unrolls \(n\) iterations of the Primal Dual algorithm, \(\tilde{x}_t\) the primal variable after \(nk\) iterations, \(k \in \mathbb{N}\).
Let's start by learning a simple linear operator
Formulation of the simplified problem
In the previous general model, if we pose \( H = I_n \), \( b = -2 img_{obs}\) and \(L : x \mapsto \sum_{(i, j) \in \mathcal{E}} \|x_i -x_j \|_1\), we can get back to the usual ROF model, as formulated in the Chambolle Pock paper: $$ \begin{align} & \underset{x}{\text{minimize}} & & \| x - img_{obs} \|_2^2 + \frac{\lambda}{2} \left\| Lx \right\|_1 \end{align} $$ Since this model has been studied in the previous blog post, we are going to introduce the linear operator \(L : x ; w \mapsto \sum_{(i, j) \in \mathcal{E}} w_{i,j} \|x_i -x_j \|_1\), i.e \(L = W \odot \nabla\), \(\odot \) being the Hadamard product here. A calculation shows that \(L\) is a linear operator, and that its adjoint \(L^T\) is \( L^T : y \mapsto - div(W \odot y) \).
Now let's talk a little bit about the form of the matrix \(W = (w_{i,j})_{ij} \); the operator \(L \) is supposed to be learning for any image that we take as an input for our algorithm. So if neighboring pixels have a very high absolute difference in one image, a new independant image is very unlikely to have the same high differences at the same locations in the images. This leads to the intuition that \(\forall (i, j), \ (w_{i,j})_{ij} \simeq c \). So we are going to pose : \(\forall (i, j), \ \mathbb{w}_{i,j} = \lambda_1 + \lambda_2 \exp (- L(x_i, x_j; \mathbb{w}) \), and try and learn the parameters \(\theta = (\lambda_1 , \lambda_2, \mathbb{w}) \). Since our output here is structured, and that \(L\) is the pairwise operator in our problem formulation, it would be interesting to try and to learn \( \mathbb{w} \) with a couple of convolutional layers in the neural net.
An example
Here are the noised, denoised and original images. In this first new scenario, we are going to minimize the Mean Squared Error between the Ground Truth and the primal variable after \( n\) iterations of the primal dual algorithm: $$ \begin{align} & \underset{w}{\text{minimize}} & & \sum{\left\| x_i^{*} - x_i^{GT} \right\|_2^2 } \\ & ST & & x_i^{*} = \underset{x}{\arg \min P(x; w)} \end{align} $$ Each function was optimized for 200 steps and the trained optimizers were unrolled for 20 steps, with the following set of parameters: \(\sigma_n = [0.01, 0.05, 0.1], \lambda_{ROF}=7.0, \theta = 0.75, \tau = 0.01 \)
If we now use the trained network on test images, which has the same amount of noise than in the training image:
It seems to denoise pretty nicely, as can be seen in the following table. The average running time in the testing setup in \(0.06\) seconds.
| Image | Lena | Barbara | Boats | Lake |
|---|---|---|---|---|
| PNSR noisy image | 19.9797562 | 19.9797562 | 19.9797562 | 19.9797562 |
| PSNR denoised image | 32.7784948 | 25.4279597 | 28.1104372 | 27.5150026 |
| Processing time (s) | 0.0576860904 s | 0.0734817981 s | 0.0620040893 s |
Python Code Example with PyTorch
Here is how I organized the code with PyTorch, with GPU support. Every function needed in the Primal Dual Net is coded as a nn.Module submodule, to facilitate the modularity of this framework, and ease the addition of further experiments:
from torch.autograd import Variable
import torch
import torch.nn as nn
class ForwardWeightedGradient(nn.Module):
def __init__(self):
super(ForwardWeightedGradient, self).__init__()
def forward(self, x, w, dtype=torch.cuda.FloatTensor):
"""
:param x: PyTorch Variable [1xMxN]
:param w: PyTorch Variable [2xMxN]
:param dtype: Tensor type
:return: PyTorch Variable [2xMxN]
"""
im_size = x.size()
gradient = Variable(torch.zeros((2, im_size[1], im_size[2])).type(dtype)) # Allocate gradient array
# Horizontal direction
gradient[0, :, :-1] = x[0, :, 1:] - x[0, :, :-1]
# Vertical direction
gradient[1, :-1, :] = x[0, 1:, :] - x[0, :-1, :]
gradient = gradient * w
return gradient
class BackwardWeightedDivergence(nn.Module):
def __init__(self):
super(BackwardWeightedDivergence, self).__init__()
def forward(self, y, w, dtype=torch.cuda.FloatTensor):
"""
:param y: PyTorch Variable, [2xMxN], dual variable
:param dtype: tensor type
:return: PyTorch Variable, [1xMxN], divergence
"""
im_size = y.size()
y_w = w.cuda() * y
# Horizontal direction
d_h = Variable(torch.zeros((1, im_size[1], im_size[2])).type(dtype))
d_h[0, :, 0] = y_w[0, :, 0]
d_h[0, :, 1:-1] = y_w[0, :, 1:-1] - y_w[0, :, :-2]
d_h[0, :, -1] = -y_w[0, :, -2:-1]
# Vertical direction
d_v = Variable(torch.zeros((1, im_size[1], im_size[2])).type(dtype))
d_v[0, 0, :] = y_w[1, 0, :]
d_v[0, 1:-1, :] = y_w[1, 1:-1, :] - y_w[1, :-2, :]
d_v[0, -1, :] = -y_w[1, -2:-1, :]
# Divergence
div = d_h + d_v
return div
class PrimalWeightedUpdate(nn.Module):
def __init__(self, lambda_rof, tau):
super(PrimalWeightedUpdate, self).__init__()
self.backward_div = BackwardWeightedDivergence()
self.tau = tau
self.lambda_rof = lambda_rof
def forward(self, x, y, img_obs, w):
"""
:param x: PyTorch Variable [1xMxN]
:param y: PyTorch Variable [2xMxN]
:param img_obs: PyTorch Variable [1xMxN]
:return:Pytorch Variable, [1xMxN]
"""
x = (x + self.tau * self.backward_div.forward(y, w) +
self.lambda_rof * self.tau * img_obs) / (1.0 + self.lambda_rof * self.tau)
return x
class DualWeightedUpdate(nn.Module):
def __init__(self, sigma):
super(DualWeightedUpdate, self).__init__()
self.forward_grad = ForwardWeightedGradient()
self.sigma = sigma
def forward(self, x_tilde, y, w):
"""
:param x_tilde: PyTorch Variable, [1xMxN]
:param y: PyTorch Variable, [2xMxN]
:param w: PyTorch Variable, [2xMxN]
:return: PyTorch Variable, [2xMxN]
"""
y = y + self.sigma * self.forward_grad.forward(x_tilde, w)
return y
class PrimalRegularization(nn.Module):
def __init__(self, theta):
super(PrimalRegularization, self).__init__()
self.theta = theta
def forward(self, x, x_tilde, x_old):
"""
:param x: PyTorch Variable, [1xMxN]
:param x_tilde: PyTorch Variable, [1xMxN]
:param x_old: PyTorch Variable, [1xMxN]
:return: PyTorch Variable, [1xMxN]
"""
x_tilde = x + self.theta * (x - x_old)
return x_tilde
class PrimalDualNetwork(nn.Module):
def __init__(self, w, max_it=10, lambda_rof=4.0, sigma=1. / (7.0 * 0.01), tau=0.01, theta=0.5):
"""
:param w: PyTorch Variable, [2xMxN]
:param max_it: int
:param lambda_rof: float
:param sigma: float
:param tau: float
:param theta: float
"""
super(PrimalDualNetwork, self).__init__()
self.max_it = max_it
self.dual_update = DualWeightedUpdate(sigma)
self.prox_l_inf = ProximalLinfBall()
self.primal_update = PrimalWeightedUpdate(lambda_rof, tau)
self.primal_reg = PrimalRegularization(theta)
self.energy_primal = PrimalEnergyROF()
self.energy_dual = DualEnergyROF()
self.pe = 0.0
self.de = 0.0
self.w = w
self.clambda = lambda_rof
def forward(self, img_obs):
"""
Forward function for the PrimalDualNet.
:param img_obs: PyTorch Variable, [1xMxN]
:return: PyTorch Variable, [1xMxN]
"""
x = img_obs.clone().cuda()
x_tilde = img_obs.clone().cuda()
img_size = img_obs.size()
y = Variable(torch.ones((img_size[0] + 1, img_size[1], img_size[2]))).cuda()
for it in range(self.max_it):
# Dual update
y = self.dual_update.forward(x_tilde, y, self.w.cuda())
y = self.prox_l_inf.forward(y, 1.0)
# Primal update
x_old = x
x = self.primal_update.forward(x, y, img_obs, self.w.cuda())
# Smoothing
x_tilde = self.primal_reg.forward(x, x_tilde, x_old)
# Compute energies
self.pe = self.energy_primal.forward(x, img_obs.cuda(), self.w, self.clambda)
self.de = self.energy_dual.forward(y, img_obs, self.w)
return x_tilde
And now here is what the main looks like:
from __future__ import print_function import numpy as np from PIL import Image import matplotlib.pyplot as plt import torch import torch.nn as nn from torch.autograd import Variable import torchvision.transforms as transforms import png import argparse from primal_dual_model import PrimalDualNetwork, ForwardGradient, BackwardDivergence, ForwardWeightedGradient if __name__ == '__main__': parser = argparse.ArgumentParser(description='Run Primal Dual Net.') parser.add_argument('--use_cuda', type=bool, default=True, help='Flag to use CUDA, if available') parser.add_argument('--max_it', type=int, default=20, help='Number of iterations in the Primal Dual algorithm') parser.add_argument('--max_epochs', type=int, default=200, help='Number of epochs in the Primal Dual Net') parser.add_argument('--lambda_rof', type=float, default=7., help='Lambda parameter in the ROF model') parser.add_argument('--theta', type=int, default=0.75, help='Regularization parameter in the Primal Dual algorithm') parser.add_argument('--tau', type=int, default=0.01, help='Parameter in Primal') parser.add_argument('--save_flag', type=bool, default=True, help='Flag to save or not the result images') args = parser.parse_args() max_epochs = args.max_epochs max_it = args.max_it lambda_rof = args.lambda_rof theta = args.theta tau = args.tau sigma = 1. / (lambda_rof * tau) # cuda use_cuda = (torch.cuda.is_available() and args.use_cuda) dtype = torch.cuda.FloatTensor if use_cuda else torch.FloatTensor # Transforms PIL <-> PyTorch tensors pil2tensor = transforms.ToTensor() tensor2pil = transforms.ToPILImage() # Create image to noise and denoise / train the network on sigma_n = 0.1 # Noise variance img_ = Image.open("images/image_Lena512.png") h, w = img_.size img_ref = Variable(pil2tensor(img_).type(dtype)) noise = torch.ones(img_ref.size()) noise = Variable(noise.normal_(0.0, sigma_n).type(dtype)) img_obs = img_ref + noise img_n = img_obs.data.cpu().numpy().reshape((512, 512)) x = Variable(img_obs.data.clone().type(dtype)) x_tilde = Variable(img_obs.data.clone().type(dtype)) img_size = img_ref.size() y = Variable(torch.zeros((img_size[0]+1, img_size[1], img_size[2])).type(dtype)) y = ForwardGradient().forward(x) g_ref = y.clone() # Net approach w = nn.Parameter(torch.rand(y.size()).type(dtype)) n_w = torch.norm(w, 1, dim=0) plt.figure() plt.imshow(n_w.data.cpu().numpy()) plt.colorbar() net = PrimalDualNetwork(w, max_it=max_it, lambda_rof=lambda_rof, sigma=sigma, tau=tau, theta=theta) criterion = torch.nn.MSELoss(size_average=False) optimizer = torch.optim.Adam(net.parameters(), lr=1e-2) params = list(net.parameters()) loss_history = [] primal_history = [] dual_history = [] gap_history = [] learning_rate = 1e-4 for t in range(max_epochs): # Forward pass: Compute predicted image by passing x to the model x_pred = net(x) # Compute and print loss loss = criterion(x_pred, img_ref) print(t, loss.data[0]) loss_history.append(loss.data[0]) # Zero gradients, perform a backward pass, and update the weights. optimizer.zero_grad() loss.backward() optimizer.step() # Compute energies and store them for plotting pe = net.pe.data[0] de = net.de.data[0] primal_history.append(pe) dual_history.append(de) gap_history.append(pe - de) f, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, sharex='col', sharey='row') ax1.imshow(np.array(img_)) ax1.set_title("Reference image") ax2.imshow(np.array(tensor2pil(img_obs.data.cpu())).reshape(512, 512)) ax2.set_title("Observed image") ax3.imshow(np.array(tensor2pil(x_pred.data.cpu())).reshape(512, 512)) ax3.set_title("Denoised image") ax4.imshow(np.abs(np.array(tensor2pil(img_obs.data.cpu())) - np.array(tensor2pil(x_pred.data.cpu())))) # Save results if specified so: if args.save_flag: f = open('lena_noised.png', 'wb') w1 = png.Writer(512, 512, greyscale=True) w1.write(f, np.array(tensor2pil(img_obs.data.cpu()))) f.close() f_res = open('lena_denoised.png', 'wb') w1.write(f_res, np.array(tensor2pil(x_pred.data.cpu())).reshape(512, 512)) f_res.close() # Test image img_t = Image.open("images/image_Barbara512.png") h, w1 = img_t.size img_ref_t = Variable(pil2tensor(img_t).type(dtype)) noise_2 = torch.ones(img_ref_t.size()) noise_2 = Variable(noise_2.normal_(0.0, sigma_n).type(dtype)) img_obs_t = img_ref_t + noise img_dn = net.forward(img_obs_t) f2, ((ax21, ax22), (ax23, ax24)) = plt.subplots(2, 2, sharex='col', sharey='row') ax21.imshow(np.array(img_t)) ax21.set_title("Reference image") ax22.imshow(img_obs_t.data.cpu().mul_(255).numpy().reshape(512, 512)) ax22.set_title("Observed image") ax23.imshow(img_dn.data.cpu().mul_(255).numpy().reshape(512, 512)) ax23.set_title("Denoised image")
The code (and images as well) is available in its full here.
In the next part of this ticket, I will implement the general form of the aforementionned problem.
You can leave comments down here, or contact me through the contact form of this blog if you have questions or remarks on this post!


v3.0.1
npm install mq-js --save
This documentation is best viewed on a Desktop computer or laptop for the sake of viewing the examples at all screen sizes.
mq-js was inspired by the mq-scss Sass mixin. I wanted to use media queries in JavaScript in a similar sort of way to how I was using media queries in my Sass code.
This documentation assumes that you have the ability to use ES6 JavaScript syntax in your project. mq-js will work in environments that don't support es6 JavaScript syntax however the syntax will be different to what is documented. View the full documentation for ES5 (IE friendly) examples.
If you are new to Node and npm, read this beginners guide on how to get set up. You will also need JavaScript bundling software such as Browserify, Rollup, or Webpack integrated into your build process for mq-js to work.
Once that is all set up, install mq-js using npm.
npm install mq-js --save
Now, create this simple mq.js file to set up your website breakpoints.
Now import the mq
variable into your main/component JavaScript file.
Note: mq.max
is inclusive of the given screen size and mq.min
is exclusive of the given screen size. This is to avoid any potential 1px overlap issues where both statements return true at the same time. It is also designed to align with how mq-scss works.
It is also worth noting that you can save your breakpoints into a json file and import that instead. This can make the breakpoints a bit more portable.
If you are using mq-scss in your project and have enabled em-conversions, you will need to enable them in mq-js as well.
In the following example, the button will only turn green if you click it when the browser window is at a maximum width of 980px wide (ie. at or below the "medium" breakpoint).
To help you figure out when the button is able to be clicked, the border of the button will turn green when it is clickable.
As you can see, you can easily access the "medium"
breakpoint that you defined earlier by simply parsing a string as the breakpoint value. There is no need to import the breakpoints
variable from the mq.js
file to use the "medium"
breakpoint.
You can also use it in an if statement sort of format as shown below. All mq-js functions can be used in this if
statement format. For the sake of brevity though, I'm just showing the callback
format in the rest of these examples.
Notice that using the callback method gives you access to the screen size data at the time the button is clicked. The if statement format does not (or at least not without custom code).
Also notice that I'm not binding and unbinding the click function on screen resize. By placing mq
inside the click function, it behaves very similarly to how media queries behave in css.
One last thing. If you prefer to explicitly state that the media query is targeting "width", mq.max
can also be written as mq.maxWidth
. It has identical functionality, it's just personal preference.
In the following example, the button will only turn green if you click it when the browser window is at a minimum width of 981px wide (ie. above the "medium" breakpoint).
You might be wondering why 981px instead of 980px. While mq.max
is inclusive of the breakpoint value, mq.min
is exclusive of the breakpoint value. This serves two important purposes:
It prevents bugs from occurring in that 1px sweet spot where both would otherwise return true.
It aligns with how mq-scss handles min and max width to prevent odd bugs where JavaScript is firing but the styles aren't there to support it (or vice versa).
Also, like with mq.max
, it can be written like mq.minWidth
instead if you prefer.
mq.inside
works in much the same way as it does in mq-scss.
Instead of stating one value then calling the function, you state two values then call the function.
You can state the breakpoint values in any order but the function needs to be called last (if at all). The function will be triggered if the screen width sits between the two breakpoint values when it is called.
The smaller value always gets +1 for consistency with the mq-scss mixin and to prevent 1px overlap issues.
It can also be written as mq.insideWidth
if you prefer.
mq.outside
does what you would expect. It is the opposite of mq.inside
. It will only fire if the screen width is outside of the two breakpoint values.
It can also be written as mq.outsideWidth
if you prefer.
One of the major features found in mq-scss is that you can easily create meaningful media query variables.
Here is how to do a similar sort of thing in mq-js:
Just because you have access to the breakpoint values, it doesn't mean that you have to use them. mq-js still works if you just parse solid numbers into it.
Remember that if you use mq.min
it will be like adding 1 pixel to the value that you provide.
What if you only want to tweak one of the normal breakpoint values so that it is just a few pixels larger than the base break point? That is when it is useful to import the breakpoint
variable.
In the following example, I'm importing the breakpoints variable and using it to add 30px to the "medium"
breakpoint.
mq-js is just a simple function that checks the current window size when it is called. It doesn't do anything fancy when you resize the window.
The following example uses Debounce to prevent the function from firing until the window has finished resizing. This improves performance and can avoid odd bugs from occurring.
The button will activate when the screen is at the correct width and deactivate when not. Clicking the button does nothing.
As of version 2.0.0, mq-js supports various plugins that allow it to fully replicate everything that mq-scss can do.
These plugins can be loaded from the mq.js file to avoid the need for loading them every time you wish to use them.
Alternatively, you can load all of the plugins in one statement by simply importing mq-js/ultimate
;
This one is pretty straight forward, it works in pretty much the same way as min
, max
, inside
and outside
except it targets height instead of width. Available functions are mq.minHeight
, mq.maxHeight
, mq.insideHeight
and mq.outsideHeight
.
The height plugin is the main reason why I have provided the alternate names for the width based commands. When you are combining height and width media queries in an if statement it can be easier to read mq.minWidth(800) && mq.minHeight(400)
than mq.min(800) && mq.minHeight(400)
.
This one is even simpler. One little gotcha is that there is no "square" orientation type in css. There is only portrait and landscape types. Square screen types seem to fall under portrait
in css so I've made square screen types fall under portrait
in mq-js as well.
This one is a little more complex. The ratio's for mq-js are always formatted as [width] / [height]
. Ratios work off of width more so than height in CSS so mq-js does as well. So a ratio of 2 / 3
is counted as being larger than a ratio of 1 / 3
since 2 / 3
is wider than 1 / 3
.
With this in mind, the ratio plugin provides you with all the typical ranges but for ratios.
You can also provide the ratio as a string instead.
This plugin is a bit different to the others. The primary purpose of this plugin is to fire off functions when a media query either enters or leaves a defined screen size range.
It takes a function that returns an mq-js screen-check result as it's first parameter and a callback function as it's second parameter. It will then call the callback function every time the screen-check result changes from true
to false
or false
to true
.
mq-js has a lot of complicated functionality that I needed to be able to test effectively. Doing it with demos by eye wasn't cutting it.
I built a custom unit testing framework to do this.
I needed the testing framework to do the following:
I'm pretty happy with the testing framework that I came up with. If you would like to see it in action, open Firefox and press the button below.
To view the detailed test results, click on one of the windows that appear and press the F12 key to bring up dev tools. Click on the "console" tab to view the test results.
The tests don't work properly in Chrome because window.outerWidth - window.innerWidth
does not have a stable value in Chrome.


By default, a global variable is put in the fabric L2 memory, as it is the biggest one. The following macros can be used to change this behavior and specify in which memory it must be placed.
This has to be used on the declaration of the variable. A few other behavior can also be specified:
- A variable can sometimes be put in the short section, resulting in better optimized accesses as the address generation can then be done with only 1 instruction instead of 2 or 3.
- A variable is by default using a global address which allows the variable to be accessed from anywhere. For some memories, it is possible to ask for a local address so that the variable can be access as a local one on each cluster.
◆ RT_CL_ALIAS_DATA
Put the data inside each cluster memory, using a local address.
◆ RT_CL_DATA
Put the data inside cluster 0 memory, using a gobal address.
◆ RT_CL_SHORT_DATA
Put the data inside each cluster memory, using a short local address.
◆ rt_section_cluster_text_size()
| static int rt_section_cluster_text_size |
( |
| ) |
|
|
inlinestatic |
The runtime code used by the cluster is put into a specific section so that it can be better controlled. This function can be used to get the size of this section.
◆ rt_section_cluster_text_start()
| static void* rt_section_cluster_text_start |
( |
| ) |
|
|
inlinestatic |
The runtime code used by the cluster is put into a specific section so that it can be better controlled. This function can be used to get the start address of this section.


4
5 # This file is free software; the Free Software Foundation
6 # gives unlimited permission to copy and/or distribute it,
7 # with or without modifications, as long as this notice is preserved.
8
9 # This program is distributed in the hope that it will be useful,
10 # but WITHOUT ANY WARRANTY, to the extent permitted by law; without
11 # even the implied warranty of MERCHANTABILITY or FITNESS FOR A
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13
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22
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25
26 dnl Copyright © 2004 Scott James Remnant<EMAIL_ADDRESS>27 dnl Copyright © 2012-2015 Dan Nicholson<EMAIL_ADDRESS>28 dnl
29 dnl This program is free software; you can redistribute it and/or modify
30 dnl it under the terms of the GNU General Public License as published by
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32 dnl (at your option) any later version.
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38 dnl
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184 # Put the nasty error message in config.log where it belongs
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279 m4_popdef([pkg_description])
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283 dnl PKG_CHECK_VAR(VARIABLE, MODULE, CONFIG-VARIABLE,
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297 ])dnl PKG_CHECK_VAR
298
299 # Copyright (C) 2002-2018 Free Software Foundation, Inc.
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311 [am__api_version='1.16'
312 dnl Some users find AM_AUTOMAKE_VERSION and mistake it for a way to
313 dnl require some minimum version. Point them to the right macro.
314 m4_if([$1], [1.16.1], [],
315 [AC_FATAL([Do not call $0, use AM_INIT_AUTOMAKE([$1]).])])dnl
316 ])
317
318 # _AM_AUTOCONF_VERSION(VERSION)
|
4
5 # This file is free software; the Free Software Foundation
6 # gives unlimited permission to copy and/or distribute it,
7 # with or without modifications, as long as this notice is preserved.
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10 # but WITHOUT ANY WARRANTY, to the extent permitted by law; without
11 # even the implied warranty of MERCHANTABILITY or FITNESS FOR A
12 # PARTICULAR PURPOSE.
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33 dnl
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35 dnl WITHOUT ANY WARRANTY; without even the implied warranty of
36 dnl MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
37 dnl General Public License for more details.
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39 dnl You should have received a copy of the GNU General Public License
40 dnl along with this program; if not, write to the Free Software
41 dnl Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA
42 dnl 02111-1307, USA.
43 dnl
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48 dnl program.
49
50 dnl PKG_PREREQ(MIN-VERSION)
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54 dnl Verify that the version of the pkg-config macros are at least
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71 dnl PKG_PROG_PKG_CONFIG([MIN-VERSION])
72 dnl ----------------------------------
73 dnl Since: 0.16
74 dnl
75 dnl Search for the pkg-config tool and set the PKG_CONFIG variable to
76 dnl first found in the path. Checks that the version of pkg-config found
77 dnl is at least MIN-VERSION. If MIN-VERSION is not specified, 0.9.0 is
78 dnl used since that's the first version where most current features of
79 dnl pkg-config existed.
80 AC_DEFUN([PKG_PROG_PKG_CONFIG],
81 [m4_pattern_forbid([^_?PKG_[A-Z_]+$])
82 m4_pattern_allow([^PKG_CONFIG(_(PATH|LIBDIR|SYSROOT_DIR|ALLOW_SYSTEM_(CFLAGS|LIBS)))?$])
83 m4_pattern_allow([^PKG_CONFIG_(DISABLE_UNINSTALLED|TOP_BUILD_DIR|DEBUG_SPEW)$])
84 AC_ARG_VAR([PKG_CONFIG], [path to pkg-config utility])
85 AC_ARG_VAR([PKG_CONFIG_PATH], [directories to add to pkg-config's search path])
86 AC_ARG_VAR([PKG_CONFIG_LIBDIR], [path overriding pkg-config's built-in search path])
147 else
148 _pkg_short_errors_supported=no
149 fi[]dnl
150 ])dnl _PKG_SHORT_ERRORS_SUPPORTED
151
152
153 dnl PKG_CHECK_MODULES(VARIABLE-PREFIX, MODULES, [ACTION-IF-FOUND],
154 dnl [ACTION-IF-NOT-FOUND])
155 dnl --------------------------------------------------------------
156 dnl Since: 0.4.0
157 dnl
158 dnl Note that if there is a possibility the first call to
159 dnl PKG_CHECK_MODULES might not happen, you should be sure to include an
160 dnl explicit call to PKG_PROG_PKG_CONFIG in your configure.ac
161 AC_DEFUN([PKG_CHECK_MODULES],
162 [AC_REQUIRE([PKG_PROG_PKG_CONFIG])dnl
163 AC_ARG_VAR([$1][_CFLAGS], [C compiler flags for $1, overriding pkg-config])dnl
164 AC_ARG_VAR([$1][_LIBS], [linker flags for $1, overriding pkg-config])dnl
165
166 pkg_failed=no
167 AC_MSG_CHECKING([for $1])
168
169 _PKG_CONFIG([$1][_CFLAGS], [cflags], [$2])
170 _PKG_CONFIG([$1][_LIBS], [libs], [$2])
171
172 m4_define([_PKG_TEXT], [Alternatively, you may set the environment variables $1[]_CFLAGS
173 and $1[]_LIBS to avoid the need to call pkg-config.
174 See the pkg-config man page for more details.])
175
176 if test $pkg_failed = yes; then
177 AC_MSG_RESULT([no])
178 _PKG_SHORT_ERRORS_SUPPORTED
179 if test $_pkg_short_errors_supported = yes; then
180 $1[]_PKG_ERRORS=`$PKG_CONFIG --short-errors --print-errors --cflags --libs "$2" 2>&1`
181 else
182 $1[]_PKG_ERRORS=`$PKG_CONFIG --print-errors --cflags --libs "$2" 2>&1`
183 fi
184 # Put the nasty error message in config.log where it belongs
185 echo "$$1[]_PKG_ERRORS" >&AS_MESSAGE_LOG_FD
186
187 m4_default([$4], [AC_MSG_ERROR(
279 m4_popdef([pkg_description])
280 ])dnl PKG_NOARCH_INSTALLDIR
281
282
283 dnl PKG_CHECK_VAR(VARIABLE, MODULE, CONFIG-VARIABLE,
284 dnl [ACTION-IF-FOUND], [ACTION-IF-NOT-FOUND])
285 dnl -------------------------------------------
286 dnl Since: 0.28
287 dnl
288 dnl Retrieves the value of the pkg-config variable for the given module.
289 AC_DEFUN([PKG_CHECK_VAR],
290 [AC_REQUIRE([PKG_PROG_PKG_CONFIG])dnl
291 AC_ARG_VAR([$1], [value of $3 for $2, overriding pkg-config])dnl
292
293 _PKG_CONFIG([$1], [variable="][$3]["], [$2])
294 AS_VAR_COPY([$1], [pkg_cv_][$1])
295
296 AS_VAR_IF([$1], [""], [$5], [$4])dnl
297 ])dnl PKG_CHECK_VAR
298
299 dnl PKG_WITH_MODULES(VARIABLE-PREFIX, MODULES,
300 dnl [ACTION-IF-FOUND],[ACTION-IF-NOT-FOUND],
301 dnl [DESCRIPTION], [DEFAULT])
302 dnl ------------------------------------------
303 dnl
304 dnl Prepare a "--with-" configure option using the lowercase
305 dnl [VARIABLE-PREFIX] name, merging the behaviour of AC_ARG_WITH and
306 dnl PKG_CHECK_MODULES in a single macro.
307 AC_DEFUN([PKG_WITH_MODULES],
308 [
309 m4_pushdef([with_arg], m4_tolower([$1]))
310
311 m4_pushdef([description],
312 [m4_default([$5], [build with ]with_arg[ support])])
313
314 m4_pushdef([def_arg], [m4_default([$6], [auto])])
315 m4_pushdef([def_action_if_found], [AS_TR_SH([with_]with_arg)=yes])
316 m4_pushdef([def_action_if_not_found], [AS_TR_SH([with_]with_arg)=no])
317
318 m4_case(def_arg,
319 [yes],[m4_pushdef([with_without], [--without-]with_arg)],
320 [m4_pushdef([with_without],[--with-]with_arg)])
321
322 AC_ARG_WITH(with_arg,
323 AS_HELP_STRING(with_without, description[ @<:@default=]def_arg[@:>@]),,
324 [AS_TR_SH([with_]with_arg)=def_arg])
325
326 AS_CASE([$AS_TR_SH([with_]with_arg)],
327 [yes],[PKG_CHECK_MODULES([$1],[$2],$3,$4)],
328 [auto],[PKG_CHECK_MODULES([$1],[$2],
329 [m4_n([def_action_if_found]) $3],
330 [m4_n([def_action_if_not_found]) $4])])
331
332 m4_popdef([with_arg])
333 m4_popdef([description])
334 m4_popdef([def_arg])
335
336 ])dnl PKG_WITH_MODULES
337
338 dnl PKG_HAVE_WITH_MODULES(VARIABLE-PREFIX, MODULES,
339 dnl [DESCRIPTION], [DEFAULT])
340 dnl -----------------------------------------------
341 dnl
342 dnl Convenience macro to trigger AM_CONDITIONAL after PKG_WITH_MODULES
343 dnl check._[VARIABLE-PREFIX] is exported as make variable.
344 AC_DEFUN([PKG_HAVE_WITH_MODULES],
345 [
346 PKG_WITH_MODULES([$1],[$2],,,[$3],[$4])
347
348 AM_CONDITIONAL([HAVE_][$1],
349 [test "$AS_TR_SH([with_]m4_tolower([$1]))" = "yes"])
350 ])dnl PKG_HAVE_WITH_MODULES
351
352 dnl PKG_HAVE_DEFINE_WITH_MODULES(VARIABLE-PREFIX, MODULES,
353 dnl [DESCRIPTION], [DEFAULT])
354 dnl ------------------------------------------------------
355 dnl
356 dnl Convenience macro to run AM_CONDITIONAL and AC_DEFINE after
357 dnl PKG_WITH_MODULES check. HAVE_[VARIABLE-PREFIX] is exported as make
358 dnl and preprocessor variable.
359 AC_DEFUN([PKG_HAVE_DEFINE_WITH_MODULES],
360 [
361 PKG_HAVE_WITH_MODULES([$1],[$2],[$3],[$4])
362
363 AS_IF([test "$AS_TR_SH([with_]m4_tolower([$1]))" = "yes"],
364 [AC_DEFINE([HAVE_][$1], 1, [Enable ]m4_tolower([$1])[ support])])
365 ])dnl PKG_HAVE_DEFINE_WITH_MODULES
366
367 # Copyright (C) 2002-2018 Free Software Foundation, Inc.
368 #
369 # This file is free software; the Free Software Foundation
370 # gives unlimited permission to copy and/or distribute it,
371 # with or without modifications, as long as this notice is preserved.
372
373 # AM_AUTOMAKE_VERSION(VERSION)
374 # ----------------------------
375 # Automake X.Y traces this macro to ensure aclocal.m4 has been
376 # generated from the m4 files accompanying Automake X.Y.
377 # (This private macro should not be called outside this file.)
378 AC_DEFUN([AM_AUTOMAKE_VERSION],
379 [am__api_version='1.16'
380 dnl Some users find AM_AUTOMAKE_VERSION and mistake it for a way to
381 dnl require some minimum version. Point them to the right macro.
382 m4_if([$1], [1.16.1], [],
383 [AC_FATAL([Do not call $0, use AM_INIT_AUTOMAKE([$1]).])])dnl
384 ])
385
386 # _AM_AUTOCONF_VERSION(VERSION)
|


java.lang.Object Direction
public class Direction
An immutable class that describes a returned axis direction from the joypad. This includes the direction and it's strength.
The user should never need to create these themselves, instead they will be created and returned for you by the GamePad class.
| Constructor Summary | |
|---|---|
Direction(int angle,
float strength)
Creates a new direction. |
| Method Summary | |
|---|---|
int |
getAngle()
Angles are in degrees from 0 to 359, with 0 pointing to the east. |
double |
getAngleRadians()
Returns the same value as the getAngle method, however this is in radians instead of degrees. |
float |
getStrength()
Strength represents how far the axis is pushed in the given direction. |
| Methods inherited from class java.lang.Object |
|---|
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Constructor Detail |
|---|
public Direction(int angle, float strength)
The
- angle for this direction in degrees.How
- strong the direction is moving in the given angle.| Method Detail |
|---|
public int getAngle()
public double getAngleRadians()
Returns the same value as the getAngle method, however this is in radians instead of degrees.
This is useful for when you are using the static trigonomic methods in the Math class, like Math.cos() and Math.sin().
public float getStrength()


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This page demonstrates the widgets and theme you selected in Download Builder. Please make sure you are using them with a compatible jQuery version.


... but with rising numbers of trash produced each year, we all can help a little bit by separating our trash correctly. While reducing waste production is the best option, correct recycling can help with the sorting process of the trash afterwards, or make the reuse of important materials possible or keep harmful substances out of the enviroment. There are a lot of different rules and you can find differences between almost all cities and districts. Some allow meat in the compost, some don’t. In some places old pots and pans are allowed in the yellow recycling bin and elsewhere they aren’t. We took Berlin and the rules of the BSR as an example, but it is always best to check online what applies in your area. There are also some general rules and often wrongly recycled items, which we collected in an explorative picture puzzle. Find the recycable items and learn more about how to dispose of them correctly and why it is important. Everything applies only to unusable items, there are a lot of great ways to gift or sell usable but unwanted items, for some ideas check here.
Check out the code in our GitHub repository or play with a simplified version of this article.


| File | Statements | Branches | Functions | Lines | |||||
|---|---|---|---|---|---|---|---|---|---|
| app/ | 100% | 12/12 | 100% | 0/0 | 100% | 2/2 | 100% | 11/11 | |
| app/footer/ | 100% | 5/5 | 100% | 0/0 | 100% | 1/1 | 100% | 4/4 | |
| app/home/ | 40.58% | 56/138 | 25% | 2/8 | 20.51% | 8/39 | 39.1% | 52/133 | |
| app/navbar/ | 100% | 8/8 | 100% | 0/0 | 100% | 1/1 | 100% | 7/7 | |
| app/shared/ | 100% | 1/1 | 100% | 0/0 | 100% | 0/0 | 100% | 1/1 | |
| app/shared/constant/ | 100% | 3/3 | 100% | 0/0 | 100% | 0/0 | 100% | 3/3 |


{% extends 'layout-2-1-bleedbar.html' %} {# Import organisms and molecules used in the template. #} {% import 'molecules/hero.html' as hero %} {% block title -%} TODO: This is a prototype page for testing landing page layouts. Remove when fully implemented elsewhere. {%- endblock %} {# TODO: Add the following molecules - Post feed - Grey well - Sidebar with related content and mailing list #} {% block content_main %}


above |
Li The Clinging, Flame |
|
below |
Li The Clinging, Flame |
The Clinging. Perseverance furthers.
It brings success.
Care of the cow brings good fortune.
That which is bright rises twice:
The image of Fire.
Thus the great man, by perpetuating this brightness,
Illumines the four quarters of the world.
Nine at the beginning means:
The footprints run crisscross.
If one is seriously intent, no blame.
Six in the second place means:
Yellow light. Supreme good fortune.
Nine in the third place means:
In the light of the setting sun,
Men either beat the pot and sing
Or loudly bewail the approach of old age.
Misfortune.
Nine in the fourth place means:
Its coming is sudden;
It flames up, dies down, is thrown away.
Six in the fifth place means:
Tears in floods, sighing and lamenting.
Good fortune.
Nine at the top means:
The king uses him to march forth and chastise.
Then it is best to kill the leaders
And take captive the followers. No blame.