Hey there,
I have a window with a backbuffer and it's doing something strange and I don't understand how.
When I present the backbuffer to the window it has a dark grey border around the edge of the client area, which is being produced by the backbuffer.
I cannot figure out where this border is coming from. It doesn't make sense because my BitBlt() is:
1 2 3
BitBlt(hWndDC, 0, 0, ClientWidth, ClientHeight,
hSrcDC, 0, 0,
SRCCOPY); // just an example
(Where the backbuffer is the same demensions as the client area)
The border is equal all the way around the client area, which is the most confusing of all. If I had simply got the width and height of the client area wrong, it wouldn't have an equal edge all the way round...


The Angular CDK Tree module provides a way to easily and declaratively create Tree interfaces.
To get started, import CdkTreeModule
into the NgModule where you want to use cdk-tree
features:
For more details on the implementation of the @angular/cdk/tree, check the documentation here
CdkTreeModule
This example shows how easy it is to create a cdk-tree
element. Our examples are using nui-expander
css styles which we recommend to use. To add nui-expander
styles, add .expander()
mixin to the
component styles. In the result, appropriate css classes are generated which are used in the examples. For example
.nui-tree__body
applies dotted line to the body of branch, or .nui-tree_header
applies
cursor: pointer
. Look into examples to find more css classes or extend existing css rules to achieve your goal.
To apply unique styles for the particular node, provide icon/css class/etc. to this node in the dataSource
,
and use it as usually in the template of component. Check the source code below:
To implement lazy-loading on CdkTree
component branches, use renderNodeChanges
method,
in order to synchronize data-source with DOM representation after an asynchronous operation has finished. Pay attention
to the isLoading
flag into yours node objects while implementing lazy-loading.
For handling state of checkboxes SelectionModel
is used. You can easily grab all the selected items using SelectionModel.selected
or subscribe to the changes
using SelectionModel.changed
. Check one of the checkboxes to see it in action.
This example also shows how to implement "indeterminate" state of checkbox for branches.
In case some selectable options are to be lazy loaded, it is possible to load additional selectable options asynchronously, while still be able to track the selection properly. In the following example there are several places where some additional options are being loaded on demand. Once triggered, if the parent selectable node is selected, all the lazy-loaded selectables will get selected once they're loaded.
If you have many items on leaf nodes, you might be interested in loading the data by parts. You can use pagination as one of the options for such a purpose.
The example below shows how to do that. In terms of this example, it's assumed that an API can return a necessary set of data for the node by it's identificator. When you expand the node, it will load the first page and then will display a paginator. At this point, on the first data fetch, an API should return a total amount of items to pass it to the paginator.
It's also recommended to use nui-paginator
for handling the pagination.
To customize tree nodes, add any template. Below you see several typical cases with adding additional templates (menu, tag) into the tree.
The example below shows how to load data in portions using "Load More"
It's possible the case when there're too many items on the leaf node. In this case, it's also possible to display all the items on the separate screen.
For example, inside the nui-dialog
.
Current example uses Nova test BE service to get the data.
It can send the data by some portions for a certain criteria.
In this current example, there're 3 nodes that fetch the data per location, the service is implementing Nova
DataSourceService
.


$(selector).writemaths()
adds a bit of MathJax magic to editable areas so that when you're writing LaTeX, you get an instant preview just above the cursor.
For example, click inside these LaTeX expressions:
$e^{\pi i} + 1 = 0$
\[e^{\pi i} + 1 = 0\]
\begin{align}
x &= 1 \\
y &= 2 \\
\end{align}
Of course, it works when maths is in the middle of a line, like $\log_2 \left( \frac{1}{x+2} \right)$ is.
But stuff inside a <code>
tag doesn't count: $e^{\pi i}+1 = 0$
.
It works in <textarea>
tags too:
And <input>
tags, of course:
This plugin needs rangy-core.js for contenteditable
areas. For textarea
and input
tags, it needs textinputs_jquery.js.
Made by cp in 2012-2013.


BEST launched online pizza ordering in 2007, and in 2008 revolutionized the online delivery experience by launching the innovative Pizza Builder and Domino's Tracker tools. In 2018, Domino’s Pizza Mauritius launched www.dominos.mu which is the first online ordering platform of its kind in the country. The Domino's Pizza Tracker is another innovation in food delivery, allowing customers to follow the progress of the order from the time it is placed until they receive it. We also added pizza deals and offers to the website in order to provide even more value to our customers. Our innovations simply help us get better at what we love to do most: deliver great pizza to your door, efficiently and enthusiastically!
vegetable for small pizza:1500frw
vegetable for medium pizza:2500frw
vegetable for big pizza:3500frw
onion sausage for small pizza:1500frw
onion sausage for medium pizza:2500frw
onion sausage for big pizza:3500frw
sugar pine for small pizza:1500frw
sugar pine for medium pizza:2500frw
sugar pine for big pizza:3500frw


Press release
More Traineeships also announced by leading employers.
Even more people will get an opportunity to earn while they learn, after more than 20,000 new apprenticeships have been pledged by employers as part of National Apprenticeship Week.
From Lloyds Banking Group committing to hiring 5,000 apprentices, Greene King and Whitbread pledging 2,000 a piece and EE, Mitchells and Butlers and Starbucks each announcing 1,000 vacancies, announcements have been made by hundreds of businesses around the country.
And it’s not just big corporates that are showing their commitment to apprenticeships – 47% of the employers creating new apprenticeships are small and medium sized enterprises ( SMEs ). Smaller organisations announcing plans to take on apprentices include Cambridgeshire retailer Crafting Time, Kent web designers Interactive Red, London charity Lives Not Knives, Grimsby electrical contractors HYBRiD, Oldham construction firm Emanuel Whittaker and Penzance customer services company TR18 Contact.
Other companies that have pledged new apprenticeships include BT, ITV, Network Rail, Be Wiser Insurance, Central Bedfordshire Council, Sheffield Teaching Hospitals, Ashtead Plant Hire, Southend Citizen Advice Bureau and Virgin Media. Meanwhile, Capita Personal Independence Payment have pledged to make 5% of its non-healthcare workforce apprentices and launched a drive to hire more disabled young people onto apprenticeships.
And National Apprenticeship Week has also seen a boost to the new traineeships, which give young people the work preparation training, English, maths and work experience they need to get an apprenticeship or other job.
Employers including the BBC and Port of Tilbury have pledged to give more young people traineeships – as have Liverpool Football Club through a partnership with Myerscough College. Meanwhile, Westminster Kingsway College has launched a new Parliamentary Traineeship programme so 16 to 23 year olds can get a chance to gain experience in the corridors of power.
Business Secretary Vince Cable said:
The government is putting an end to the damaging divide between vocational and academic learning. We are committing the biggest ever investment in apprenticeships and are on track to create 2 million apprenticeships over the course of this parliament.
The resounding success of this year’s National Apprenticeship Week demonstrates that apprenticeships are deservedly becoming more popular with business of all sizes. Apprentices already contribute £1.8 billion to the economy and with these further commitments, thousands more people will now be able to get the practical skills they need to build careers and help firms grow.
Today’s (7 March 2014) announcement follows new research revealing that nearly half (44%) of employers intend to take on 1 or more apprentice in the next 5 years. And research from the Association of Accounting Technicians revealed that apprentices delivered around £1.8 billion of net economic benefits to UK organisations in the last year.
The week has also seen the prime minister announce the government’s commitment to creating 2 million apprenticeships over the lifetime of the current parliament. David Cameron also announced the next round of trailblazer employers ; 29 new groups of employers who will lead the transformation of apprenticeships. The new trailblazers, spanning sectors from accountancy to aviation, nursing to retail, include small businesses and sector specialists as well as some big names such as Tata Steel, John Lewis, British Airways and BMW.
Other announcements during the week included the launch of new higher, degree level, apprenticeships in space engineering and nursing. And to help bring more SME employers on board, further support has been announced including a new online mentoring service, Apprenticemakers. Meanwhile, the AvLive feed of vacancies which training providers and partners can use to promote apprenticeship vacancies has been upgraded following a successful 6-month testing period.
Skills and Enterprise Minister Matthew Hancock MP said:
We want to make going to university or into an apprenticeship the new norm, and over half of young people say they would like to do an apprenticeship if one were available. So it’s great that as National Apprenticeship Week 2014 comes to a close we can announce that 20,432 new positions are to be created.
In order to give young people the best chance possible of securing an Apprenticeship or job and unlock their potential, we need to help them become work-ready. The new traineeships will be crucial to that.
Apprenticeships are delivering for employers, young people, adults and the economy and are increasing at a record rate. Nearly every employer that takes on an apprentice reports benefits to their business, including the ability to grow their own talents.
Now in its seventh year, National Apprenticeship Week is designed to celebrate apprenticeships and the positive impact they have on individuals, businesses and the wider economy. The overarching theme for 2014 has been Great Apprenticeships, to demonstrate that apprenticeships are great for business and personal careers and create opportunities for both employers and apprentices.
Visit apprenticeships.org.uk or https://bitly.com/Traineeships2014 for more information.
1.The ‘20,000 apprenticeships pledged’ figure is sourced from monitoring press coverage during National Apprenticeship Week, through press releases sent to National Apprenticeship Service and registrations on the National Apprenticeship Service Pledgeometer.
The National Apprenticeship Service supports and co-ordinates the delivery of apprenticeships and traineeships throughout England.
It is responsible for increasing the number of apprenticeship and traineeship opportunities and providing a dedicated, responsive service for both employers and learners. This includes simplifying the process of recruiting an apprentice or trainee through apprenticeship vacancies, an online system where employers can advertise their vacancies and potential apprentices or trainees can apply.
Apprenticeships bring considerable value to organisations, employers, individuals and the economy. Businesses across the country are increasingly realising the enormous benefits that apprenticeships create, not only in terms of developing a highly skilled workforce but also by boosting productivity and staff retention.
Research shows that apprenticeships are an optimal way of training, developing and skilling people for the future, helping businesses to secure a supply of people with the skills and qualities they need and which are often not available on the external job market.
A traineeship is an education and training programme with a work placement that is focused on giving young people the skills and experience that employers are looking for. At its core are work preparation training, English and maths for those who need it, and a high quality work experience placement.
Traineeships are for young people aged 16 to 23 who are motivated to get a job but who lack the skills and experience that employers are looking for. Traineeships prepare young people for their future careers by helping them to become ‘work ready’. They give young people the skills and experience to be able to compete for an apprenticeship or other job.
Traineeships are a stepping stone to future success for young people, businesses and the wider economy.
There has never been a better time to employ an apprentice, or start an apprenticeship.
Visit apprenticeships.org.uk or call 08000 150 600 to find out more.
The Skills Funding Agency is a partner organisation of the Department for Business, Innovation and Skills ( BIS ). Its job is to fund and promote adult further education ( FE ) and skills training in England, including traineeships and apprenticeships, in a way which supports economic growth. The agency delivers £4.1 billion of skills training through contracts with over 1,000 colleges, private training organisations and employers, with a highly effective supply chain that means FE meets local need in the most cost-effective way.
Within the Skills Funding Agency, there are 2 customer-facing services: the National Apprenticeship Service, which is responsible for increasing the number of traineeship and apprenticeship opportunities, and the National Careers Service, which provides impartial careers information, advice and guidance online, by telephone or face-to-face.
5.The government’s economic policy objective is to achieve ‘strong, sustainable and balanced growth that is more evenly shared across the country and between industries’. It set 4 ambitions in the ‘Plan for Growth’ , published at Budget 2011:
Work is underway across government to achieve these ambitions, including progress on more than 250 measures as part of the Growth Review. Developing an Industrial Strategy gives new impetus to this work by providing businesses, investors and the public with more clarity about the long-term direction in which the government wants the economy to travel.
Published: 7 March 2014


The Flatworm Docs rendering script is designed to be very simple, but provide enough formatting necessary for documenting JavaScript libraries.
A lot of its inspiration came from Read the Docs and the Sphinx project.
Flatworm Docs are made up of fragments. A fragment is either a lone body of markdown text, or a directive for specialized formatting, which may itself have body.
A section has its TITLE in an H1 font. Sections are linked to in Table of Contents and have a dividing line drawn above them. If an option is specified, it is avaialble as a name for intern linking. There should only be one _section:
per page.
A subsection has its TITLE in an H2 font. Subsections are linked to in Table of Contents and have a dividing line drawn above them. If an option is specified, it is avaialble as a name for internal linking.
A heading has its TITLE in an H3 font. If an option is specified, it is available as a name for internal linking.
A definition has its TERM bolded and the markdown body is indented.
A property has its JavaScript SIGNATURE formatted and the markdown body is indented.
A code reads the FILENAME and depending on the extension adjusts it.
For JavaScript files, the file is executed, with //!
replaced with the result of the last statement and //!error
is replaced with the throw error. If the error state does not agree, rendering fails.
A toc injects a Table of Contents, loading each line of the body as a filename and recursively loads the toc if present, otherwise all the sections and subsections.
A null is used to terminated a directive. For example, after a definition, the bodies are indented, so a null can be used to reset the indentation.
The markdown is simple and does not have the flexibility of other dialects, but allows for bold, italic, underlined, monospaced
, superscript and striked text, supporting links and lists.


New customer?start here
You have no items in your shopping cart.
All prescription lenses are anti-scratch, coated with an anti-reflective (anti-glare) layer, and 100% UV protectant at no additional cost
All prescription glasses come with a premium case and a cleaning cloth
WOMEN
MEN
KIDS
Innovative Lens Technologies
Our unique software is a pioneer in the industry. Our advanced algorithm assess your prescription, pupillary distance, frame, as well as lenst type to provide the most comfortable fit. Our Payne Glasses Measure technology makes it possible to craft comfortable prescription glasses for customers all without needing to visit a store.
GLASSES
QUICK LINKS
ABOUT
FEATURED LENSES
BE THE FIRST TO KNOW
Get the latest information on New Products, Events and Offers. Sign up today
Enter your email address
Email<EMAIL_ADDRESS>Call :<PHONE_NUMBER>
Text :<PHONE_NUMBER>
Chat : Search or Chat
© 2017-2018 Payne Glasses LLC. All Rights Reserved.


dmidecode command
linux의 dmidecode은 DMI(alternatively known as SMBIOS) 테이블 내용을 사람이 읽을 수 형태로 보여주는 명령어 입니다.
이 명령어를 사용하려면 root
권한이 필요 합니다.
한번에 많은 정보를 보여주기 때문에 more
를 이용하여 화면 단위로 내용을 보면 유용합니다.
$> sudo dmidecode | more
# dmidecode 3.0
Getting SMBIOS data from sysfs.
SMBIOS 2.7 present.
76 structures occupying 3048 bytes.
Table at 0x000EB530.

유용한 옵션
-s
: DMI 문자열에 관련된 정보를 보여주는 옵션
$> sudo dmidecode -s
String keyword expected
Valid string keywords are:
bios-vendor
bios-version
bios-release-date
system-manufacturer
system-product-name
system-version
system-serial-number
system-uuid
baseboard-manufacturer
baseboard-product-name
baseboard-version
baseboard-serial-number
baseboard-asset-tag
chassis-manufacturer
chassis-type
chassis-version
chassis-serial-number
chassis-asset-tag
processor-family
processor-manufacturer
processor-version
processor-frequency
$>sudo dmidecode -s processor-version
Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz
-t
: 타입에 관련된 정보를 보여주는 옵션
$> sudo dmidecode -t
dmidecode: option requires an argument -- 't'
Type number or keyword expected
Valid type keywords are:
bios
system
baseboard
chassis
processor
memory
cache
connector
slot
$> sudo dmidecode -t processor
# dmidecode 3.0
Getting SMBIOS data from sysfs.
SMBIOS 2.7 present.
Handle 0x0041, DMI type 4, 42 bytes
Processor Information
Socket Designation: SOCKET 0
Type: Central Processor
Family: Core i7
Manufacturer: Intel
ID: C3 06 03 00 FF FB EB BF
Signature: Type 0, Family 6, Model 60, Stepping 3
Flags:
FPU (Floating-point unit on-chip)
VME (Virtual mode extension)
DE (Debugging extension)
PSE (Page size extension)
TSC (Time stamp counter)
MSR (Model specific registers)
PAE (Physical address extension)
MCE (Machine check exception)
CX8 (CMPXCHG8 instruction supported)
APIC (On-chip APIC hardware supported)
SEP (Fast system call)
MTRR (Memory type range registers)
PGE (Page global enable)
MCA (Machine check architecture)
CMOV (Conditional move instruction supported)
PAT (Page attribute table)
PSE-36 (36-bit page size extension)
CLFSH (CLFLUSH instruction supported)
DS (Debug store)
ACPI (ACPI supported)
MMX (MMX technology supported)
FXSR (FXSAVE and FXSTOR instructions supported)
SSE (Streaming SIMD extensions)
SSE2 (Streaming SIMD extensions 2)
SS (Self-snoop)
HTT (Multi-threading)
TM (Thermal monitor supported)
PBE (Pending break enabled)
Version: Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz
Voltage: 1.0 V
External Clock: 100 MHz
Max Speed: 7000 MHz
Current Speed: 3700 MHz
Status: Populated, Enabled
Upgrade: Other
L1 Cache Handle: 0x0004
L2 Cache Handle: 0x0005
L3 Cache Handle: 0x0006
Serial Number: Not Specified
Asset Tag: Fill By OEM
Part Number: Fill By OEM
Core Count: 4
Core Enabled: 1
Thread Count: 2
Characteristics:
64-bit capable
dmidecode –help
$>dmidecode --help
Usage: dmidecode [OPTIONS]
Options are:
-d, --dev-mem FILE Read memory from device FILE (default: /dev/mem)
-h, --help Display this help text and exit
-q, --quiet Less verbose output
-s, --string KEYWORD Only display the value of the given DMI string
-t, --type TYPE Only display the entries of given type
-u, --dump Do not decode the entries
--dump-bin FILE Dump the DMI data to a binary file
--from-dump FILE Read the DMI data from a binary file
-V, --version Display the version and exit


Resources
Falling Fruit is a celebration of the overlooked culinary bounty of our city streets. By quantifying this resource on an interactive map, Falling Fruit hopes to facilitate intimate connections between people, food, and the natural organisms growing in our neighborhoods.
Survey Results
In October 2014, GleanSLO polled the community for an Operations and Strategic Planning Survey. A summary of the results can be found here.
Food Preservation
The Master Food Preservers (MFP) program is now taking calls about questions on food safety, canning fruits and vegetables, freezing, drying, pickling and jelly and jam making!
Call their hotline on Wednesdays between 1:00pm - 3:00pm,<PHONE_NUMBER>. More info here.
Climate Friendly Food
GleanSLO is a proud partner of OutsideInSLO because we believe what we choose to eat matters to our health and our climate.
OutsideInSLO is the first public health campaign linking climate change and health impacts in California. The goal of OutsideInSLO is to educate the community on the connections between climate change and health while empowering the community to make healthy choices which have multiple benefits.
“We at the SLO Food Bank take climate change seriously by rescuing safe food that would go to the landfill and gleaning fresh produce locally through GleanSLO. It's part of our mission to help build a healthier community." - Carl Hansen, CEO Food Bank Coalition of SLO County
Download the climate friendly food brochure here.
 We create amazing school gardens that power healthy, happy, smart youth.
For more information click here


guard GFP expression cell lines cells enhancer Fig. T-DNA epidermal gene trap DNA Plant stomatal GUS activity GAL Arabidopsis genes insert used using RNA cells, vascular plants root {border-style development flanking px;} upstream #ffffff;} analysis expressed fragment fragments leaf tissue cDNA line patterns Dof reporter transcription Leonhardt cell-specific detected genomic plant promoter putative samples elements fragmentation inserts leaves transformed uidA Supplementary mesophyll GCP JXB Physiology indicated number obtained thaliana wild-type Haseloff Plesch adjacent binding coding expressing growth independent observed protoplasts results sequences specific Cell Guard KC, Kwak Pandey acid, amino cells. containing described factor method pools position purity px; seedlings types water {font-family (data Atg, Biology Dodd Galbiati Hugouvieux KS-, PCR Schroeder Yang acid data enriched font-size fusion isolated protein protoplasting region regulatory relative respective selected shown). single (see HIC KAT TAC Table carried construct contained did double; grown identification identify isolation lines. material molecular online). pavement reported represent responses (DD), (E, Analysis Atg C). D). Desikan Epidermal Lines Poethig RT-PCR Science abscisic amplified approximately assess border-color border-width capture complex confirmed control demonstrated derived direction drive expression, factors greater kDa kanamycin located markers mature pool primers protoplast responsible roots shown significantly silencing solid; stably track tracking useful {font-style {font-weight ACT Atg) Baker Birnbaum Cary Hetherington However, IAA J-, KS, KS- Laplaze Leica Methods Molecular Müller-Röber Stomatal Tsugeki USA) Wang Webb Zimmermann activation asymmetric bold border channel characterization compared contrast contribution courier; detectable differences driving em; following free fused hypocotyl individuals intergenic involved lateral likely lines, location meristemoid methods mother pattern phenotypic plates potassium prepared present provided pt;} range resulting right role seedlings. sequence shoot signal site sites small stress sufficient suggest tissue, transduction types, utility yield


See: Description
| Class | Description |
|---|---|
| AbstractBagDecorator |
Decorates another
Bag to provide additional behaviour. |
| AbstractMapBag |
Abstract implementation of the
Bag interface to simplify the creation
of subclass implementations. |
| AbstractMapBag.MutableInteger |
Mutable integer class for storing the data.
|
| AbstractSortedBagDecorator |
Decorates another
SortedBag to provide additional behaviour. |
| HashBag |
Implements
Bag , using a HashMap to provide the
data storage. |
| PredicatedBag |
Decorates another
Bag to validate that additions
match a specified predicate. |
| PredicatedSortedBag |
Decorates another
SortedBag to validate that additions
match a specified predicate. |
| SynchronizedBag |
Decorates another
Bag to synchronize its behaviour
for a multi-threaded environment. |
| SynchronizedSortedBag |
Decorates another
SortedBag to synchronize its behaviour
for a multi-threaded environment. |
| TransformedBag |
Decorates another
Bag to transform objects that are added. |
| TransformedSortedBag |
Decorates another
SortedBag to transform objects that are added. |
| TreeBag |
Implements
SortedBag , using a TreeMap to provide
the data storage. |
| TypedBag |
Decorates another
Bag to validate that elements added
are of a specific type. |
| TypedSortedBag |
Decorates another
SortedBag to validate that elements added
are of a specific type. |
| UnmodifiableBag |
Decorates another
Bag to ensure it can't be altered. |
| UnmodifiableSortedBag |
Decorates another
SortedBag to ensure it can't be altered. |
This package contains implementations of the
Bag
and
SortedBag
interfaces.
A bag stores an object and a count of the number of occurences of the object.
The following implementations are provided in the package:
The following decorators are provided in the package:
Copyright © 2001–2015 The Apache Software Foundation. All rights reserved.


Apps to improve your Miixperience.
INCLUDES:
+ Samsung Health - Samsung Health provides core features to keep up your body fit and healthy. It will record and analyze your daily activities and habits to help maintain successful diet and lead healthy lifestyle. ... Manage your fitness activities, track your weight and monitor your diet using Samsung Galaxy and Gear devices.
+ Samsung Connect - Allows you to easily search and connect using Bluetooth or Wi-Fi to find other mobile devices, smart TVs and or streaming devices such as Chromecast or Amazon Fire TV stick. You can use Quick Connect to share content such as images, videos, and audio.
+ Finder - Finder is a powerful search application that allows you to find what you want in an instant by searching the content on your Galaxy smartphone and on the web as well.
+ Magnifier widget - Now you can use your Galaxy device as the perfect digital magnifier. There is not need to take a magnifying glass when reading small labels.
Note that these apps are optional as they use too much system resources.


::USER::
-user_id
-user_name
-password
-email
-staff_level
-last_ip
-last_login
-last_update
-last_active
-failed_logins
-avatar_link
-gender
-village
-level
-rank
-health
-max_health
-stamina
-max_stamina
-chakra
-max_chakra
-regen_rate
-exp
-bloodline_id
-bloodline_name
-awake
-battle_id
-location(x.y)
-in_village (boolean, determined in class from location + village location comparison)
-train_type
-train_gain
-train_time
-money
-premium_credits
-premium_credits_purchased
-pvp_wins
-pvp_losses
-ai_wins
-ai_losses
-monthly_pvp
-ninjutsu skill
-genjutsu skill
-taijutsu skill
-bloodline_skill
-cast_speed
-speed
-strength
-endurance
-intelligence
-willpower
Bloodline
-bloodline id
-bloodline name
-register_date
::USER INVENTORY::
-user_id
jutsu (Below is JSON encoded into text column)
--jutsu_id
--level
--exp
items (Below is JSON encoded into text column)
-item_id
-quantity
bloodline_jutsu (Below is JSON encoded into text column)
--jutsu_id
--level
--exp
equipped_jutsu (Below is JSON encoded into text column)
-jutsu_id
equipped_items (Below is JSON encoded into text column)
-item_id
::JOURNALS::
-user_id
-journal
::AI OPPONENTS::
-ai_id
-rank
-name
-max_health
-ninjutsu skill
-genjutsu skill(offense + defense)
-taijutsu skill(offense + defense)
-speed(taijutsu accuracy)
-strength(taijutsu damage)
-cast_speed(ninjutsu defense vs taijutsu)
-endurance(taijutsu defense)
-intelligence(genjutsu offense + resistance)
-willpower(all defense)
-moves(json encoded text): [battle_text, power]
::JUTSU::
-jutsu_id
-name
-jutsu_type (ninjutsu, genjutsu, taijutsu)
-rank (student, genin, etc.)
-level (0 for unlearned jutsu (SCROLL), 1-100 for other)
-power
-hand_seals (1-2-3-4-5-6)
-element (melee, spirit, poison, fire, etc.)
-parent_jutsu (for chained multi-stage moves)
-purchase_type (1 = default, 2 = purchasable, 3 = non-purchasable)
-purchase_cost
-use_cost
-use_type (projectile, physical, barrier)
-description
-battle_text
-effect
-effect_amount
-effect_length
::ITEM::
-item_id
-name
-rank
-purchase_type(1 = purchasable, 2 = event)
-purchase_cost
-use_type (1 = weapon, 2 = armor, 3 = consumable)
-effect
-effect_amount
::BLOODLINE::
-bloodline_id (int)
-name (string - 100 chars)
-clan_id (int)
-rank (lesser, common, greater, elite, legendary)
-passive_boosts (string - variable)
-json: 1-3x(effect, amount, power)
-combat_boosts(string - variable)
-json: 1-3x(effect, amount, power)
-moves(string - variable)
-json: [JUTSU data structure]
Bloodlines will have set amount of power points (100 would be full for a legendary)
These points can be set for each boost
Effects will be static numbers (not %) which are increased by bloodline skill
::BLOODLINE::
-bloodline_id
-passive_boosts(with actual %s)
-combat_boosts(with actual %s)
-moves (id, level, exp)
::TAVERN CHAT::
-post_id
-user_name
-message
-time
-staff_level
::PRIVATE MESSAGES::
-message_id
-sender
-recipient
-subject
-message
-time
-read
-staff_level
::NEWS POSTS::
-post_id
-sender
-title
-message
-time
::VILLAGES::
-village_id
-name
-location
-points
::BANNED IPS::
-id
-ip_address
-ban_level
::REPORTS::
report_id
report_type
content_id
user_id
reporter_id
staff_level
reason
notes
status
time
::RANKS::
rank_id
name
base_level
max_level
base_stats
stats_per_level
health_gain
pool_gain
stat_cap
::CLANS::
clan_id
village
name
bloodline_only
boost
boost_amount
points
leader
elder_1
elder_2
logo
::CLANS::
mission_id
name
rank (1 = d-rank, 2 = c-rank, 3 = B-rank, 4 = A-rank, 5 = S-rank)
mission_type (1 = village, 2 = clan, 3 = special)
stages(json encode)
-stage_id
-action_type (travel, search)
-action_data (location to travel to/search for)
-location_radius (radius to search/generate dynamic location in)
-description (text displayed to user)
::TEAMS::
team_id
village
name
boost
boost_amount
points
monthly_points
leader
members(JSON array of userids)
mission_id
mission_stage
logo


The [ My KBase Lab Application ] was developed by the KBase Team. For questions and comments email<EMAIL_ADDRESS>This project relies on user input and testing, so please contact us with any suggestions or comments.


20K
Members
12
Countries
3K
Hotels
7
Partners
Wisata Popular
Something that you never try
before in this world
Our Networks
Companies are trusted us
more than just a trip
They Are Loving Us
Moments were giving them
the best experience
Angga Risky
“ It was glorious and I could not stop to say wohooo for every single moment Dankeeeeee “
Trip To Ubud
Shayna
“ The trip was amazing and I saw something beautiful in that Island that makes me want to learn more “
Trip To Nusa Penida
Shabrina
“ I loved it when the waves was shaking harder — I was scared too “
Trip To Karimun Jawa


Home
Setup
Installation
Download
History
Frontend
Configuration
Monitoring
Troubleshooting
Factory
Configuration
Monitoring
Troubleshooting
Tutorials
Submitting a Job
Cloud Systems
Setting a VM
Batch Jobs
FAQ
Design
Frontend
Frontend Elements
Factory
Factory Entry
Glideins
Data Exchange
Advanced Configuration
HTCondor Organization Attributes
HTCondor Configuration
Custom Variables
Custom Scripts
GlideinWMS Tools
Documentation
API Documentation
References
Sitemap
{{ content }}


Listen to the gaffer-tape-inspired designer's show soundtrack, and read about his cut-and-shut knits, leather shirts and use of girl models for menswear
Listen to the gaffer-tape-inspired designer's show soundtrack, and read about his cut-and-shut knits, leather shirts and use of girl models for menswear
Listen to the gaffer-tape-inspired designer's show soundtrack, and read about his cut-and-shut knits, leather shirts and use of girl models for menswear
Listen to the gaffer-tape-inspired designer's show soundtrack, and read about his cut-and-shut knits, leather shirts and use of girl models for menswear
Listen to the gaffer-tape-inspired designer's show soundtrack, and read about his cut-and-shut knits, leather shirts and use of girl models for menswear


optimsimplex: R Port of the 'Scilab' Optimsimplex Module
Provides a building block for optimization algorithms
based on a simplex. The 'optimsimplex' package may be used in the
following optimization methods: the simplex method of Spendley
et al. (1962) <doi:10.1080/00401706.1962.10490033>, the method of
Nelder and Mead (1965) <doi:10.1093/comjnl/7.4.308>, Box's algorithm for
constrained optimization (1965) <doi:10.1093/comjnl/8.1.42>, the
multi-dimensional search by Torczon (1989)
<http://www.cs.wm.edu/~va/research/thesis.pdf>, etc...
| Version: |
1.0-7 |
| Depends: |
optimbase (≥ 1.0-8), methods |
| Published: |
2018-02-13 |
| Author: |
Sebastien Bihorel, Michael Baudin (author of the original
module) |
| Maintainer: |
Sebastien Bihorel <sb.pmlab at gmail.com> |
| License: |
CeCILL-2 |
| NeedsCompilation: |
no |
| Materials: |
README ChangeLog |
| In views: |
Optimization |
| CRAN checks: |
optimsimplex results |
Downloads:
Reverse dependencies:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=optimsimplex
to link to this page.


The Online Reaction Time Test
Instructions:
Click the large button on the right to begin.
Wait for the stoplight to turn green.
When the stoplight turns green, click the large button quickly!
Click the large button again to continue.
Test
Number
Reaction
Time (in seconds)
The stoplight
to watch.
The button
to click.
1
2
3
4
5
AVG.


|
||||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object coins.mdf.TreatNode coins.mdf.TreatIf
This class treats `IF statement'. In MDF module, the `IF statement' is a kind of special statement because it include the flow information.
| Field Summary | |
protected MdfEnvironment |
env
|
protected MacroFlowGraph |
mfg
|
protected Stmt |
root
|
protected SymTable |
symTab
|
protected java.util.LinkedList |
visited
|
| Constructor Summary | |
(package private) |
TreatIf(MdfEnvironment e,
MacroFlowGraph g,
IfStmt stmt,
java.util.LinkedList list,
MdfConditions c)
Constructor: |
| Method Summary | |
protected boolean |
isChange(MacroTask mt,
Label label)
Get whether the specified macro task has the specified label. |
protected BlockStmt |
makeJumpBlk(Label label)
Make a new block structure which include a new JUMP statement. |
(package private) void |
replace(MacroTask mt,
Label label)
Replace the `then part' and `else part' of the current IF statement. |
protected void |
replaceReturn(Label label,
DeclareLocalVariables decLocal)
Replace the return statement into the jump statement. |
| Methods inherited from class java.lang.Object |
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Field Detail |
protected MdfEnvironment env
protected SymTable symTab
protected MacroFlowGraph mfg
protected java.util.LinkedList visited
protected Stmt root
| Constructor Detail |
TreatIf(MdfEnvironment e, MacroFlowGraph g, IfStmt stmt, java.util.LinkedList list, MdfConditions c)
e
- The environment of the MDF moduleg
- The current macro flow graphstmt
- The current IF statementlist
- The list of the labels which the MDF module is already treatedc
- The current conditions| Method Detail |
void replace(MacroTask mt, Label label)
mt
- The current macro tasklabel
- The target labelprotected void replaceReturn(Label label, DeclareLocalVariables decLocal)
label
- The label of the final return statementdecLocal
- The declaration of the local variablesprotected BlockStmt makeJumpBlk(Label label)
label
- The target label
protected boolean isChange(MacroTask mt, Label label)
mt
- The target macro tasklabel
- The target label
|
||||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |


Enumerators are a great way to search and sort through collection of data within ruby. Today we are going to focus on the group_by Enumerable and its uses.
This basically says that what you call on group_by to sort your information you will decide how you want to group your information using the block of code and it will return a hash (a directory of unique keys and their values.)
Why would you need the group_by Enumerable you ask? Well if you are looking to make groups of specific information and narrow down your search this is a huge help.
You can see for the number 1 through 6 it grouping them by their divisibility by 3. 3 and 6 are evenly divisable by 3 with zero remainders, 1 and 4 have one remainder, 2 and 5 have 2 remainders.
Lets say you are an eagerly waiting parents for your first new born and you have run into the predicament of what to call you child. You have a big list of names what was to narrow it down a bit. Group_by can help you out. Lets say you wanted to choose your babies name based on the length of the name, you want something short and sweet. You can do this:
Maybe, you are dead set on having good initials, you can group by the first letter of the name like this:
While these are very basic and simple examples. With good ruby knowledge you can group by number of occurrences with the array or even by a specific string.


Providing functions to diagnose and make inferences from various linear models, such as those obtained from 'aov', 'lm', 'glm', 'gls', 'lme', and 'lmer'. Inferences include predicted means and standard errors, contrasts, multiple comparisons, permutation tests and graphs.
| Version: | 1.0.1 |
| Depends: | R (≥ 3.0.0), lme4, nlme, parallel |
| Imports: | ggplot2, graphics, grDevices, Matrix, methods, numDeriv, pbkrtest, plyr, stats, utils |
| Published: | 2018-08-23 |
| Author: | Dongwen Luo, Siva Ganesh and John Koolaard |
| Maintainer: | Dongwen Luo <dongwen.luo at agresearch.co.nz> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | https://CRAN.R-project.org/package=predictmeans |
| NeedsCompilation: | no |
| CRAN checks: | predictmeans results |
| Reference manual: | predictmeans.pdf |
| Package source: | predictmeans_1.0.1.tar.gz |
| Windows binaries: | r-devel: predictmeans_1.0.1.zip, r-release: predictmeans_1.0.1.zip, r-oldrel: predictmeans_1.0.1.zip |
| OS X binaries: | r-release: predictmeans_1.0.1.tgz, r-oldrel: predictmeans_1.0.1.tgz |
| Old sources: | predictmeans archive |
Please use the canonical form https://CRAN.R-project.org/package=predictmeans to link to this page.


The-Good-Travel
Place name
Trip Place
HAWAII
ITALY
LONDON
MALAYSIA
PARIS
SLOVAKIA
Type Of Transport
Car
Plane
Trian
Ship
submit


Argo provides predefined configurations, which differ in performance characteristics, so that you can tailor your storage performance to the needs of your applications.
Please review the selected configuration and enter the volume properties.


Too Young to Marry
Too Young to Marry is a movie starring Nina Dobrev, Dillon Casey, and Polly Draper. Connecticut high school students Max Doyle and Jessica Carpenter fall in love and feel making love isn't enough, so they brave everyone's objections...


{% extends "base.mail.html" %}
{% load i18n %}
{% block content %}
{% blocktrans with sender_name=sender.full_name receiver_name=receiver.short_name task_title=task.title motivation=motivation %}
Hello {{ receiver_name }},
Good news! {{ sender_name }} applied for your task
{{ task_title }}.
{% endblocktrans %}
{% if motivation %}
{% blocktrans with sender_name=sender.full_name %}
{{ sender_name }} replied with the following motivation to your task:
{% endblocktrans %}
{{ motivation }}
{% endif %}
{% blocktrans %}
Please accept or reject this application, he or she will receive an
email when you have confirmed the status.
{% endblocktrans %}
{% endblock %}
{% block action %}
{% trans 'Confirm now' context 'email' %}
{% endblock %}


PowerShell - Using the Help System
in Blog
Barely Scratching the surface with PowerShell’s Basic Commands
Objective
To document some use cases on how to the the Help System to find what you need in PowerShell
Prerequisites
General Windows Operating System Knowledge
PowerShell version 5 or higher
Using the PowerShell Help System
Update your PowerShell Help Files
Ensures you have the latest help files
- Requires internet connection
- You may notice a few error messages while trying to update all of the modules. This is normal
Update-Help
Get a list of Modules that support Updatable Help
Get-Module -ListAvailable | Where-Object -Property HelpInfoUri
Obtain full help file from on a specific cmdlet
Get-Help Get-Service -Full
Get the online version of a help file (opens in a broser window)
Get-Help Get-Process -Online
External Links
Microsoft PowerShell Documentation
PowerShell MasterClass YouTube Series - By John Saville
PowerShell in a Month of Lunches - By Don Jones
Channel 9 Series - Getting Started with Powershell
Summary
- Have a question?
- Find an error?
- Have a suggestion on how to improve this page?


Consuming ZPT-Sharp as an API
ZPT-Sharp comes with two applications out of the box: an ASP.NET MVC view engine, and a command-line rendering app. It is also available as an API for consumption by your own applications.
The documentation presented here is intended to provide a high-level look at how to use that API. It is not detailed technical reference; there is in-depth class documentation available for that purpose. The information here links to that documentation where appropriate, providing context and an entry-point to the reference material.
Referencing ZPT-Sharp
To begin, you will need to reference ZPT-Sharp from your own project; there are two assemblies which are important.
First there is the abstractions assembly: CSF.Zpt.Abstractions.dll
.
This assembly contains no real logic, only interfaces, abstract types and a few data-carrying types.
Second is the main assembly: CSF.Zpt.dll
.
This assembly contains the concrete implementations of the core ZPT-Sharp logic.
Thus, the ZPT-Sharp implementation logic is separated from the interfaces by assembly; you may choose to split your own logic in a similar manner. To do this, reference the main assembly only from a small assembly of your own, which is only responsible for constructing instances of the concrete ZPT-Sharp types. Then, the remainder of your application need only reference the abstractions assembly, and deal with the API using its interfaces.
Plugin assemblies
ZPT-Sharp has extension points which are plugin-based. In almost all use-cases, you do not need to reference the plugin assemblies from your application. You will need to ensure that the relevant plugins are installed though; ZPT-Sharp won't do anything useful if no plugins are installed.
To install plugins, copy the relevant assemblies to your application's bin
directory.
Then, and add appropriate content to
your application's configuration file.
API use-cases
The following section deals with - what are expected to be - the most common use-cases for consuming the ZPT-Sharp API. Where applicable, links are provided to the relevant reference documentation for any types which are mentioned.
Render a single document
In this API use-case, you begin with either a System.IO.FileInfo
or a System.IO.Stream
.
This file or stream contains a single ZPT document.
Your aim is to render that document either to a System.IO.TextWriter
or to a System.String
.
The minimal steps to do this is as follows:
-
Construct an instance of
ZptDocumentFactory
(found in the main assembly). This type implements the interfaceIZptDocumentFactory
(found in the abstractions assembly). -
Use an appropriate overload of
CreateDocument
from the factory to get an object which implementsIZptDocument
(found in the abstractions assembly). -
Call an appropriate overload of the document's
Render
method.
There are optional parameters available for both the CreateDocument
and Render
methods.
These allow you to customise how the document is created and/or rendered.
Of note, are the following capabilities:
-
Specify an
System.Text.Encoding
toCreateDocument
to specify the input encoding. -
Specify a
RenderingMode
toCreateDocument
to specify whether the source document is HTML or XML. This parameter is mandatory when the input is aStream
, as there is no file extension from which the type can be auto-detected. -
Specify a model to
Render
to provide the representation of the model (in the MVC sense) to be rendered. -
Provide an instance of
IRenderingSettings
toRender
to control other aspects of the rendering process. It might be convenient to use aRenderingSettingsFactory
to create the settings instance.
Render a native document
This use case is similar to the above, for rendering a single document; the key differences are your desired start and end points.
In this scenario, you begin with a concrete document implementation object such as a
System.Xml.Linq.XDocument
or an
HTML Agility Pack HtmlDocument
.
Your goal is to render this to another instance of the same type.
This is a scenario where it becomes necessary to directly reference a plugin assembly from your application. You will need to reference the document provider plugin for the applicable document type.
In the plugin assembly, you will find a document provider type (a type which implements
IZptDocumentProvider
).
Instantiate this directly - the plugin specification requires it to have a public parameterless constructor.
On the concrete provider type You will find an overload of CreateDocument
which does not appear on the interface.
This extra overload of CreateDocument
will accept an instance of the native document type, and will return an instance of a concrete ZPT document type.
That concrete ZPT document type will, in turn, expose a rendering method which does not appear on the
IZptDocument
interface.
That extra rendering method will render the document and return an instance of the native document type.
Otherwise, apart from instantiating the document type directly and using an alternative rendering method, this use-case is identical in principle to the one above.
Batch-rendering multiple documents
ZPT-Sharp also provides a batch document renderer, which can iterate through many documents and render them all with a single API call. Typically, this is used to render all of the documents contained within a given directory path (and its subdirectories).
To use this functionality, you should first call the static method
BatchRenderingOptions.Build()
(this type is found in the main assembly).
This method provides a fluent interface which guides you through the valid combinations of options, returning an object which implements
IBatchRenderingOptions
(found in the abstractions assembly).
Consider the following example:
var batchOptions = BatchRenderingOptions
.Build()
.FromDirectory("my/input/path")
.ToDirectory("my/output/path");
Next, instantiate one of the following two types, dependant upon your use-case (both are found in the main assembly):
Both of these types implement
IBatchRenderer
(abstractions assembly).
The difference between them is how they respond to errors/unhandled exceptions whilst rendering.
The regular batch renderer will stop upon the first error in rendering and abort all further operations, raising an exception.
The error-tolerant batch renderer will skip rendering documents which fail, continuing with the rest.
The error-tolerant version will only raise exceptions for truly fatal errors which prevent all rendering,
such as an input directory which does not exist.
Once you have a batch renderer, call an appropriate overload of its Render
method.
As with the use-cases above you may optionally provide rendering settings, in exactly the same manner as already described for single-document rendering.


Note
Click here to download the full example code
Quick Start Tutorial for Compiling Deep Learning Models¶
Author: Yao Wang
This example shows how to build a neural network with NNVM python frontend and generate runtime library for Nvidia GPU with TVM. Notice that you need to build TVM with cuda and llvm enabled.
Overview for Supported Hardware Backend of TVM¶
The image below shows hardware backend currently supported by TVM:
In this tutorial, we’ll choose cuda and llvm as target backends. To begin with, let’s import NNVM and TVM.
import numpy as np
import nnvm.compiler
import nnvm.testing
import tvm
from tvm.contrib import graph_runtime
Define Neural Network in NNVM¶
First, let’s define a neural network with nnvm python frontend. For simplicity, we’ll use pre-defined resnet-18 network in NNVM. Parameters are initialized with Xavier initializer. NNVM also supports other model formats such as MXNet, CoreML, ONNX and Tensorflow.
In this tutorial, we assume we will do inference on our device
and the batch size is set to be 1.  We can call the nnvm.symbol.debug_str
to show the network structure.
batch_size = 1
num_class = 1000
image_shape = (3, 224, 224)
data_shape = (batch_size,) + image_shape
out_shape = (batch_size, num_class)
net, params = nnvm.testing.resnet.get_workload(
layers=18, batch_size=batch_size, image_shape=image_shape)
print(net.debug_str())
Compilation¶
Next step is to compile the model using the NNVM/TVM pipeline. Users can specify the optimization level of the compilation. Currently this value can be 0 to 3. The optimization passes include operator fusion, pre-computation, layout transformation and so on.
nnvm.compiler.build
returns three components: the execution graph in
json format, the TVM module library of compiled functions specifically
for this graph on the target hardware, and the parameter blobs of
the model. During the compilation, NNVM does the graph-level
optimization while TVM does the tensor-level optimization, resulting
in an optimized runtime module for model serving.
We’ll first compile for Nvidia GPU. Behind the scene, nnvm.compiler.build first does a number of graph-level optimizations, e.g. pruning, fusing, etc., then registers the operators (i.e. the nodes of the optimized graphs) to TVM implementations to generate a tvm.module. To generate the module library, TVM will first transfer the High level IR into the lower intrinsic IR of the specified target backend, which is CUDA in this example. Then the machine code will be generated as the module library.
opt_level = 3
target = tvm.target.cuda()
with nnvm.compiler.build_config(opt_level=opt_level):
graph, lib, params = nnvm.compiler.build(
net, target, shape={"data": data_shape}, params=params)
Run the generate library¶
Now we can create graph runtime and run the module on Nvidia GPU.
# create random input
ctx = tvm.gpu()
data = np.random.uniform(-1, 1, size=data_shape).astype("float32")
# create module
module = graph_runtime.create(graph, lib, ctx)
# set input and parameters
module.set_input("data", data)
module.set_input(**params)
# run
module.run()
# get output
out = module.get_output(0, tvm.nd.empty(out_shape))
# convert to numpy
out.asnumpy()
# Print first 10 elements of output
print(out.asnumpy().flatten()[0:10])
Save and Load Compiled Module¶
We can also save the graph, lib and parameters into files and load them back in deploy environment.
# save the graph, lib and params into separate files
from tvm.contrib import util
temp = util.tempdir()
path_lib = temp.relpath("deploy_lib.tar")
lib.export_library(path_lib)
with open(temp.relpath("deploy_graph.json"), "w") as fo:
fo.write(graph.json())
with open(temp.relpath("deploy_param.params"), "wb") as fo:
fo.write(nnvm.compiler.save_param_dict(params))
print(temp.listdir())
# load the module back.
loaded_json = open(temp.relpath("deploy_graph.json")).read()
loaded_lib = tvm.module.load(path_lib)
loaded_params = bytearray(open(temp.relpath("deploy_param.params"), "rb").read())
input_data = tvm.nd.array(np.random.uniform(size=data_shape).astype("float32"))
module = graph_runtime.create(loaded_json, loaded_lib, tvm.gpu(0))
module.load_params(loaded_params)
module.run(data=input_data)
out = module.get_output(0).asnumpy()
Total running time of the script: ( 0 minutes 0.000 seconds)


 Reminds me of the "good" old days when she was 6 months old.  Reminds me of the "good" old days when she was 6 months old. Now where's my coffee?
7/13/2009, 8:57:42 AM
Favs: 0
Retweets: 0
link


Chapter 2 Analyzing Texts
Learning Objectives
- perform different frequency counts and generate plots
- use the
widyr
package to calculate co-ocurrance- use
igraph
andggraph
to plot a co-ocurrance graph- import and export a Document-Term Matrix into
tidytext
- use the
sentiments
dataset fromtidytext
to perform a sentiment analysis
Now that we’ve read in our text and metadata, tokenized and cleaned it a little, let’s move on to some analysis.
First, we’ll make sure we have loaded the libraries we’ll need.
library(tidyverse)
library(tidytext)
Let’s remind ourselves of what our data looks like.
tidy_sotu_words
#> # A tibble: 778,161 x 7
#> president year years_active party sotu_type doc_id word
#> <chr> <int> <chr> <chr> <chr> <chr> <chr>
#> 1 Abraham Lin… 1861 1861-1865 Republ… written abraham-linc… fellow
#> 2 Abraham Lin… 1861 1861-1865 Republ… written abraham-linc… citizens
#> 3 Abraham Lin… 1861 1861-1865 Republ… written abraham-linc… senate
#> 4 Abraham Lin… 1861 1861-1865 Republ… written abraham-linc… house
#> 5 Abraham Lin… 1861 1861-1865 Republ… written abraham-linc… represe…
#> 6 Abraham Lin… 1861 1861-1865 Republ… written abraham-linc… midst
#> 7 Abraham Lin… 1861 1861-1865 Republ… written abraham-linc… unprece…
#> 8 Abraham Lin… 1861 1861-1865 Republ… written abraham-linc… politic…
#> 9 Abraham Lin… 1861 1861-1865 Republ… written abraham-linc… troubles
#> 10 Abraham Lin… 1861 1861-1865 Republ… written abraham-linc… gratitu…
#> # … with 778,151 more rows
2.1 Frequencies
Since our unit of analysis at this point is a word, let’s do some straightforward counting to figure out which words occur most frequently in the corpus as a whole.
tidy_sotu_words %>%
count(word, sort = TRUE)
#> # A tibble: 29,558 x 2
#> word n
#> <chr> <int>
#> 1 government 7573
#> 2 congress 5759
#> 3 united 5102
#> 4 people 4219
#> 5 country 3564
#> 6 public 3413
#> 7 time 3138
#> 8 war 2961
#> 9 american 2853
#> 10 world 2581
#> # … with 29,548 more rows
We can pipe this into ggplot
to make a graph of the words that occur more that 2000 times. We count the words and use geom_col
to represent the n values.
tidy_sotu_words %>%
count(word) %>%
filter(n > 2000) %>%
mutate(word = reorder(word, n)) %>% # reorder values by frequency
ggplot(aes(word, n)) +
geom_col(fill = "gray") +
coord_flip() # flip x and y coordinates so we can read the words better
What if we want to check the most common words per speech for a single president and see which of the top words apppear in which speech?
tidy_sotu_words %>%
filter(president == "Barack Obama") %>%
count(doc_id, word) %>%
filter(n > 20) %>%
mutate(word = reorder(word, n)) %>%
ggplot(aes(word, n, fill=doc_id)) +
geom_col() +
coord_flip()
CHALLENGE: In any given year, how often is the word ‘peace’ used and how often is the word ‘war’ used? Make a bar chart that shows for each year the proportion of each of these two words out of the total of how often both words are used. Bonus: use decades instead of years.
As another example let us calculate the average number of words per speech for each president. How long was the average speech of each president and who are the top ‘wordiest’ presidents?
First we summarize the words per president per speech
tidy_sotu_words %>%
count(president, doc_id)
#> # A tibble: 236 x 3
#> president doc_id n
#> <chr> <chr> <int>
#> 1 Abraham Lincoln abraham-lincoln-1861.txt 2578
#> 2 Abraham Lincoln abraham-lincoln-1862.txt 3088
#> 3 Abraham Lincoln abraham-lincoln-1863.txt 2398
#> 4 Abraham Lincoln abraham-lincoln-1864.txt 2398
#> 5 Andrew Jackson andrew-jackson-1829.txt 3849
#> 6 Andrew Jackson andrew-jackson-1830.txt 5428
#> 7 Andrew Jackson andrew-jackson-1831.txt 2612
#> 8 Andrew Jackson andrew-jackson-1832.txt 2881
#> 9 Andrew Jackson andrew-jackson-1833.txt 2869
#> 10 Andrew Jackson andrew-jackson-1834.txt 4952
#> # … with 226 more rows
Then we use the output table and group it by president. That allows us to calculate the average number of words per speech.
tidy_sotu_words %>%
count(president, doc_id) %>%
group_by(president) %>%
summarize(avg_words = mean(n)) %>%
arrange(desc(avg_words))
#> # A tibble: 41 x 2
#> president avg_words
#> <chr> <dbl>
#> 1 William Howard Taft 9126.
#> 2 William McKinley 7797
#> 3 Jimmy Carter 7673.
#> 4 Theodore Roosevelt 7356
#> 5 James K. Polk 6920.
#> 6 Grover Cleveland 5736.
#> 7 James Buchanan 5409
#> 8 Benjamin Harrison 5308.
#> 9 Rutherford B. Hayes 4411
#> 10 Martin Van Buren 4286.
#> # … with 31 more rows
2.2 Term frequency
Often a raw count of a word is less important than understanding how often that word appears relative to the total number of words in a text. This ratio would be the term frequency. We can use dplyr
to calculate it like this:
tidy_sotu_words %>%
count(doc_id, word, sort = T) %>% # count n for each word
group_by(doc_id) %>%
mutate(n_tot = sum(n), # count total number of words per doc
term_freq = n/n_tot)
#> # A tibble: 352,846 x 5
#> # Groups: doc_id [236]
#> doc_id word n n_tot term_freq
#> <chr> <chr> <int> <int> <dbl>
#> 1 harry-s-truman-1946.txt dollars 207 12614 0.0164
#> 2 jimmy-carter-1980b.txt congress 204 16128 0.0126
#> 3 harry-s-truman-1946.txt war 201 12614 0.0159
#> 4 william-howard-taft-1910.txt government 164 11178 0.0147
#> 5 james-k-polk-1846.txt mexico 158 7023 0.0225
#> 6 richard-m-nixon-1974b.txt federal 141 9996 0.0141
#> 7 harry-s-truman-1946.txt million 138 12614 0.0109
#> 8 harry-s-truman-1946.txt fiscal 129 12614 0.0102
#> 9 jimmy-carter-1981.txt administration 129 16595 0.00777
#> 10 william-howard-taft-1912.txt government 129 10215 0.0126
#> # … with 352,836 more rows
Let’s graph the term frequency for one of these speeches so we can understand the frequency distribution of words over a text.
tidy_sotu_words %>%
filter(doc_id == "harry-s-truman-1946.txt") %>%
count(doc_id, word) %>% # count n for each word
group_by(doc_id) %>%
mutate(n_tot = sum(n), # count total number of words per doc
term_freq = n/n_tot) %>%
ggplot(aes(term_freq)) +
geom_histogram()
This distribution makes sense. Most words are used relatively rarely in a text. Only a few have a high term frequency.
We could keep filtering this data to see which terms have high frequency, thus maybe increased significance, for different presidents and different particular speeches.
CHALLENGE: Pick one president. For each of his speeches, which is the term with highest term frequency? Create a table as output. (Hint:
top_n
might be useful)
2.3 Tf-idf
So far we’ve been looking at term frequency per document. What if we want to know about words that seem more important based on the contents of the entire corpus?
For this, we can use term-frequency according to inverse document frequency, also callled tf-idf. Tf-idf measures how important a word is within a corpus by scaling term frequency per document according to the inverse of the term’s document frequency (number of documents within the corpus in which the term appears divided by the number of documents).
The tf-idf value will be:
- lower for words that appear in many documents in the corpus, and lowest when the word occurs in virtually all documents.
- high for words that appear many times in few documents in the corpus, this lending high discriminatory power to those documents.
The tidytext
package includes a function bind_tf_idf
. It takes a table that contains one-row-per-term-per-document, the name of the column that contains the words (terms), the name of the column which contains the doc-id, and the name of the column that contains the document-term counts.
So below we aggregate our tibble with the word tokens to create the one-row-per-term-per-document table and then pipe it into the bind_tf_idf
function.
tidy_sotu_words %>%
count(doc_id, word, sort = TRUE) %>% # aggregate to count n for each word
bind_tf_idf(word, doc_id, n)
#> # A tibble: 352,846 x 6
#> doc_id word n tf idf tf_idf
#> <chr> <chr> <int> <dbl> <dbl> <dbl>
#> 1 harry-s-truman-1946.txt dollars 207 0.0164 0.612 1.00e-2
#> 2 jimmy-carter-1980b.txt congress 204 0.0126 0.00425 5.37e-5
#> 3 harry-s-truman-1946.txt war 201 0.0159 0.0345 5.50e-4
#> 4 william-howard-taft-1910.… government 164 0.0147 0.00425 6.23e-5
#> 5 james-k-polk-1846.txt mexico 158 0.0225 0.810 1.82e-2
#> 6 richard-m-nixon-1974b.txt federal 141 0.0141 0.293 4.14e-3
#> 7 harry-s-truman-1946.txt million 138 0.0109 0.728 7.96e-3
#> 8 harry-s-truman-1946.txt fiscal 129 0.0102 0.494 5.05e-3
#> 9 jimmy-carter-1981.txt administration 129 0.00777 0.282 2.19e-3
#> 10 william-howard-taft-1912.… government 129 0.0126 0.00425 5.36e-5
#> # … with 352,836 more rows
We can see in the output that our function added three columns to our aggregated table: which contain term frequency, inverse document frequency and Tf-idf.
Let’s look at some of the words in the corpus that have the highest tf-idf scores, which means words that are particularly distinctive for their documents.
tidy_sotu_words %>%
count(doc_id, word, sort = TRUE) %>%
bind_tf_idf(word, doc_id, n) %>%
arrange(desc(tf_idf))
#> # A tibble: 352,846 x 6
#> doc_id word n tf idf tf_idf
#> <chr> <chr> <int> <dbl> <dbl> <dbl>
#> 1 lyndon-b-johnson-1966.txt vietnam 32 0.0152 2.42 0.0367
#> 2 jimmy-carter-1980a.txt soviet 31 0.0218 1.47 0.0321
#> 3 george-w-bush-2003.txt hussein 19 0.00811 3.85 0.0313
#> 4 george-w-bush-2003.txt saddam 19 0.00811 3.67 0.0298
#> 5 franklin-d-roosevelt-1943.txt 1942 13 0.00758 3.85 0.0292
#> 6 dwight-d-eisenhower-1961.txt 1953 23 0.00747 3.85 0.0288
#> 7 john-adams-1800.txt gentlemen 8 0.0153 1.80 0.0275
#> 8 benjamin-harrison-1892.txt 1892 40 0.00741 3.52 0.0261
#> 9 franklin-d-roosevelt-1942.txt hitler 7 0.00527 4.77 0.0251
#> 10 herbert-hoover-1930.txt 1928 14 0.00711 3.52 0.0250
#> # … with 352,836 more rows
To understand the occurrence of the years as being particularly distinctive we might need to look more closely at the speeches themselves, and determine whether the years are significant or whether they need to be removed from the text either permanently in the clean up or temporarily using filter
.
CHALLENGE: Pick the same president you chose above. For each of his speeches, which is the term with highest tf-idf? Create a table as output. (Hint: Remember to group by doc_id before you use top_n)
2.4 N-Grams
We mentioned n-grams in the intro, but let’s revisit them here and take a look at the most common bigrams in the speeches. Remember we use the unnest_token
function on our texts and explicitly tell it to generate bigrams:
sotu_whole %>%
unnest_tokens(bigram, text, token = "ngrams", n = 2) # create bigram
#> # A tibble: 1,964,976 x 7
#> president year years_active party sotu_type doc_id bigram
#> <chr> <int> <chr> <chr> <chr> <chr> <chr>
#> 1 Abraham Lin… 1861 1861-1865 Repub… written abraham-lin… fellow ci…
#> 2 Abraham Lin… 1861 1861-1865 Repub… written abraham-lin… citizens …
#> 3 Abraham Lin… 1861 1861-1865 Repub… written abraham-lin… of the
#> 4 Abraham Lin… 1861 1861-1865 Repub… written abraham-lin… the senate
#> 5 Abraham Lin… 1861 1861-1865 Repub… written abraham-lin… senate and
#> 6 Abraham Lin… 1861 1861-1865 Repub… written abraham-lin… and house
#> 7 Abraham Lin… 1861 1861-1865 Repub… written abraham-lin… house of
#> 8 Abraham Lin… 1861 1861-1865 Repub… written abraham-lin… of repres…
#> 9 Abraham Lin… 1861 1861-1865 Repub… written abraham-lin… represent…
#> 10 Abraham Lin… 1861 1861-1865 Repub… written abraham-lin… in the
#> # … with 1,964,966 more rows
Let’s see the most common bigrams:
sotu_whole %>%
unnest_tokens(bigram, text, token = "ngrams", n = 2) %>%
count(bigram, sort = TRUE) # count ocurrences and sort descending
#> # A tibble: 469,092 x 2
#> bigram n
#> <chr> <int>
#> 1 of the 33610
#> 2 in the 12499
#> 3 to the 11643
#> 4 for the 6892
#> 5 and the 6224
#> 6 by the 5606
#> 7 of our 5172
#> 8 the united 4767
#> 9 united states 4760
#> 10 it is 4756
#> # … with 469,082 more rows
Ok, so we again need to remove the stopwords. First let us separate the two words into two columns “word1” and “word2” with separate
from the tidyr
package:
sotu_whole %>%
unnest_tokens(bigram, text, token = "ngrams", n = 2) %>%
separate(bigram, c("word1", "word2"), sep = " ")
#> # A tibble: 1,964,976 x 8
#> president year years_active party sotu_type doc_id word1 word2
#> <chr> <int> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 Abraham Li… 1861 1861-1865 Repub… written abraham-l… fellow citiz…
#> 2 Abraham Li… 1861 1861-1865 Repub… written abraham-l… citiz… of
#> 3 Abraham Li… 1861 1861-1865 Repub… written abraham-l… of the
#> 4 Abraham Li… 1861 1861-1865 Repub… written abraham-l… the senate
#> 5 Abraham Li… 1861 1861-1865 Repub… written abraham-l… senate and
#> 6 Abraham Li… 1861 1861-1865 Repub… written abraham-l… and house
#> 7 Abraham Li… 1861 1861-1865 Repub… written abraham-l… house of
#> 8 Abraham Li… 1861 1861-1865 Repub… written abraham-l… of repre…
#> 9 Abraham Li… 1861 1861-1865 Repub… written abraham-l… repre… in
#> 10 Abraham Li… 1861 1861-1865 Repub… written abraham-l… in the
#> # … with 1,964,966 more rows
Now we use dplyr’s filter
function to select only the words in each column that are not in the stopwords.
sotu_whole %>%
unnest_tokens(bigram, text, token = "ngrams", n = 2) %>%
separate(bigram, c("word1", "word2"), sep = " ") %>% # separate into cols
filter(!word1 %in% stop_words$word) %>% # remove stopwords
filter(!word2 %in% stop_words$word)
#> # A tibble: 215,992 x 8
#> president year years_active party sotu_type doc_id word1 word2
#> <chr> <int> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 Abraham Li… 1861 1861-1865 Republ… written abraham-… fellow citiz…
#> 2 Abraham Li… 1861 1861-1865 Republ… written abraham-… unpre… polit…
#> 3 Abraham Li… 1861 1861-1865 Republ… written abraham-… polit… troub…
#> 4 Abraham Li… 1861 1861-1865 Republ… written abraham-… abund… harve…
#> 5 Abraham Li… 1861 1861-1865 Republ… written abraham-… pecul… exige…
#> 6 Abraham Li… 1861 1861-1865 Republ… written abraham-… forei… natio…
#> 7 Abraham Li… 1861 1861-1865 Republ… written abraham-… profo… solic…
#> 8 Abraham Li… 1861 1861-1865 Republ… written abraham-… solic… chief…
#> 9 Abraham Li… 1861 1861-1865 Republ… written abraham-… domes… affai…
#> 10 Abraham Li… 1861 1861-1865 Republ… written abraham-… dislo… porti…
#> # … with 215,982 more rows
Lastly, we re-unite the two word columns into back into our bigrams and save it into a new table sotu_bigrams
.
sotu_bigrams <- sotu_whole %>%
unnest_tokens(bigram, text, token = "ngrams", n = 2) %>%
separate(bigram, c("word1", "word2"), sep = " ") %>% # separate into cols
filter(!word1 %in% stop_words$word) %>% # remove stopwords
filter(!word2 %in% stop_words$word) %>%
unite(bigram, word1, word2, sep = " ") # combine columns
sotu_bigrams %>%
count(bigram, sort = TRUE)
#> # A tibble: 129,622 x 2
#> bigram n
#> <chr> <int>
#> 1 federal government 479
#> 2 american people 428
#> 3 june 30 325
#> 4 fellow citizens 296
#> 5 public debt 283
#> 6 public lands 256
#> 7 health care 240
#> 8 social security 232
#> 9 post office 202
#> 10 annual message 200
#> # … with 129,612 more rows
(Bonus question: What happened on that June 30th?)
A bigram can also be treated as a term in a document in the same way that we treated individual words. That means we can look at tf-idf values in the same way. For example, we can find out the most distinct bigrams that the presidents uttered in all their respective speeches taken together.
We group by president and bigram and then bind the tf-idf value with the bind_tf_idf
function.
sotu_bigrams %>%
count(president, bigram) %>%
bind_tf_idf(bigram, president, n) %>%
arrange(desc(tf_idf))
#> # A tibble: 169,227 x 6
#> president bigram n tf idf tf_idf
#> <chr> <chr> <int> <dbl> <dbl> <dbl>
#> 1 George W. Bush al qaida 35 0.00628 3.02 0.0190
#> 2 John Adams john adams 3 0.00510 3.71 0.0189
#> 3 John Adams amity commerce 5 0.00850 2.10 0.0179
#> 4 John Adams st croix 5 0.00850 2.10 0.0179
#> 5 John Adams 6th article 4 0.00680 2.33 0.0158
#> 6 John Adams 7th article 4 0.00680 2.33 0.0158
#> 7 John Adams commissioners acting 3 0.00510 3.02 0.0154
#> 8 John Adams damages sustained 3 0.00510 3.02 0.0154
#> 9 William J. Clinton 21st century 59 0.00830 1.77 0.0147
#> 10 John Adams commissioners appointed 7 0.0119 1.23 0.0146
#> # … with 169,217 more rows
CHALLENGE: Again, pick the same president you chose above. For each of his speeches, which is the bigram with highest tf-idf? Create a table as output.
2.5 Co-occurrence
Co-occurrences give us a sense of words that appear in the same text, but not necessarily next to each other.
For this section we will make use of the widyr
package. The function which helps us do this is the pairwise_count()
function. It lets us count common pairs of words co-appearing within the same speech.
Behind the scenes, this function first turns our table into a wide matrix. In our case that matrix will be made up of the individual words and the cell values will be the counts of in how many speeches they co-occur, like this:
#> we thus have
#> we NA 4 5
#> thus 4 NA 2
#> have 5 2 NA
It then will turn the matrix back into a tidy form, where each row contains the word pairs and the count of their co-occurrence. Since we don’t care about the order of the words, we will not count the upper triangle of the wide matrix, which leaves us with:
#>
#> we thus 4
#> we have 5
#> thus have 2
we | thus | 4 we | have | 5 thus | have | 2
Since processing the entire corpus would take too long here, we will only look at the last 100 words of each speech: which words occurr most commonly together at the end of the speeches?
library(widyr)
sotu_word_pairs <- sotu_whole %>%
mutate(speech_end = word(text, -100, end = -1)) %>% # extract last 100 words
unnest_tokens(word, speech_end) %>% # tokenize
filter(!word %in% stop_words$word) %>% # remove stopwords
pairwise_count(word, doc_id, sort = TRUE, upper = FALSE) # don't include upper triangle of matrix
sotu_word_pairs
#> # A tibble: 125,576 x 3
#> item1 item2 n
#> <chr> <chr> <dbl>
#> 1 god bless 37
#> 2 god america 35
#> 3 bless america 30
#> 4 people country 26
#> 5 world god 22
#> 6 god people 22
#> 7 government people 21
#> 8 congress people 21
#> 9 public country 21
#> 10 god nation 21
#> # … with 125,566 more rows
To visualize the co-occurrence network of words that occur together at the end of 10 or more speeches, we use the igraph
package to convert our table into a network graph and the ggraph
package which adds functionality to ggplot to make it easier to plot a network.
library(igraph)
library(ggraph)
sotu_word_pairs %>%
filter(n >= 10) %>% # only word pairs that occur 10 or more times
graph_from_data_frame() %>% #convert to graph
ggraph(layout = "fr") + # place nodes according to the force-directed algorithm of Fruchterman and Reingold
geom_edge_link(aes(edge_alpha = n, edge_width = n), edge_colour = "tomato") +
geom_node_point(size = 5) +
geom_node_text(aes(label = name), repel = TRUE,
point.padding = unit(0.2, "lines")) +
theme_void()
There are alternative approaches for this as well. See for example the findAssocs
function in the tm
package.
2.6 Document-Term Matrix
A document-term matrix (DTM) is a format which is frequently used in text analysis. It is a matrix where we can see the counts of each term per document. In a DTM each row represents a document, each column represents a term, and the cell values are the counts of the occurrences of the term for the particular document.
tidytext
provides functionality to convert to and from DTMs, if for example, your analyis requires specific functions that require you to use a different R package which only works with DTM objects.
The cast_dtm
function can be used to create a DTM object from a tidy table.
Let’s assume that for some reason we want to use the findAssoc
function from the tm
package.
First we use dplyr to create a table with the document name, the term, and the count.
# make a table with document, term, count
tidy_sotu_words %>%
count(doc_id, word)
#> # A tibble: 352,846 x 3
#> doc_id word n
#> <chr> <chr> <int>
#> 1 abraham-lincoln-1861.txt 1,470,018 1
#> 2 abraham-lincoln-1861.txt 1,500 1
#> 3 abraham-lincoln-1861.txt 100,000 1
#> 4 abraham-lincoln-1861.txt 102,532,509.27 1
#> 5 abraham-lincoln-1861.txt 12,528,000 1
#> 6 abraham-lincoln-1861.txt 13,606,759.11 1
#> 7 abraham-lincoln-1861.txt 1830 1
#> 8 abraham-lincoln-1861.txt 1859 1
#> 9 abraham-lincoln-1861.txt 1860 2
#> 10 abraham-lincoln-1861.txt 1861 6
#> # … with 352,836 more rows
Now we cast it as a DTM.
sotu_dtm <- tidy_sotu_words %>%
count(doc_id, word) %>%
cast_dtm(doc_id, word, n)
class(sotu_dtm)
#> [1] "DocumentTermMatrix" "simple_triplet_matrix"
Finally, let’s use it in the tm
package.
library(tm)
# look at the terms with tm function
Terms(sotu_dtm) %>% tail()
#> [1] "queretaro" "refreshments" "schleswig" "sedulous"
#> [5] "subagents" "transcript"
# most frequent terms
findFreqTerms(sotu_dtm, lowfreq = 5000)
#> [1] "congress" "government" "united"
# find terms associated with ...
findAssocs(sotu_dtm, "citizen", corlimit = 0.5)
#> $citizen
#> laws citizenship protection contained entitled government
#> 0.62 0.59 0.56 0.55 0.53 0.53
#> citizens postmaster careful question report suits
#> 0.52 0.52 0.51 0.51 0.51 0.51
Conversely, tidytext
implements the tidy
function (originally from the broom
package) to import DocumentTermMatrix objects. Note that it only takes the cells from the DTM that are not 0, so there will be no rows with 0 counts.
2.7 Sentiment analysis
tidytext
comes with a dataset sentiments
which contains several sentiment lexicons, where each word is attributed a certain sentiment, like this:
sentiments
#> # A tibble: 6,786 x 2
#> word sentiment
#> <chr> <chr>
#> 1 2-faces negative
#> 2 abnormal negative
#> 3 abolish negative
#> 4 abominable negative
#> 5 abominably negative
#> 6 abominate negative
#> 7 abomination negative
#> 8 abort negative
#> 9 aborted negative
#> 10 aborts negative
#> # … with 6,776 more rows
Here we will take a look at how the sentiment of the speeches change over time. We will use the lexicon from Bing Liu and collaborators, which assigns positive/negative labels for each word:
bing_lex <- get_sentiments("bing")
bing_lex
#> # A tibble: 6,786 x 2
#> word sentiment
#> <chr> <chr>
#> 1 2-faces negative
#> 2 abnormal negative
#> 3 abolish negative
#> 4 abominable negative
#> 5 abominably negative
#> 6 abominate negative
#> 7 abomination negative
#> 8 abort negative
#> 9 aborted negative
#> 10 aborts negative
#> # … with 6,776 more rows
We can use these sentiments attached to each word and join them to the words of our speeches. We will use inner_join
from dplyr
. It will take all rows with words from tidy_sotu_words
that match words in bing_lex
, eliminating rows where the word cannot be found in the lexicon. Since our columns to join on have the same name (word
) we don’t need to explicitly name it.
sotu_sentiments <- tidy_sotu_words %>%
inner_join(bing_lex) # join to add semtinemt column
sotu_sentiments
#> # A tibble: 105,206 x 8
#> president year years_active party sotu_type doc_id word sentiment
#> <chr> <int> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 Abraham L… 1861 1861-1865 Repub… written abraham-… trou… negative
#> 2 Abraham L… 1861 1861-1865 Repub… written abraham-… grat… positive
#> 3 Abraham L… 1861 1861-1865 Repub… written abraham-… unus… negative
#> 4 Abraham L… 1861 1861-1865 Repub… written abraham-… abun… positive
#> 5 Abraham L… 1861 1861-1865 Repub… written abraham-… pecu… negative
#> 6 Abraham L… 1861 1861-1865 Repub… written abraham-… prof… positive
#> 7 Abraham L… 1861 1861-1865 Repub… written abraham-… soli… negative
#> 8 Abraham L… 1861 1861-1865 Repub… written abraham-… disl… negative
#> 9 Abraham L… 1861 1861-1865 Repub… written abraham-… dest… negative
#> 10 Abraham L… 1861 1861-1865 Repub… written abraham-… disr… negative
#> # … with 105,196 more rows
Finally we can visualize the proportion of positive sentiment (out of the total of positive and negative) in US State of the Union Addresses over time like this:
sotu_sentiments %>%
count(year, sentiment) %>% # group by year and sentiment
spread(sentiment, n) %>% # spread by seniment
mutate(pct_positive_sentiment = positive/(negative + positive) * 100) %>% # calculate %
ggplot(aes(year, pct_positive_sentiment)) +
geom_line(color="gray") +
geom_smooth(span = 0.3, se = FALSE) + # for easier viewing
geom_hline(yintercept = 50, linetype="dotted") + # 50% as reference
scale_x_continuous(breaks = seq(1790, 2016, by = 10)) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))


Domain:
Master password:
Generated password:
Hash algorithm:
Generated passwords length:
Character set: Alphas Alphanumerics Alphanumerics & symbols Hexadecimal Numbers Symbols Custom...
Custom character set:
Use l33t: Never Before hashing After hashing Before and after hashing
L33t level:
Modifier:
Password prefix:
Password suffix:


The Journey to the Center of the Earth
by Jules Verne
Chapter XXXV. AN ELECTRIC STORM
Additional Information
- Year Published: 1871
- Language: English
- Country of Origin: France
- Source: Verne, J. (1871) A Journey to the Center of the Earth (Frederick Amadeus Malleson, Trans.) London: Ward, Lock, &Co., Ltd. (Original work published 1864)
-
Readability:
- Flesch–Kincaid Level: 6.0
- Word Count: 2,024
- Genre: Adventure
- ✎ Cite This
Downloads
Friday, August 21. – On the morrow the magnificent geyser has disappeared. The wind has risen, and has rapidly carried us away from Axel Island. The roarings become lost in the distance.
The weather – if we may use that term – will change before long. The atmosphere is charged with vapours, pervaded with the electricity generated by the evaporation of saline waters. The clouds are sinking lower, and assume an olive hue. The electric light can scarcely penetrate through the dense curtain which has dropped over the theatre on which the battle of the elements is about to be waged.
I feel peculiar sensations, like many creatures on earth at the approach of violent atmospheric changes. The heavily voluted cumulus clouds lower gloomily and threateningly; they wear that implacable look which I have sometimes noticed at the outbreak of a great storm. The air is heavy; the sea is calm.
In the distance the clouds resemble great bales of cotton, piled up in picturesque disorder. By degrees they dilate, and gain in huge size what they lose in number. Such is their ponderous weight that they cannot rise from the horizon; but, obeying an impulse from higher currents, their dense consistency slowly yields. The gloom upon them deepens; and they soon present to our view a ponderous mass of almost level surface. From time to time a fleecy tuft of mist, with yet some gleaming light left upon it, drops down upon the dense floor of grey, and loses itself in the opaque and impenetrable mass.
The atmosphere is evidently charged and surcharged with electricity. My whole body is saturated; my hair bristles just as when you stand upon an insulated stool under the action of an electrical machine. It seems to me as if my companions, the moment they touched me, would receive a severe shock like that from an electric eel.
At ten in the morning the symptoms of storm become aggravated. The wind never lulls but to acquire increased strength; the vast bank of heavy clouds is a huge reservoir of fearful windy gusts and rushing storms.
I am loth to believe these atmospheric menaces, and yet I cannot help muttering:
"Here's some very bad weather coming on."
The Professor made no answer. His temper is awful, to judge from the working of his features, as he sees this vast length of ocean unrolling before him to an indefinite extent. He can only spare time to shrug his shoulders viciously.
"There's a heavy storm coming on," I cried, pointing towards the horizon. "Those clouds seem as if they were going to crush the sea."
A deep silence falls on all around. The lately roaring winds are hushed into a dead calm; nature seems to breathe no more, and to be sinking into the stillness of death. On the mast already I see the light play of a lambent St. Elmo's fire; the outstretched sail catches not a breath of wind, and hangs like a sheet of lead. The rudder stands motionless in a sluggish, waveless sea. But if we have now ceased to advance why do we yet leave that sail loose, which at the first shock of the tempest may capsize us in a moment?
"Let us reef the sail and cut the mast down!" I cried. "That will be safest."
"No, no! Never!" shouted my impetuous uncle. "Never! Let the wind catch us if it will! What I want is to get the least glimpse of rock or shore, even if our raft should be smashed into shivers!"
The words were hardly out of his mouth when a sudden change took place in the southern sky. The piled–up vapours condense into water; and the air, put into violent action to supply the vacuum left by the condensation of the mists, rouses itself into a whirlwind. It rushes on from the farthest recesses of the vast cavern. The darkness deepens; scarcely can I jot down a few hurried notes. The helm makes a bound. My uncle falls full length; I creep close to him. He has laid a firm hold upon a rope, and appears to watch with grim satisfaction this awful display of elemental strife.
Hans stirs not. His long hair blown by the pelting storm, and laid flat across his immovable countenance, makes him a strange figure; for the end of each lock of loose flowing hair is tipped with little luminous radiations. This frightful mask of electric sparks suggests to me, even in this dizzy excitement, a comparison with preadamite man, the contemporary of the ichthyosaurus and the megatherium. [1]
[1] Rather of the mammoth and the mastodon. (Trans.)
The mast yet holds firm. The sail stretches tight like a bubble ready to burst. The raft flies at a rate that I cannot reckon, but not so fast as the foaming clouds of spray which it dashes from side to side in its headlong speed.
"The sail! the sail!" I cry, motioning to lower it.
"No!" replies my uncle.
"Nej!" repeats Hans, leisurely shaking his head.
But now the rain forms a rushing cataract in front of that horizon toward which we are running with such maddening speed. But before it has reached us the rain cloud parts asunder, the sea boils, and the electric fires are brought into violent action by a mighty chemical power that descends from the higher regions. The most vivid flashes of lightning are mingled with the violent crash of continuous thunder. Ceaseless fiery arrows dart in and out amongst the flying thunder–clouds; the vaporous mass soon glows with incandescent heat; hailstones rattle fiercely down, and as they dash upon our iron tools they too emit gleams and flashes of lurid light. The heaving waves resemble fiery volcanic hills, each belching forth its own interior flames, and every crest is plumed with dancing fire. My eyes fail under the dazzling light, my ears are stunned with the incessant crash of thunder. I must be bound to the mast, which bows like a reed before the mighty strength of the storm.
(Here my notes become vague and indistinct. I have only been able to find a few which I seem to have jotted down almost unconsciously. But their very brevity and their obscurity reveal the intensity of the excitement which dominated me, and describe the actual position even better than my memory could do.)
Sunday, 23. – Where are we? Driven forward with a swiftness that cannot be measured.
The night was fearful; no abatement of the storm. The din and uproar are incessant; our ears are bleeding; to exchange a word is impossible.
The lightning flashes with intense brilliancy, and never seems to cease for a moment. Zigzag streams of bluish white fire dash down upon the sea and rebound, and then take an upward flight till they strike the granite vault that overarches our heads. Suppose that solid roof should crumble down upon our heads! Other flashes with incessant play cross their vivid fires, while others again roll themselves into balls of living fire which explode like bombshells, but the music of which scarcely–adds to the din of the battle strife that almost deprives us of our senses of hearing and sight; the limit of intense loudness has been passed within which the human ear can distinguish one sound from another. If all the powder magazines in the world were to explode at once, we should hear no more than we do now.
From the under surface of the clouds there are continual emissions of lurid light; electric matter is in continual evolution from their component molecules; the gaseous elements of the air need to be slaked with moisture; for innumerable columns of water rush upwards into the air and fall back again in white foam.
Whither are we flying? My uncle lies full length across the raft.
The heat increases. I refer to the thermometer; it indicates . . . (the figure is obliterated).
Monday, August 24. – Will there be an end to it? Is the atmospheric condition, having once reached this density, to become final?
We are prostrated and worn out with fatigue. But Hans is as usual. The raft bears on still to the south–east. We have made two hundred leagues since we left Axel Island.
At noon the violence of the storm redoubles. We are obliged to secure as fast as possible every article that belongs to our cargo. Each of us is lashed to some part of the raft. The waves rise above our heads.
For three days we have never been able to make each other hear a word. Our mouths open, our lips move, but not a word can be heard. We cannot even make ourselves heard by approaching our mouth close to the ear.
My uncle has drawn nearer to me. He has uttered a few words. They seem to be 'We are lost'; but I am not sure.
At last I write down the words: "Let us lower the sail."
He nods his consent.
 Mast and sail flew up in an instant together, and I saw them carried up to prodigious height, resembling in appearance a pterodactyle, one of those strong birds of the infant world.
We lay there, our blood running cold with unspeakable terror. The fireball, half of it white, half azure blue, and the size of a ten–inch shell, moved slowly about the raft, but revolving on its own axis with astonishing velocity, as if whipped round by the force of the whirlwind. Here it comes, there it glides, now it is up the ragged stump of the mast, thence it lightly leaps on the provision bag, descends with a light bound, and just skims the powder magazine. Horrible! we shall be blown up; but no, the dazzling disk of mysterious light nimbly leaps aside; it approaches Hans, who fixes his blue eye upon it steadily; it threatens the head of my uncle, who falls upon his knees with his head down to avoid it. And now my turn comes; pale and trembling under the blinding splendour and the melting heat, it drops at my feet, spinning silently round upon the deck; I try to move my foot away, but cannot.
A suffocating smell of nitrogen fills the air, it enters the throat, it fills the lungs. We suffer stifling pains.
Why am I unable to move my foot? Is it riveted to the planks? Alas! the fall upon our fated raft of this electric globe has magnetised every iron article on board. The instruments, the tools, our guns, are clashing and clanking violently in their collisions with each other; the nails of my boots cling tenaciously to a plate of iron let into the timbers, and I cannot draw my foot away from the spot.
Ah! what a flood of intense and dazzling light! the globe has burst, and we are deluged with tongues of fire!
Then all the light disappears. I could just see my uncle at full length on the raft, and Hans still at his helm and spitting fire under the action of the electricity which has saturated him.
But where are we going to? Where?
* * * *
Tuesday, August 25. – I recover from a long swoon. The storm continues to roar and rage; the lightnings dash hither and thither, like broods of fiery serpents filling all the air. Are we still under the sea? Yes, we are borne at incalculable speed. We have been carried under England, under the channel, under France, perhaps under the whole of Europe.
* * * *
A fresh noise is heard! Surely it is the sea breaking upon the rocks! But then . . . .


{% extends 'commercialoperator/emails/base_email.html' %} {% block content %}
Your licence to conduct commercial operations on Conservation and Land Management Act 1984 lands and waters has been approved and expires on {{proposal.approval.expiry_date}}. A copy of your licence is attached to this email and can be accessed in the Commercial Operator Licensing System (COLS) under your account, click here to login.
Parks and Activities
The activities and parks you are permitted to operate within are listed in Schedule 1 of your licence.
Commercial Operator Handbook - Licence Conditions
All operators are required to adhere to the licence conditions listed in the Commercial Operator Handbook. A copy of the handbook can be downloaded from the {{settings.DEP_NAME}} website:
{{settings.COLS_HANDBOOK_URL}}Please ensure you have a copy of this handbook on hand either in hardcopy or an electronic version.
Additional Licence Conditions Additional licence conditions are listed in schedule 2 of the licence. If you are required to submit additional documentation based on these conditions, it will show under the “Compliance with requirements” section on your COLS homepage. You will need to login to your account and ensure you have completed any compliance requirements by the due date by uploading the requested documents.
If you have any enquiries or need assistance with the online system please contact a licensing office on {{settings.DEP_PHONE}} or email {{settings.SUPPORT_EMAIL}}
{% endblock %}


The Editor provides basic and rich text editing capabilities across numerous modules including Announcements, FAQ, Events and HTML/HTML Pro. The Basic Text Box associated with the Editor is the same for all DNN versions. The Rich Text Editor (RTE) associated with the Editor provider will depend upon the DNN version and edition of you are using.
This manual details how to use two different providers for the RadEditor control, the DotNetNuke.RadEditorProvider (default) and the TelerikEditorProvider, which are both included with DNN 6+.
Other RTE's which can be integrated with DNN include the FCKEditor and the Free Text Box.
The Default Editor for DNN 6.0+ (DotNetNuke.RadEditorProvider for the RadEditor)
Related Topics:


Play with the available acessories in your face.
 Maximum 300 Kb.
Save Picture
Funny Face - 43º WorldSkills Competition - São Paulo - 2015. All rights reserved.


{% extends 'base.html' %} {% load crispy_forms_tags %} {% block title %}
Edit treatment sheet information | Output this sheet as a PDF
{{ sheet.comment|linebreaks }}


The generated system implementation includes a powerful and
appreciable set of architectural features, capabilities and best-practices.
Client/Server System Architecture
Peer-to-Peer System Architecture
Hub-and-Spoke System Architecture
Multicast System Architecture


Off-Topic
I was blacklisted just because i went down my friends house and used hexus pls unblacklist me
(13)
Hexus got deleted yes no more topics please
(2)
Where do I download the current version of hexus?
(3)
The Discord Servers
(2)
Hexus hub (NEW FEATURE IN THE WORKS)
(16)
Hexus crashing (Windows 10)
(2)
You Guys Got Minecraft?
(2)
Its live
(2)
Buddhism hotline
(1)
May i ask
(3)
Help me please i need some tech geek REPLY ASAP
(3)
Question for Owners
(16)
Sooo, here is my first post, on this site
(4)
Best admin script?

(2)
Randomley Met Custom
(5)
I cant get on discord?
(2)
Synapse or RC7
(
2
)
(31)
Binaries/Hexus.dll error?
(4)
Umm wut dark theme?
(7)
Well?
(1)
Suggestion for Hexus
(2)
← previous page
next page →


Log Problem
Problem ID:
*PHP generated from database*
Employee ID:
*PHP generated from database*
Software:
Photoshop
Chrome
Visual Studio
Git
Operating System:
Windows 10
Windows 8.1
Mac OS X
Ubuntu
Category:
Software
Hardware
Network
Database
Priority:
High
Medium
Low
Problem resolved
Assigned specialist:
Select specialist from Specialist Search
Log Problem


This form is provided by templatemo. Credit goes to shopify for credit card icons. Please enter your payment information below.


Use a Groovy script to define the parameter.
The Script will be evaluated first, and if it fails (e.g. throws an exception), the Fallback Script will be used as a fallback.
This script integrates with Script Security plugin to allow regular Jenkins users to configure this parameter, while limiting what they can do for security.
If this script is used for any of the HTML choice types of an Active Choices Reactive Reference Parameter,
the resulting HTML output will be sanitized to remove everything but basic formatting, like script
tags, unless the script runs outside the sandbox.
This mode requires approval from a Jenkins administrator to prevent cross-site scripting (HTML) and arbitrary code execution (Groovy).


{% extends "base_page.html" %} {% load staticfiles %} {% block "metadesc" %} Search for courses with advanced filters, see your schedule as you build it, and get a link to share with your advisor or on facebook! {% endblock %} {% block "ogdesc" %} Search for courses with advanced filters, see your schedule as you build it, and get a link to share with your advisor or on facebook! {% endblock %} {% block "title" %}Course Planner and Reviews{% endblock %} {% block "page_title" %}Course Planner and Reviews{% endblock %} {% block "css" %} {{ block.super }} {% endblock %} {% block "content_class" %}schedule{% endblock %} {% block "breadcrumbs" %}
{% endblock %}


title = "Home" url = "/" layout = "default" is_hidden = 0 ==
You can monitor each coin rate and generate graphs
You can import transactions via CSV files or via API (currently GDAX, Cryptopia & Binance are supported) or manually add an entry
You can check the market status for all the coin/pair on GDAX, Cryptopia & binance exchange from here


Rugby Construction needed a website that could showcase not only their completed projects, but also their projects on-the-go. The philosophy behind this was that in-progress photos communicated the realities of building a home — something that customers could benefit from. As well, sales people and project managers could use in-progress photos to highlight building techniques that might suit a customer's needs. The cutting-edge building technologies used by the company were brought to attention through the blog, which also featured stories about the building methods used in basement dig-downs and glass solariums — work for which the company was known.
The front page opened with a randomly selected gallery slideshow of completed projects.
While the project gallery sorted by completed and in-progress projects.
The single-project page featured a touch-scrolling image gallery, as well as 'inspiration galleries' which were used by salespeople to direct customer requests.
As the name of the company suggests, the owners and staff were keen players of Rugby.
The company focused on building a community around their business and used much of their promotions budget to fund youth Rugby around the city. Jerseys were designed for a youth rugby team.
Additional designs were made for email and direct mail campaigns, construction hoarding (the display fencing that surrounds building sites), safety signage, sales pitch-books, sub-project identities and letterhead. A tradeshow booth was designed that featured an astroturf rugby pitch and 'locker room' — complete with custom digital displays — which was featured at tradeshows across the Lower Mainland.
The construction business is a competitive one, and effectively communicating a company's strengths helps to differentiate it from the rest of the field. A custom website build in conjunction with supplementary design material clearly articulated the company's philosophy that home building was a process rather than a finished product — and ultimately placed the customer's experience first.


Make your spinner
size
radius
thickness
count
space
fromcolor
tocolor
stopcolor
linewidth
linecolor
refreshRate


Welcome to my GitHub page Senpai ( ͡° ͜ʖ ͡°) (づ｡◕‿‿◕｡)づ Sorry for the inconvenience but we’re performing some maintenance at the moment(─‿‿─). If you need to you can always notice me ( ͡° ͜ʖ ͡°), otherwise we’ll be back online shortly! If you want to notice me you can sign up HERE — FU∆D R∃Z∀


Emma. S
....Learning, Creating, Accepting, Searching....
Values, Stengths and Identity
go to page here..
Technical post for Sprint 1
go to page here..
Learning Reflection
go to page here..
CSS: Inline vs Inline-Block
go to page here..
Technical Blog - Javascript
go to page here..
Emotional Intelligence
go to page here..
Problem Solving
go to page here..
Growth Mindset and Neuroplasticity
go to page here..
Foundations Reflections
go to page here..


This is a paragraph
This is line two. This is continued I'm still writing in line two
This is line three
Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
This is text that is in bold
this is text that is in italics
This is text that is in bold
this is text that is in italics
I want to be together in group One