The cold weather doesn't have to stop your outside training, so wear the correct clothing and enjoy winter.
Cold weather doesn't have to put a freeze on your outdoor exercise program. If you take precautions, you can still work out when the weather turns chilly.
Clothing is critical, says the American College of Sports Medicine (ACSM). Although a double-thick cotton sweatshirt may seem like a good choice, it doesn't insulate nearly as well as synthetic fabrics such as polypropylene.
Don't overdress. You can overheat even in below-freezing temperatures. If you dress too warmly, you'll sweat a lot. Then, when cold winds hit, perspiration will rapidly evaporate, chilling you. You want to limit perspiration and keep it away from both your skin and the outside air.
The ACSM recommends that you wear a synthetic material like polypropylene against your skin. This will allow the sweat to pass through the fabric away from your body. The second layer should be wool or cotton to soak up moisture. The third layer should be chosen for its ability to keep the cold air out — something lightweight and artificial, such as Gore-Tex material. Layering also helps regulate your temperature. If you get too warm, you can strip off a layer.
You can lose a tremendous amount of heat through your uncovered head, so wear a hat, cap, or hood.
Your feet get cold first. Wear appropriate boots or shoes, insulate your feet with warm socks, and keep your feet dry. Water can cause body-heat losses up to 25 times greater than cold air alone. Because of the large surface area to volume, your hands are also more susceptible to cold. Choose mittens over gloves because the fingers can warm each other and the mitten decreases the exposed surface area.
If you can see your breath, you're seeing moisture leave your body. So drink plenty of fluids, particularly if the air is cold and dry.
Cold is a stress on the body, and so is exercise. Together they may be too much for someone not in optimal health. Talk to your doctor before you start a winter exercise program.
People who have diabetes, who take certain medications, or who are older adults are at greater risk for a drop in body temperature in cold weather.
Warming up before exercising is always important but even more so when it gets cold. If the weather is particularly cold, it's probably too cold for you to exercise outdoors. If you do have to go out in extreme cold, cover all exposed skin to prevent frostbite.
Cold air doesn't damage the lungs. Even very cold air is warmed to body temperature by the time it hits the lungs. But for some people with asthma, cold air can trigger an attack.


| HP+ | MP+ | SP+ | LP+ | WT+ |
|---|---|---|---|---|
| Init.+ | Speed+ | Fly+ | Swim+ | CrP |
| Resistances & Bonuses | ||||||||
|---|---|---|---|---|---|---|---|---|
| Disease % | Fire | Frost | Shock | Poison | Magic | Tough | Silver | Sunlight |
| Attribute | Toggle |
|---|---|
| 1/2 Fatigue Penalty | |
| 1/2 Speed | |
| Add 1/2 Speed | |
| 1/2 Wound Penalty | |
| Wounded Penalty to -30 | |
| Define as Mechanical |


My name is Sean McDermott. I grew up in a Central Oregon town know as Bend. Since I can remember, I've had an intense passion for music. I started playing religiously in my 7th grade year and have not been able to even think about quitting since.
It is my desire to learn computer programming and web design so that I have a career in which I am constatnly able to learn new information and be presented with challenges daily. While the task seems daunting now, I'm confident that with hard work and dedication, I will have the tools and skills I need to grow into this new career and life path.


Represents metagenomic samples on the Euclidean space to examine similarity amongst samples by studying clusters in the model. Given the matrix of metagenomic counts for samples, this package (1) quantifies dissimilarity between samples using Kendall's tau-distance, (2) constructs multidimensional models of different dimension, and (3) plots the models for visualization and comparison.
| Version: | 1.1.0 |
| Depends: | R (≥ 3.1.0) |
| Imports: | gWidgets, RGtk2, gWidgetsRGtk2, MASS, cluster, rgl, GUniFrac, ape |
| Suggests: | rglwidget |
| Published: | 2016-02-14 |
| Author: | Deepak Nag Ayyala [aut, cre], Shili Lin [aut] |
| Maintainer: | Deepak Nag Ayyala <deepaknagayyala at gmail.com> |
| License: | LGPL-3 |
| URL: | https://www.r-project.org, http://www.stat.osu.edu/~statgen/SOFTWARE/GrammR/ |
| NeedsCompilation: | yes |
| CRAN checks: | GrammR results |
| Reference manual: | GrammR.pdf |
| Package source: | GrammR_1.1.0.tar.gz |
| Windows binaries: | r-devel: GrammR_1.1.0.zip, r-release: GrammR_1.1.0.zip, r-oldrel: GrammR_1.1.0.zip |
| OS X binaries: | r-release: not available, r-oldrel: not available |
| Old sources: | GrammR archive |
Please use the canonical form https://CRAN.R-project.org/package=GrammR to link to this page.


Before Change
for paragraph in block.paragraphs:
block_words.extend(paragraph.words)
block_symbols = []
for word in block_words:
block_symbols.extend(word.symbols)
block_text = ""
for symbol in block_symbols:
block_text = block_text + symbol.text
print("Block Content: {}".format(block_text))
print("Block Bounds:\n {}".format(block.bounding_box))
// [END migration_document_text_detection]
// [END def_detect_document]
After Change
paragraph.confidence))
for word in paragraph.words:
word_text = "".join([
symbol.text for symbol in word.symbols
])
print("Word text: {} (confidence: {})".format(
word_text, word.confidence))
for symbol in word.symbols:
print("\tSymbol: {} (confidence: {})".format(


| Staking Requirement: {{staking_requirement}} | Last Reward At Height/Index: {{last_reward_at_block}}/{{last_reward_at_block_tx_index}} |
| Total Contributed: {{total_contributed}} | Operator Cut: {{operator_cut}}% |
| Total Reserved: {{total_reserved}} | Register Height: {{register_height}} |
| Last Uptime Proof: {{last_uptime_proof}} |
{{master_node_registered_text}}
| Contributor | Amount | Reserved |
| {{address}} | {{amount}} | {{reserved}} |


How old are I’m A Celebrity star Kate Garraway’s children?
Kate Garraway’s husband Derek Draper and their two children Darcey and William appeared on Good Morning Britain to talk about her time on I’m A Celebrity so far.
Derek, 52, sat next to the young ‘uns that Kate was previously seen getting emotional over in the jungle when she spoke about how she was missing them.
Derek quipped: ‘I never get a mention by the way. “Oh I miss the kids”, what about me?’
With Kate’s time in the jungle having only just started, let’s take a closer look at the family she’s been missing so much.
How old are Kate Garraway’s children?
Kate Garraway daughter’s Darcey is 13 and her son William is 10.
On this show, Kate was seen getting emotional when she spoke about how much she missed her children, saying she could no longer ‘remember what their heads smelled like’.
Advertisement Advertisement
When the family appeared on Good Morning Britain, Derek, a former political lobbyist, gushed about his wife, saying: ‘Take it from someone who knows – she never looks better than first thing in the morning.
‘When we go on romantic breaks I beg her not to put make-up on. She’s naturally beautiful and adorable and awesome, and she’s smashing it in the jungle.’
He added: ‘Darcey, Bill and me are so proud.’
Darcey meanwhile said right off the bat that she was embarrassed when her mum said she feared creepy crawlies were going to crawl up her ‘noo-noo’ during a challenge.
She had some kind words to say too, as she later said: ‘She’s so genuine and she will keep trying and she won’t put herself first, she’s like so selfless she’ll just do everything for everyone else before she thinks of herself.’
Earlier this week, it came out that Kate’s children are the reason why she decided to go on I’m A Celebrity, with Kate telling Hello before she trekked into the jungle: ‘Darcey really got into the last few series.
Advertisement Advertisement
‘[She] kept saying “You should so do this mum.”’

‘It’s been a war of attrition.’
Catch I’m A Celebrity tonight at 9pm on ITV.


[gecode-users] Watching Propagators
Max Ostrowski
ChaosAngel at gmx.de
Mon Jun 20 10:05:12 CEST 2011
Sorry for reposting, i already asked this question 2 years ago, but now i managed to read more of the manual and my questions will be more refined.
For each constraint that i post, i want to find out which variables are propagated by the corresponding propagator.
Also the ordering would be interesting. I need this to build an SMT like System, to generate a "reason" why a certain reified constraint became true/false.
So can i somehow "watch" the propagate function, testing if the subscribed variables are pruned?
I first thought of "copying" the post functions of Gecode and implementing a "wrapper" propagator that wraps around the functions.
But i think this will not do the work, as propagators can be disposed and replaced at runtime, right ?
Do you have any other idea how i can find out which constraint is responsible for pruning the search space?
I want to avoid to modify Gecode, as i want to stay up to date.
This is also the reason why i do not simply want to copy all propagators to implement BookKeeping myself.
Best,
Max
--
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belohnen Sie mit bis zu 50,- Euro! https://freundschaftswerbung.gmx.de
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Download the data
The data behind Justice Divided were obtained from public data sources. They have been cleaned and documented. Now, we're pleased to make them available to you.
Each of the following data files is available in CSV format, which can be opened as plain text or with any spreadsheet editor, such as Google Sheets or Microsoft Excel. The composite dataset that powers the map of per-district disparity is also available as GeoJSON.
Available datasets
Juvenile arrests by demographic (2013 - early 2017)
Source: Chicago Police Department
Obtained by: DataMade
Description:
This dataset contains age, sex, race/ethnicity, statute violated, statute description, and charge type (misdemeanor or felony) for all juvenile arrests made by the Chicago Police Department from Jan. 1, 2013 to March 2017.
Download (CSV)
Juvenile arrrests by Chicago police district (2014)
Source: Chicago Police Department
Obtained by: Mariame Kaba / Project NIA
Description:
Originally published in the third edition of Arresting Justice, a semi-annual analysis of juvenile arrests in Chicago, this dataset contains counts of all juvenile arrests made by the Chicago Police Department in 2014 per police district, by race/ethnicity and age.
Download (CSV)
Chicago police district juvenile population by race (2014)
Source: American Community Survey five-year estimates
Obtained by: DataMade
Description:
This dataset contains the number of boys and girls between the ages of 10 and 17 in each Chicago police district for the following racial/ethnic categories: White, not Hispanic; Black only; and Hispanic.
These counts were compiled using census_area, a Python library for querying Census and American Community Survey data using custom boundaries, and police district boundaries from the City of Chicago data portal.
Download (CSV)
Chicago police district profiles
Source: Chicago Police Department / American Community Survey five-year estimates
Description:
This dataset contains police district name and number, plus count and percent total for youth population and juvenile arrests by the following racial/ethnic categories: White, not Hispanic; Black only; and Hispanic.
It combines information from juvenile arrests by Chicago police district, Chicago police district juvenile population by race, and police district boundaries from the City of Chicago data portal.
Download (CSV) Download (GeoJSON)
Illinois Department of Juvenile Justice exits to Chicago ZIP codes (2009 - 2015)
Source: Illinois Department of Juvenile Justice
Obtained by: Dan Cooper / Adler University
Description:
This dataset contains counts of juvenile exits to Chicago from Illinois Department of Juvenile Justice institutions by ZIP, gender, and race/ethnicity from 2009 to 2015.
The source dataset contains exits to Cook County. It was filtered using ZIP codes from the City of Chicago data portal.
Download (CSV)


The following predefined operations are supplied for MPI_REDUCE and related functions MPI_ALLREDUCE, MPI_REDUCE_SCATTER, MPI_SCAN, and MPI_EXSCAN. These operations are invoked by placing the following in op.
Name Meaning
MPI_MAX maximum
MPI_MIN minimum
MPI_SUM sum
MPI_PROD product
MPI_LAND logical and
MPI_BAND bit-wise and
MPI_LOR logical or
MPI_BOR bit-wise or
MPI_LXOR logical exclusive or (xor)
MPI_BXOR bit-wise exclusive or (xor)
MPI_MAXLOC max value and location
MPI_MINLOC min value and location
The two operations MPI_MINLOC and MPI_MAXLOC are discussed separately in Section MINLOC and MAXLOC . For the other predefined operations, we enumerate below the allowed combinations of op and datatype arguments. First, define groups of MPI basic datatypes in the following way.
C integer: MPI_INT, MPI_LONG, MPI_SHORT,
MPI_UNSIGNED_SHORT, MPI_UNSIGNED,
MPI_UNSIGNED_LONG,
MPI_LONG_LONG_INT,
MPI_LONG_LONG (as synonym),
MPI_UNSIGNED_LONG_LONG,
MPI_SIGNED_CHAR, MPI_UNSIGNED_CHAR
Fortran integer: MPI_INTEGER
Floating point: MPI_FLOAT, MPI_DOUBLE, MPI_REAL,
MPI_DOUBLE_PRECISION
MPI_LONG_DOUBLE
Logical: MPI_LOGICAL
Complex: MPI_COMPLEX
Byte: MPI_BYTE
Now, the valid datatypes for each option is specified below.
Op Allowed Types
MPI_MAX, MPI_MIN C integer, Fortran integer, Floating point
MPI_SUM, MPI_PROD C integer, Fortran integer, Floating point, Complex
MPI_LAND, MPI_LOR, MPI_LXOR C integer, Logical
MPI_BAND, MPI_BOR, MPI_BXOR C integer, Fortran integer, Byte
The following examples use intracommunicators.
Example
A routine that computes the dot product of two vectors that are distributed across a group of processes and returns the answer at node zero.
SUBROUTINE PAR_BLAS1(m, a, b, c, comm) REAL a(m), b(m) ! local slice of array REAL c ! result (at node zero) REAL sum INTEGER m, comm, i, ierr ! local sum sum = 0.0 DO i = 1, m sum = sum + a(i)*b(i) END DO ! global sum CALL MPI_REDUCE(sum, c, 1, MPI_REAL, MPI_SUM, 0, comm, ierr) RETURN
Example
A routine that computes the product of a vector and an array that are distributed across a group of processes and returns the answer at node zero.
SUBROUTINE PAR_BLAS2(m, n, a, b, c, comm) REAL a(m), b(m,n) ! local slice of array REAL c(n) ! result REAL sum(n) INTEGER n, comm, i, j, ierr ! local sum DO j= 1, n sum(j) = 0.0 DO i = 1, m sum(j) = sum(j) + a(i)*b(i,j) END DO END DO ! global sum CALL MPI_REDUCE(sum, c, n, MPI_REAL, MPI_SUM, 0, comm, ierr) ! return result at node zero (and garbage at the other nodes) RETURN


An in-browser scripting environment for geographic data.
This application provides a scripting environment to explore and process geographic data.
Input layers can be added from GeoJSON URL endpoints and JavaScript can be written
to create a new GeoJSON layer visible on the map. Additional information is available in
the project
README.
Click here to load example data that can be
examined and modified. The example data was obtained using the
EONET API,
which is made available by NASA. Original data sources are embedded within
the GeoJSON.


My First Heading
My first paragraph.
This is another paragraph.
Name:
My first paragraph.
My special paragraph.
W3Schools.com
W3Schools.com
Demonstration Box
list item 1
list
item
2


"Navigation bar" Menu widget on left, a.k.a vertical MenuBar
MenuBar on top
page level context menu (right-click anywhere on page)
form widget context menu (right-click on textbox widget)
Example of programatically created menu
Note: while some accelerator (shortcut) keys are displayed in the context menu, they are not actually hooked up to the corresponding actions (if any), they need to be setup explicitly by the user
Click button below to create special menu on this div.


Reports FlexUnit 4 [Test] annotated methods that are located inside a class extending the
abstract
FlexUnit 1 or Flunit class TestCase. Mixing FlexUnit API's like this is confusing and can lead to problems running the tests.


Chicago Theatre
175 N State St Chicago, IL 60601
the chicagotheatre.com<PHONE_NUMBER>
Four Seasons
120 E Delaware PI Chicago,
IL 60611 fourseasons.com<PHONE_NUMBER>
Venue
The conference is held at the Chicago Theatre, a beautiful historical landmark. The conference
Hotel for season, is a short walk away dowm state street.
175 N State St Chicago, IL 60601
the chicagotheatre.com<PHONE_NUMBER>
120 E Delaware PI Chicago,
IL 60611 fourseasons.com<PHONE_NUMBER>


Twitter Intros Topics as a Way to Personalize Interests Based Timelines
by George KamauTwitter really wants people to stick to its platform especially for power users and still be able to attract new users and keep them. The social media giant hopes Twitter Topics will kill these two birds with one stone. Twitter announced it was experimenting with Topics mid-August and has now begun rolling it out.
How Topics will work
Twitter will begin by scanning incoming tweets for interest-based keywords(not images or videos). The company then checks the see if the tweets are from credible people, looks at their engagement by seeing how many people are actually interested in this topic to have interacted with said tweet either by liking, retweeting or replying to it. If the said tweet(s) have more engagement, they make it to Topics.
Interest-based follow model
By following topics, you’ll begin seeing tweets associated with that interest even from people you don’t actually follow. Twitter is now moving to an interest-based follow model.
Topics involved
For now, Twitter will allow users to follow 300+ subjects in their timeline and Topics will range from sports, gaming and entertainment. Politics will not be included – the company recently banned political ads.
Topics will come in handy to new users who are still figuring out who to follow the sign-up process can be intimidating even after Twitter suggests to you who to follow.
Twitter Topics would have been heaven-sent to me when I first joined in Twitter – I now follow almost 5000 tweeps; don’t judge me.
I am excited that even current and older users will use Topics to discover new interests and keep up with them in a more easier way.
The future of topics and upcoming features
Twitter is planning to add more topics in future updates. The company is also testing what it calls “Narrow Casting” where you’ll be able to tweet to followers of a particular subject.
The Twitter team is also working a feature to mute topics.
Another feature is will also be the ability to browse topics on the explore page and to view topics on a dedicated list.
Concerns about Topics
“As with any machine learning problem, we learn the most from putting this in customers’ hands, and seeing what they engage with so we can build better models. Our goal is to get this out as quickly as possible and get people using it so we can improve our algorithms,” Rob Bishop, Product at Twitter said in a Verge interview.
Availability
Twitter Topics will be available worldwide on November 13th.


ng-scaffolding
is an opinionated kickstarter for web development projects.
It's an attempt to create a simple starter for new web sites and apps: just download it and start coding.
The goal is to have everything you need to get started out of the box; of course it has slick styles and icons,
but it also has a best practice directory structure to ensure maximum code reuse.
And it's all tied together with a robust build system chock-full of some time-saving goodness.
Because my team and I make web apps, and last year AngularJS became our client-side framework of choice. We start websites the same way every time: create a directory structure, copy and ever-so-slightly tweak some config files from an older project, and yada yada, etc., and so on and so forth. Why are we repeating ourselves? We wanted a starting point; a set of best practices that we could identify our projects as embodying and a set of time-saving wonderfulness, because time is money.
There are other similar projects out there, but none of them suited our needs.
Some are awesome but were just too much, existing more as reference implementations, when we really just wanted a kickstarter.
Others were just too little, with puny build systems and unscalable architectures. So we designed ng-scaffolding
to be just right.
This is not an example of an AngularJS app. This is a kickstarter.
If you're looking for an example of what a complete, non-trivial AngularJS app that does something real looks like, complete with a REST backend and authentication and authorization,
then take a look at angular-app
, which does just that, and does it well.
This section is just a quick introduction to all the stuff that comes pre-packaged with ng-boilerplate
.
For information on how to use it, see the project page at GitHub.


Group console messages
: Profiler
Profile
Steps to reproduce
Open Firebug
Enable the
Console
and
Script
panels and switch to the
Console
panel
Press the
Profile
button above
Expected result
There should be two profiler logs (results) in the console. They must not be grouped.


Departs every Friday 17:45 at the bus station next to auditorium. (Duck pond 17:40, opposite side of W8 17:42) You can get off at Central City Terminal or Sinchon rotary.
Bus fee is 9000 won. You should send your money to the bank account you are assigned in the reservation system. Note that one person can get only one ticket.
There is no bus driven by KAIST on the way back. I recommend you to ride express bus from Central City to Yuseong express bus terminal and then take bus #104 to return. For additional information to return to KAIST, use Kakao Map or Naver Map.
Reservation can be made from Sunday 17:45 to Friday 13:45. The bus may not be available when the friday is a holiday.


{% block head %}
{% block title %}{% endblock %} - Dashboard
{% endblock %} {% block navigation %}
Navbar
Home
Settings
Videos and Movies
Add Imdb Widget
Search for Movies
Search for Person
Animals
Add Shiba Widget
Generate a Shiba
Add Dog Widget
Generate a Dog
Add Cat Widget
Generate a Cat
Food
Add Food Widget
Generate a random recipe
Search for a recipe by it's name
Search for a recipe by it's ingredients
Search for a recipe by it's category
Search for a recipe by it's area
Logout
{% endblock %}
{% block content %} {% endblock %}


--- layout: post title: Storyboards church date: '2017-10-09T11:30:25+02:00' categories: - Storyboards - Sketches picture_url: https://film-Storyboards.tumblr.com/post/166221041868/Storyboards-church ---


chem package¶
Submodules¶
chem.ions module¶
-
chem.ions.
chloride_ion_ratios
(array, show=True, save=False, fname=None, figsize=(6, 6))[source]¶ Plot chloride vs major ion ratios.
Args:
Parameters: - array – A one or two dimensional array containing in mM/L in the order of ‘Ca’, ‘Mg’, ‘Na’, ‘K’, ‘HCO3’, ‘CO3’, ‘Cl’, ‘SO4’.
- show – If True shows plot else returns plot object.
- save – Save the plot default is False
- fname – Filename default is none
- figsize – Figure size tuple default (8, 3)
Returns: A plot of chloride vs major ion ratios.
-
chem.ions.
chloride_vs_stable_isotopes
(chloride, d18O, d2H, show=True, save=False, fname=None, figsize=(8, 3))[source]¶ Plot stable isotopes vs chloride.
Args:
Parameters: - chloride – A one dimensional array containing chloride in mM/L
- d18O – A one dimensional array containing d18O in VSMOW.
- d2H – A one dimensional array containing d2H in VSMOW.
- show – If True shows plot else returns plot object.
- save – Save the plot default is False
- fname – Filename default is none
- figsize – Figure size tuple default (8, 3)
Returns: A plot of stable isotopes vs chloride.
-
chem.ions.
mM
(array)[source]¶ Convert from milligrams per litre to milli-moles per litre.
Args:
Parameters: array – A one dimensional array or two dimensional pandas dataframe containing in mg/L in the order of ‘Ca’, ‘Mg’, ‘Na’, ‘K’, ‘HCO3’, ‘CO3’, ‘Cl’, ‘SO4’ Returns: The input array in mM/L
-
chem.ions.
meq
(array)[source]¶ Convert from milligrams per litre to milli-equivilents per litre.
Args:
Parameters: array – A one dimensional array or two dimensional pandas dataframe containing in mg/L in the order of ‘Ca’, ‘Mg’, ‘Na’, ‘K’, ‘HCO3’, ‘CO3’, ‘Cl’, ‘SO4’ Returns: The input array in meq/L
-
chem.ions.
schoeller
(array, labels=None, show=True, save=False, fname=None, figsize=(6, 4), cmap='tab20')[source]¶ Create a Schoeller diagram.
Args:
Parameters: - array – A one dimensional array or pandas dataframe containing in mM/L in the order of ‘Ca’, ‘Mg’, ‘Na’, ‘K’, ‘HCO3’, ‘CO3’, ‘Cl’, ‘SO4’
- labels – If a pandas datframe include labels for each row as an array.
- show – If True shows plot else returns plot object.
- save – Save the plot default is False
- fname – Filename default is none
- figsize – Figure size tuple default (6, 4)
- cmap – The colourmap default is “tab 20”
Returns: A schoeller digram of the data
chem.module_io module¶
-
chem.module_io.
output
(figure, show, save, fname)[source]¶ To save, show, or return plot object.
Parameters: - figure – The figure object.
- show – Boolean to show the plot default is True.
- save – Boolean to save the plot default is False.
- fname – File name to save the plot default is None.
Returns: Either ave, show, or return plot object.
chem.piper module¶
-
chem.piper.
piper
(array, alphalevel=1, color=True, show=True, save=False, fname=None, figsize=(4, 4))[source]¶ Create a Piper plot using the code by Peeters, (2014).
Args:
Parameters: - array – A one or two dimensional array containing in mM/L in the order of ‘Ca’, ‘Mg’, ‘Na’, ‘K’, ‘HCO3’, ‘CO3’, ‘Cl’, ‘SO4’.
- alphalevel – transparency level of points. If 1, points are opaque
- color – boolean, use background coloring of Piper plot default True
- show – If True shows plot else returns plot object.
- save – Save the plot default is False
- fname – Filename default is none
- figsize – Figure size tuple default (8, 3)
Returns: Piperplot
Code from https://github.com/inkenbrandt/Peeters_Piper/blob/master/peeter_piper.py:
Citation:
@article {GWAT:GWAT12118, author = {Peeters, Luk}, title = {A Background Color Scheme for Piper Plots to Spatially Visualize Hydrochemical Patterns}, journal = {Groundwater}, volume = {52}, number = {1}, publisher = {Blackwell Publishing Ltd}, issn = {1745-6584}, url = {http://dx.doi.org/10.1111/gwat.12118}, doi = {10.1111/gwat.12118}, pages = {2–6}, year = {2014}, }


// Created by Jason Keene and Eno Compton. package main import "fmt" func main() { fmt.Println("Episode 1") e := LoadEpisode() e.Play() // ☜ }


Challenge a Penalty Charge Notice
Your challenge options
You were entitled to {% if (ticked == 'K') %} dental treatment {% else %} free prescriptions {% endif %}
We will help you sort this out as quickly as possible.
It may be that your personal details on your exemption or benefit don’t match the details at your {% if (ticked == 'K') %} dental practice. {% else %} GP surgery. {% endif %} We will ask a few simple questions so that we can confirm your exemption.
You were not entitled to free {% if (ticked == 'K') %} dental treatment {% else %} prescriptions {% endif %} but have an exceptional reason
If you were not entitled to free {% if (ticked == 'K') %} dental treatment {% else %} prescriptions {% endif %} but have an exceptional reason why you made the claim, you can ask for a review of your Penalty Charge Notice.
Find out if you can ask for a review.


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Multiple pages, Custom Graphics, Informational, Interactive, Video Hosting, Social Media Integration, Manage Your Own Content (Posting), Applications, and More.
Custom Advertising & Marketing
Targeted Online Ad Campaigns, Social Media Marketing, Print Materials including: Postcards, Brochures, Business Cards, Posters, Magnets, Stickers
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{% include 'components/info_card.html' with id='plantType' title='Plant Type' body=instance.plant_type %}
{% include 'components/info_card.html' with title='Status' body=instance.get_status_element|safe %}
{% include 'components/info_card.html' with id='moistureThreshold' title='Moisture Threshold' body=instance.moisture_threshold %}
{% include 'components/info_card.html' with id='wateringDuration' title='Watering Duration' body=instance.watering_duration %}


--- layout: default ---
Demonstrating APIs & Json Web Token Creation/Authentication
This project uses the Rails 5 API setting for the backend and a jekyll site generator to compile various mini sites using JS FrontEnd Frameworks(React) via custom scripts. The frontend repo is currently hosted from a github: railsapijwt-frontend. The purpose of this is to learn about APIs and Javascript Front-End Frameworks.
View the API README for more info.


Equanimity (Latin: æquanimitas, having an even mind; aequus even; animus mind/soul) is a state of psychological stability and composure which is undisturbed by experience of or exposure to emotions, pain, or other phenomena that may cause others to lose the balance of their ...


While common thought is that a great new business idea comes in the form of an epiphany, I beg to differ. For me, there wasn’t one single moment in which I thought “I have to do this, it’s time to do this”, but rather a series of events throughout my life that led me to create Listant.
I contribute the creation of Listant to two main areas: Where I grew up, and who I am.
My value of community is heavily influenced by where I grew up: Iowa. When I say ‘Iowa’, most people will immediately think of corn and flat land; I think of the people and the strong communities they built. I was fortunate to be surrounded by genuine, kind-hearted, and helpful people that shared a great sense of community. People routinely go out of their way to help their neighbors by shoveling driveways after a snowfall, pitching in with yard work, or by coming together to overcome any other issue that our neighborhood faced.
Growing up in this environment of selflessness is what led me to the foundational idea of Listant: Neighbors helping Neighbors. However, the piece of the puzzle that seemed to be missing was the connections. I knew there was an opportunity to accomplish even more...If only everyone knew about and had access to the opportunities to do so.
Sarah’s neighbor, John from two blocks over, would probably be glad to mow her lawn for her, but how would he know that it needs to be done? There needed to be a way for those connections be made; I knew Listant could be the solution.
I have always been the type of person who values simplicity, and I knew that Listant needed to hold simplicity as a virtue. Whether it is studying for a test, shoveling snow, or doing the dishes, I always look for the most efficient way to get things done. The dots began to connect, and from people, community, and simplicity, Listant was born.
Listant enhances and expands neighborly connections to benefit all members of your community. Whether it is connecting with a neighbor to walk your dog so you can study for a big test, or shoveling your neighbor’s sidewalk because they just had surgery, Listant is there for both the one who works and the one who outsources.
Download TODAY in both the Apple & Google Play stores!


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LaraCraft
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Non-GLSL types that are used to define precision-based types. More...
Non-GLSL types that are used to define precision-based types.
The GLSL language allows the user to define the precision of a particular variable. In OpenGL's GLSL, these precision qualifiers have no effect; they are there for compatibility with OpenGL ES's precision qualifiers, where they do have an effect.
C++ has no language equivalent to precision qualifiers. So GLM provides the next-best thing: a number of typedefs of the Template types that use a particular precision.
None of these types make any guarantees about the actual precision used.
| typedef mediump_float glm::float_t |
Definition at line 70 of file type_float.hpp.
| typedef detail::tvec2<bool, highp> glm::highp_bvec2 |
2 components vector of high precision bool numbers. There is no guarantee on the actual precision.
Definition at line 149 of file type_vec.hpp.
| typedef detail::tvec3<bool, highp> glm::highp_bvec3 |
3 components vector of high precision bool numbers.
Definition at line 259 of file type_vec.hpp.
| typedef detail::tvec4<bool, highp> glm::highp_bvec4 |
4 components vector of high precision bool numbers.
Definition at line 354 of file type_vec.hpp.
| typedef detail::tmat2x2<double, highp> glm::highp_dmat2 |
2 columns of 2 components matrix of high precision floating-point numbers.
Definition at line 466 of file type_mat.hpp.
| typedef detail::tmat2x2<double, highp> glm::highp_dmat2x2 |
2 columns of 2 components matrix of high precision floating-point numbers.
Definition at line 484 of file type_mat.hpp.
| typedef detail::tmat2x3<double, highp> glm::highp_dmat2x3 |
2 columns of 3 components matrix of high precision floating-point numbers.
Definition at line 507 of file type_mat.hpp.
| typedef detail::tmat2x4<double, highp> glm::highp_dmat2x4 |
2 columns of 4 components matrix of high precision floating-point numbers.
Definition at line 530 of file type_mat.hpp.
| typedef detail::tmat3x3<double, highp> glm::highp_dmat3 |
3 columns of 3 components matrix of high precision floating-point numbers.
Definition at line 576 of file type_mat.hpp.
| typedef detail::tmat3x2<double, highp> glm::highp_dmat3x2 |
3 columns of 2 components matrix of high precision floating-point numbers.
Definition at line 553 of file type_mat.hpp.
| typedef detail::tmat3x3<double, highp> glm::highp_dmat3x3 |
3 columns of 3 components matrix of high precision floating-point numbers.
Definition at line 594 of file type_mat.hpp.
| typedef detail::tmat3x4<double, highp> glm::highp_dmat3x4 |
3 columns of 4 components matrix of high precision floating-point numbers.
Definition at line 617 of file type_mat.hpp.
| typedef detail::tmat4x4<double, highp> glm::highp_dmat4 |
4 columns of 4 components matrix of high precision floating-point numbers.
Definition at line 686 of file type_mat.hpp.
| typedef detail::tmat4x2<double, highp> glm::highp_dmat4x2 |
4 columns of 2 components matrix of high precision floating-point numbers.
Definition at line 640 of file type_mat.hpp.
| typedef detail::tmat4x3<double, highp> glm::highp_dmat4x3 |
4 columns of 3 components matrix of high precision floating-point numbers.
Definition at line 663 of file type_mat.hpp.
| typedef detail::tmat4x4<double, highp> glm::highp_dmat4x4 |
4 columns of 4 components matrix of high precision floating-point numbers.
Definition at line 704 of file type_mat.hpp.
| typedef detail::tvec2<double, highp> glm::highp_dvec2 |
2 components vector of high double-precision floating-point numbers. There is no guarantee on the actual precision.
Definition at line 86 of file type_vec.hpp.
| typedef detail::tvec3<double, highp> glm::highp_dvec3 |
3 components vector of high double-precision floating-point numbers. There is no guarantee on the actual precision.
Definition at line 197 of file type_vec.hpp.
| typedef detail::tvec4<double, highp> glm::highp_dvec4 |
4 components vector of high double-precision floating-point numbers.
Definition at line 300 of file type_vec.hpp.
| typedef highp_float_t glm::highp_float |
High precision floating-point numbers. There is no guarantee on the actual precision.
Definition at line 67 of file type_float.hpp.
| typedef detail::highp_int_t glm::highp_int |
High precision signed integer. There is no guarantee on the actual precision.
Definition at line 121 of file type_int.hpp.
| typedef detail::tvec2<int, highp> glm::highp_ivec2 |
2 components vector of high precision signed integer numbers. There is no guarantee on the actual precision.
Definition at line 107 of file type_vec.hpp.
| typedef detail::tvec3<int, highp> glm::highp_ivec3 |
3 components vector of high precision signed integer numbers. There is no guarantee on the actual precision.
Definition at line 218 of file type_vec.hpp.
| typedef detail::tvec4<int, highp> glm::highp_ivec4 |
4 components vector of high precision signed integer numbers.
Definition at line 318 of file type_vec.hpp.
| typedef detail::tmat2x2< float, highp > glm::highp_mat2 |
2 columns of 2 components matrix of high precision floating-point numbers. There is no guarantee on the actual precision.
Definition at line 79 of file type_mat.hpp.
| typedef detail::tmat2x2< float, highp > glm::highp_mat2x2 |
2 columns of 2 components matrix of high precision floating-point numbers. There is no guarantee on the actual precision.
Definition at line 100 of file type_mat.hpp.
| typedef detail::tmat2x3< float, highp > glm::highp_mat2x3 |
2 columns of 3 components matrix of high precision floating-point numbers. There is no guarantee on the actual precision.
Definition at line 126 of file type_mat.hpp.
| typedef detail::tmat2x4< float, highp > glm::highp_mat2x4 |
2 columns of 4 components matrix of high precision floating-point numbers. There is no guarantee on the actual precision.
Definition at line 152 of file type_mat.hpp.
| typedef detail::tmat3x3< float, highp > glm::highp_mat3 |
3 columns of 3 components matrix of high precision floating-point numbers. There is no guarantee on the actual precision.
Definition at line 204 of file type_mat.hpp.
| typedef detail::tmat3x2< float, highp > glm::highp_mat3x2 |
3 columns of 2 components matrix of high precision floating-point numbers. There is no guarantee on the actual precision.
Definition at line 178 of file type_mat.hpp.
| typedef detail::tmat3x3< float, highp > glm::highp_mat3x3 |
3 columns of 3 components matrix of high precision floating-point numbers. There is no guarantee on the actual precision.
Definition at line 225 of file type_mat.hpp.
| typedef detail::tmat3x4< float, highp > glm::highp_mat3x4 |
3 columns of 4 components matrix of high precision floating-point numbers. There is no guarantee on the actual precision.
Definition at line 251 of file type_mat.hpp.
| typedef detail::tmat4x4< float, highp > glm::highp_mat4 |
4 columns of 4 components matrix of high precision floating-point numbers. There is no guarantee on the actual precision.
Definition at line 330 of file type_mat.hpp.
| typedef detail::tmat4x2< float, highp > glm::highp_mat4x2 |
4 columns of 2 components matrix of high precision floating-point numbers. There is no guarantee on the actual precision.
Definition at line 277 of file type_mat.hpp.
| typedef detail::tmat4x3< float, highp > glm::highp_mat4x3 |
4 columns of 3 components matrix of high precision floating-point numbers. There is no guarantee on the actual precision.
Definition at line 303 of file type_mat.hpp.
| typedef detail::tmat4x4< float, highp > glm::highp_mat4x4 |
4 columns of 4 components matrix of high precision floating-point numbers. There is no guarantee on the actual precision.
Definition at line 351 of file type_mat.hpp.
| typedef detail::highp_uint_t glm::highp_uint |
High precision unsigned integer. There is no guarantee on the actual precision.
Definition at line 142 of file type_int.hpp.
| typedef detail::tvec2<uint, highp> glm::highp_uvec2 |
2 components vector of high precision unsigned integer numbers. There is no guarantee on the actual precision.
Definition at line 128 of file type_vec.hpp.
| typedef detail::tvec3<uint, highp> glm::highp_uvec3 |
3 components vector of high precision unsigned integer numbers. There is no guarantee on the actual precision.
Definition at line 239 of file type_vec.hpp.
| typedef detail::tvec4<uint, highp> glm::highp_uvec4 |
4 components vector of high precision unsigned integer numbers.
Definition at line 336 of file type_vec.hpp.
| typedef detail::tvec2< float, highp > glm::highp_vec2 |
2 components vector of high single-precision floating-point numbers. There is no guarantee on the actual precision.
High Single-precision floating-point vector of 2 components.
Definition at line 65 of file type_vec.hpp.
| typedef detail::tvec3< float, highp > glm::highp_vec3 |
3 components vector of high single-precision floating-point numbers. There is no guarantee on the actual precision.
High Single-precision floating-point vector of 3 components.
Definition at line 176 of file type_vec.hpp.
| typedef detail::tvec4< float, highp > glm::highp_vec4 |
4 components vector of high single-precision floating-point numbers.
High Single-precision floating-point vector of 4 components.
Definition at line 282 of file type_vec.hpp.
| typedef mediump_int glm::int_t |
Definition at line 145 of file type_int.hpp.
| typedef detail::tvec2<bool, lowp> glm::lowp_bvec2 |
2 components vector of low precision bool numbers. There is no guarantee on the actual precision.
Definition at line 163 of file type_vec.hpp.
| typedef detail::tvec3<bool, lowp> glm::lowp_bvec3 |
3 components vector of low precision bool numbers.
Definition at line 271 of file type_vec.hpp.
| typedef detail::tvec4<bool, lowp> glm::lowp_bvec4 |
4 components vector of low precision bool numbers.
Definition at line 366 of file type_vec.hpp.
| typedef detail::tmat2x2<double, lowp> glm::lowp_dmat2 |
2 columns of 2 components matrix of low precision floating-point numbers.
Definition at line 454 of file type_mat.hpp.
| typedef detail::tmat2x2<double, lowp> glm::lowp_dmat2x2 |
2 columns of 2 components matrix of low precision floating-point numbers.
Definition at line 472 of file type_mat.hpp.
| typedef detail::tmat2x3<double, lowp> glm::lowp_dmat2x3 |
2 columns of 3 components matrix of low precision floating-point numbers.
Definition at line 495 of file type_mat.hpp.
| typedef detail::tmat2x4<double, lowp> glm::lowp_dmat2x4 |
2 columns of 4 components matrix of low precision floating-point numbers.
Definition at line 518 of file type_mat.hpp.
| typedef detail::tmat3x3<float, lowp> glm::lowp_dmat3 |
3 columns of 3 components matrix of low precision floating-point numbers.
Definition at line 564 of file type_mat.hpp.
| typedef detail::tmat3x2<double, lowp> glm::lowp_dmat3x2 |
3 columns of 2 components matrix of low precision floating-point numbers.
Definition at line 541 of file type_mat.hpp.
| typedef detail::tmat3x3<double, lowp> glm::lowp_dmat3x3 |
3 columns of 3 components matrix of low precision floating-point numbers.
Definition at line 582 of file type_mat.hpp.
| typedef detail::tmat3x4<double, lowp> glm::lowp_dmat3x4 |
3 columns of 4 components matrix of low precision floating-point numbers.
Definition at line 605 of file type_mat.hpp.
| typedef detail::tmat4x4<double, lowp> glm::lowp_dmat4 |
4 columns of 4 components matrix of low precision floating-point numbers.
Definition at line 674 of file type_mat.hpp.
| typedef detail::tmat4x2<double, lowp> glm::lowp_dmat4x2 |
4 columns of 2 components matrix of low precision floating-point numbers.
Definition at line 628 of file type_mat.hpp.
| typedef detail::tmat4x3<double, lowp> glm::lowp_dmat4x3 |
4 columns of 3 components matrix of low precision floating-point numbers.
Definition at line 651 of file type_mat.hpp.
| typedef detail::tmat4x4<double, lowp> glm::lowp_dmat4x4 |
4 columns of 4 components matrix of low precision floating-point numbers.
Definition at line 692 of file type_mat.hpp.
| typedef detail::tvec2<double, lowp> glm::lowp_dvec2 |
2 components vector of low double-precision floating-point numbers. There is no guarantee on the actual precision.
Definition at line 100 of file type_vec.hpp.
| typedef detail::tvec3<double, lowp> glm::lowp_dvec3 |
3 components vector of low double-precision floating-point numbers. There is no guarantee on the actual precision.
Definition at line 211 of file type_vec.hpp.
| typedef detail::tvec4<double, lowp> glm::lowp_dvec4 |
4 components vector of low double-precision floating-point numbers.
Definition at line 312 of file type_vec.hpp.
| typedef lowp_float_t glm::lowp_float |
Low precision floating-point numbers. There is no guarantee on the actual precision.
Definition at line 53 of file type_float.hpp.
| typedef detail::lowp_int_t glm::lowp_int |
Low precision signed integer. There is no guarantee on the actual precision.
Definition at line 107 of file type_int.hpp.
| typedef detail::tvec2<int, lowp> glm::lowp_ivec2 |
2 components vector of low precision signed integer numbers. There is no guarantee on the actual precision.
Definition at line 121 of file type_vec.hpp.
| typedef detail::tvec3<int, lowp> glm::lowp_ivec3 |
3 components vector of low precision signed integer numbers. There is no guarantee on the actual precision.
Definition at line 232 of file type_vec.hpp.
| typedef detail::tvec4<int, lowp> glm::lowp_ivec4 |
4 components vector of low precision signed integer numbers.
Definition at line 330 of file type_vec.hpp.
| typedef detail::tmat2x2< float, lowp > glm::lowp_mat2 |
2 columns of 2 components matrix of low precision floating-point numbers. There is no guarantee on the actual precision.
Definition at line 65 of file type_mat.hpp.
| typedef detail::tmat2x2< float, lowp > glm::lowp_mat2x2 |
2 columns of 2 components matrix of low precision floating-point numbers. There is no guarantee on the actual precision.
Definition at line 86 of file type_mat.hpp.
| typedef detail::tmat2x3< float, lowp > glm::lowp_mat2x3 |
2 columns of 3 components matrix of low precision floating-point numbers. There is no guarantee on the actual precision.
Definition at line 112 of file type_mat.hpp.
| typedef detail::tmat2x4< float, lowp > glm::lowp_mat2x4 |
2 columns of 4 components matrix of low precision floating-point numbers. There is no guarantee on the actual precision.
Definition at line 138 of file type_mat.hpp.
| typedef detail::tmat3x3< float, lowp > glm::lowp_mat3 |
3 columns of 3 components matrix of low precision floating-point numbers. There is no guarantee on the actual precision.
Definition at line 190 of file type_mat.hpp.
| typedef detail::tmat3x2< float, lowp > glm::lowp_mat3x2 |
3 columns of 2 components matrix of low precision floating-point numbers. There is no guarantee on the actual precision.
Definition at line 164 of file type_mat.hpp.
| typedef detail::tmat3x3< float, lowp > glm::lowp_mat3x3 |
3 columns of 3 components matrix of low precision floating-point numbers. There is no guarantee on the actual precision.
Definition at line 211 of file type_mat.hpp.
| typedef detail::tmat3x4< float, lowp > glm::lowp_mat3x4 |
3 columns of 4 components matrix of low precision floating-point numbers. There is no guarantee on the actual precision.
Definition at line 237 of file type_mat.hpp.
| typedef detail::tmat4x4< float, lowp > glm::lowp_mat4 |
4 columns of 4 components matrix of low precision floating-point numbers. There is no guarantee on the actual precision.
Definition at line 316 of file type_mat.hpp.
| typedef detail::tmat4x2< float, lowp > glm::lowp_mat4x2 |
4 columns of 2 components matrix of low precision floating-point numbers. There is no guarantee on the actual precision.
Definition at line 263 of file type_mat.hpp.
| typedef detail::tmat4x3< float, lowp > glm::lowp_mat4x3 |
4 columns of 3 components matrix of low precision floating-point numbers. There is no guarantee on the actual precision.
Definition at line 289 of file type_mat.hpp.
| typedef detail::tmat4x4< float, lowp > glm::lowp_mat4x4 |
4 columns of 4 components matrix of low precision floating-point numbers. There is no guarantee on the actual precision.
Definition at line 337 of file type_mat.hpp.
| typedef detail::lowp_uint_t glm::lowp_uint |
Low precision unsigned integer. There is no guarantee on the actual precision.
Definition at line 128 of file type_int.hpp.
| typedef detail::tvec2<uint, lowp> glm::lowp_uvec2 |
2 components vector of low precision unsigned integer numbers. There is no guarantee on the actual precision.
Definition at line 142 of file type_vec.hpp.
| typedef detail::tvec3<uint, lowp> glm::lowp_uvec3 |
3 components vector of low precision unsigned integer numbers. There is no guarantee on the actual precision.
Definition at line 253 of file type_vec.hpp.
| typedef detail::tvec4<uint, lowp> glm::lowp_uvec4 |
4 components vector of low precision unsigned integer numbers.
Definition at line 348 of file type_vec.hpp.
| typedef detail::tvec2< float, lowp > glm::lowp_vec2 |
2 components vector of low single-precision floating-point numbers. There is no guarantee on the actual precision.
Low single-precision floating-point vector of 2 components.
Definition at line 79 of file type_vec.hpp.
| typedef detail::tvec3< float, lowp > glm::lowp_vec3 |
3 components vector of low single-precision floating-point numbers. There is no guarantee on the actual precision.
Low single-precision floating-point vector of 3 components.
Definition at line 190 of file type_vec.hpp.
| typedef detail::tvec4< float, lowp > glm::lowp_vec4 |
4 components vector of low single-precision floating-point numbers.
Low single-precision floating-point vector of 4 components.
Definition at line 294 of file type_vec.hpp.
| typedef detail::tvec2<bool, mediump> glm::mediump_bvec2 |
2 components vector of medium precision bool numbers. There is no guarantee on the actual precision.
Definition at line 156 of file type_vec.hpp.
| typedef detail::tvec3<bool, mediump> glm::mediump_bvec3 |
3 components vector of medium precision bool numbers.
Definition at line 265 of file type_vec.hpp.
| typedef detail::tvec4<bool, mediump> glm::mediump_bvec4 |
4 components vector of medium precision bool numbers.
Definition at line 360 of file type_vec.hpp.
| typedef detail::tmat2x2<double, mediump> glm::mediump_dmat2 |
2 columns of 2 components matrix of medium precision floating-point numbers.
Definition at line 460 of file type_mat.hpp.
| typedef detail::tmat2x2<double, mediump> glm::mediump_dmat2x2 |
2 columns of 2 components matrix of medium precision floating-point numbers.
Definition at line 478 of file type_mat.hpp.
| typedef detail::tmat2x3<double, mediump> glm::mediump_dmat2x3 |
2 columns of 3 components matrix of medium precision floating-point numbers.
Definition at line 501 of file type_mat.hpp.
| typedef detail::tmat2x4<double, mediump> glm::mediump_dmat2x4 |
2 columns of 4 components matrix of medium precision floating-point numbers.
Definition at line 524 of file type_mat.hpp.
| typedef detail::tmat3x3<double, mediump> glm::mediump_dmat3 |
3 columns of 3 components matrix of medium precision floating-point numbers.
Definition at line 570 of file type_mat.hpp.
| typedef detail::tmat3x2<double, mediump> glm::mediump_dmat3x2 |
3 columns of 2 components matrix of medium precision floating-point numbers.
Definition at line 547 of file type_mat.hpp.
| typedef detail::tmat3x3<double, mediump> glm::mediump_dmat3x3 |
3 columns of 3 components matrix of medium precision floating-point numbers.
Definition at line 588 of file type_mat.hpp.
| typedef detail::tmat3x4<double, mediump> glm::mediump_dmat3x4 |
3 columns of 4 components matrix of medium precision floating-point numbers.
Definition at line 611 of file type_mat.hpp.
| typedef detail::tmat4x4<double, mediump> glm::mediump_dmat4 |
4 columns of 4 components matrix of medium precision floating-point numbers.
Definition at line 680 of file type_mat.hpp.
| typedef detail::tmat4x2<double, mediump> glm::mediump_dmat4x2 |
4 columns of 2 components matrix of medium precision floating-point numbers.
Definition at line 634 of file type_mat.hpp.
| typedef detail::tmat4x3<double, mediump> glm::mediump_dmat4x3 |
4 columns of 3 components matrix of medium precision floating-point numbers.
Definition at line 657 of file type_mat.hpp.
| typedef detail::tmat4x4<double, mediump> glm::mediump_dmat4x4 |
4 columns of 4 components matrix of medium precision floating-point numbers.
Definition at line 698 of file type_mat.hpp.
| typedef detail::tvec2<double, mediump> glm::mediump_dvec2 |
2 components vector of medium double-precision floating-point numbers. There is no guarantee on the actual precision.
Definition at line 93 of file type_vec.hpp.
| typedef detail::tvec3<double, mediump> glm::mediump_dvec3 |
3 components vector of medium double-precision floating-point numbers. There is no guarantee on the actual precision.
Definition at line 204 of file type_vec.hpp.
| typedef detail::tvec4<double, mediump> glm::mediump_dvec4 |
4 components vector of medium double-precision floating-point numbers.
Definition at line 306 of file type_vec.hpp.
| typedef mediump_float_t glm::mediump_float |
Medium precision floating-point numbers. There is no guarantee on the actual precision.
Definition at line 60 of file type_float.hpp.
Medium precision signed integer. There is no guarantee on the actual precision.
Definition at line 114 of file type_int.hpp.
| typedef detail::tvec2<int, mediump> glm::mediump_ivec2 |
2 components vector of medium precision signed integer numbers. There is no guarantee on the actual precision.
Definition at line 114 of file type_vec.hpp.
| typedef detail::tvec3<int, mediump> glm::mediump_ivec3 |
3 components vector of medium precision signed integer numbers. There is no guarantee on the actual precision.
Definition at line 225 of file type_vec.hpp.
| typedef detail::tvec4<int, mediump> glm::mediump_ivec4 |
4 components vector of medium precision signed integer numbers.
Definition at line 324 of file type_vec.hpp.
| typedef detail::tmat2x2< float, mediump > glm::mediump_mat2 |
2 columns of 2 components matrix of medium precision floating-point numbers. There is no guarantee on the actual precision.
Definition at line 72 of file type_mat.hpp.
| typedef detail::tmat2x2< float, mediump > glm::mediump_mat2x2 |
2 columns of 2 components matrix of medium precision floating-point numbers. There is no guarantee on the actual precision.
Definition at line 93 of file type_mat.hpp.
| typedef detail::tmat2x3< float, mediump > glm::mediump_mat2x3 |
2 columns of 3 components matrix of medium precision floating-point numbers. There is no guarantee on the actual precision.
Definition at line 119 of file type_mat.hpp.
| typedef detail::tmat2x4< float, mediump > glm::mediump_mat2x4 |
2 columns of 4 components matrix of medium precision floating-point numbers. There is no guarantee on the actual precision.
Definition at line 145 of file type_mat.hpp.
| typedef detail::tmat3x3< float, mediump > glm::mediump_mat3 |
3 columns of 3 components matrix of medium precision floating-point numbers. There is no guarantee on the actual precision.
Definition at line 197 of file type_mat.hpp.
| typedef detail::tmat3x2< float, mediump > glm::mediump_mat3x2 |
3 columns of 2 components matrix of medium precision floating-point numbers. There is no guarantee on the actual precision.
Definition at line 171 of file type_mat.hpp.
| typedef detail::tmat3x3< float, mediump > glm::mediump_mat3x3 |
3 columns of 3 components matrix of medium precision floating-point numbers. There is no guarantee on the actual precision.
Definition at line 218 of file type_mat.hpp.
| typedef detail::tmat3x4< float, mediump > glm::mediump_mat3x4 |
3 columns of 4 components matrix of medium precision floating-point numbers. There is no guarantee on the actual precision.
Definition at line 244 of file type_mat.hpp.
| typedef detail::tmat4x4< float, mediump > glm::mediump_mat4 |
4 columns of 4 components matrix of medium precision floating-point numbers. There is no guarantee on the actual precision.
Definition at line 323 of file type_mat.hpp.
| typedef detail::tmat4x2< float, mediump > glm::mediump_mat4x2 |
4 columns of 2 components matrix of medium precision floating-point numbers. There is no guarantee on the actual precision.
Definition at line 270 of file type_mat.hpp.
| typedef detail::tmat4x3< float, mediump > glm::mediump_mat4x3 |
4 columns of 3 components matrix of medium precision floating-point numbers. There is no guarantee on the actual precision.
Definition at line 296 of file type_mat.hpp.
| typedef detail::tmat4x4< float, mediump > glm::mediump_mat4x4 |
4 columns of 4 components matrix of medium precision floating-point numbers. There is no guarantee on the actual precision.
Definition at line 344 of file type_mat.hpp.
Medium precision unsigned integer. There is no guarantee on the actual precision.
Definition at line 135 of file type_int.hpp.
| typedef detail::tvec2<uint, mediump> glm::mediump_uvec2 |
2 components vector of medium precision unsigned integer numbers. There is no guarantee on the actual precision.
Definition at line 135 of file type_vec.hpp.
| typedef detail::tvec3<uint, mediump> glm::mediump_uvec3 |
3 components vector of medium precision unsigned integer numbers. There is no guarantee on the actual precision.
Definition at line 246 of file type_vec.hpp.
| typedef detail::tvec4<uint, mediump> glm::mediump_uvec4 |
4 components vector of medium precision unsigned integer numbers.
Definition at line 342 of file type_vec.hpp.
| typedef detail::tvec2< float, mediump > glm::mediump_vec2 |
2 components vector of medium single-precision floating-point numbers. There is no guarantee on the actual precision.
Medium Single-precision floating-point vector of 2 components.
Definition at line 72 of file type_vec.hpp.
| typedef detail::tvec3< float, mediump > glm::mediump_vec3 |
3 components vector of medium single-precision floating-point numbers. There is no guarantee on the actual precision.
Medium Single-precision floating-point vector of 3 components.
Definition at line 183 of file type_vec.hpp.
| typedef detail::tvec4< float, mediump > glm::mediump_vec4 |
4 components vector of medium single-precision floating-point numbers.
Medium Single-precision floating-point vector of 4 components.
Definition at line 288 of file type_vec.hpp.
| typedef unsigned int glm::uint |
Unsigned integer type.
Definition at line 171 of file type_int.hpp.
| typedef mediump_uint glm::uint_t |
Definition at line 157 of file type_int.hpp.


Top in soft sweatshirt fabric. Slightly looser fit with dropped shoulders and ribbing around the neckline, cuffs and hem. Soft brushed inside.
Hoodie in soft sweatshirt fabric with a lined, drawstring hood, long sleeves, kangaroo pocket and ribbing at the cuffs and hem. Soft brushed inside
Sleeveless, fitted, calf-length dress in a fine rib knit. Deep v-neck, decorative buttons down the front and a detachable tie belt at the waist
Loose-fitting, knitted cardigan in a boxy style with a V-neck and buttons down the front. Dropped shoulders and extra-long sleeves with ribbed cuffs
Oversized, straight-cut jacket in twill made from a linen blend. Collar, notch lapels, buttons at the front, low dropped shoulders and large patch front pockets. Satin lining
Two-button jacket in cotton twill with notch lapels, a chest pocket and fake flap pockets at the front. Contrasting colour elbow patches, decorative buttons at the cuffs and a single back vent. Lined
Pyjamas in soft cotton jersey with a print motif. Long-sleeved top with dropped shoulders and ribbing around the neckline, cuffs and hem. Bottoms with an elasticated waist


Woman who humiliated homeless man by throwing milkshake on him spared jail
A woman who threw a milkshake on a homeless man as he was being punched and kicked on the ground, has been spared jail.
Dana Hooper, 24, was ordered to do unpaid work after a clip of her throwing liquid over the 60-year-old man as he was being attacked, was viewed thousands of times.
As Nick Hulley lay on the floor in considerable pain, defending himself against a vicious attack, Hooper threw her milkshake all over him, Brighton Magistrates court heard.
The incident took place on October 13, after Hooper followed Mr Hulley after an earlier confrontation.
Hooper, from Portslade, pleaded guilty to common assault by throwing her drink over Mr Hulley.
District Judge Teresa Szagun told Hooper her sentence was increased by the humiliation she subjected him to.
She said: ‘I found it was an aggravating feature, you were prepared to continue following him – whilst he was being assaulted by others – to add to the humiliation by the action you took.’
Advertisement Advertisement
Suzanne Soros prosecuting said independent witnesses described being concerned for Mr Hulley’s safety and trying to intervene.
Ms Soros said: ‘She carried on abusing him even after people were saying to leave him alone.
‘They describe kicking while he was on the floor and he was shouting and groaning in pain.’
Mr Hulley has not come forward to make a complaint, the court heard, and there would be no financial compensation for him.
Hooper was with a group of friends on the Sunday evening.
Ed Fish, defending, said there had been a confrontation between Hooper, her friends and Mr Hulley.
The court heard Hooper has been afraid to leave home since the video went viral.
The judge ordered Hooper to serve a 12 month community order, including 12 rehabilitation days and 80 hours unpaid work.
She will also be under electronic weekend curfew and will have £175 costs and surcharge deducted from her benefits.
Peter Derbyshire, 37, of Bognor Regis is charged with assault by beating of Mr Hulley.
He is due to appear at Brighton Magistrates court at the end of the month.
Got a story for Metro.co.uk?
Get in touch with our news team by emailing us at<EMAIL_ADDRESS>For more stories like this, check our news page.


It has been a while ago since I received this book for review, initially I started reading it on my way to Exchange Connections in Las Vegas on my iPad mini. And the last few weeks I finished reading the book. So what do I think of the book? The book is a must have resource for everyone who is working with Exchange 2013. Tony Redmond is amazing good in describing the several parts of Exchange related to mailbox and high availability. Yes you are reading it correct this book only describes the mailbox an high availability of Exchange 2013. For the Client Access ,Connectivity and UM part of Exchange 2013 you will have to read the book from Paul Robichaux.
Tony starts with describing some important things you will have to consider when implementing Exchange 2013. This starts with a discussion about the architecture and continuous with several decisions you have to make before implementing Exchange 2013. Then he continuous to describe which preparations you have to make before you can implement Exchange 2013. After these topics have been described Tony describes how to install it and the basic management tools you will need to use to manage your Exchange 2013 and in more detail how to manage your mailboxes.
Starting from chapter 8 the book makes a very deep dive. For those who are already familiar with Exchange 2013 and want to skip the basic things this is were you should start reading the book. In very deep detail Tony describes several topics among them:
- how does the store work
- the database availability group
- migrating mailboxes
- compliance management
- public folders and site mailboxes
For example in the _how does the store work _chapter will describe what is under the hood of the store. When you have read this chapter you will probably have a better understanding of the store process and how complex the product Exchange is under the hood. After the store is described the book continuous with the database availability group. In this chapter Tony describes several topics related to the DAG including: design considerations, daily administration tasks and several important components of the DAG among them the active manager.
Once the mailbox and high availability part have been discussed the book continues with the mailbox migration process. In this chapter the components are discussed which are key components of the migration process and of course how to perform the migration. This is not limited to only the cmdlets but also contains several practical tips from Tony about planning.
Some people are already working with the compliance part but some haven’t probably touched it. Both groups will benefit from this chapter as it describes some basic things but contains enough detail so people with experience will also learn a lot from this chapter. Compliance is a topic which becomes more important now-a-days since more and more companies will start implementing it either due to company regulations or government regulations.
In the last chapter Tony describes one of the probably most discussed topics of Exchange. Will it be there in the next release? How long will Microsoft Public Folders? These are just a few questions you will hear every time during the development phase of the next version of Exchange. In Exchange 2013 the Public Folders have changed a lot since the public folders can now be part of a normal mailbox database so no separate public folder database anymore. But how to get from the “legacy” Public Folder to the “new” Public Folder. Read Tony’s book and you have some good info on how to do this.
So what’s the conclusion of this review? The book is a must read for everybody who works with Exchange. It does not contain the basic information which most books do but describes several things in very deep detail. Besides this Tony also provides some additional information which really adds value to the book.
So if you got curious about the book just press the link below to buy the book:
Buy Microsoft Exchange Server 2013 Inside Out: Mailbox and High Availability


Direct and private cell number +447024075280 Direct and private Telephone +<PHONE_NUMBER>02 Direct and private fax number +<PHONE_NUMBER>83 Dear Friend, I am Mr. T. smith, deputy senior regional manager at Lloyds TSB Commercial Finance in Yorkshire. I have urgent and very confidential business proposition for you. On June 6, 2000, Mr. Barry Kelly made a numbered time (Fixed) Deposit for forty-eight months, (being four years) valued at US$40,000,000.00, (forty Million, Dollars) in my branch. Upon maturity, I sent a routine notification to his forwarding address but got no reply. After a month, we sent a reminder and finally we discovered from his employers, that Mr. Barry Kelly died from tsunami on his holidays in Thailand 26 December 2004. On further investigation, I found out that he died without making a WILL and all attempts to trace his next of kin was fruitless. I therefore made further investigation and discovered that Mr. Barry Kelly did not declare any kin or relations in all his official documents, including his Bank Deposit paperwork in my Bank. This sum of US$40,000,000 has carefully been moved out of my bank to a security company for Safekeeping .No one will ever come forward to claim it. According to British Law, at the expiration of (three) years, the money will revert to the ownership of the British Government if nobody applies to claim the fund. Consequently, my proposal is that I will like you to stand in as the owner of the money I deposited it in a security company in two trunk boxes though the security company does not know the contents of the boxes as I tagged them to be photographic materials for export. I am writing you because I as a public servant (Manager with the bank), I cannot operate a foreign account or have an account that is more than $1m. I want to present you as the owner of the boxes in the security company so you can be able to claim them with the help of my attorney. This is simple. I will like you to provide immediately your full names and address so that the Attorney will prepare the necessary documents which will put you in place as the as the owner of the boxes. The money will be moved out for us to share in the ratio of 70% for me and 30% for you. There is no risk at all as all the paperwork for this transaction will be done by the Attorney and this will guarantees the successful execution of this transaction. If you are interested, please reply immediately via my email address upon your response, I shall then provide you with more details and relevant documents that will help you understand the transaction. Please observe utmost confidentiality, and rest assured that this transaction would be most profitable for both of us because I shall require your assistance to invest my share in (Buying of properties like houses, hotels and doing charity works etc) Please do send me your private/mobile telephone fax number so that we can have a smooth communication. Awaiting your urgent reply ONLY via my email<EMAIL_ADDRESS>) Thanks and regards. TONY SMITH


{% extends "base.html" %} {% load bootstrap3 %} {% block title %}Edit {{ feedlist }}{% endblock %} {% block javascript %} > {% endblock %} {% block meta %} {% endblock %} {% block content %} View this list
You may add additional information for this list. The e-mail address will never be visible but may be used to recover the secret url to the page you see right now. There is no way to edit the list without this link:
Please bookmark (or save elsewhere): http://feedshare.net{{ feedlist.get_edit_url }}
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Current Identity Update
Does your identity need a facelift? Let Asset update your current identity
Already have an identity package but it needs to be updated? Then this is the selection for you.
Copy/text for your*
- Business card
- Letterhead
- Envelope
*Doesn't include printing


High performance blockchain technology is necessary for cryptocurrencies and smart contract platforms to provide a viable alternative to existing financial platforms. BitShares is designed from the ground up to process more transactions every second than VISA and MasterCard combined. With Delegated Proof of Stake, the BitShares network can confirm transactions in an average of just 1 second, limited only by the speed of light.
Overview
To achieve this industry-leading performance, BitShares has borrowed lessons learned from the LMAX Exchange, which is able to process 6 million transactions per second. Among these lessons are the following key points:
- Keep everything in memory.
- Keep the core business logic in a single thread.
- Keep cryptographic operations (hashes and signatures) out of the core business logic.
- Divide validation into state-dependent and state-independent checks.
- Use an object oriented data model.
By following these simple rules, BitShares is able to process 100,000 transactions per second without any significant effort devoted to optimization. Future optimizations are expected to bring the performance of BitShares to levels similar to LMAX.
It should be noted that the performance achieved by BitShares is highly dependent upon having a compatible transaction protocol. It would not be possible to achieve the same level of performance in a protocol where the Core Business Logic is run in a virtual machine that performs cryptographic operations and references all objects with hash identifiers. Blockchains are inherently single-threaded, and the performance of a single core of a CPU is the most limited and least scalable resource of all. BitShares is designed to get the most out of this single thread of execution.
Background
A blockchain is a global ledger that orders transactions, whereby each transaction deterministically modifies a shared global state at a specified timestamp. The order in which transactions are processed can change the validity of other transactions. For example, you cannot withdraw money from your bank account until after your paycheck deposit has cleared. It becomes impossible to know whether or not a transaction is valid until after all prior transactions that impact a particular account have been processed.
In theory, transactions for two unrelated accounts can be processed at the same time, provided that they do not share any common dependency. In practice, the cost of identifying which transactions are truly independent of each other on a ledger empowered by smart contracts with arbitrary conditions is intractable. The only way to be sure that two transactions are truly independent is by maintaining completely separate ledgers and then periodically transferring value between them. An analogy could be made to the performance trade offs in the design of Non-Uniform Memory Access (NUMA) vs Uniform Memory Access.
In practice, Uniform Memory Access is much easier for developers to design for, and has lower costs. NUMA architectures are usually adopted as a last resort when building supercomputers or giant clusters.
The computer industry has grown to realize that scaling performance through parallelism is nowhere near as easy as the early days when all that was necessary was to increase the clock speed of the CPU. It is for this reason that CPU designers pushed the single-threaded performance to the limits before attempting to adopt a multi-threaded approach to increase performance. When multi-threading is not enough, then, and only then, is cluster computing considered an option.
Many in the cryptocurrency industry have attempted to solve the scalability issue by immediately moving to a “cluster” solution without fully exploring what is technologically possible on a single core of a single computer.
LMAX Disruptor
The LMAX Disruptor provides a case study on an architecture with a high degree of scalability and performance, showing what is achievable within a single execution thread.  They have been generous enough to share what they have learned publicly.
Here is a brief overview of their architecture:
The Business Logic Processor is where all of the sequential transactions and order matching is processed. It is a single thread that is able to process millions of orders per second. This architecture is readily ported to the realm of cryptocurrencies and blockchain designs.
The role of the Input Disruptor is to gather orders from users from many different sources and assign them a deterministic order. After assigning them an order they are replicated, logged, and broadcast to many redundant business logic processors. The tasks of the Input Disruptor are embarrassingly parallel and easily farmed out to a cluster of computers.
After the Business Logic Processor has processed the inputs, an Output Disruptor takes care of notifying anyone who cares about the results. This is also an embarrassingly parallel task.
Ultimately, LMAX was able to process 6 million transactions per second through the Business Logic Processor using a single core of a commodity CPU using the Java virtual machine. If LMAX can achieve 6 million transactions per second, then certainly there is no need for cryptocurrency and smart contract platforms to reach for clustered solutions when they are not even processing 10 transactions per second.
High Performance Blockchains
To implement a high performance blockchain, BitShares must adopt the same techniques used by LMAX. Several key fundementals must be met:
- Keep everything in memory.
- Avoid synchronization primitives (locks, atomic operations).
- Minimize unnecessary computation in the business logic processor.
Memory is becoming cheaper every day because it is extremely parallel in its design. The amount of information that is required to track the account balance and permissions of every person on the Internet is less than 1 Terabyte of RAM, which can be purchased for less than $15,000 and installed on commodity (high-end) server motherboards. Long before 3 billion people adopt the system, this kind of hardware will be in the average desktop.
The real bottleneck is not the memory requirements, but the bandwidth requirements. At 1 million transactions per second and 256 bytes per transaction, the network would require 256 megabytes per second (1 Gbit/sec). This kind of bandwidth is not widely available to the average desktop; however, this level of bandwidth is a fraction of the 100 Gbit/s that Internet 2 furnishes to more than 210 U.S. educational institutions, 70 corporations, and 45 non-profit and government agencies.
Therefore, blockchain technology can easily keep everything in RAM and scale to handle millions of transactions per second if it is designed properly.
 Traditional blockchain designs use cryptographic hashes to generate globally unique IDs that are statistically guaranteed to never have a collision. The problem with these hashes is that they require significantly more memory and more CPU cycles to manipulate. It takes significantly more CPU time to look up an account record by hash than with a direct array index. For example, 64 bit integers are easier to compare and manipulate than 160+bit IDs. Larger hash IDs means there is less room in the CPU cache and that more memory is required. On modern operating systems, infrequently accessed RAM is compressed, but hash identifiers are random data that is not compressible.
Fortunately, blockchains give us a means to globally assign unique IDs that do not conflict with one another, so it is possible to completely remove the need to use hash-based identifiers (Bitcoin addresses) to refer to an account, balance, or permission.
Remove Signature Verification from Business Logic Processor
All transactions on cryptocurrency networks depend upon cryptographic signatures to validate permissions. In the general case, the permissions required can change as a result of other transactions. This means that permissions need to be defined in terms that require no cryptographic calculations within the Business Logic Processor.
To do this, every public key needs to be assigned a unique and immutable ID. After an ID has been assigned, the Input Disruptor(s) can verify that the signature provided matches the ID specified. By the time the transaction makes it to the Business Logic Processor, the only remaining step is to check the IDs.
This same technique can be used to remove pre-condition checking on any immutable object with a static ID.
Design Transactions for Static Validation
Many transaction properties can be checked statically, without the need to reference the current global state.
These checks include range checking of parameters, de-duplication of inputs, sort order of arrays, etc.
Generally speaking, many checks can be performed if the transaction includes the data it “assumes” about
the global state. After these checks are performed, all that is necessary for the Business Logic Processor to do is
make sure the “assumptions” are still true, which can usually be boiled down to checking a modification timestamp on
objects referenced relative to the time the transaction was signed.
Smart Contracts
Many blockchains are adopting a general purpose scripting language to define all operations. These designs end up defining the “Business Logic Processor” as a virtual machine and all transactions are defined as scripts to be run by the virtual machine. This approach takes the single-threaded limitations of a real CPU and compounds them by forcing everything through a virtual CPU. A virtual CPU, even with Just-In-Time compilation, will always be slower than a real CPU, but pure speed of calculation isn’t the only issue with the “everything is a script” approach.
When transactions are defined at such a low level, it means that most of the static checks and cryptographic operations get sucked back into the Business Logic Processing and the overall throughput falls. A scripting engine should never require a cryptographic signature check to be performed even if it is done through a native call.
Based upon the lessons we learn from LMAX, we know that a virtual machine for a blockchain should be designed with single-threaded performance in mind. This means it should be optimized for Just-In-Time compilation from the beginning, and that the most frequently used smart contracts should be supported natively by the blockchain, leaving only the rarely-used custom contracts to run in a virtual machine. These custom contracts should be designed around performance, which means the Virtual Machine should limit the addressable memory to something that will fit within the CPU cache.
Object Oriented Data Model
One of the benefits of keeping everything in memory is that the software can be designed to mimic the real-world relationships of data. This means that the Business Logic Processor can quickly follow in-memory pointers to the data it needs, rather than being forced to perform expensive database queries. It also means that data can be accessed without copying it, and that the data can be modified in-place. This single optimization offers an order-of-magnitude performance gain over using a database-based approach.
Transaction Sizes
A blockchain that processes 100,000 transactions every second generates a lot of data. The average size of a transaction on competing networks, such as Ripple and Bitcoin, is about 250 bytes. A similar transaction on BitShares averages just 100 bytes. In other words, competing systems require 2.5 times the bandwidth to propagate the same number of transactions. Assuming a gigabit connection to the internet, it would take about 0.1 seconds just to transfer a block containing 100,000 transactions. Competing networks would require 0.25 seconds. After latency and multiple hops on a peer-to-peer network are factored in, it becomes clear that transaction size directly impacts the block interval, and therefore the confirmation latency.
Transaction sizes are often an indication of the amount of data the CPU must process in its critical path. Therefore, they serve as an indication of how soon the single threaded performance of a CPU will be hit.
Some optimizations are possible in all protocols if they assume that all nodes have prior knowledge of all broadcast transactions and only require the ordered list of transaction IDs to broadcast every block. This would be an implementation detail.
Conclusion
Designing a high-performance blockchain isn’t rocket-science, and doesn’t require complex, hard-to-understand protocols, nor does it require dividing processing among all the nodes on the network. Instead, all that is necessary to build a high-performance blockchain is to remove all calculations that are not part of the critical, order-dependent, evaluation from the core business logic, and to design a protocol that facilitates these kinds of optimizations. This is what BitShares has done.


Provides transfusion-related differential tests on Near-infrared spectroscopy (NIRS) time series with detection limit, which contains two testing statistics: Mean Area Under the Curve (MAUC) and slope statistic. This package applied a penalized spline method within an imputation setting. Testing is conducted by a nested permutation approach within imputation. Refer to Guo et al (2018) <arXiv:1801.08153> for further details.
| Version: | 1.0 |
| Depends: | R (≥ 3.1.0), ggplot2, mgcv, gridExtra |
| Published: | 2018-01-29 |
| Author: | Yikai Wang and Ying Guo |
| Maintainer: | Yikai Wang <yikai.wang at emory.edu> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| CRAN checks: | NIRStat results |
| Reference manual: | NIRStat.pdf |
| Package source: | NIRStat_1.0.tar.gz |
| Windows binaries: | r-devel: NIRStat_1.0.zip, r-release: NIRStat_1.0.zip, r-oldrel: NIRStat_1.0.zip |
| OS X binaries: | r-release: NIRStat_1.0.tgz, r-oldrel: NIRStat_1.0.tgz |
Please use the canonical form https://CRAN.R-project.org/package=NIRStat to link to this page.


--- prev_page: '/tutorial/tutorial_publishing.html' next_page: '/recipes/recipes_intro.html' --- {% raw %}
# HIDDEN
# Clear previously defined variables
%reset -f
nbinteract
was designed to make interactive explanations easy to create. In this tutorial, we will show the process of writing a simulation from scratch and visualizing the results interactively.
In this section, you will create an interactive simulation of the Monty Hall Problem. You may continue writing code in the notebook from the previous section or create a new one for this section.
The Monty Hall Problem (Wikipedia) is a famous probability problem that has stumped many, mathematicians included. The problem goes something like this:
Suppose you're on a game show, and you're given the choice of three doors: Behind one door is a car; behind the others, goats. You pick a door, say No. 1, and the host, who knows what's behind the doors, opens another door, say No. 3, which has a goat.  Is it to your advantage to switch your choice?
Perhaps unintuitively, you will win the prize about twice as often if you switch doors. We can show this through simulation.
One way to write an interactive explanation is to write functions and create interactions for each one as applicable. Composing the functions allows you to create more complicated processes. nbinteract
also provides tools for interactive visualizations as we will soon see.
Let's start with defining a function to simulate one round of the Monty Hall Problem.
from ipywidgets import interact
import numpy as np
import random
PRIZES = ['Car', 'Goat 1', 'Goat 2']
def monty_hall(example_num=0):
'''
Simulates one round of the Monty Hall Problem. Outputs a tuple of
(result if stay, result if switch, result behind opened door) where
each results is one of PRIZES.
'''
pick = random.choice(PRIZES)
opened = random.choice(
[p for p in PRIZES if p != pick and p != 'Car']
)
remainder = next(p for p in PRIZES if p != pick and p != opened)
return (pick, remainder, opened)
Note that the example_num
argument is passed in but not used in the monty_hall
function. Although it's unneeded for the function, it is easier to use interact
to call functions when they have arguments to manipulate:
interact(monty_hall, example_num=(0, 100));
By interacting with the function above, we are able to informally verify that the function never allows the host to open a door with a car behind it. Even though the function is random we are able to use interaction to examine its long-term behavior!
We'll continue by defining a function to simulate a game of Monty Hall and output the winning strategy for that game:
def winner(example_num=0):
'''
Plays a game of Monty Hall. If staying with the original door wins
a car, return 'stay'. Otherwise, the remaining door contains the car
so 'switch' would have won.
'''
picked, _, _ = monty_hall()
return 'stay' if picked == 'Car' else 'switch'
interact(winner, example_num=(0, 100));
Again, a bit of interaction lets us quickly examine the behavior of winner
. We can see that switch
appears more often than stay
.
nbinteract
¶Let's create an interactive bar chart of the number of times each strategy wins. We'll use nbinteract
's plotting functionality.
nbi.bar
creates a bar chart:
import nbinteract as nbi
nbi.bar(['a', 'b'], [4, 6])
To make an interactive chart, pass a response function in place of one or both of bar
's arguments.
# This function generates the x-values
def categories(n):
return list('abcdefg')[:n]
# This function generates the y-values (heights of bars)
# The y response function always takes in the x-values as its
# first argument
def offset_y(xs, offset):
num_categories = len(xs)
return np.arange(num_categories) + offset
# Each argument of the response functions is passed in as a keyword
# argument to `nbi.bar` in the same format as `interact`
nbi.bar(categories, offset_y, n=(1, 7), offset=(0, 10))
Now, let's turn back to our original goal: plotting the winners as games are played.
We can call winner
many times and use nbi.bar
to show the bar chart as it's built over the trials.
Note that we compute the results before defining our function won
. This has two benefits over running the simulation directly in won
:
won
, moving the slider from 500 to a different number back to 500 will give us a slightly different bar chart.categories = ['stay', 'switch']
winners = [winner() for _ in range(1000)]
# Note that the the first argument to the y response function
# will be the x-values which we don't need
def won(_, num_games):
'''
Outputs a 2-tuple of the number of times each strategy won
after num_games games.
'''
return (winners[:num_games].count('stay'),
winners[:num_games].count('switch'))
nbi.bar(categories, won, num_games=(1, 1000))
Note that by default the plot will adjust its y-axis to match the limits of the data. We can manually set the y-axis limits to better visualize this plot being built up. We will also add labels to our plot:
options = {
'title': 'Number of times each strategy wins',
'xlabel': 'Strategy',
'ylabel': 'Number of wins',
'ylim': (0, 700),
}
nbi.bar(categories, won, options=options, num_games=(1, 1000))
We can get even fancy and use the Play
widget from ipywidgets
to animate the plot.
from ipywidgets import Play
nbi.bar(categories, won, options=options,
num_games=Play(min=0, max=1000, step=10, value=0, interval=17))
Now we have an interactive, animated bar plot showing the distribution of wins over time for both Monty Hall strategies. This is a convincing argument that switching is better than staying. In fact, the bar plot above suggests that switching is about twice as likely to win as staying!
Is switching actually twice as likely to win? We can again use simulation to answer this question by simulating sets of 50 games at a time. recording the proportion of times switch
wins.
def prop_wins(sample_size):
'''Returns proportion of times switching wins after sample_size games.'''
return sum(winner() == 'switch' for _ in range(sample_size)) / sample_size
interact(prop_wins, sample_size=(10, 100));
We can then define a function to play sets of games and generate a list of win proportions for each set:
def generate_proportions(sample_size, repetitions):
'''
Returns an array of length reptitions. Each element in the list is the
proportion of times switching won in sample_size games.
'''
return np.array([prop_wins(sample_size) for _ in range(repetitions)])
interact(generate_proportions, sample_size=(10, 100), repetitions=(10, 100));
Interacting with generate_proportions
shows the relationship between its arguments sample_size
and repetitions
more quickly than reading the function itself!
We can then use nbi.hist
to show these proportions being computed over runs.
Again, we pre-compute the simulations and interact with a function that takes a slice of the simulations to make the interaction faster.
# Play the game 10 times, recording the proportion of times switching wins.
# Repeat 100 times to record 100 proportions
proportions = generate_proportions(sample_size=10, repetitions=100)
def props_up_to(num_sets):
return proportions[:num_sets]
nbi.hist(props_up_to, num_sets=Play(min=0, max=100, value=0, interval=50))
As with last time, it's illustrative to specify the limits of the axes:
options = {
'title': 'Distribution of win proportion over 100 sets of 10 games when switching',
'xlabel': 'Proportions',
'ylabel': 'Percent per area',
'xlim': (0.3, 1),
'ylim': (0, 3),
'bins': 7,
}
nbi.hist(props_up_to, options=options, num_sets=Play(min=0, max=100, value=0, interval=50))
We can see that the distribution of wins is centered at roughly 0.66 but the distribution almost spans the entire x-axis. Will increasing the sample size make our distribution more narrow? Will increasing repetitions do the trick? Or both? We can find out through simulation and interaction.
We'll start with increasing the sample size:
varying_sample_size = [generate_proportions(sample_size, repetitions=100)
for sample_size in range(10, 101)]
def props_for_sample_size(sample_size):
return varying_sample_size[sample_size - 10]
changed_options = {
'title': 'Distribution of win proportions as sample size increases',
'ylim': (0, 6),
'bins': 20,
}
nbi.hist(props_for_sample_size,
options={**options, **changed_options},
sample_size=Play(min=10, max=100, value=10, interval=50))
So increasing the sample size makes the distribution narrower. We can now see more clearly that the distribution is centered at 0.66.
We can repeat the process for the number of repetitions:
varying_reps = [generate_proportions(sample_size=10, repetitions=reps) for reps in range(10, 101)]
def props_for_reps(reps):
return varying_reps[reps - 10]
changed_options = {
'title': 'Distribution of win proportions as repetitions increase',
'ylim': (0, 5),
}
nbi.hist(props_for_reps,
options={**options, **changed_options},
reps=Play(min=10, max=100, value=10, interval=50))
Our distribution gets smoother as the number of repetitions increases since we have more proportions to plot. However, the spread of the distribution remains the same.
Through simulation, we've shown that increasing the sample size causes our distribution of proportions to become more narrowly distributed. Increasing the number of repetitions makes our distribution look smoother when plotted but doesn't affect the distribution spread.
We've also seen through increasing the sample size shows that our distribution is centered at 0.66. This implies that the probability of winning the Monty Hall game when switching is around 0.66, and thus the probability of winning the Monty Hall game when staying is around 0.33.
Through this extended example, we've shown how interaction and visualization can help explore ideas and convincingly explain concepts. Congrats on making it through the tutorial! Let's now publish your results to the web.
Like the previous section of the tutorial, you should open a terminal and run the following command:
nbinteract {notebook}
Where {notebook}
is replaced with the name of the notebook you wish to convert (e.g. nbinteract monty.ipynb
).  For example, if my
username is SamLau95
, my URL is:
SamLau95.github.io/nbinteract-tutorial/monty.html


getFieldVersionHistory
ListItem
Takes an array of field names, finds the version history for field, and returns a snapshot of the object at each version. If no fields are provided, we look at the field definitions in the model and pull all non-readonly fields. The only way to do this that I've been able to get working is to get the version history for each field independently and then build the history by combining the server responses for each requests into a snapshot of the object.
getFieldVersionHistory([fieldNames]);
| Param | Type | Details |
|---|---|---|
| [fieldNames] | string[] | An array of field names that we're interested in. |
| object | promise - containing array of changes |
Assuming we have a modal form where we want to display each version of the title and project fields of a given list item.
myGenericListItem.getFieldVersionHistory(['title', 'project']) .then(function(versionHistory) { // We now have an array of every version of these fields $scope.versionHistory = versionHistory; };


seattle_flu_incidence_mapper package¶
Subpackages¶
Submodules¶
seattle_flu_incidence_mapper.apikey_auth module¶
seattle_flu_incidence_mapper.app module¶
seattle_flu_incidence_mapper.config module¶
seattle_flu_incidence_mapper.generic_models module¶
-
seattle_flu_incidence_mapper.generic_models.
create
()[source]¶ This function creates a new pathogen_model in the pathogen_models structure based on the passed in pathogen_model data :return: 201 on success, 406 on pathogen_model exists
-
seattle_flu_incidence_mapper.generic_models.
delete
(pathogen_model_id)[source]¶ This function deletes a pathogen_model from the pathogen_models structure :param pathogen_model_id: Id of the pathogen_model to delete :return: 200 on successful delete, 404 if not found
-
seattle_flu_incidence_mapper.generic_models.
model_file
(modelId)[source]¶ Returns the model file to the user :param pathogen_model_id: :return:
-
seattle_flu_incidence_mapper.generic_models.
read
(model_id)[source]¶ This function responds to a request for /api/pathogen_models/{pathogen_model_id} with one matching pathogen_model from pathogen_models :param model_id: Id of pathogen_model to find :return: pathogen_model matching id
-
seattle_flu_incidence_mapper.generic_models.
read_all
()[source]¶ This function responds to a request for /api/pathogen_models with the complete lists of models :return: json string of list of models
-
seattle_flu_incidence_mapper.generic_models.
update
(pathogen_model_id, pathogen_model)[source]¶ This function updates an existing pathogen_model in the pathogen_models structure :param pathogen_model_id: Id of the pathogen_model to update in the pathogen_models structure :param pathogen_model: pathogen_model to update :return: updated pathogen_model structure
seattle_flu_incidence_mapper.jwt module¶
seattle_flu_incidence_mapper.model_store module¶
seattle_flu_incidence_mapper.orm_config module¶
seattle_flu_incidence_mapper.query_model module¶
-
seattle_flu_incidence_mapper.query_model.
execute_model_query
(socket, file_format, outfile)[source]¶ - Parameters
file_format –
outfile –
socket –
- Returns


Guillermo Rojas Hernandez progress:
Yang Hao progress:
Natasha Rice progress:
Siddharth Basu progress:
Guillermo Rojas Hernandez progress:
Yang Hao progress:
Natasha Rice progress:
Siddharth Basu progress:
Guillermo Rojas Hernandez progress:
Yang Hao progress:
Natasha Rice progress:
Siddharth Basu progress:
The data utilized was chosen by the professor of the course, Dr. Andy Johnson.
For the application, we created bar charts and pie charts using the ggplot library once the data was processed into R Studio.
Data Manipulation
Libraries Used: