Lists
Unordered lists
Normal unordered lists
Normal unordered regular list
- first point of the list
- second point of the list
- third point of the list
Normal unordered nested list
- first point of the list
- second point of the list
- third point of the list
- first point of the list
- second point of the list
- third point of the list
Normal unordered list with skipped element
- first point of the list
- second point of the list
- third skipped point of the list
Normal unordered blank list
- first point of the blank list
- second point of the blank list
- third point of the blank list
Special unordered lists
Special unordered regular list
- first point of the list
- second point of the list
- third point of the list
Special unordered nested list
- first point of the list
- second point of the list
- third point of the list
- first point of the list
- second point of the list
- third point of the list
Special unordered list with skipped element
- first point of the list
- second point of the list
- third skipped point of the list
Special unordered blank list
- first point of the blank list
- second point of the blank list
- third point of the blank list
Ordered lists
Normal ordered lists
Normal ordered regular list
- first point of the list
- second point of the list
- third point of the list
Normal ordered nested list
- first point of the list
- second point of the list
- third point of the list
- first point of the list
- second point of the list
- third point of the list
Normal ordered list with skipped element
- first point of the list
- second point of the list
- third skipped point of the list
Normal ordered blank list
- first point of the blank list
- second point of the blank list
- third point of the blank list
Special ordered lists
Special ordered regular list
- first point of the list
- second point of the list
- third point of the list
Special ordered nested list
- first point of the list
- second point of the list
- third point of the list
- first point of the list
- second point of the list
- third point of the list
Special ordered list with skipped element
- first point of the list
- second point of the list
- third skipped point of the list
Special ordered blank list
- first point of the list
- second point of the list
- third point of the blank list
Description lists
Normal description list
- Description one
- the pattern of narrative development that aims to make vivid a place, object, character, or group.
Description is one of four rhetorical modes (also known as modes of discourse),
along with exposition, argumentation, and narration.
In practice it would be difficult to write literature that drew on just one of the four basic modes.
- Description two
- the pattern of narrative development that aims to make vivid a place, object, character, or group.
Description is one of four rhetorical modes (also known as modes of discourse),
along with exposition, argumentation, and narration.
In practice it would be difficult to write literature that drew on just one of the four basic modes.
- Description three
- the pattern of narrative development that aims to make vivid a place, object, character, or group.
Description is one of four rhetorical modes (also known as modes of discourse),
along with exposition, argumentation, and narration.
In practice it would be difficult to write literature that drew on just one of the four basic modes.
Special description list
- Description one
- the pattern of narrative development that aims to make vivid a place, object, character, or group.
Description is one of four rhetorical modes (also known as modes of discourse),
along with exposition, argumentation, and narration.
In practice it would be difficult to write literature that drew on just one of the four basic modes.
- Description two
- the pattern of narrative development that aims to make vivid a place, object, character, or group.
Description is one of four rhetorical modes (also known as modes of discourse),
along with exposition, argumentation, and narration.
In practice it would be difficult to write literature that drew on just one of the four basic modes.
- Description three
- the pattern of narrative development that aims to make vivid a place, object, character, or group.
Description is one of four rhetorical modes (also known as modes of discourse),
along with exposition, argumentation, and narration.
In practice it would be difficult to write literature that drew on just one of the four basic modes.


public interface HttpHandler
See HttpServer
.
A filter is an HttpHandler that may forward the request to another handler, possibly transforming the request beforehand, and/or the response afterwards.
We recommend that a filter is created by either a constructor or a factory method that accepts the target handler. For example:
HttpHandler handler; handler = new MyAppHandler(); handler = new FooFilter(handler); handler = BarFilter.create(handler, params); ... HttpServer server = new HttpServer(handler);
| Abstract Method | |
|---|---|
Async<HttpResponse> |
handle(HttpRequest request)
Generate a response for the request.
|
Async<HttpResponse> handle(HttpRequest request)
This is an Async action. The action must eventually complete with a
non-null HttpResponse
, or an exception.
Note that HttpResponseImpl
is a subtype of Async<HttpResponse>
,
therefore it can be returned from this method.
The response status code must not be 1xx(e.g. "100 Continue"); 1xx responses are handled automatically by lower layers.
For HttpServer handler:
handle(request)
is invoked for every incoming request.
It's always invoked on a fiber
,
with the request as the fiber-local request
.
If this action fails with an exception, HttpServer
will generate
a simple internal error
response.
request entity is not sharable; its body can be read only once.
after handle(request)
completes,
the request body must not be read any more.
HEAD requests can be treated the same as GET requests; the server will not read the response entity body.
conditional GET requests can be treated the same as normal GET requests;
they are handled
automatically
by default.
Range requests can be treated the same as normal GET requests;
they are handled
automatically
by default.


Visualize popular sorting algorithms
Generates a bar graph visualization of an array, sorts it using one out of 10 predefined algorithms and updates the graph in realtime.


${withdrawRequest.user.name} USD ${withdrawRequest.amount | usdFormat} Approve Reject Details Submitted: ${withdrawRequest.created_at} Requested amount: ${withdrawRequest.amount | usdFormat} To address: ${withdrawRequest.to_address} Status: ${withdrawRequest.completed ? 'Completed' : 'Not yet completed'}


Please enter your email address: {{ form.username(size=80) }}
Please enter your password: {{ form.password(size=80) }}
New here?? Get yourself registered straight away!!


{% extends 'program_base.html' %} {% load bootstrap3 %} {% load bornhack %} {% block title %} Facility Feedback for {{ team.name }} Team | {{ block.super }} {% endblock %} {% block content %}
No unhandled feedback found for any Facilities managed by {{ team.name }} Team. Good job!
Backoffice {% endif %}


The final step is to sign and submit your application. This is to confirm that you have finished your application and that all the information is correct.
Please agree to the following:
"The person signing is furnishing true information and to advise that person that the application is being made in connection with the receipt of Federal funds;
School officials may verify the information on the application; and
Deliberate misrepresentation of the information may subject the applicant to prosecution under State and Federal statutes."


{% extends 'base.html' %} {% load crispy_forms_tags %} {% block content %}
Please enter the email address or mobile number you used to sign up for a learning circle to opt out.
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Komodo
equaliser
100 messages
Before 05-Dec-19 12:00 AM
Gateway
BOT
08-Jul-18 09:29 PM
[dscrdeql] <!!!
Thorth
!!!> Morning @jellysmash
[dscrdeql] <!!!
Thorth
!!!> Did u eat something .... or
(edited)
[dscrdeql] <!!!
Thorth
!!!>
[dscrdeql] <Catalyst> @Thorth we learn new things everyday :)
[dscrdeql] <!!!
Thorth
!!!> Yeap... btw ... your work in what domain ?
[dscrdeql] <Catalyst> @jellysmash get well soon mate :)
[dscrdeql] <Catalyst> My work.. Am a telecommunication engineer :)
[dscrdeql] <!!!
Thorth
!!!> Aaaa nice
[dscrdeql] <Catalyst> Major in Antenna and Microwaves
[dscrdeql] <jellysmash> @Thorth hey they're
[dscrdeql] <jellysmash> No I don't think I ate something bad
[dscrdeql] <jellysmash> Just caught it from kid
[dscrdeql] <jellysmash> And they must have caught it from someone else
[dscrdeql] <!!!
Thorth
!!!> Nice... i'm an system architect... automatic system engineer.... but on software... and i what to go to hardware
[dscrdeql] <!!!
Thorth
!!!> Auch @jellysmash
[dscrdeql] <!!!
Thorth
!!!> Hope u get better soon
[dscrdeql] <jellysmash> @Thorth didn't know you were a systems architect
[dscrdeql] <jellysmash> So you write white papers?
[dscrdeql] <!!!
Thorth
!!!> Yeap a lot of... instructions... flux... logical... and some programs sometimes
[dscrdeql] <jellysmash> Cool
[dscrdeql] <Catalyst> Woah cool you mean automation right?
[dscrdeql] <DaylightHours> Why they don't call them black papers
[dscrdeql] <Catalyst> @DaylightHours that racist :P
[dscrdeql] <jellysmash> Lol
[dscrdeql] <MySoulisNotForSale> lol
[dscrdeql] <!!!
Thorth
!!!> My college name was... computer science and automatics
[dscrdeql] <DaylightHours> Lol
[dscrdeql] <Catalyst> Got it
[dscrdeql] <jellysmash> Maybe that means automation?
[dscrdeql] <jellysmash> As well
[dscrdeql] <Catalyst> Yes it means that :)
[dscrdeql] <!!!
Thorth
!!!> Yeap
[dscrdeql] <Catalyst> Jinx ahah
[dscrdeql] <!!!
Thorth
!!!> From a satelits to a nuclear power
[dscrdeql] <!!!
Thorth
!!!>
[dscrdeql] <AndronicusBass-Founder> @Catalyst ah, my dad was a telecommunications engineer
[dscrdeql] <AndronicusBass-Founder> setup satelites in indonesia
[dscrdeql] <Catalyst> Yay!!
[dscrdeql] <jellysmash> Nuclear power.....leave one bug in system and kabooom
[dscrdeql] <!!!
Thorth
!!!> @jellysmash
[dscrdeql] <Catalyst> The bug might become mutant and later turn into Wolverine :P
[dscrdeql] <Catalyst> Well I wanted to do Space engineering but it had no space in Pakistan at that time :P
[dscrdeql] <Catalyst> The puns are real ^
[dscrdeql] <!!!
Thorth
!!!> @Faisal lol yesterday was on tv X-MEN 2 ... with wolvagine as i call it
[dscrdeql] <!!!
Thorth
!!!>
[dscrdeql] <Catalyst> Noice
[dscrdeql] <Catalyst> I love his girlfriend too ahem
[dscrdeql] <jellysmash> @Thorth wolvagine...lol
[dscrdeql] <Catalyst> WT..I just saw the name ahaha
[dscrdeql] <Catalyst> Why would you call him that
[dscrdeql] <!!!

Thorth
!!!> Badin... i hear before this name
[dscrdeql] <!!!
Thorth
!!!> Hm.....
[dscrdeql] <!!!
Thorth
!!!> But where...
[dscrdeql] <Catalyst> :P
[dscrdeql] <jellysmash> @Faisal where is badeen...is that in Indonesia?
[dscrdeql] <CowboyCrypto- Founder> Just catching up, thats is pretty awesome Thort
[dscrdeql] <CowboyCrypto- Founder> Thorth*
[dscrdeql] <CowboyCrypto- Founder> what is thorth is short for Thor The Hero? lol
[dscrdeql] <!!!
Thorth
!!!> Thor
[dscrdeql] <!!!
Thorth
!!!> But from Stargate
[dscrdeql] <!!!
Thorth
!!!>
[dscrdeql] <!!!
Thorth
!!!> From there i got the ideea
[dscrdeql] <!!!
Thorth
!!!>
[dscrdeql] <!!!
Thorth
!!!> But u can call it as u said
[dscrdeql] <!!!
Thorth
!!!> Me like
[dscrdeql] <jellysmash> Have you got a cape as well @Thorth?
[dscrdeql] <sisana10> Always wanted to watch stargate
[dscrdeql] <CowboyCrypto- Founder> hahaha thats awesome I was totally just joking around
[dscrdeql] <!!!
Thorth
!!!> Lol @jellysmash
[dscrdeql] <!!!
Thorth
!!!>
[dscrdeql] <!!!
Thorth
!!!> Yeap
Gateway
BOT
08-Jul-18 10:21 PM
[dscrdeql] <NWhiskeyTango>
all
[dscrdeql] <NWhiskeyTango> Long day
[dscrdeql] <NWhiskeyTango> Hope all is well
[dscrdeql] <AndronicusBass-Founder>
[dscrdeql] <AndronicusBass-Founder> I'm slowing down lol
Gateway
BOT
08-Jul-18 10:32 PM
[dscrdeql] <jellysmash> @NWhiskeyTango it's Monday morning here
Gateway
BOT
08-Jul-18 10:45 PM
[dscrdeql] <jellysmash> At least it nice and sunny today
[dscrdeql] <Catalyst> Good night guys
[dscrdeql] <Catalyst>
Gateway
BOT
08-Jul-18 10:54 PM
[dscrdeql] <jellysmash> good night @Catalyst
[dscrdeql] <jellysmash> Ok
[dscrdeql] <jellysmash> get zee zzzzzzzzzzzzzzzzzZZZ
[dscrdeql] <cypherzero> good night
[dscrdeql] <jellysmash> it's gonna be a bright, bright sunshine day
[dscrdeql] <sourge> night catalyst
[dscrdeql] <tanya> i am in that "i dont want to work" mood
[dscrdeql] <tanya> need to check how much xp i got today
[dscrdeql] <tanya> hmm, not much
[dscrdeql] <aFastHero> A week away from home, finally made it back today!
[dscrdeql] <aFastHero> Feels nice to be back in my own domicile!
[dscrdeql] <tanya> welcome back
[dscrdeql] <AndronicusBass-Founder> Nice, camping always good to get away but coming home is always a pleasure.
[dscrdeql] <MySoulisNotForSale> did you mention earlier you're familiar with solar power?

Gateway
BOT
08-Jul-18 11:48 PM
[dscrdeql] <CowboyCrypto- Founder> ?rank
[dscrdeql] <donaldmathers>
Gateway
BOT
08-Jul-18 11:57 PM
[dscrdeql] <jellysmash> level 80, that's high


--- layout: snippet title: "Loading buttons" permalink: /examples/buttons/loading/ sitemap: false --- Primary Custom loading template


Ghost of von Hellmann:
Find out what has happened in the forest!
I'd have one of my servants show you the way, but they're all busy. Come back later!


所有类
BaseDataSet
EditPlayer
EditPlayerAction
EditTeams
EditTeamsAction
EditWinners
EditWinnersAction
Emailer
EmailPicks
EmailScores
FileCache
Game
GameManager
Home
HTMLConstants
HTMLFlexiWriter
HTMLWriter
Initialization
Instructions
Login
MadnessConstants
MadnessDatabase
MadnessServlet
MadnessWriter
MadnessWriterSkin1
MetaLog
MultiMap
Picks
PicksAction
PicksCSV
Player
PlayerManager
PlayerWithScores
Scoreboard
String1
StyleSheet
Team
TeamManager
Web1
WebImage


Jake Marshall
Many young surfers have the potential to make an impact on our sport, but none look more poised to do so than
Jake Marshall. Raised on the rippable beachbreaks and reefs of San Diego's North County, Jake has developed a
solid base of smooth rail work as well as the kind of flair that few 14-year-old surfers can match. His
progression is due in no small part to the numerous world-class talents in his neighborhood that have taught him
a thing or two about tearing Seaside apart. "I see Rob Machado, Josh Kerr, Damien Hobgood, and Taylor Knox out
all the time, and it's really fun to surf with guys on that level," says Jake. ""I look at them and think,
'That's how I have to surf if I want to make the Tour one day.' That really helps me push my limits." Already,
he's had remarkable success in a jersey, including a recent win at the U.S. Surfing Championships at Lower
Trestles. But while surf stardom seems inevitable for Jake, he's still just a kid, and knows where his
priorities should be. "I surf and try to improve all the time, but I still put a lot of emphasis on school,"
says Jake. "I know that education is really important, and if surfing doesn't work out, it's always good to have
a backup plan."


Posted by Klojure on May 3, 2016
Posted by beautifulSoup on April 18, 2016
Posted by beautifulSoup on April 12, 2016
Posted by beautifulSoup on April 11, 2016
Posted by beautifulSoup on April 8, 2016
Posted by beautifulSoup on April 1, 2016
Posted by beautifulSoup on March 31, 2016
Posted by beautifulSoup on March 23, 2016


Coding4noobies Session1 Task list
- Display the current time in the playground section
- Implement the average algo in the playground section
- Create a form a text input where you can pass in the numbers, separated by comma
- Create a button that onClick calls the wrapCalculateAverage function
- Create an html tag with id=calculatedAverage that will be populated by the value calculated by the function
- Create the wrapCalculateAverage that:
- gets the string representing the comma separated numbers
- splits the string based on comma to create an array of strings
- Convers the string array into a nubmer array
- Passes the array to the existing calculateAverage function
- Gets the average and sets it in the calculatedAverage tag
- Just like before, implement Find the largest number in array algo. This time, instead of using onlick, use addEventListener


Questioner
☰
Meetups
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user
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☰ Your upcoming Meetups
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{% extends "global/Page.html" %} {% load staticfiles otree %} {% block title %} Further Instructions {% endblock %} {% block content %}
The three participants of your group are labelled A, B and C.
After the Letter Counting task you will get feedback about the number of sequences you solved and your earnings. Subsequently, you will have the opportunity to make an investment and, finally, you will see the results for the group.
After completion of all rounds you will be asked to fill a short survey before the session ends.
Next we will ask you about your opinion about the luck involved in the game and your beliefs regarding the production of the other players. After that, you may need to wait for other participants to get ready.


${site.name} Search Result
Search Term
${search-term}
Categories
${categories}
Search Title Only
${title-only}
Has Image?
${has-image}
Price Range
${price-range}
Description
Price
Post Date
Location
Image


ISBN Search
ISBN-13:<PHONE_NUMBER>592
ISBN-10:<PHONE_NUMBER>
Authors: Willie L. Pritchett; David De Smet
Binding: Paperback
Publisher: Packt Publishing
Published: October 2013
List Price: $41.99
$34.25
+3.99 shipping
Ready to ship
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$38.54
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--- title: "Statistics About COVID-19 Cases in Toronto" author: "Frida Gomam" date: 2020-07-25T21:13:14-05:00 categories: ["R"] tags: ["R Markdown", "plot", "regression"] ---
This website highlights four different data about the COVID-19 cases in Toronto. They are gender,age group, source of infection, outbreak associated types respectively.
All of this data sets are categorical rather than numerical, so my thoughts and finds are based on the internal analysis of each single set of data. In the following discussion, you will see 1.The ratio of men to women in Toronto’s COVID-19 cases 2. What has the greatest impact on the spread of the virus? 3. What kind of population are susceptible? 4. and the outbreak associated types.
The data set I choose is supported from Toronto Open Data Portal.The topic of data set is about COVID-19 cases in Toronto.This data set contains demographic, geographic, and severity information for all confirmed and probable cases reported to and managed by Toronto Public Health since the first case was reported in January 2020 (reference at bottom).
library("tidyverse")
## ── Attaching packages ────────────────────────── tidyverse 1.3.0 ──
## ✓ ggplot2 3.3.2 ✓ purrr 0.3.4
## ✓ tibble 3.0.3 ✓ dplyr 1.0.2
## ✓ tidyr 1.1.2 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.5.0
## ── Conflicts ───────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
summary(cars)
## speed dist
## Min. : 4.0 Min. : 2.00
## 1st Qu.:12.0 1st Qu.: 26.00
## Median :15.0 Median : 36.00
## Mean :15.4 Mean : 42.98
## 3rd Qu.:19.0 3rd Qu.: 56.00
## Max. :25.0 Max. :120.00
fit <- lm(dist ~ speed, data = cars)
fit
##
## Call:
## lm(formula = dist ~ speed, data = cars)
##
## Coefficients:
## (Intercept) speed
## -17.579 3.932
You can also embed plots. See Figure 1 for example:
par(mar = c(0, 1, 0, 1))
pie(
c(280, 60, 20),
c('Sky', 'Sunny side of pyramid', 'Shady side of pyramid'),
col = c('#0292D8', '#F7EA39', '#C4B632'),
init.angle = -50, border = NA
)
title = About COVID-19 Cases in Toronto, publisher = Toronto Public Health, year = 2020, website link = https://open.toronto.ca/dataset/covid-19-cases-in-toronto/


Instructions:
Mix flour and salt in a bowl. Beat in half of the milk and whisk to a smooth batter.
Beat in the rest of the milk and the eggs.
Melt the butter in the frying pan and whisk into the batter. Stir thin pancakes of the batter in a frying pan or pancake pan.


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7 Chinese held in kidnap try
by Itchie G. CabayanSEVEN Chinese nationals who allegedly attempted to kidnap a couple — a male Chinese and a female Taiwanese — were arrested by the Manila Police District-Plaza Miranda Poliçe.Community Precinct.
Already turned over to the MPD-General Assignment and Investigation Section (MPD-GAIS) for proper disposition were suspects Fen Xiu TIan, 24; Kang Xin Hang, 30; Li Dong, 22; Yao Shu, 23; Li You Bang, 28; Li Bing, 20 and Chan Dang, 28, all Chinese nationals and residents of 20th Floor, Medical Plaza located at Amorsolo corner Dela Rosa Streets, Legaspi Village, Makati City.
A probe by GAIS showed that the alleged attempted kidnapping took place at around 6:20 p.m. in front of the Sun Star Grand Hotel on Quezon Boulevard in Quiapo, Manila the other night. The victims were identified as Xu Jun, 30 and Chloe Yuan, 35, a Chinese and a Taiwanese, respectively.
It was learned that the two were about to check in at the said hotel but it was fully-booked.
Owing to this, they asked a hotel employee to book them a cab service so they can proceed to a hotel in Las Pinas City.


PART VIII. USE OF SENIOR-SPECIFIC CERTIFICATIONS
AND PROFESSIONAL DESIGNATIONS
Note
Part heading amended by L 2012, c 34, §10.
[§431:10D-641] Purpose. The purpose of this part is to protect consumers from misleading and fraudulent marketing practices with respect to the use of senior-specific certifications and professional designations in the solicitation of, sale of, or purchase of, or advice given in connection with a life insurance or annuity product. [L 2011, c 108, pt of §7]


Streaming Music Controller
Buy me a beer 🍺🥰
Rate this extension 🤩
Press the following keyboard hotkey to control your music:
⏯to
play/pause
your music
⏩to skip to the
next
track
⏪to skip to the
previous
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Report an issue 🚨
Fork me on Github 🤘


Use the imaging APIs (Windows.Graphics.Imaging) to read and edit bitmap properties and apply transformations such as scale, crop and rotate.
Scale: |
|
Scaled width: |
|
Scaled height: |
|
User rotation: |
|
EXIF orientation: |


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Submit ideas, give feedback, start a discussion, and help keep Agilex ahead of the competition with enterprising and inventive new suggestions


--- layout: page title: About header-img: "img/about-bg.png" ---
Taylor Ishimoto is a full-time wedding photographer trying to discover her next big plan. Join her as she documents her experience becoming a Software engineer without a college degree.


Download Code
In this tutorial we will apply the DyCon toolbox to find a control to the semi-discrete semi-linear heat equation.
where $N^2A$ is the discretization of the Laplacian in 1d in $N$ nodes. We are looking for a control that after time $T$ steers the system near zero.
Setup
In this tutorial we need DyCon Toolbox, to install it we will have to write the following in our MATLAB console:
unzip('https://github.com/DeustoTech/DyCon-Computational-Platform/archive/master.zip')
addpath(genpath(fullfile(cd,'DyCon-toolbox-master')))
Methodology
In order to do so we will frame the problem as a minimization of a functional and we will apply gradient descent to find it. Note that the convexity of the functional is not proven, therefore, we will obtain a local minima for the functional. The functional considered will be:
where by $| \cdot |_{L^2}$ we understand the discrete $L^2$ norm.
Once this control is computed for a certain N, we will think on a dynamical system that models an opinion dynamics with $N$ agents communicating through a chain.
\begin{equation}\label{m1}y_t-\frac{1}{N}Ay=G(y)+Bv\end{equation}
The goal will be to compute also the control $v$ thinking model \eqref{m1} as if it was a semidiscretization of a heat equation with diffusivity $\frac{1}{N^3}$. Furthermore we will also compute the control for model \eqref{m1} with the non-linearity being non-homogeneous on $N$ and a time horizon being $T_N=N^3T$.
clear all
import casadi.*
Definition of the time
ts = SX.sym('t');
%% Discretization of the space
N = 30;
xi = 0; xf = 1;
xline = linspace(xi,xf,N);
Interior Control region between 0.5 and 0.8
w1 = 0.5; w2 = 0.8;
D = 1;
[A,B,count] = GetABmatrix(N,w1,w2,D);
we define symbolically the vectors of the state and the control
Ys = SX.sym('y',[N 1]);
Us = SX.sym('u',[count 1]);
We create the ODE object Our ODE object will have the semi-discretization of the semilinear heat equation. We set also initial conditions, define the non linearity and the interaction of the control to the dynamics.
Initial condition
Diffusion part: the discretization of the 1d Laplacian
Definition of the non-linearity
G = casadi.Function('NLT',{Ys},{10*Ys.*exp(-Ys.^2)});%%
and we define the part of the dynamics corresponding to the nonlinearity
Putting all the things together
F = casadi.Function('F',{ts,Ys,Us},{A*Ys + B*Us + G(Ys)});
%% Creation of the ODE object
%% Time horizon
T = 1;
We create the ODE-object and we change the resolution to $dt=0.01$ in order to see the variation in a small time scale. We will get the values of the solution in steps of size odeEqn.dt, if we do not care about modifying this parameter in the object, we might get the solution in certain time steps that will hide part of the dynamics.
Nt = 100;
tspan = linspace(0,T,Nt);
ipde = semilinearpde1d(Ys,Us,A,B,G,tspan,xline);
ipde.InitialCondition = Y0;
We solve the equation and we plot the free solution applying solve to odeEqn and we plot the free solution.
Yfree = solve(ipde,ZerosControl(ipde));
Yfree = full(Yfree);
figure;
surf(Yfree','EdgeColor','none');
title('Free Dynamics')
xlabel('space discretization')
ylabel('Time')
yticks([1 Nt])
yticklabels([tspan(1) tspan(end)])
We create the object that collects the formulation of an optimal control problem by means of the object that describes the dynamics odeEqn, the functional to minimize Jfun and the time horizon T
L = Function('L' ,{ts,Ys,Us},{ Us.'*Us });
Psi = Function('Psi',{Ys} ,{ 1e6*(Ys.'*Ys) });
iocp = ocp(ipde,L,Psi);
We apply the steepest descent method to obtain a local minimum (our functional might not be convex).
U0 =ZerosControl(ipde);
[OptControl ,OptState] = IpoptSolver(iocp,U0);
This is Ipopt version 3.12.3, running with linear solver mumps.
NOTE: Other linear solvers might be more efficient (see Ipopt documentation).
Number of nonzeros in equality constraint Jacobian...: 19236
Number of nonzeros in inequality constraint Jacobian.: 0
Number of nonzeros in Lagrangian Hessian.............: 3900
Total number of variables............................: 3900
variables with only lower bounds: 0
variables with lower and upper bounds: 0
variables with only upper bounds: 0
Total number of equality constraints.................: 3000
Total number of inequality constraints...............: 0
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 0
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 3.0000000e+07 9.05e+00 9.67e-01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 2.4544321e+05 1.22e-01 7.92e+00 -1.0 3.10e+01 -2.0 1.00e+00 1.00e+00f 1
2 1.8219937e+06 4.24e-03 1.73e+00 -1.0 1.31e+01 -1.6 1.00e+00 1.00e+00h 1
3 7.1220860e+04 1.67e-02 2.92e+00 -1.0 9.12e+00 -2.1 1.00e+00 1.00e+00f 1
4 8.9356500e+03 3.97e-04 8.51e-02 -1.0 2.41e+00 -2.5 1.00e+00 1.00e+00f 1
5 3.1075917e+03 1.06e-04 3.19e-03 -2.5 2.96e+00 -3.0 1.00e+00 1.00e+00f 1
6 1.3016546e+03 5.20e-05 1.19e-03 -3.8 3.60e+00 -3.5 1.00e+00 1.00e+00h 1
7 4.5871538e+02 5.64e-05 4.84e-04 -5.7 4.41e+00 -4.0 1.00e+00 1.00e+00h 1
8 1.6346578e+02 6.55e-05 1.92e-04 -5.7 5.26e+00 -4.4 1.00e+00 1.00e+00h 1
9 9.7049320e+01 1.20e-04 3.78e-05 -5.7 3.10e+00 -4.9 1.00e+00 1.00e+00h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 4.0069187e+01 9.26e-03 1.98e-05 -5.7 1.48e+01 - 1.00e+00 5.00e-01h 2
11 1.6748754e+01 4.28e-04 2.92e-06 -5.7 4.34e+00 - 1.00e+00 1.00e+00h 1
12 1.6371256e+01 4.58e-06 9.87e-09 -5.7 1.88e-01 - 1.00e+00 1.00e+00h 1
13 1.6367177e+01 1.03e-11 1.11e-13 -8.6 7.84e-04 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 13
(scaled) (unscaled)
Objective...............: 8.1835886066017175e-04 1.6367177213203433e+01
Dual infeasibility......: 1.1096830919432588e-13 2.2193661838865175e-09
Constraint violation....: 1.0345974077452524e-11 1.0345974077452524e-11
Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00
Overall NLP error.......: 1.0345974077452524e-11 2.2193661838865175e-09
Number of objective function evaluations = 19
Number of objective gradient evaluations = 14
Number of equality constraint evaluations = 19
Number of inequality constraint evaluations = 0
Number of equality constraint Jacobian evaluations = 14
Number of inequality constraint Jacobian evaluations = 0
Number of Lagrangian Hessian evaluations = 13
Total CPU secs in IPOPT (w/o function evaluations) = 0.436
Total CPU secs in NLP function evaluations = 0.057
EXIT: Optimal Solution Found.
t_proc [s] t_wall [s] n_eval
nlp_f 0.000826 0.000828 19
nlp_g 0.00916 0.00917 19
nlp_grad_f 0.00139 0.00139 15
nlp_hess_l 0.0175 0.0174 13
nlp_jac_g 0.0333 0.0333 15
solver 0.507 0.501 1
Elapsed time is 0.636870 seconds.
plotSHE(OptState,OptControl,Yfree,YT,xline,tspan,Nt)
Collective behavior dynamics
Now we apply the same procedure for the collective behavior dynamics. We will employ a function that does the algorithm explained before for the semilinear heat equation having the chance to set a diffusivity constant.
For the simulation of the model in collective behavior we will employ a diffusivity $D=\frac{1}{N^3}$.
D = 1/(N^3);
[A,B,count] = GetABmatrix(N,w1,w2,D);
%%
G = casadi.Function('NLT',{Ys},{10*Ys.*exp(-Ys.^2)});%%
%%
T = 1; Nt = 100;
tspan = linspace(0,T,Nt);
%%
ipde = semilinearpde1d(Ys,Us,A,B,G,tspan,xline);
ipde.InitialCondition = Y0;
%%
iocp = ocp(ipde,L,Psi);
U0 =ZerosControl(ipde);
%%[OptControl ,OptState] = ArmijoGradient(iocp,U0,'MaxIter',200);
[OptControl ,OptState] = IpoptSolver(iocp,U0);
This is Ipopt version 3.12.3, running with linear solver mumps.
NOTE: Other linear solvers might be more efficient (see Ipopt documentation).
Number of nonzeros in equality constraint Jacobian...: 19236
Number of nonzeros in inequality constraint Jacobian.: 0
Number of nonzeros in Lagrangian Hessian.............: 3900
Total number of variables............................: 3900
variables with only lower bounds: 0
variables with lower and upper bounds: 0
variables with only upper bounds: 0
Total number of equality constraints.................: 3000
Total number of inequality constraints...............: 0
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 0
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 3.0000000e+07 1.00e+00 9.72e-01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 1.1211823e+08 6.93e-02 1.07e+01 -1.7 1.75e+00 0.0 1.00e+00 1.00e+00h 1
2 1.0201471e+08 2.53e-02 2.23e+00 -1.7 3.50e+00 -0.5 1.00e+00 1.00e+00f 1
3 8.8642262e+07 4.18e-02 5.31e-01 -1.7 2.13e+02 - 1.00e+00 1.00e+00f 1
4 8.2505158e+07 1.39e+00 3.13e+00 -1.7 2.72e+03 - 1.00e+00 1.00e+00F 1
5 7.9212105e+07 9.68e+00 5.20e+00 -1.7 1.38e+03 - 1.00e+00 1.00e+00f 1
6 7.8291279e+07 6.94e-02 2.84e+00 -1.7 2.05e+03 - 1.00e+00 1.00e+00f 1
7 7.8090072e+07 1.57e+00 4.98e-01 -1.7 7.87e+02 - 1.00e+00 1.00e+00f 1
8 7.8026943e+07 1.54e-01 8.25e-02 -1.7 3.33e+02 - 1.00e+00 1.00e+00f 1
9 7.8491521e+07 4.41e-02 7.64e+00 -2.5 2.10e+00 -1.0 1.00e+00 1.00e+00h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
...
...
54 7.8022238e+07 4.03e-07 8.33e-10 -5.7 2.15e-02 - 1.00e+00 1.00e+00h 1
55 7.8022238e+07 9.83e-12 1.82e-12 -8.6 1.02e-04 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 55
(scaled) (unscaled)
Objective...............: 3.9011119130960064e+03 7.8022238261920124e+07
Dual infeasibility......: 1.8189894035458565e-12 3.6379788070917130e-08
Constraint violation....: 9.8296371042749797e-12 9.8296371042749797e-12
Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00
Overall NLP error.......: 9.8296371042749797e-12 3.6379788070917130e-08
Number of objective function evaluations = 111
Number of objective gradient evaluations = 56
Number of equality constraint evaluations = 111
Number of inequality constraint evaluations = 0
Number of equality constraint Jacobian evaluations = 56
Number of inequality constraint Jacobian evaluations = 0
Number of Lagrangian Hessian evaluations = 55
Total CPU secs in IPOPT (w/o function evaluations) = 1.374
Total CPU secs in NLP function evaluations = 0.308
EXIT: Optimal Solution Found.
t_proc [s] t_wall [s] n_eval
nlp_f 0.00624 0.00627 111
nlp_g 0.0634 0.0633 111
nlp_grad_f 0.00762 0.00768 57
nlp_hess_l 0.0868 0.0846 55
nlp_jac_g 0.149 0.149 57
solver 1.74 1.73 1
Elapsed time is 1.874814 seconds.
plotSHE(OptState,OptControl,Yfree,YT,xline,tspan,Nt)
Now we will change also the time horizon and we will incorporate a non-homogeneous non-linearity, we will just divide the non-linearity $G$ by $N^3$
D = 1/(N^2);
[A,B,count] = GetABmatrix(N,w1,w2,D);
%%
G = casadi.Function('NLT',{Ys},{10*Ys.*exp(-Ys.^2)/N^2} );%%
%%
T = N^2; Nt = 100;
tspan = linspace(0,T,Nt);
%%
ipde = semilinearpde1d(Ys,Us,A,B,G,tspan,xline);
ipde.InitialCondition = Y0;
%%
iocp = ocp(ipde,L,Psi);
U0 =ZerosControl(ipde);
[OptControl ,OptState] = IpoptSolver(iocp,U0);
This is Ipopt version 3.12.3, running with linear solver mumps.
NOTE: Other linear solvers might be more efficient (see Ipopt documentation).
Number of nonzeros in equality constraint Jacobian...: 19236
Number of nonzeros in inequality constraint Jacobian.: 0
Number of nonzeros in Lagrangian Hessian.............: 3900
Total number of variables............................: 3900
variables with only lower bounds: 0
variables with lower and upper bounds: 0
variables with only upper bounds: 0
Total number of equality constraints.................: 3000
Total number of inequality constraints...............: 0
inequality constraints with only lower bounds: 0
inequality constraints with lower and upper bounds: 0
inequality constraints with only upper bounds: 0
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
0 3.0000000e+07 9.05e+00 1.17e+02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0
1 2.8754020e+04 1.48e-01 4.01e+00 -1.0 2.73e+00 0.0 1.00e+00 1.00e+00f 1
2 3.0839145e+03 1.91e-02 3.08e-01 -1.0 9.19e-01 -0.5 1.00e+00 1.00e+00h 1
3 9.2703794e+02 1.17e-03 2.31e-02 -1.7 2.08e-01 -1.0 1.00e+00 1.00e+00h 1
4 4.2707593e+02 2.49e-03 9.65e-03 -3.8 2.61e-01 -1.4 1.00e+00 1.00e+00h 1
5 2.2851845e+02 1.36e-03 2.60e-03 -3.8 2.10e-01 -1.9 1.00e+00 1.00e+00h 1
6 1.5158663e+02 1.25e-03 9.80e-04 -3.8 2.38e-01 -2.4 1.00e+00 1.00e+00h 1
7 1.0229631e+02 2.37e-03 4.26e-04 -5.7 3.11e-01 -2.9 1.00e+00 1.00e+00h 1
8 8.6220145e+01 1.84e-01 3.73e-04 -5.7 2.84e+01 - 1.00e+00 1.25e-01h 4
9 1.6663658e+00 6.98e-02 4.71e-05 -5.7 2.20e+00 - 1.00e+00 1.00e+00h 1
iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls
10 1.1303218e-01 1.61e-02 9.28e-07 -5.7 4.53e-01 - 1.00e+00 1.00e+00h 1
11 3.7959122e-02 7.79e-03 2.54e-08 -5.7 2.84e-01 - 1.00e+00 1.00e+00h 1
12 2.1316195e-02 1.77e-03 4.98e-09 -5.7 1.39e-01 - 1.00e+00 1.00e+00h 1
13 1.8447375e-02 6.30e-06 5.26e-11 -5.7 8.15e-03 - 1.00e+00 1.00e+00h 1
14 1.8440851e-02 2.26e-09 9.54e-15 -8.6 1.61e-04 - 1.00e+00 1.00e+00h 1
Number of Iterations....: 14
(scaled) (unscaled)
Objective...............: 9.2204254694796125e-07 1.8440850938959225e-02
Dual infeasibility......: 9.5381182640930876e-15 1.9076236528186175e-10
Constraint violation....: 2.2559639156760625e-09 2.2559639156760625e-09
Complementarity.........: 0.0000000000000000e+00 0.0000000000000000e+00
Overall NLP error.......: 2.2559639156760625e-09 2.2559639156760625e-09
Number of objective function evaluations = 19
Number of objective gradient evaluations = 15
Number of equality constraint evaluations = 19
Number of inequality constraint evaluations = 0
Number of equality constraint Jacobian evaluations = 15
Number of inequality constraint Jacobian evaluations = 0
Number of Lagrangian Hessian evaluations = 14
Total CPU secs in IPOPT (w/o function evaluations) = 0.415
Total CPU secs in NLP function evaluations = 0.065
EXIT: Optimal Solution Found.
t_proc [s] t_wall [s] n_eval
nlp_f 0.000898 0.000903 19
nlp_g 0.00904 0.00905 19
nlp_grad_f 0.0017 0.00169 16
nlp_hess_l 0.0214 0.0214 14
nlp_jac_g 0.0354 0.035 16
solver 0.489 0.487 1
Elapsed time is 0.616536 seconds.
plotSHE(OptState,OptControl,Yfree,YT,xline,tspan,Nt)


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Wind Energy calculator
Wind speed
Degree of line of best fit:
Polynomial in degree 3
Polynomial in degree 4
Polynomial in degree 7
Polynomial in degree 7, with zeros removed
(A higher degree will have greater accuracy on average, but will take longer to compute. This difference will be imperceptible to a human, but noticeable if you are automating multiples of requests.
Further, several input values in the data set used to train this model returned an output of zero. It might be unfair to deem these as ''outliers'' in the data, as it seems equally plausible that a turbine malfunctioned, returning zero energy. Given that there is a nonzero chance of this occurring, removing these values would seem to artificially inflate the expected output value. Therefore, for completeness, the fourth option returned here has these values removed from the training set, but the others do not.)
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{% for file in files %}
Abstract
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I'm a Software Developer based in San Francisco, California.
BIO
Strong interest in full-stack development focusing on well designed, clean, responsive, and scalable code. I strive on creating/implementing/testing features that improves overall user experience, learning new technologies, and optimizing services to scale. I have experience developing in an Agile environment and enjoy solving challenges that arise during the software development cycle.
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Charty Usage
You can write Markdown!
- item1
- item2
require 'charty'
charty = Charty::Plotter.new(:pyplot)
bar = charty.bar do
series [0,1,2,3,4], [10,40,20,90,70]
series [0,1,2,3,4], [90,80,70,60,50]
series [0,1,2,3,4,5,6,7,8], [50,60,20,30,10, 90, 0, 100, 50]
range x: 0..10, y: 1..100
xlabel 'foo'
ylabel 'bar'
title 'bar plot'
end
bar.render("bar_matplot.png")


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source file: /Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/botocore/retryhandler.py
file stats: 194 lines, 129 executed: 66.5% covered
1. # Copyright (c) 2012-2013 Mitch Garnaat http://garnaat.org/ 2. # Copyright 2012-2014 Amazon.com, Inc. or its affiliates. All Rights Reserved. 3. # 4. # Licensed under the Apache License, Version 2.0 (the "License"). You 5. # may not use this file except in compliance with the License. A copy of 6. # the License is located at 7. # 8. # http://aws.amazon.com/apache2.0/ 9. # 10. # or in the "license" file accompanying this file. This file is 11. # distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF 12. # ANY KIND, either express or implied. See the License for the specific 13. # language governing permissions and limitations under the License. 14. 15. import random 16. import functools 17. import logging 18. from binascii import crc32 19. 20. from botocore.vendored.requests import ConnectionError, Timeout 21. from botocore.vendored.requests.packages.urllib3.exceptions import ClosedPoolError 22. 23. from botocore.exceptions import ChecksumError, EndpointConnectionError 24. 25. 26. logger = logging.getLogger(__name__) 27. # The only supported error for now is GENERAL_CONNECTION_ERROR 28. # which maps to requests generic ConnectionError. If we're able 29. # to get more specific exceptions from requests we can update 30. # this mapping with more specific exceptions. 31. EXCEPTION_MAP = { 32. 'GENERAL_CONNECTION_ERROR': [ 33. ConnectionError, ClosedPoolError, Timeout, 34. EndpointConnectionError 35. ], 36. } 37. 38. 39. def delay_exponential(base, growth_factor, attempts): 40. """Calculate time to sleep based on exponential function. 41. 42. The format is:: 43. 44. base * growth_factor ^ (attempts - 1) 45. 46. If ``base`` is set to 'rand' then a random number between 47. 0 and 1 will be used as the base. 48. Base must be greater than 0, otherwise a ValueError will be 49. raised. 50. 51. """ 52. if base == 'rand': 53. base = random.random() 54. elif base <= 0: 55. raise ValueError("The 'base' param must be greater than 0, " 56. "got: %s" % base) 57. time_to_sleep = base * (growth_factor ** (attempts - 1)) 58. return time_to_sleep 59. 60. 61. def create_exponential_delay_function(base, growth_factor): 62. """Create an exponential delay function based on the attempts. 63. 64. This is used so that you only have to pass it the attempts 65. parameter to calculate the delay. 66. 67. """ 68. return functools.partial( 69. delay_exponential, base=base, growth_factor=growth_factor) 70. 71. 72. def create_retry_handler(config, operation_name=None): 73. checker = create_checker_from_retry_config( 74. config, operation_name=operation_name) 75. action = create_retry_action_from_config( 76. config, operation_name=operation_name) 77. return RetryHandler(checker=checker, action=action) 78. 79. 80. def create_retry_action_from_config(config, operation_name=None): 81. # The spec has the possibility of supporting per policy 82. # actions, but right now, we assume this comes from the 83. # default section, which means that delay functions apply 84. # for every policy in the retry config (per service). 85. delay_config = config['__default__']['delay'] 86. if delay_config['type'] == 'exponential': 87. return create_exponential_delay_function( 88. base=delay_config['base'], 89. growth_factor=delay_config['growth_factor']) 90. 91. 92. def create_checker_from_retry_config(config, operation_name=None): 93. checkers = [] 94. max_attempts = None 95. retryable_exceptions = [] 96. if '__default__' in config: 97. policies = config['__default__'].get('policies', []) 98. max_attempts = config['__default__']['max_attempts'] 99. for key in policies: 100. current_config = policies[key] 101. checkers.append(_create_single_checker(current_config)) 102. retry_exception = _extract_retryable_exception(current_config) 103. if retry_exception is not None: 104. retryable_exceptions.extend(retry_exception) 105. if operation_name is not None and config.get(operation_name) is not None: 106. operation_policies = config[operation_name]['policies'] 107. for key in operation_policies: 108. checkers.append(_create_single_checker(operation_policies[key])) 109. retry_exception = _extract_retryable_exception( 110. operation_policies[key]) 111. if retry_exception is not None: 112. retryable_exceptions.extend(retry_exception) 113. if len(checkers) == 1: 114. # Don't need to use a MultiChecker 115. return MaxAttemptsDecorator(checkers[0], max_attempts=max_attempts) 116. else: 117. multi_checker = MultiChecker(checkers) 118. return MaxAttemptsDecorator( 119. multi_checker, max_attempts=max_attempts, 120. retryable_exceptions=tuple(retryable_exceptions)) 121. 122. 123. def _create_single_checker(config): 124. if 'response' in config['applies_when']: 125. return _create_single_response_checker( 126. config['applies_when']['response']) 127. elif 'socket_errors' in config['applies_when']: 128. return ExceptionRaiser() 129. 130. 131. def _create_single_response_checker(response): 132. if 'service_error_code' in response: 133. checker = ServiceErrorCodeChecker( 134. status_code=response['http_status_code'], 135. error_code=response['service_error_code']) 136. elif 'http_status_code' in response: 137. checker = HTTPStatusCodeChecker( 138. status_code=response['http_status_code']) 139. elif 'crc32body' in response: 140. checker = CRC32Checker(header=response['crc32body']) 141. else: 142. # TODO: send a signal. 143. raise ValueError("Unknown retry policy: %s" % config) 144. return checker 145. 146. 147. def _extract_retryable_exception(config): 148. applies_when = config['applies_when'] 149. if 'crc32body' in applies_when.get('response', {}): 150. return [ChecksumError] 151. elif 'socket_errors' in applies_when: 152. exceptions = [] 153. for name in applies_when['socket_errors']: 154. exceptions.extend(EXCEPTION_MAP[name]) 155. return exceptions 156. 157. 158. class RetryHandler(object): 159. """Retry handler. 160. 161. The retry handler takes two params, ``checker`` object 162. and an ``action`` object. 163. 164. The ``checker`` object must be a callable object and based on a response 165. and an attempt number, determines whether or not sufficient criteria for 166. a retry has been met. If this is the case then the ``action`` object 167. (which also is a callable) determines what needs to happen in the event 168. of a retry. 169. 170. """ 171. 172. def __init__(self, checker, action): 173. self._checker = checker 174. self._action = action 175. 176. def __call__(self, attempts, response, caught_exception, **kwargs): 177. """Handler for a retry. 178. 179. Intended to be hooked up to an event handler (hence the **kwargs), 180. this will process retries appropriately. 181. 182. """ 183. if self._checker(attempts, response, caught_exception): 184. result = self._action(attempts=attempts) 185. logger.debug("Retry needed, action of: %s", result) 186. return result 187. logger.debug("No retry needed.") 188. 189. 190. class BaseChecker(object): 191. """Base class for retry checkers. 192. 193. Each class is responsible for checking a single criteria that determines 194. whether or not a retry should not happen. 195. 196. """ 197. def __call__(self, attempt_number, response, caught_exception): 198. """Determine if retry criteria matches. 199. 200. Note that either ``response`` is not None and ``caught_exception`` is 201. None or ``response`` is None and ``caught_exception`` is not None. 202. 203. :type attempt_number: int 204. :param attempt_number: The total number of times we've attempted 205. to send the request. 206. 207. :param response: The HTTP response (if one was received). 208. 209. :type caught_exception: Exception 210. :param caught_exception: Any exception that was caught while trying to 211. send the HTTP response. 212. 213. :return: True, if the retry criteria matches (and therefore a retry 214. should occur. False if the criteria does not match. 215. 216. """ 217. # The default implementation allows subclasses to not have to check 218. # whether or not response is None or not. 219. if response is not None: 220. return self._check_response(attempt_number, response) 221. elif caught_exception is not None: 222. return self._check_caught_exception( 223. attempt_number, caught_exception) 224. else: 225. raise ValueError("Both response and caught_exception are None.") 226. 227. def _check_response(self, attempt_number, response): 228. pass 229. 230. def _check_caught_exception(self, attempt_number, caught_exception): 231. pass 232. 233. 234. class MaxAttemptsDecorator(BaseChecker): 235. """Allow retries up to a maximum number of attempts. 236. 237. This will pass through calls to the decorated retry checker, provided 238. that the number of attempts does not exceed max_attempts. It will 239. also catch any retryable_exceptions passed in. Once max_attempts has 240. been exceeded, then False will be returned or the retryable_exceptions 241. that was previously being caught will be raised. 242. 243. """ 244. def __init__(self, checker, max_attempts, retryable_exceptions=None): 245. self._checker = checker 246. self._max_attempts = max_attempts 247. self._retryable_exceptions = retryable_exceptions 248. 249. def __call__(self, attempt_number, response, caught_exception): 250. should_retry = self._should_retry(attempt_number, response, 251. caught_exception) 252. if should_retry: 253. if attempt_number >= self._max_attempts: 254. # explicitly set MaxAttemptsReached 255. if response is not None and 'ResponseMetadata' in response[1]: 256. response[1]['ResponseMetadata']['MaxAttemptsReached'] = True 257. logger.debug("Reached the maximum number of retry " 258. "attempts: %s", attempt_number) 259. return False 260. else: 261. return should_retry 262. else: 263. return False 264. 265. def _should_retry(self, attempt_number, response, caught_exception): 266. if self._retryable_exceptions and \ 267. attempt_number < self._max_attempts: 268. try: 269. return self._checker(attempt_number, response, caught_exception) 270. except self._retryable_exceptions as e: 271. logger.debug("retry needed, retryable exception caught: %s", 272. e, exc_info=True) 273. return True 274. else: 275. # If we've exceeded the max attempts we just let the exception 276. # propogate if one has occurred. 277. return self._checker(attempt_number, response, caught_exception) 278. 279. 280. class HTTPStatusCodeChecker(BaseChecker): 281. def __init__(self, status_code): 282. self._status_code = status_code 283. 284. def _check_response(self, attempt_number, response): 285. if response[0].status_code == self._status_code: 286. logger.debug( 287. "retry needed: retryable HTTP status code received: %s", 288. self._status_code) 289. return True 290. else: 291. return False 292. 293. 294. class ServiceErrorCodeChecker(BaseChecker): 295. def __init__(self, status_code, error_code): 296. self._status_code = status_code 297. self._error_code = error_code 298. 299. def _check_response(self, attempt_number, response): 300. if response[0].status_code == self._status_code: 301. actual_error_code = response[1].get('Error', {}).get('Code') 302. if actual_error_code == self._error_code: 303. logger.debug( 304. "retry needed: matching HTTP status and error code seen: " 305. "%s, %s", self._status_code, self._error_code) 306. return True 307. return False 308. 309. 310. class MultiChecker(BaseChecker): 311. def __init__(self, checkers): 312. self._checkers = checkers 313. 314. def __call__(self, attempt_number, response, caught_exception): 315. for checker in self._checkers: 316. checker_response = checker(attempt_number, response, 317. caught_exception) 318. if checker_response: 319. return checker_response 320. return False 321. 322. 323. class CRC32Checker(BaseChecker): 324. def __init__(self, header): 325. # The header where the expected crc32 is located. 326. self._header_name = header 327. 328. def _check_response(self, attempt_number, response): 329. http_response = response[0] 330. expected_crc = http_response.headers.get(self._header_name) 331. if expected_crc is None: 332. logger.debug("crc32 check skipped, the %s header is not " 333. "in the http response.", self._header_name) 334. else: 335. actual_crc32 = crc32(response[0].content) & 0xffffffff 336. if not actual_crc32 == int(expected_crc): 337. logger.debug( 338. "retry needed: crc32 check failed, expected != actual: " 339. "%s != %s", int(expected_crc), actual_crc32) 340. raise ChecksumError(checksum_type='crc32', 341. expected_checksum=int(expected_crc), 342. actual_checksum=actual_crc32) 343. 344. 345. class ExceptionRaiser(BaseChecker): 346. """Raise any caught exceptions. 347. 348. This class will raise any non None ``caught_exception``. 349. 350. """ 351. def _check_caught_exception(self, attempt_number, caught_exception): 352. # This is implementation specific, but this class is useful by 353. # coordinating with the MaxAttemptsDecorator. 354. # The MaxAttemptsDecorator has a list of exceptions it should catch 355. # and retry, but something needs to come along and actually raise the 356. # caught_exception. That's what this class is being used for. If 357. # the MaxAttemptsDecorator is not interested in retrying the exception 358. # then this exception just propogates out past the retry code. 359. raise caught_exception


TinyCLR OS
TinyCLR is our own operating system and allows you to program the FEZ in C# or Visual Basic using Microsoft's Visual Studio development environment.
To start using TinyCLR with the FEZ you must first:
- Set up your computer: Install Visual Studio, the TinyCLR OS extensions and NuGet packages.
- Set up your FEZ: Install the GHI bootloader (if not installed already) and latest TinyCLR firmware.
Setting Up Your Computer
Instructions for setting up your computer are found here on the [Getting Started]under the TinyCLR section of our documentation.
Setting Up the FEZ
The following instructions explain how to install the bootloader and TinyCLR firmware on the FEZ. The bootloader should be installed on the FEZ already. It does not need to be reinstalled unless it becomes deleted or corrupted.
The firmware may come pre-installed on the FEZ, but it might not be the latest version. If you are having problems or just want to make sure your FEZ is up to date with the latest release, use the TinyCLR Config tool to install the latest firmware. We have also included instructions for manually installing the firmware if that's what you prefer.
Loading the GHI Bootloader v2
The bootloader comes pre-installed on the FEZ and should not need to be reinstalled unless it is erased or becomes corrupted. Also, some advanced programming techniques require you to erase the bootloader (for example Mbed).
If you do need to reinstall the bootloader instructions are found on the STM32 Bootloader page under Loading the GHI Bootloader v2. When you are asked to put the FEZ in DFU mode, hold down the BOOT0 button, press and release the RESET button, and then release the BOOT0 button.
Loading the Firmware
Tip
First make sure you have bootloader v2 loaded. This needs to be done only once.
Using TinyCLR Config
Our TinyCLR Config tool includes multiple features useful for working with TinyCLR-OS-enabled devices. It simplifies the firmware update and it includes options for accessing the TinyCLR firmware at runtime.
Using this tool is the recommended path; however, the instructions for manually loading the firmware are included below. Read more on the TinyCLR Config page.
Manually Loading the Firmware
We recommend using the TinyCLR Config tool to update the firmware. As a backup, use these instructions:
- Download and save the latest FEZCLR firmware
- Put the FEZ in bootloader mode: Hold down BTN1, press and release the RESET button, and then release BTN1.
- Open any terminal software, for example Tera Term,
- Select serial and pick the COM port associated with your board. (If unsure, check Device Manager)
- Press
V
and then enter. The FEZ will respond with the installed boot loader version number (v2.x.x) - Press
U
and then enter to start the upload firmware procedure. - Press
Y
to confirm then enter. The FEZ will respond withCCCC
... - Go to
File
->Transfer
->XMODEM
->Send
and then check the1K
option. - Select the firmware file you downloaded in step 1.
- When the transfer is complete, press the RESET button on the FEZ.
Check out our TinyCLR Tutorials!
Visit our main website at www.ghielectronics.com and our community forums at forums.ghielectronics.com.


Full Name: Christopher Julianus P
Born: July 31, 1998
IPB University
Informatics Management
Associate's Degree / GPA 3.61
Experience in PHP, MySQL, JavaScript and good understanding with MVC, OOP, and RESTful API.
Top 10 Finalist at Indonesian Vocational Olympics (OLIVIA UNAIR) Branch of Web Competition
Menjalankan Praktik Kerja Lapangan (PKL) di BPPT khususnya Pusat Pelayanan Teknologi
Fresh Graduate at IPB University


MSGCMP
Section: GNU (1)Updated: November 2007
Index Return to Main Contents
NAME
msgcmp - compare message catalog and templateSYNOPSIS
msgcmp [OPTION] def.po ref.potDESCRIPTION
Compare two Uniforum style .po files to check that both contain the same set of msgid strings. The def.po file is an existing PO file with the translations. The ref.pot file is the last created PO file, or a PO Template file (generally created by xgettext). This is useful for checking that you have translated each and every message in your program. Where an exact match cannot be found, fuzzy matching is used to produce better diagnostics.
Mandatory arguments to long options are mandatory for short options too.
Input file location:
- def.po
- translations
- ref.pot
- references to the sources
- -D, --directory=DIRECTORY
- add DIRECTORY to list for input files search
Operation modifiers:
- -m, --multi-domain
- apply ref.pot to each of the domains in def.po
- --use-fuzzy
- consider fuzzy entries
- --use-untranslated
- consider untranslated entries
Input file syntax:
- -P, --properties-input
- input files are in Java .properties syntax
- --stringtable-input
- input files are in NeXTstep/GNUstep .strings syntax
Informative output:
- -h, --help
- display this help and exit
- -V, --version
- output version information and exit
AUTHOR
Written by Peter Miller.REPORTING BUGS
Report bugs to<EMAIL_ADDRESS>Copyright © 1995-1998, 2000-2007 Free Software Foundation, Inc. License GPLv3+: GNU GPL version 3 or later <http://gnu.org/licenses/gpl.html>This is free software: you are free to change and redistribute it. There is NO WARRANTY, to the extent permitted by law.
SEE ALSO
The full documentation for msgcmp is maintained as a Texinfo manual. If the info and msgcmp programs are properly installed at your site, the command- info msgcmp
should give you access to the complete manual.
Index
This document was created by man2html, using the manual pages.
Time: 05:29:06 GMT, December 24, 2015


All about the world's most magical island.
My vacation in Tahiti changed my life!
If you have never gone to Tahiti, you have never lived.
Tahiti est ma destination favorite au monde!
one rejects, dislikes, or avoids pleasure itself, because it is pleasure, but because those who do not know how to pursue pleasure rationally encounter consequences that are extremely painful. Nor again is there anyone who loves or pursues or desires to obtain pain of itself, because it is pain, but because occasionally circumstances occur in which toil and pain can procure him some great pleasure. To take a trivial example, which of us ever undertakes laborious physical exercise, except to obtain some advantage from it? But who has any right to find fault with a man who chooses to enjoy a pleasure that has no annoying consequences, or one who avoids a pain that produces no resultant pleasure?"
one rejects, dislikes, or avoids pleasure itself, because it is pleasure, but because those who do not know how to pursue pleasure rationally encounter consequences that are extremely painful. Nor again is there anyone who loves or pursues or desires to obtain pain of itself, because it is pain, but because occasionally circumstances occur in which toil and pain can procure him some great pleasure. To take a trivial example, which of us ever undertakes laborious physical exercise, except to obtain some advantage from it? But who has any right to find fault with a man who chooses to enjoy a pleasure that has no annoying consequences, or one who avoids a pain that produces no resultant pleasure?"
one rejects, dislikes, or avoids pleasure itself, because it is pleasure, but because those who do not know how to pursue pleasure rationally encounter consequences that are extremely painful. Nor again is there anyone who loves or pursues or desires to obtain pain of itself, because it is pain, but because occasionally circumstances occur in which toil and pain can procure him some great pleasure. To take a trivial example, which of us ever undertakes laborious physical exercise, except to obtain some advantage from it? But who has any right to find fault with a man who chooses to enjoy a pleasure that has no annoying consequences, or one who avoids a pain that produces no resultant pleasure?"


--- layout: page title: "The Little Book on CoffeeScript" prev: OEBPS/ch02.html next: OEBPS/ch02s03.html book_path: books/alex-maccaw-the-little-book-on-coffeescript-_oeb/ --- {% include JB/setup %} {% raw %}
How about defining class (i.e., static) properties? Well, it turns
out that inside a class definition, this
refers to the class object. In other words,
you can set class properties by setting them directly on this
.
class Animal this.find = (name) -> Animal.find("Parrot")
In fact, as you may remember, CoffeeScript aliases this
to @
,
which lets you write static properties even more succinctly:
class Animal @find: (name) -> Animal.find("Parrot")


Architects release haunting acoustic version of A Wasted Hymn
by https://www.facebook.com/metalhammer/Architects have released a video to accompany their acoustic version of A Wasted Hymn - original track featured on their latest album Holy Hell
Brighton’s finest Architects have released a video to accompany their acoustic version of A Wasted Hymn, with the original version appearing on their 2018 album Holy Hell.
The new version features visuals of stars and constellations, fitting in with the kaleidoscopic graphics on Holy Hell, which came out in November 2018 via Epitaph Records.
It was the band's first album since the death of guitarist Tom Searle, who passed away in 2016 after a long battle with cancer.
In Metal Hammer's review of the 2018 album, we said Holy Hell “isn’t the best Architects album, but it doesn’t have to be. It deviates away from the previous two albums into something more fractured, missing the mark at times, but still able to deliver knockout blows when it counts.
“As Sam sings he’ll ‘always carry the cross’ on closer A Wasted Hymn, the emotional force of the previous 40 minutes strikes in the chest, flooding all senses, highlighting just how much of Tom is in this album and in Architects.”
The band have released a number of revisited tracks from Holy Hell, including a Spotify Session recorded at London's famous Abbey Road Studios earlier in the year.


Quickstart¶
This page demonstrates how to use the Python module mushi
to infer demographic history and mutation spectrum history
We use mushi
to infer effective population size history and history of the mutation process, which we can think of as the mutation rate function over time for each of many mutation types. In mushi
, we use coalescent theory and optimization techniques to learn about this history from the \(k\)-SFS: a matrix whose columns are sample frequency spectra (SFS) for each mutation type (or just the standard SFS if we are only interested in inferring demographic history).
Imports¶
We first import the mushi
package, and a few other standard packages.
[1]:
import mushi
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
/Users/williamdewitt/Applications/miniconda2/envs/mushidocs/lib/python3.7/site-packages/traitlets/traitlets.py:3036: FutureWarning: --rc={'figure.dpi': 96} for dict-traits is deprecated in traitlets 5.0. You can pass --rc <key=value> ... multiple times to add items to a dict.
FutureWarning,
Reading sample frequency spectrum data¶
The most important class in the mushi
package is the kSFS
, which holds site frequency spectrum (SFS) data, or \(k\)-SFS data for multiple mutation types. There are several ways to instantiate a kSFS
object (for details read the API documentation):
1. From a numpy
array¶
Here we demonstrate a kSFS
instantiated from a numpy array, and then a plot of the resulting SFS.
[2]:
x = 1e6 / np.arange(1, 200)
ksfs = mushi.kSFS(X=x)
ksfs.plot_total()
plt.xscale('log')
plt.yscale('log')
/Users/williamdewitt/Applications/miniconda2/envs/mushidocs/lib/python3.7/site-packages/jax/lib/xla_bridge.py:116: UserWarning: No GPU/TPU found, falling back to CPU.
warnings.warn('No GPU/TPU found, falling back to CPU.')
2. From a tsv file¶
For example, suppose a file ksfs.tsv
contains \(k\)-SFS data (in the format generated by the seperate mutyper
package), or simply an SFS with sample frequencies in the first column and variant counts in the second. The kSFS
is instantiated as
ksfs = mushi.kSFS(file='ksfs.tsv')
3. By simulation¶
We can also instantiate by only specifying the number of number of sampled haplotypes \(n\), then generating simulated SFS or \(k\)-SFS data by specifying a demographic history and a optionally a mutation spectrum history. We will proceed with this approach for this demonstration.
[3]:
ksfs = mushi.kSFS(n=200)
We now define a true demographic history for simulation (note that for inference on real data, this is not necessary). In mushi
, the demographic history is denoted by the haploid effective population size history \(\eta(t)\) (“eta” of t), and can be constructed using the class mushi.eta
.
Note that for diploids, :math:`eta(t)=2N(t)`.
We instantiate demographic history with a vector of times (into the past) when the population size changes, and a vector of population sizes in each interval. Here we define a simple bottleneck history, and plot it.
[4]:
t = np.logspace(0, 4, 100)
y = 1e4 * np.ones(t.size + 1)
y[40:60] /= 4
eta_true = mushi.eta(t, y)
eta_true.plot()
Now we simulate SFS data using this demographic history. We also specify a constant mutation rate (mutations per genome per generation), a rate of ancestral misidentification, and a random seed.
[5]:
mu0 = 50
ksfs.simulate(eta_true, mu0, r=0.02, seed=1)
ksfs.plot_total()
plt.xscale('log')
plt.yscale('log')
Demographic inference¶
With SFS data defined, we can attempt to infer demographic history, and ask if it resembles the true history we used to simulate the data.
Demographic inference requires an estimate of the total mutation rate, and optional regularization penalties.
Regularization parameters¶
The most important regularization parameter is the trend penalty, a tuple
(k, λ)
. This indicates a trend penalty of orderk
and strengthλ
. Inference will favor piece-wise polynomial solutions, with pieces of orderk
. For example using a 0th order penalty (as we do below) will favor piece-wise constant solutions. Using a 1st order penalty would allow linear pieces, and a 2nd order penalty would allow quadratic pieces, etc. Multiple trend penalty tuples can be supplied, which will result in solutions that are a mix of pieces of different orders.An additional
ridge_penalty
parameter improves convergence without noticably affecting the solution, so we recommend using a small one, typically1e-4
.
We use a single 0th order trend penalty, to favor piecewise constant solutions. The choice of which trend orders to use is a model selection choice that should be judiciously chosen, possible based on domain knowledge about the population under study.
We also can specify the number of iterations (if we want more than the default), and a verbosity level to print info about progress.
[6]:
ksfs.infer_eta(mu0,
(0, 1e2), # <-- trend penalty
ridge_penalty = 1e-4,
max_iter=300,
verbose=True)
initial objective -4.249421e+07
iteration 300, objective -4.269e+07, relative change 2.524e-08
maximum iteration 300 reached with relative change in objective function 2.5e-08
We can now plot the fit to the SFS, as well as the inferred history alongside the true history. The inferred history is contained in the attribute eta
of the kSFS
object.
[7]:
plt.subplot(121)
ksfs.plot_total()
plt.xscale('log')
plt.yscale('log')
plt.subplot(122)
eta_true.plot(label='true')
ksfs.eta.plot(label='inferred')
plt.legend();
[7]:
<matplotlib.legend.Legend at 0x7fc6ee7c22d0>
The ancestral misidentification rate was also inferred (to fit the “smile”), and we can check that it is near the true value used to simulate.
[8]:
ksfs.r
[8]:
DeviceArray(0.02017785, dtype=float64)
Folded SFS¶
For non-model organisms, it is often difficult to polarize the ancestral state of SNPs. The SFS can be supplied with reference polarized data, and inference can be performed using the flag folded=True
, to use the folded SFS (aggregating each sample frequence with its complement). We can repeate the above inference using folded SFS inference to get similar results.
[9]:
ksfs.infer_eta(mu0,
(0, 1e2),
ridge_penalty = 1e-4,
folded=True,
max_iter=300, verbose=True)
plt.subplot(121)
ksfs.plot_total(folded=True)
plt.xscale('log')
plt.yscale('log')
plt.subplot(122)
eta_true.plot(label='true')
ksfs.eta.plot(label='inferred')
plt.legend();
initial objective -4.442544e+07
iteration 300, objective -4.443e+07, relative change 2.147e-11
maximum iteration 300 reached with relative change in objective function 2.1e-11
[9]:
<matplotlib.legend.Legend at 0x7fc6f1ba1d90>
Mutation spectrum history (mush) inference¶
We now consider data that is resolved into different mutation types (e.g. 96 triplet mutation types), and we have an SFS for each type as a separate column of a \(k\)-SFS matrix. As described above, a kSFS
can be instantiated from a numpy array, or loaded from a tsv file. Here we’ll again simulate one, so we first need to define a true mutation spectrum history.
In mushi
, the mush is denoted by a time dependent mutation rate history vector \(\boldsymbol\mu(t)\) (“mu” of t), and can be constructed using the class mushi.mu
. Each component of the vector is the mutation rate for one mutation type (e.g. one triplet type, like AAA>ACA
).
We instantiate mush with a vector of times (into the past) when the mush changes, and a matrix of mutation rate vectors in each interval. Here we define a a 96 dimensional history where all are constant in time except two: a pulse and a monotonic ramp. Here again, note that this step would not be necessary for inference on real data, but is needed for this demonstration.
To simplify things, we’ll aggregate all the constant mutation types, so we have mutation types: TCC>TTC
, GAA>GGA
, and None
(which will contain the remaining 94 mutation types.
[10]:
Z = np.ones((t.size + 1, 3))
# pulse
Z[40:70, 0] *= 2
Z[:40, 0] *= 1.2
Z[:, 0] *= 2
# ramp
Z[25:75, 1] *= np.linspace(2, 1, 50)
Z[:25, 1] *= 2
# flat
Z[:, 2] *= 94
# We model a TCC>TTC, it's revertant type GAA>GGA (for ancestral misidentification),
# and aggregate category for the remainder of mutations
mutation_types = ['TCC>TTC', 'GAA>GGA', None]
mu_true = mushi.mu(t, Z, mutation_types)
mu_true.plot(('TCC>TTC', 'GAA>GGA'), alpha=0.75, lw=3)
Now simulate new \(k\)-SFS data assuming this mush, and plot just the first two (non-constant) components.
Using the flag clr=True
when plotting the \(k\)-SFS shows relative mutation counts (using a compositional transform) at each time, so it makes it easier to visualize.
[11]:
ksfs = mushi.kSFS(n=200)
ksfs.simulate(eta_true, mu_true, r=0.02, seed=1)
plt.subplot(121)
ksfs.plot_total()
plt.xscale('log')
plt.yscale('log')
plt.subplot(122)
ksfs.plot(('TCC>TTC', 'GAA>GGA'), clr=True)
[12]:
mu0 = Z[0, :].sum()
ksfs.infer_eta(mu0,
(0, 1e2),
ridge_penalty = 1e-4,
max_iter=300, verbose=True)
plt.subplot(121)
ksfs.plot_total()
plt.xscale('log')
plt.yscale('log')
plt.subplot(122)
eta_true.plot(label='true')
ksfs.eta.plot(label='inferred')
plt.legend();
initial objective -8.880823e+07
iteration 300, objective -8.918e+07, relative change 1.540e-08
maximum iteration 300 reached with relative change in objective function 1.5e-08
[12]:
<matplotlib.legend.Legend at 0x7fc6f2ccd690>
[13]:
ksfs.infer_mush((0, 1e2), (1, 1e0), # <-- mixed trend penalties
ridge_penalty = 1e-4,
max_iter=300, verbose=True)
plt.subplot(121)
ksfs.plot(('TCC>TTC', 'GAA>GGA'), clr=True)
plt.subplot(122)
mu_true.plot(('TCC>TTC', 'GAA>GGA'), ls='--')
ksfs.mu.plot(('TCC>TTC', 'GAA>GGA'))
initial objective -8.770428e+07
iteration 300, objective -8.771e+07, relative change 3.642e-09
maximum iteration 300 reached with relative change in objective function 3.6e-09
[ ]:


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| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
public interface PhysicsConnector
A simulated physical connector, useable independently of the underlying physics engine.
| Method Summary | |
|---|---|
void |
connect()
Connect this connector to another connector, if this connector is in the immediate proximity |
void |
disconnect()
|
Connector |
getModel()
|
java.lang.String |
getName()
|
boolean |
hasProximateConnector()
Return true if is there is a connector available for interaction, false otherwise |
boolean |
isConnected()
Returns true if this connector is connected to another connector, false otherwise |
boolean |
isDisconnected()
|
void |
setConnectorBehaviorHandler(ConnectorBehaviorHandler handler)
Set the connector behavior handler for this connector |
void |
setConnectorColor(java.awt.Color color)
|
void |
setModel(Connector connector)
Set the model-level connector representing this connector simulation |
void |
setRotation(PhysicsQuaternionHolder rot)
|
void |
setTimeToConnect(float time)
|
void |
setTimeToDisconnect(float time)
|
void |
update()
Update it this connector has another connector near enough to connect to |
| Methods inherited from interface ussr.physics.PhysicsEntity |
|---|
clearDynamics, getPosition, getRotation, reset, setPosition, setRotation |
| Method Detail |
|---|
boolean hasProximateConnector()
boolean isConnected()
void connect()
other
- the connector to connect tovoid setModel(Connector connector)
connector
- the connector model to associate with this connectorConnector getModel()
void disconnect()
boolean isDisconnected()
void setConnectorColor(java.awt.Color color)
void update()
void setRotation(PhysicsQuaternionHolder rot)
void setConnectorBehaviorHandler(ConnectorBehaviorHandler handler)
handler
- the connector behavior handlerConnectorBehaviorHandler
void setTimeToConnect(float time)
void setTimeToDisconnect(float time)
java.lang.String getName()
|
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THE TWELFTH CAKE.
THE custom, apparently a very ancient one, of putting certain, articles into a rich cake, is still preserved in many districts. Usually, sixpence, a wedding-ring, and a silver thimble are employed. These are mixed up with the dough, and baked in the cake. At night the cake is divided. The person who secures the sixpence will not want money for that year; the one who has the, ring will be the first married; and the possessor of the thimble will die an old maid.
"Then also every householder,
To his abilitie
Doth make a mighty cake, that may
Suffice his companie:
Herein a pennie doth he put,
Before it come to fire;
This he divides according as
His household doth require,
And every peece distributethAs round about they stand,
Which in their names unto the poor
Is given out of hand.
But who so chanceth on the peece
Wherein the money lies,
Is counted king amongst them all;
And is with shoutes and cries
Exalted to the heavens up."
--Naogeorgus's Popish Kingdom.


{% extends "layout.html" %} {% block title %}Home{% endblock %} {% block content %}
This is a sample application to demonstrate authenticating against github oauth, using Flask, on Heroku.


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33. The Story of the Farmer
One day, Jesus was teaching a very large crowd of people near the shore of the lake. So many people came to hear him that Jesus got into a boat at the edge of the water in order to have enough space to speak to them. He sat in the boat and taught the people.
Jesus told this story. "A farmer went out to plant some seed. As he was spreading the seeds by hand, some seeds happened to fall on the path, and the birds came and ate all of those seeds."
"Other seeds fell on rocky ground, where there was very little soil. The seeds in the rocky ground sprouted quickly, but their roots were not able to go deep into the soil. When the sun came up and it got hot, the plants withered and died."
"Still other seeds fell among thorn bushes. Those seeds began to grow, but the thorns choked them out. So the plants that grew from the seeds in the thorny ground did not produce any grain."
"Other seeds fell into good soil. These seeds grew up and produced 30, 60, or even 100 times as much grain as the seed that had been planted. He who has ears, let him hear!"
This story confused the disciples. So Jesus explained, "The seed is the word of God. The path is a person who hears God's word, but does not understand it, and the devil takes the word away from him."
"The rocky ground is a person who hears God's word and accepts it with joy. But when he experiences hardship or persecution, he falls away."
"The thorny ground is a person who hears God's word, but as time passes, the cares, riches, and pleasures of life choke out his love for God. As a result, the teaching he heard does not produce fruit."
"But the good soil is a person who hears the word of God, believes it, and produces fruit."
A Bible story from: Matthew 13:1-8, 18-23; Mark 4:1-8, 13-20; Luke 8:4-15


Gecode::MiniModel::BoolExpr::Node Class Reference
#include <minimodel.hh>
Detailed Description
Node for Boolean expression.Definition at line 466 of file minimodel.hh.
Public Member Functions | |
| Node (void) | |
| Default constructor. | |
| bool | decrement (void) |
| Decrement reference count and possibly free memory. | |
| int | post (Space *home, NodeType t, BoolVarArgs &b, int i, IntConLevel icl, PropKind pk) const |
| Post propagators for nested conjunctive and disjunctive expression. | |
| void | post (Space *home, BoolVar b, IntConLevel icl, PropKind pk) const |
| Post propagators for expression. | |
| BoolVar | post (Space *home, IntConLevel icl, PropKind pk) const |
| Post propagators for expression. | |
| void | post (Space *home, bool t, IntConLevel icl, PropKind pk) const |
| Post propagators for relation. | |
Static Public Member Functions | |
| static void * | operator new (size_t size) |
| Memory management. | |
| static void | operator delete (void *p, size_t size) |
| Memory management. | |
Public Attributes | |
| unsigned int | use |
| Nodes are reference counted. | |
| unsigned int | same |
| Number of variables in subtree with same type (for AND and OR). | |
| NodeType | t |
| Type of expression. | |
| Node * | l |
| Subexpressions. | |
| Node * | r |
| BoolVar | x |
| Possibly a variable. | |
| LinRel< IntVar > | rl_int |
| Possibly a reified linear relation over integer variables. | |
| LinRel< BoolVar > | rl_bool |
| Possibly a reified linear relation over Boolean variables. |
Constructor & Destructor Documentation
| Gecode::MiniModel::BoolExpr::Node::Node | ( | void | ) | [inline] |
Member Function Documentation
| bool Gecode::MiniModel::BoolExpr::Node::decrement | ( | void | ) |
| int Gecode::MiniModel::BoolExpr::Node::post | ( | Space * | home, | |
| NodeType | t, | |||
| BoolVarArgs & | b, | |||
| int | i, | |||
| IntConLevel | icl, | |||
| PropKind | pk | |||
| ) | const |
Post propagators for nested conjunctive and disjunctive expression.
Definition at line 83 of file bool-expr.cc.
| void Gecode::MiniModel::BoolExpr::Node::post | ( | Space * | home, | |
| BoolVar | b, | |||
| IntConLevel | icl, | |||
| PropKind | pk | |||
| ) | const |
| BoolVar Gecode::MiniModel::BoolExpr::Node::post | ( | Space * | home, | |
| IntConLevel | icl, | |||
| PropKind | pk | |||
| ) | const |
| void Gecode::MiniModel::BoolExpr::Node::post | ( | Space * | home, | |
| bool | t, | |||
| IntConLevel | icl, | |||
| PropKind | pk | |||
| ) | const |
| void * Gecode::MiniModel::BoolExpr::Node::operator new | ( | size_t | size | ) | [inline, static] |
| void Gecode::MiniModel::BoolExpr::Node::operator delete | ( | void * | p, | |
| size_t | size | |||
| ) | [inline, static] |
Member Data Documentation
| unsigned int Gecode::MiniModel::BoolExpr::Node::use |
| unsigned int Gecode::MiniModel::BoolExpr::Node::same |
Number of variables in subtree with same type (for AND and OR).
Definition at line 471 of file minimodel.hh.
Definition at line 475 of file minimodel.hh.
Possibly a reified linear relation over integer variables.
Definition at line 479 of file minimodel.hh.
Possibly a reified linear relation over Boolean variables.
Definition at line 481 of file minimodel.hh.
The documentation for this class was generated from the following files:
- gecode/minimodel.hh (Revision: 7354)
- gecode/minimodel/bool-expr.cc (Revision: 6312)