Go to the source code of this file.
Classes | |
| struct | PxVehicleTypes |
| struct | PxVehicleConcreteType |
| An enumeration of concrete vehicle classes inheriting from PxBase. More... | |
| struct | PxVehicleUpdateMode |
Macros | |
| #define | PX_MAX_NB_WHEELS (20) |
| This number is the maximum number of wheels allowed for a vehicle. More... | |
| #define | PX_DEBUG_VEHICLE_ON (1) |
| Compiler setting to enable recording of telemetry data. More... | |
Functions | |
| PX_C_EXPORT bool PX_CALL_CONV | PxInitVehicleSDK (PxPhysics &physics, PxSerializationRegistry *serializationRegistry=NULL) |
| Initialize the PhysXVehicle library.  More... | |
| void | PxVehicleSetBasisVectors (const PxVec3 &up, const PxVec3 &forward) |
| Set the basis vectors of the vehicle simulation. More... | |
| void | PxVehicleSetUpdateMode (PxVehicleUpdateMode::Enum vehicleUpdateMode) |
| Set the effect of PxVehicleUpdates to be either to modify each vehicle's rigid body actor. More... | |
| void | PxVehicleSetSweepHitRejectionAngles (const PxF32 pointRejectAngle, const PxF32 normalRejectAngle) |
| Set threshold angles that are used to determine if a wheel hit is to be resolved by vehicle suspension or by rigid body collision. More... | |
| void | PxVehicleSetMaxHitActorAcceleration (const PxF32 maxHitActorAcceleration) |
| Determine the maximum acceleration experienced by PxRigidDynamic instances that are found to be in contact with a wheel. More... | |


As in many South American countries, armed robbery and violent crime does occur. The UK Foreign Office recommend exercising caution when travelling in Guyana, however most visits are trouble-free. The local authorities have been working hard over the last few years to improve the security situation for visiting yachts and are keen to develop yachting tourism.
Currently however (August 2014) fishermen in and around the Corentyne River (which is the border between Guyana and Suriname) are being violently attacked by pirates and losing anchors and fishing gear. This area should be avoided.
Georgetown is notorious for petty street crime. Do not walk alone at night, or even during the day. Whilst no reports of attacks on cruising yachts have been received, pirates attacked a number of fishing vessels in Feburary 2012 in the Pomeroon River just off the village of Cozier.
The Caribbean Safety and Security Net<EMAIL_ADDRESS>gather information by anchorage or by island, so sailors can plan their cruising in the Caribbean with an eye to appropriate behaviour and precautions wherever they decide to go.
Should you have information about a security incident, as well as contacting Noonsite please also forward details to the Caribbean Safety and Security Net, as theirs is the most comprehensive source of Caribbean security incidents against sailors on the net. Please be sure to include boat name, date of incident and anchorage/port where the incident took place.
Last updated August 2014.
The climate is hot and humid, especially from August to October. The wet seasons are from April to August, and November to January. Guyana lies outside of the hurricane belt.
Meteo France Outre-mer (in French)
For links to free global weather information, forecast services and extreme weather information see the Noonsite Weather Page
Bartica * , Georgetown * , Parika *
* indicates port of entry


Team: Chicago Bulls
Position: Guard
Height: 6' 6"
Weight: 195 lbs.
Born: 2/17/63, Brooklyn, New York
College: North Carolina
| Season | G | Min | FG | 3pt | FT | Reb | Ast | Stl | Blk | Tot | PG |
| 95-96 | 82 | 3090 | .495 | .427 | .834 | 543 | 352 | 180 | 42 | 2491 | 30.4 |
| CAREER | 766 | 29600 | .512 | .332 | .844 | 4879 | 4377 | 2025 | 739 | 24489 | 32.0 |


{% extends 'tests/base.html' %} {% block content %}
Ans: {{question.correct_answer}}
Q-ID:{{question.id}}
A) {{question.question_choice_1}}
B) {{question.question_choice_2}}
C) {{question.question_choice_3}}
D) {{question.question_choice_4}}
You must have Tutor-level access to view this page.
{% endif %} {% endblock %}


TwilightPunkster K64
09-Mar-19 07:35 AM
Still not clear. I kind of want a very specific scenario or better a set of scenarios so I can forge this in the game. There are so many ways to cheat in these games (scruffs, trainers, etc.) and primarily focussing on that will be out of scope for a demo. If its good enough, we can actually plan a good game, this is just "hey, we jammed this baby"
one idea I had was to have cryptokitty orcs
having their own dna, which could be blended if you sacrifice them to the Ogre god, having the best of two attributes
That being said, you keep units as "collectable cards" like as in Magic: The Gathering; prior the game begins you can pick 5 units from your "unlimited deck".
so instead of keeping resources, you will have to begin each map with minimal resources and work out your economy.
@FishyGuts is your artist friend someone who can whip out a model fast?
there should be a komodo mascotte xD


Download Code
Background and motivation
Let us consider the random heat equation described
by the following system:
for $G$ a domain, $G_0\subset G$ a subdomain,
$f$ a control, $y^0$ the initial configuration
and $\alpha$ the diffusivity coefficient, which is a positive
random variable with density function $\rho$.
We have that the averaged solution of (1)
is given by:
We want to determine if, given a positive random variable
$\alpha$ and an initial configuration $y^0\in L^2(G)$, there
is some $f\in L^2((0,T)\times G_0)$ such that $\tilde y(T,\cdot)=0$.
In order to illustrate the effect of averaging in
the dynamics, let us study the dynamics of
(1) when $G=\mathbb R^d$ and $f=0$.
As averaging and the Fourier transform commute, we work on the
Fourier transform of the fundamental solution of the
heat equation, which is given by:
Consequently, the Fourier transform of the average
of the fundamental solutions is given by:
i.e. the Laplace transform of $\rho$ evaluated in $|\xi|^2t$.
In particular, for $r\in(0,1)$ if $\rho(\alpha)\sim_{0^+} e^{-C\alpha^{-\frac{r}{1-r}}}$
we have that:
when $|\xi|^2t\to+\infty$, which can be proved by the Laplace method.
Thus, for those density functions
the averaged dynamics in $\mathbb R^d$ has a fractional nature. As it is proved in [1],
for $G$ bounded this is also true and we have the usual controllability
and observability results of fractional dynamics;
that is, (2) implies that
the averaged unique continuation is preserved,
but (2) preserves the null averaged observability
if and only if $r>1/2$, being the threshold density functions those
which satisfy:
Some numerical simulations
Let us now illustrate the difference between several probability distributions for the diffusion through numerical simulations. For that, we recall that
the optimal control is given by $\varphi(t,x;\phi)1_{G_0}$, for $\varphi$ the
averaged solution of
and $\phi$ the state which minimizes the functional:
Due to the hardness of the numerical computations in higher dimensions and to get
better illustrations we work in $d=1$, and in particular in $G=(0,\pi)$.
We also consider $G_0=(1,2)$, $T=1$ and $y^0=\frac{1}{2}$.
Moreover, to illustrate the difference between diffusivities inside and
outside the null controllability regime, we consider $\rho=1_{(1,2)}$, which is inside,
and $\rho=1_{(0,1)}$, which is outside.
In order to numerically implement this problem, we approximate it by
minimizing $J$ in $V_M:=\langle e_i \rangle_{i=1}^M$
for ${e_i}$ the eigenfunctions of the Dirichlet Laplacian
and for $M\in{40,50,60}$.
Since $V_M$ is a finite dimensional space, computing the
minimum of $J$ is equivalent to solving numerically a linear system,
which can be easily done by using any numerical computing environment
(in our case MATLAB). We have the following illustrations:
First, we illustrate in Figure 1
(resp. in Figure 2)
the controls induced by the minimum of $J$ for $\rho=1_{(1,2)}$
(resp. for $\rho=1_{(0,1)}$).
For $\rho=1_{(1,2)}$ the
sequence of controls converges, which is something that can be seen in an even
more clear way when $t\in[0,1/2]$. Of course, the closer the time is to $1$,
the more slowly the punctual values of the control converges pointwise with $M$
(and in $t=1$ it diverges),
which is a well-known behaviour when
controlling a parabolic dynamics (see, for instance,
[2], [3] and [4]).
However, for $\rho=1_{(0,1)}$ the sequence of controls
diverges, which is something that we can appreciate in a more
detailed way when $t\in[0,1/2]$.
Figure 1:
The optimal control for $\rho=1_{(1,2)}$ and $y^0=\frac{1}{2}$
induced by the minimum of the functional $J$ in $V_{40}$, $V_{50}$
and $V_{60}$.
In the left column we illustrate the whole controls, whereas in the right
column we illustrate the controls with the time variable zoomed in $[0,1/2]$.
Figure 2:
The optimal control for $\rho=1_{(0,1)}$ and $y^0=\frac{1}{2}$
induced by the minimum of the functional $J$ in $V_{40}$, $V_{50}$
and $V_{60}$.
In the left column we illustrate the whole controls, whereas in the right
column we illustrate the controls with the time variable zoomed in $[0,1/2]$.
Next, we show in Figure 3
the canonical prolongation of the previously obtained controls to $t=0$.
Again, for $\rho=1_{(1,2)}$ we have a clear convergence,
whereas for $\rho=1_{(0,1)}$ it diverges.
Figure 3:
The natural extensions to $t=0$ of the controls
induced by the minimum of the functional $J$ in $V_{40}$, $V_{50}$
and $V_{60}$ with $y^0=\frac{1}{2}$. In the left figure
we have considered $\rho =1_{(1,2)}$ and in the right one $\rho=1_{(0,1)}$.
Finally, we illustrate in Figure 4 the state at $t=1$
of the respective solutions of the averaged heat equation
with the previously obtained controls.
For $\rho=1_{(1,2)}$ the solution converges smoothly to $0$,
whereas for $\rho=1_{(0,1)}$ the solution diverges.
Figure 4:
The state in time $t=1$ of the averaged solutions of the heat equation after
applying the control induced by the minimum of $J$ in $V_{40}$, $V_{50}$
and $V_{60}$ with $y^0=\frac{1}{2}$. In the left figure
we have considered $\rho =1_{(1,2)}$ and in the right one $\rho=1_{(0,1)}$.
Bibliography
[1] J. A. Bárcena-Petisco, E. Zuazua, Averaged dynamics and control for heat equations with random diffusion . Preprint https://hal.archives-ouvertes.fr/hal-02958671/
[2] E. Fernández-Cara and A. Münch. Strong convergent approximations of null controls for the 1D
heat equation. SeMA journal, 61(1):49-78, 2013.
[3] R. Glowinski and J. L. Lions. Exact and approximate controllability for distributed parameter
systems. Acta Numer., 1:269-378, 1994.
[4] A. Münch and E. Zuazua. Numerical approximation of null controls for the heat equation: illposedness
and remedies. Inverse Probl., 26(8):085018, 2010.


Before RSS took over, I had a folder named "Daily" which had links to all of the URLs that I surfed to on a daily basis. One problem was that for the sites that changed daily, it was a pain to decide what was new. An even bigger problem was the ones that didn't change daily. Eventually, the pain of surfing to them daily was outweighed by the pleasure I would get when those sites were finally updated.
Sure, RSS is useful to track Scoble as he shirks his moving duties, but even more useful to track Tim when he eventually does update his blog. With RSS, I don't have to experience any pain surfing to Tim's blog day after day. Instead, I let SharpReader poll Tim's blog and when it's finally updated, I'll know about it. All gain; no pain.
What that means, unfortunately, is that I only read blogs with RSS feeds, because I've long ago gotten out of the habit of surfing my "Daily" folder. So when Josh Trupin (editor of MSDN Magazine) gave his reasons for being out of the office on Monday, he had to send me an email with a link to the description of his horrific train injury detailed on his blog. As fun a writer as Josh is, I won't go back (unless Google brings me there) 'til he's got an RSS feed. I've been hurt before; now I'm older and wiser and require an RSS feed before I'm willing to wed myself to a blog.


Record number of women to stand in General Election next month
More women than ever are set to stand in the General Election next month.
Labour has set a record with more than 50 per cent of its candidates being women, which is a historic first for any major political party in the UK.
By contrast, about three in 10 of those standing for the Conservatives and the Liberal Democrats are women.
Two of the smaller parties – the Greens and the SNP – have higher levels of female representation than both the Tories and the Lib Dems put together.
According to provisional analysis collated by PA news agency, 1,120 female candidates have registered to stand – making up 34 per cent of the total.
And while there is still some way to go for women to be given equal representation, this is the highest proportion of female candidates ever in a general election.
Advertisement Advertisement
Figures have continued to climb from 215 which saw just 26 per cent, while 2017 saw this rise to 29 per cent.
Labour is fielding 631 candidates in this election – which does not include the newly elected Speaker, Lindsay Hoyle – and 333 of these are women, making up 53 per cent of candidates.
The overall number of candidates in December’s poll is currently 3,322, which is a slight increase from 3,304 who stood in 2017.
However, this number could change over the next few days.
For Labour, the number of female candidates has seen a big jump from 42 per cent from the 2017 election.
For the Conservatives, 190 of their 635 candidates standing in this election are women, making a total of 30 per cent – this is a small increase on the last election of 29 per cent.
Of the 611 Lib Dem candidates, 188 (31 per cent) are women – up from 29 per cent last time.
The Greens have 498 candidates and 204 (41 per cent) are women, while the 20 of the SNP’s 59 candidates are women – 34 per cent.
Meanwhile, exactly a quarter of Plaid Cymru’s candidates are women: nine out of 36.
Female candidates comprise just 54 of the Brexit Party’s total of 275, or 20 per cent.
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.


Back to all the things we're doing
About the digital portfolio
The digital portfolio belongs to the Home Office. You can view the Home Office Digital blog.
Back to all the things we're doing
The digital portfolio belongs to the Home Office. You can view the Home Office Digital blog.


PostgreSQL/psycopg Backend
This are notes about PostqreSQL backend based on the psycopg adapter for Roundup issue tracker.
Prerequisites
To use PostgreSQL as backend for storing roundup data, you should additionally install:
PostgreSQL 8.x or higher - http://www.postgresql.org/
The psycopg python interface to PostgreSQL:
Running the PostgreSQL unit tests
The user that you’re running the tests as will need to be able to access the postgresql database on the local machine and create and drop databases. See the config values in ‘test/db_test_base.py’ about which database connection, name and user will be used.
Credit
The postgresql backend was originally submitted by Federico Di Gregorio <fog@initd.org>


18 October 1996
Disease Outbreak Reported
As at 18 October, 18 cases of Ebola haemorrhagic fever have been reported of which 11 were fatal in the Booué region. Two patients have recovered and five are still under treatment. Three of the four new cases were in children who had been in close contact with previously registered cases. Four patients are currently under treatment and two are recovering. A further 111 contacts have been identified. Most (100) are in the Ogooué-Ivindo Province but a few have been reported Moyen Oogoué, Latoursville, Oogoué Lolo and Libreville situated between 300 and 500 km from Booué. The Ministry of Health has taken precautions to assure that these contacts are under strict surveillance.
An isolation ward has been prepared in Booué Hospital and samples for laboratory investigation have been collected from patients and close contacts. An education and information campaign for health workers has been undertaken in regions where the epidemic has occurred and in bordering areas where there have been no reports of suspected cases.
An International Committee for Technical and Scientific Coordination was established in Gabon with the collaboration of the WHO following the announcement of an outbreak of haemorrhagic fever in the Booué region. It includes experts from the Gabonese Ministry of Health, the International Medical Research Centre in Franceville, Gabon, Coopération Française and WHO.


Monday, November 9th, 2015
Class Methods
Welcome to this week's tutorial. As always, I like to use real life analogies to get my point across. Today we are talking about class methods in Ruby, in the context of apartment hunting on Craigslist. If any of you have had the displeasure to doing this before, you'll know that these apartments often fall very short from how they were described in the ad, but you already knew this when you decided to search on Craigslist. If you wanted honesty, there are plenty of more dignified sites like StreetEasy and Naked Apartments, but you couldn't help at least glance at Craigslist, searching for that rare gem of a deal that one of your friends told you she got. Never mind that that was back in 2004 Bushwick when pizza delivery guys were getting slammed in the back of the head with 2x4's for tip money and if you worked in the city, you'd need to own a kayak beacsue the L train was so unreliable.
With the fierce competition in a market like NYC, agents need to post ads on just about every real estate site available, as many as 30 times a day. From a programmer's perspective, these agents could really benefit from using a method to generate ads quickly over and over again. The problem with coding something like this though, is that each site has a different tone and feel to it, so if we wrote an ad generator that would work for Naked Apartments, it might not necessarily for for a site like Craigslist. We would have to make a different template for each site. That's where we would use a class method.
A class is essentially a blue print for writing methods that we intend to use over and over again.  For example, a Naked Apartments headline that reads, "Bright, Roomy 2 Bed off the Myrtle-Wyckoff L/M" might be adapted for Craigslist to read "Sun-drenched, Massive 2 Bed, Steps to Jefferson L!!!!"
So since we know that our Craigslist ads need to be a bit more over the top, let's write a class that takes the facts about the apartment, and makes them sound much more appealing to the apartment-hunter.
First, let's define a class called "CraigslistApt." It's imprtant to note that class names should always begin with an uppercase letter and written in "camelcase," meaning that if there is a second word appended to the first word in the class name, that second word will start with a capital letter as well, thus mimicking the image of a camel's humps.
class CraigslistApt def initialize(neighborhood, sq_ft, bedrooms, price) @neighborhood = neighborhood @sq_ft = sq_ft @bedrooms = bedrooms @price = price end end
In the first method, "initialize," we declared "bedrooms" and "sq_ft" to be instance variables, which will allow us to use the values assigned to them in a different method within the same class. In the method, "size," we are using these values to calculate how big each bedroom is, and automatically generate a word to describe the apartment accordingly. If we did not use the @ symbol, we would not be able to access the values established in "initialize."
Now that we've declared our instance variables and can access them from anywhere in the class, let's use them to manipulate the data. We'll start by creating a method called "size" which will generate a descriptive term based on the square footage.
class CraigslistApt def initialize(neighborhood, sq_ft, bedrooms, price) @neighborhood = neighborhood @sq_ft = sq_ft @bedrooms = bedrooms @price = price end def size if @sq_ft / @bedrooms < 80 @description = "Cozy" elsif @sq_ft / @bedrooms < 100 @description = "Spacious" elsif @sq_ft / @bedrooms < 120 @description = "Massive" else @description = "OPTIMUS PRIME-SIZED" end end end
In the second block of code, above, I've created an if/else conditional statement that sets a new instance variable, "@description" to a different string to describe the apartment size, depending on the size of each bedroom. If you're wondering why I haven't accounted for a common room, it's because you know it'll just get AirBnB'd out for tenants to afford rent and not actually be considered part of the living space. We are doing this so that later in our class, we can output a string using these new instance variables, that will become a gripping Craigslist ad headline.
Using my old apartment (222 Wilson Ave.), if we want to create a new object in this class, we would do the following:
wilson_222 = CraigslistAd.new('Bushwick', 800, 2, 3000) wilson_222.size
It should be noted that technically you do not need to do something with every argument passed to a class but you do need to make sure a parameter is declared for it. For example, we assigned the following parameters to this class upon initialization:
(neighborhood, sq_ft, bedrooms, price)
But at this point, we are only working with "sq_ft" and "bedrooms."
def size if @sq_ft / @bedrooms < 80 @description = "Cozy" elsif @sq_ft / @bedrooms < 100 @description = "Spacious" elsif @sq_ft / @bedrooms < 120 @description = "Massive" else @description = "OPTIMUS PRIME-SIZED" end end
The above block of code will not output anyting when we call it because we are simply creating a new instance variable based on square feet divided by number of bedrooms. If we want to generate our headline and output a string, we need to do a few more things. For the sake of accounting for every feature we've passed to this class as an argument, let's do something with every instance variable.
class CraigslistApt def initialize(neighborhood, sq_ft, bedrooms, price) @neighborhood = neighborhood @sq_ft = sq_ft @bedrooms = bedrooms @price = price end def size if @sq_ft / @bedrooms < 80 @description = "Cozy" elsif @sq_ft / @bedrooms < 100 @description = "Spacious" elsif @sq_ft / @bedrooms < 120 @description = "Massive" else @description = "OPTIMUS PRIME-SIZED" end end end def rent if @price / @bedrooms <= 1500 @affordability = "STUPID CHEAP" elsif @price <= 2000 @affordability = "Affordable" else @affordability = "Liz Taylor Luxurious" end end def location if @neighborhood == "Williamsburg" @fantasy_neighborhood = "Prime Williamsburg!" elsif @neighborhood == "Bushwick" @fantasy_neighborhood = "Williamsburg" elsif @neighborhood == "East New York" @fantasy_neighborhood = "Bushwick" elsif @neighborhood =="Bedstuy" @fantasy_neighborhood = "Bushwick" elsif @neighborhood == "Crown Heights" @fantasy_neighborhood = "Park Slope" else @fantasy_neighborhood = "Up and coming neighborhood! Lot's of artists and musicians!" end end def ad puts "#{@description} #{@affordability} #{@bedrooms} Bed #{@fantasy_neighborhood} Apartment. WON'T LAST!!!!" end end
Now let's call the method again, and get it to print the "ad." Since the instance variables being referenced in the ad method require other methods within the class to run first, we need to call all of them like this:
wilson_222.size wilson_222.rent wilson_222.location wilson_222.ad
And our output, based on a 600 square foot, 2 bedroom apartment in Bushwick for $3000/month would be:
"OPTIMUS PRIME-SIZED STUPID CHEAP 2 Bed Williamsburg Apartment. WON'T LAST!!!!"
Hopefully this tutorial wasn't too confusing. I have to admit, I got a little carried away with poking fun at New York real estate and will probably turn this into a Javascript project at some point which will actually help NYC n00bs decode Craigslist apartment ad buzz words, but for now, it's time for me to move on to other projects! :)


Questions about Self
We all have questions that we ask ourselves and we search for answers through our family and friends. The most important lesson to learn in life is to be your own best friend. Hear what our BFF's have to say about self-love.
Why team darkskin or team lightskin?
Answers from:
Why do people judge you by what you wear and how you speak?
Answers from:
How do I love myself?
Answers from:
Why do I have to be a certain size or shape to be beautiful?
Answers from:


Dhara Patel, a Electrical and Computer Engineer studying at Franklin W. Olin College of Engineering, has a passion for astronomy and opera.
Dhara's LinkedInOriginally from Redmond, Washington, Anthony is an avid rock climber, reader, and dialectician. He is graduating from Babson College in 2019 and is starting a career in corporate finance. Eventually, he hopes to pursue a graduate degree in economics or law.
Anthony's LinkedIn


gg-tools is a set of CLI (command line interface) tools to control GoGrid cloud. It also provides a Python module with the interface to the GoGrid API. It has no additional dependencies but Python itself and should work with both CPython and Jython.
Version 0.2.0 released! Changes:
Latest release: %%VERSION%%
After you've downloaded and upacked gg-tools, just go into its directory and execute:
sudo python setup.py install
It will install both CLI tools and Python module onto your system.
All you need to do is to obtain your API key and password and put them in a file ~/.ggrc in a format:
[default] apikey: your_api_key secret: your_secret
In order to get a list of available images use gg-images-list tool. It doesn't require you to specify any arguments and is just as simple as that:
image rhel51_64_lamp (id = 21) image rhel51_64_mysql (id = 22) image rhel51_64_postgresql (id = 23) image centos51_64_apache (id = 24) image centos51_64_php (id = 25) image centos51_64_lamp (id = 26)
By example of the first line: here rhel51_64_lamp is a name of the image and 21 is its numeric id. In all gg tools if you need specify some image, you can use both id or name, so remember id or a name of the image you're interested it.
To create a server you can use gg-server-add tool. It accepts the following arguments:
As you've created your server, you'd most certainly like to log into it. :-) You can use gg-password tool to list passwords for your servers. Typical output would look this way:
id username password server 31830 root foobar1 <IP_ADDRESS> 31831 root barfoor1 <IP_ADDRESS>
A typical use would be like: gg-password | grep <IP_ADDRESS> so the grep tool leaves only server you're currently interested in.
gg-ip allows to obtain various information about IPs belonging to your account. Without specifying any arguments, gg-ip just print out all the IPs available. However, it supports various arguments which allow to perform some queries:
For example, if you want to list of all free (unassigned) public IPs, you should do:
gg-ip -t pu -s u
You can remove servers using gg-server-delete tool. You can remove it by id:
gg-server-delete -i 12345
Or you can remove it by name:
gg-server-delete -n myserv
And you can even remove a set of servers that match a regular expression:
gg-server-delete -rn 'foobar_*'
In regex mode, gg-server-delete asks confirmation before deleting servers to make sure you didn't make unexpected errors in regular expressions and don't removing something that doesn't need to be removed. However, you can pass -q key if it annoys you.
MyGSI allows to create custom server images. You can get more details on it here. The process of creating custom image with gg tools looks this way:
You need to create a new server which would serve as a basis for new image. You should use gg-server-add and pass all the arguments you usually pass to it and in addition pass -s argument which would make it a sandbox. Now, when you execute gg-server-list, each sandbox will be marked with > sign.
Once the server has provisioned, you can perform all the customizations you need. When done, execute /root/prep.py on the server and then on your own box execute:
gg-image-save -n my_new_image -s 12345 -d 'some cool image'
Where my_new_image is the name of your brand new image, 12345 is the id of the server you're using as a basis and -d is an optional switch for description of the image.
Now the new image should be available in gg-image-list and you can use it like any other usual image.
As of version 0.2.0 gg tools support having multiple config profiles. Section 'Configuration' describes how to create 'default' section. You can follow this pattern and add additional sections, like e.g. myacc2 and when you will need to switch to myacc2 you will pass -p myacc2 args to the gg tool you need to use. It should not be hard to guess that section 'default' is used by default.
Python module Documentation
Project's github page: http://github.com/novel/gg/tree/master


GraphQL Demo
This is an example application that demonstrate how to add GraphQL to your existing JAX-RS Application.
Swagger UI
(
openapi schema
)
GraphiQL
(
graphql schema
)
Examples
Source code
MicroProfile proposal


Hey there! This week I am going to discuss the differences between
As you might be the many among us who thought
---creating new instances(that falls into this specific class)
---defines the behavior of the instances
Here is an example of these two functions in action:
class Artists def initialize(first_name, last_name, artist_type) @first_name = first_name @last_name = last_name @artist_type = artist_type end def introduce "#{@first_name} #{@last_name} is a famous #{@artist_type}" end end art1 = Artists.new("Beyonce", "Knowles", "singer") puts art1.introduce
Above, with the initialize code, I am able to create an new instance 'art1' which belongs in the Artists class. Thus when I call the instance method "introduce" on my art1, it automatically plugs in the arguemetns from the initailize call and returns the result.
Now you are probably thinking: Doesn't
Just like this cup of owl looknig coffee, in
---Constructors creat new instances
---Prototypes are used to define the behaviour of instances.
JavaScript objects don’t have a formal class, and thus there’s no special syntax for defining how to create an instance or define its behaviour:
function Artists (firstName, lastName, artistType) { this.firstName = firstName; this.lastName = lastName; this.artistType = artistType; }; Artists.prototype.introduce = function () { return this.firstName + " " + this.lastName + " is a famous " + this.artistType; }; var art1 = new Artists("Beyonce", "Knowles", "singer"); console.log(art1.introduce());
As you would notice from the example above that the constructor is a function, any function can be a potential constructor in


--- # layout: vertical permalink: / ---
To decorate your
communication
& cheer up books.
To decorate your
communication
& cheer up books.
To decorate your
communication
& cheer up books.
To decorate your
communication
& cheer up books.
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You can find here my latest projects. When you click on the image, you reach the detailed page with extra info and images.
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Let’s meet and search together for a solution.


{% extends 'layout.html' %} {% block content %}
First, we need to give every sensor a name, so you can recognize them. Leave empty if you don't want to change it. Use LibreHardwareMonitor to understand the sensors.


abstract fun test(features: List<Feature>): Double
Tests the model on a set of features.
features - A set of input features.
features
Returns A mapping of the input features to an output.


Source Control Manager: git
repo:
version:
commit:
Revision Date:
Dependencies [backend]:
- hostapd
- dnsmasq
- python3 (always preinstalled on Raspbian)
- python3-automationhat (typically preinstalled on Raspbian)
Dependencies [frontend]:
- JavaScript Selector Library
- Google Material Icons
Hardware Components:
- Raspberry Pi
- Pimoroni Automation Hat


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Qpid Dispatch 1.1.0 Release Notes
Dispatch is a lightweight AMQP message router library. More about
Qpid
Dispatch.
For more information about this release, including download links and
documentation, see the release overview.
NOTE: In previous releases, inter-router and route container links were each created in their own sessions. This restricted the total number of links that can be established between routers or between routers and brokers,
since the maximum number of sessions between two containers is limited to 32,768. To avoid this limitation, DISPATCH-952 changed to use a single session for all links. In concert with this change, DISPATCH-981 ensures that links initiated by the router have their name prefixed with the originating container_id, to prevent name collisions occurring during link routing. As a result of these changes, if you upgrade to Dispatch Router 1.1.0 from a prior release, any durable subscription links being routed through a router to a broker will see their subscription name change from the brokers perspective to incorporate the container_id prefix.
New features and improvements
- DISPATCH-89 - Model the legacy topic exchange behavior of qpidd
- DISPATCH-834 - Create config tool to create/read/update/delete router config files
- DISPATCH-856 - Return router's hostname as a read-only attribute on the router entity
- DISPATCH-859 - Introduce SYSTEMD and SYSVINIT cmake switches to install files accordingly
- DISPATCH-861 - Update to recent rhea.js
- DISPATCH-864 - Remove the SYSTEMD and SYSVINIT flags introduced by DISPATCH-859
- DISPATCH-872 - Add a counter for dropped-presettleds on links
- DISPATCH-878 - qdrouterd should log real port if port 0 was specified for the listener port property in qdrouterd.conf
- DISPATCH-884 - Add schema property to allow configurable TLS protocol versions
- DISPATCH-885 - Modify qd_compose_insert_[string,symbol,binary] to add zero-length [string, symbol, binary] for null input
- DISPATCH-888 - Balanced distribution algorithm visits each link to determine the best_eligible_link
- DISPATCH-892 - Support code coverage testing
- DISPATCH-901 - add authz support to auth service plugin
- DISPATCH-911 - Add link and address level counters at the global router level
- DISPATCH-918 - Improve router config consistency and metadata
- DISPATCH-921 - Install console dependencies with npm during make install
- DISPATCH-925 - Doc: Update anchor name format
- DISPATCH-932 - Provide per-ingress router counts for deliveries on egress links
- DISPATCH-938 - Doc: Remove the "Configuration Reference"
- DISPATCH-942 - allow resumable link routes to be refused
- DISPATCH-946 - Detect if npm install needs to be executed and display a message
- DISPATCH-951 - log details for the proton found during build
- DISPATCH-963 - Router crash during shutdown in system_tests_distribution
- DISPATCH-971 - Revert DISPATCH-744 - Don't reject unsettled multicasts
- DISPATCH-972 - Dispatch Router doc should be consistent with "sudo" usage
- DISPATCH-1012 - Release undeliverable deliveries, don't hold them
Bugs fixed
- DISPATCH-580 - Log stats should be graphable
- DISPATCH-590 - List of log modules on the overview page is occationally doubled
- DISPATCH-801 - Stand-alone version of the console does not open at all when running offline
- DISPATCH-831 - Change conntector.cost default value to 1 instead of '1'
- DISPATCH-869 - Multiple brokers in a topology are displayed as a single broker
- DISPATCH-875 - Document address and link route wildcards
- DISPATCH-876 - config file linkRoute should use connection instead of connector
- DISPATCH-877 - Document how to configure TLS ciphers
- DISPATCH-879 - Document how Dispatch Router uses alternate failover URLs
- DISPATCH-880 - Document how Dispatch Router disconnects connections
- DISPATCH-886 - Console does not properly escape HTML in entity names
- DISPATCH-891 - Router incref assert in system_tests_delivery_abort
- DISPATCH-893 - Compile fails using libwebsockets 7
- DISPATCH-894 - Unable to run system tests on CentOS 6 (Python 2.6)
- DISPATCH-902 - Intermittent crash with link to broker when broker closed
- DISPATCH-905 - Dispatch Router not failing over to slave broker when master broker goes away
- DISPATCH-907 - cannot set address phase via qdmanage tool
- DISPATCH-910 - Inter-router connections with dir 'in' have no host name
- DISPATCH-912 - system_tests_user_id_proxy and system_tests_policy failing
- DISPATCH-916 - qdmanage get-attributes and get-operations not taking into account passed in type
- DISPATCH-919 - Display a warning when running Dispatch tests if python-unittest2 is not installed
- DISPATCH-922 - Subsecond timestamps improperly formatted
- DISPATCH-927 - detach not echoed back on multi-hop link route
- DISPATCH-928 - calling map_destination for 'undefined' address causes segfault
- DISPATCH-931 - Syntax error and missing dependencies in docker files
- DISPATCH-940 - When running qdrouterd with -c and -d combined, configuration file is reporting as not found
- DISPATCH-941 - Router is returning incorrect files from http get requests.
- DISPATCH-943 - segfault in remote_sasl
- DISPATCH-944 - remote_sasl plugin leaving connections open
- DISPATCH-945 - Crash on shutdown when a http+websocket connection is open
- DISPATCH-947 - system tests on master branch failing when run against latest master proton
- DISPATCH-948 - Move rhea npm dependency from dispatch-management to stand-alone
- DISPATCH-952 - qdrouterd seg fault after reporting "too many sessions"
- DISPATCH-953 - Relative path name self test fails unless dispatch is installed
- DISPATCH-954 - Fix Coverity issue on master branch
- DISPATCH-962 - links established to 'unavailable' addresses are not refused correctly
- DISPATCH-964 - Auto-links are closed with an incorrect error indication
- DISPATCH-966 - Qpid dispatch unstable inter-router connections
- DISPATCH-967 - Policy management interface does not forward log warning messages
- DISPATCH-981 - link routing results in name collisions now that single session is used
- DISPATCH-983 - link routed receiver link did not issue flow
- DISPATCH-986 - Coverity issues on master
- DISPATCH-991 - Master qdstat throws keyError when running against 1.0.1 router
- DISPATCH-992 - System test is failing in some scenarios - system_tests_delivery_abort.py
- DISPATCH-994 - segfault in qdr_link_second_attach
- DISPATCH-1021 - Router core dumps when 3 senders and 3 receivers are sending/receiving very large presettled messages to a multicast address
- DISPATCH-1022 - Router crashes when a client sends an end immediately following a detach in a link routed case
- DISPATCH-1023 - qdr_deliovery_t objects leak in two router presettled case
Tasks
- DISPATCH-833 - Remove 'Add mode' from hawtio and stand-alone consoles
- DISPATCH-862 - have the CI build log detal which commit is being used from the Proton repo
- DISPATCH-955 - Assure console works with updated schema attribute names
- DISPATCH-978 - Audit memory allocations to remove cases of uninitialized data
- DISPATCH-989 - remove defunct/broken test tool for console
Apache Qpid, Messaging built on AMQP; Copyright © 2015
The Apache Software Foundation; Licensed under
the Apache
License, Version 2.0; Apache Qpid, Qpid, Qpid Proton,
Proton, Apache, the Apache feather logo, and the Apache Qpid
project logo are trademarks of The Apache Software
Foundation; All other marks mentioned may be trademarks or
registered trademarks of their respective owners


class: center, middle, inverse, title-slide # Should we strengthen seat belt laws? ## A (semi-) Bayesian analysis ### Sam Harper ###
### 2021-03-09 --- class: center, top, inverse # .orange[**Outline**] .left[ ## .orange[**Orientation**] ## .orange[**Background**] ## .orange[**Methods**] ## .orange[**Results**] ## .orange[**Discussion**] ] --- class: center, top, inverse # .orange[**Outline**] .left[ ## .orange[**Orientation**] ## .gray[**Background**] ## .gray[**Methods**] ## .gray[**Results**] ## .gray[**Discussion**] ] --- class: center, middle # Orientation ## Bayesian framework for inference --- # Thought experiment .footnote[See Hamra et al. (2013) for intuitive description of Bayesian inference, which I modified to this example. Image credit: [Government of Canada](https://www.canada.ca/en/services/policing/police/community-safety-policing/impaired-driving/drug-impaired-driving.html)] .pull-left[ ## Suppose you are interested in the effect of exposure to cannabis on the risk of a motor vehicle crash in the next driving trip. ] .pull-right[ ![](images/cannabis-ca.png) ] --- ## Before doing your study: what do you know? .pull-left[ ### Describe your prior beliefs - Many studies have found excess risks for those consuming cannabis `\((X=1)\)` compared to those who don't `\((X=0)\)`. - You think a harmful effect is likely and a protective effect unlikely. - Based on prior literature, theory, mechanisms, biology, etc. ] -- .pull-right[ ### Quantify your prior beliefs - Translate your beliefs into probability statements. - E.g., you're 95% sure that exposure to cannabis increases the risk of a crash between 0% and 40% - Can be represented by a prior distribution: `\(N(.20, 0.1)\)`* ] .footnote[*Prior mean = average of limits = (0 + .4)/2 = 0.20; prior SD = width of interval divided by width of SD units = (|0 - 0.4| / (2x1.96)) = 0.1.] --- ## Visualizing prior information .left-column[ - Summary of our prior knowledge about the risk difference. ] .right-column[ .center[  ]] --- ## Conduct your study: what did you find? .pull-left[ ### Describe your evidence - Small pilot RCT using a driving simulator (n=100). - You find 10/50 crashed when exposed to cannabis `\((X=1)\)` and 5/50 crashed among those unexposed `\((X=0)\)` - RD=0.10 with 95%CI: (-0.04, 0.24) and variance of 0.0051 ] .pull-right[ ![](images/driving-sim.png) ] .footnote[Image credit: [Toronto Star](https://www.thestar.com/news/gta/2015/11/26/clinical-study-at-camh-tests-how-cannabis-affects-volunteers-driving-skills.html) 2015-11-26.] --- ## Adding the likelihood based on observed data .left-column[ - Plot of the likelihood based on observed data. - Note the peak of the blue curve is the maximum likelihood estimate of RD=0.10. ] .right-column[ .center[  ]] --- ## Inference: update your beliefs .footnote[Note: weights are 1/0.01 = 100 and 1/0.0051 = 196, respectively. So posterior mean = [100(0.20) + 196(0.10)]/(100 + 196) = 0.13. Variance = 1/(100 + 196) = 0.0034.] - Can combine these two pieces of evidence, since both come from normal distributions. - Inverse-variance weighting to reflect the amount of *information* given by the prior and likelihood. - Produces a summary estimate. .center[ ![:scale 80%](images/meta.png) ] --- ## Combined plot of the posterior distribution .left-column[ - Product of the prior probability and the information in the likelihood. - Revised estimate of the probability that cannabis affects crash risk. ] .right-column[ .center[  ]] --- .left-column[ .footnote[McElreath (2020)] - Normal prior not necessary - In general, the posterior `\(P(H|data)\)` is equal to `\(\frac{P(data|H)\times P(H)}{P(data)}\)` - The posterior is proportional to the product of the prior and the probability of the data. ] .right-column[ .center[ ![:scale 60%](images/non-normal-priors.png) ]] --- ## Interpretation .pull-left[ ### Frequentist inference - No probability statements about hypothesis of interest. - p-value gives evidence of compatibility of observed data with the null, given the model and assuming the null hypothesis is true. - 95%CI: If we took a large number of repeated samples and computed the 95% CI each time, then 95% of these intervals would contain the true parameter. ] .pull-right[ ### Bayesian inference: - Direct statements about probability of hypothesis from the posterior distribution. - Can say 95% probability that parameter is within the limits of the interval. - Is a learning process and is updated with new information. - But priors require subjectivity... ] --- ## Summary .right-column[ ### Honest uncertainty #### Quantitatively incorporating prior knowledge. ### Priors need defending #### Based on substance. Non-informative are rarely defensible. ### Probability statements #### More likely what you actually care about. ] --- class: center, top, inverse # .orange[**Outline**] .left[ ## .gray[**Orientation**] ## .orange[**Background**] ## .gray[**Methods**] ## .gray[**Results**] ## .gray[**Discussion**] ] --- class: center, middle # Background ## Context of seat belts and motor vehicle crash deaths --- .left-column[ - Important declines in motor vehicle crash deaths. - Many factors contributing to declines. - Yet, US lags behind other countries. - Pace of decline in US has slowed. ] .right-column[  .footnote[Source: OECD (2018), Road accidents (indicator). doi: 10.1787/2fe1b899-en (Accessed on 21 October 2018)] ] --- ## Flavors of seat belt legislation .pull-left[ ### .orange[**Primary enforcement**] ### Allows law enforcement officers to pull over drivers and ticket them if they are not wearing their seatbelts. ] -- .pull-right[ ### .orange[**Secondary enforcement**] ### Drivers pulled over for a separate violation (speeding, headlight out, etc.) can be ticketed if they are not wearing their seatbelts. ] --- .footnote[Pew Trusts, Stateline, April 28, 2017. http://bit.ly/2pFTFPp] .center[ ![:scale 70%](images/bergal-collated.png) ] --- .footnote[Source: [CDC](https://www.cdc.gov/transportationsafety/calculator/factsheet/seatbelt.html) Retreived 2021-04-09.] .pull-left[ > ### The [Centers] for Disease Control and Prevention’s systematic review of 13 high-quality studies ([Shults et al., 2004]) found that primary laws increase belt use by about 14 percentage points and reduce occupant fatalities by about 8 percent compared to secondary laws. ] .pull-right[ ![:scale 80%](images/cdc-mvscc.png) ] --- .left-column[ Current laws in existence in 2017 ] .right-column[ ![](images/laws-2017.png) .footnote[Source: Insurance Institute for Highway Safety] ] --- class: center, middle, inverse # .orange[Should Colorado upgrade to primary?] --- background-image: url(images/co-cdot.png) background-size: cover .pull-left[ > ### .white["Certainly a proven strategy that other states have adopted, and they are saving lives."] ![](images/CO-dot-primary.jpg) ] --- ### Hang on... .pull-left[ > CDOT should be concerned with building and maintaining our roads. Not joining the nanny crowd trying to protect us from our own bad decisions. > Darwinism exists for a reason. If people want to meet their windshields at 60 miles and hour and remove themselves from the gene pool, that might be good for us in the long run. > It's not a difficult sell to ask people to save their own lives. *But it shouldn't be done at gunpoint*. ] .pull-right[ ![](images/co-concerned.png) ] .footnote[http://www.9news.com, Dec 8, 2016.] --- ### Potential unintended consequences .center[ ![:scale 85%](images/aclu-disparities.png) ] .footnote[https://www.aclu.org/report/racial-disparities-florida-safety-belt-law-enforcement] --- ## Rationale for Bayesian evaluation .pull-left[ - Prior evidence on primary laws: - Strong evidence they reduce deaths and increase seat belt use; - This evidence is dated (1990s, early 2000s). - 16 states have upgraded to primary since 2000. - Concerns about unintended consequences. ] -- .pull-right[ Our aims: 1. Evaluate recent policy changes.* 2. Combine the evaluation of recent data with prior evidence. 3. Provide updated evidence on the impact of upgrading to primary enforcement. ] .footnote[*Harper and Strumpf, 2017] --- ## Intuition for Bayesian analysis .right-column[ ### Purpose #### We will generate new empirical evidence on the impact of policy changes since 2000. ### Outcomes #### Deaths per mile traveled and % of deaths wearing seat belts ### Question #### How should we approach inference from this study? ] --- ## Intuition for Bayesian analysis .pull-left[ - Frequentist analysis ignores background information. - Equivalent to belief that primary laws just as likely to: - Decrease death rates by a factor of 100 or 10. - *Increase* death rates by a factor 10 or 100. - .red[This is also a prior.] ] -- .pull-right[ - Use priors to encode existing information. - Bayesian inference explicitly incorporates prior information to estimate the posterior probability distribution: `$$\underbrace{P(\theta|D)}_{\textrm{posterior}}\propto\underbrace{P(D|\theta)}_{\textrm{ likelihood }}\times\underbrace{P(\theta)}_{\textrm{prior}}$$` ] --- class: center, middle # But where do we get our priors? --- ### Prior empirical evidence on upgrades to primary enforcement .footnote[Estimates from random effects meta-analysis. Context for RD: in 2018 33,654 MVC deaths and 3,240 billion vehicle miles traveled for a rate of 10.4 (NHTSA data)] ![](images/meta-mvc.png) -- - Mean estimate: -0.5 deaths/billion VMT (-0.7, -0.3) `\(\rightsquigarrow\)` .red[1500 deaths/yr]. - Prediction interval for new trial: (-1.97, 0.94). `\(\rightsquigarrow\)` .red[(-6000, +3000 deaths/yr).] - On relative scale `\(RR=0.95\)`, `\(95\%CI=0.83,1.09\)` --- ### Prior empirical evidence on upgrades to primary enforcement .footnote[Hedlund et al. (2008). Estimates from random effects meta-analysis.] .right-column[ .center[ 6 moderate-quality studies on proportion belted ![](images/meta-pb.png) ]] -- .left-column[ - Average estimate: 0.13 (0.10, 0.16). `\(\rightsquigarrow\)` .red[13 percentage points.] - Prediction interval for new "trial": (0.03, 0.23). - `\(RR=1.3\)`, `\(95\%CI=1.08,1.58\)` ] --- class: center, top, inverse # .orange[**Outline**] .left[ ## .gray[**Orientation**] ## .gray[**Background**] ## .orange[**Methods**] ## .gray[**Results**] ## .gray[**Discussion**] ] --- class: center, middle # Methods ## Design and analysis --- ## Semi-Bayesian analysis via data augmentation .footnote[Greenland (2006, 2007), Sullivan and Greenland (2013)] .pull-left[ - Data augmentation expresses prior information by adding empirical observations to the observed data. - Advantages: - Avoids cumbersome full Bayesian machinery (resampling, MCMC). - Approximates the posterior distribution. ] -- .pull-right[ How to execute: 1. Define priors. 2. Encode priors as observations and add to the observed data. 3. Conduct analysis on all data. 4. Regression estimate and 95% CI provide approximations to Bayesian posterior mean and credible interval. ] --- ## Why *semi-* Bayes and not fully Bayes? .right-column[ ### Focus #### Only interested in the policy effect, not other parameters. ### Clustered treatment #### Straightforward with conventional software, harder with fully specified Bayesian framework. ### Convenience #### Much less computationally intensive. ] --- ## How do we augment the observed data? .footnote[*Higgins & Spiegelhalter (2000); Greenland (2007,2008). Check these numbers yourself in Stata by typing `csi 4 4 99996 99996`] - One way to think about priors is as bets on the "true" effect. - Suppose we were 95% sure the true `\(RR\)` is between (0.25, 4). - What kind of data would correspond to the prior?* - Imagine we had a small randomized evaluation of upgrading to primary enforcement: | | Primary | Secondary | Estimates | |--------| :-------: | :--------: | -----------------------------| |Deaths | 4 | 4 | `\(RR_{prior}=1.0\)` | |Pop | 100,000 | 100,000 | `\(95\%CI_{prior}=(0.25,4.0)\)` | - Add these prior data to our observed data as an additional stratum. - Literally. --- ## Construction of priors .footnote[*NHTSA, Primary Enforcement Saves Lives (2006); CDC, Motor Vehicle Prioritizing Interventions.] - Specify prior interval for the `\(RR\)` (effect of law), translated into mean and variance. -- 3 sets of priors: 1. **Non-informative**: similar to frequentist assumptions. `\(\rightsquigarrow N(ln(1),100)\)` 2. **Empirical**: prediction interval from prior meta-analysis. - MVC: 95% bet that true `\(RR\)` between 0.83, 1.09 `\(\rightsquigarrow N(ln(0.95),0.005)\)` - %belted: 95% bet that true `\(RR\)` between 1.08, 1.58 `\(\rightsquigarrow N(ln(1.30),0.009)\)` 3. **Subjective**: from existing policy documents.* - Estimate 7-8% reduction on fatalities, 12 to 18 percentage points on seat belt use. - We add some additional uncertainty around these estimates. --- ## Summary of prior record construction .left-column[ - Recall the priors encode **information** that we append to our data. - Means adding deaths and person-time. ] .right-column[ ![](images/prior-records.png) ] --- ## Data - Person-level FARS data on fatal crashes and vehicle miles traveled (VMT) from the Fatal Analysis Reporting System (FARS), 2000-2016. - Dates of primary enforcement upgrades in each state. - Other time-varying state policies: - Speed limits; - Graduated driver's license programs; - Blood alcohol content laws. - Other time-varying state covariates: - Police per capita; - Per capita alcohol consumption; - Median income. --- ### Data structure (10 ages x 50 states x 17 years ~ n=8500) ![](images/data-structure.png) --- ### How do we add the prior? (empirical version) - First define the mean and variance for the prior ```stata set obs 1 // only adding 1 record for 1 parameter gen m = ln(0.95) // mean, i.e., RR=0.95 gen v = 0.005 // variance, equiv to 95%CI 0.83, 1.09 gen s = 25 // scaling factor ``` -- - Next set a variable representing number of prior cases, based on the variance of the prior and the scaling factor to facilitate normality (if using). ```stata gen a = round(25^2 / 0.005,0) // num of prior cases gen c = 0 // set constant to 0 for prior records, 1 for actual data ``` --- - Now set a variable for which prior is constructed to 1 for the covariate of interest, as well as equivalent person-time based on prior. Save as a dataset. ```stata gen cov = 1 / 25 gen pt = a / exp(-0.95 / 25) // person-time for prior record save "$cdir/data/derived/priorf2", replace list +-------------------------------------------------------------+ | prior m v s a c cov pt | |-------------------------------------------------------------| | 2 -.0512933 .005 25 125000 0 .04 129841.4 | +-------------------------------------------------------------+ ``` - Append this prior data to the observed data ```stata use "$cdir/data/derived/mvc-laws-prior-data.dta", clear append using "$cdir/data/derived/priorf1" ``` - Analyze the entire dataset using 'standard' methods. - No Bayesian machinery needed. --- ### Added priors (n=8503, 1 added record for each prior) ![](images/data-structure-priors.png) --- .footnote[Meyer (1995); Angrist & Pischke (2009); Ryan et al. (2015)] .left-column[ ### Study design: Difference-in-Differences Use pre/post data on treated and control groups to estimate the effect of policy. ] .right-column[ .center[ ![:scale 90%](images/ryan-fig1.png) ]] --- .left-column[ - Identification strategy: timing of legislation. - Assume that conditional on covariates, precise timing of upgrades is "as-if" random. ] .right-column[ .center[ ![:scale 90%](images/sb-laws.png) ]] --- ## Likelihood model specifications .footnote[*speed limit laws, graduated driver's license laws, BAC laws, alcohol consumption per capita, police officers per capita, state median income, police reported alcohol involved, and proportion of crash deaths on rural roads.] ### For MVC death rates - Poisson model, i.e., `\(y_{ast}\sim Poisson(\mu_{ast})\)`: `$$ln(\mu_{ast})=\alpha+{\color{red}\beta\times Primary_{st}}+\boldsymbol{\gamma}\mathbf{A}_{ast}+\boldsymbol{\delta}\mathbf{Z}_{st}+\boldsymbol{\sigma}_{s}+\boldsymbol{\tau}_{t}+ln\left(VMT_{ast}\right)$$` where: - `\({y}_{ast}\)` is deaths for age group *a*, state *s*, and time *t*. - `\(Primary=1\)` when a primary enforcement law is in effect - `\(\boldsymbol{\gamma}\)`, `\(\delta\)`, are coefficients for age groups `\((\mathbf{A}_{ast})\)`, other time-varying state covariates `\((\mathbf{Z}_{st})\)` * - `\(\boldsymbol{\sigma}\)`, and `\(\mathsf{\boldsymbol{\tau}}\)` are state and year fixed effects, respectively. - `\({VMT}_{ast}\)` is vehicle miles traveled (billions) --- ## Likelihood model specifications ### For proportion belted - Grouped logit model: `$$ln\left(p_{ast}/[1-p_{ast}]\right)=\alpha+{\color{red}\beta\times Primary_{st}}+\boldsymbol{\gamma}\mathbf{A}_{ast}+\boldsymbol{\delta}\mathbf{Z}_{st}+\boldsymbol{\sigma}_{s}+\boldsymbol{\tau}_{t}$$` where: - `\(p_{ast}\)` is the proportion of deaths wearing a seat belt in age group *a* and state *s* at time *t*. - other coefficients as before. - SEs clustered by state - Reporting marginal effects on probability scale --- class: center, top, inverse # .orange[**Outline**] .left[ ## .gray[**Orientation**] ## .gray[**Background**] ## .gray[**Methods**] ## .orange[**Results**] ## .gray[**Discussion**] ] --- ## Some descriptive comparisons .left-column[ - Important differences in covariates across levels of legislation. - Also likely for unobserved confounders. - DD approach can help. ] .right-column[ | Variable | Remained Secondary | Remained Primary | Upgrade to Primary | | -------- | :----------------: | :--------------: | :----------------: | | *Max speed limit, %*| | | | | <70mph| 60.3 | 30.1 | 35.6 | | 70mph| 9.3 | 48.2 | 57.2 | | >70mph| 30.5 | 21.7 | 7.2 | | *BAC law, %*| | | | | <0.10 | 12.8 | 4.6 | 18.3 | | <0.08 | 85.2 | 95.4 | 87.1 | ] --- .left-column[Visualization of empirical prior for MVC deaths per billion VMT] .right-column[ ![](images/prior2rd1.png) ] --- .left-column[ Visualization of empirical prior for MVC deaths per billion VMT Add likelihood ] .right-column[ ![](images/prior2rd2.png) ] --- .left-column[ Visualization of empirical prior for MVC deaths per billion VMT Add likelihood Posterior distribution ] .right-column[ ![](images/prior2rd3.png) ] --- .left-column[ Visualization of **subjective** prior for MVC deaths per billion VMT Add likelihood Posterior distribution ] .right-column[ ![](images/prior3rd3.png) ] --- ## Results for MVC deaths ![](images/t3.png) --- ## Results for proportion belted ![](images/t4.png) --- Sensitivity analysis with subjective priors (95% CIs) .center[ ![:scale 75%](images/rdplot-sens.png) ] --- ## Fully Bayesian analysis .footnote[*Rstan model bglm2 results above, 4 chains, each with iter = 2000; warmup = 1000; thin = 1; total post-warmup samples = 4000)] - Consistent with fully Bayesian estimates - Clustered standard errors more challenging in full Bayes ![](images/full-bayes.png) --- ## Added benefit of full Bayes: estimate anything! - Creating a full posterior distribution allows lots of flexibility to estimate different quantities of interest (we don't have to be slaves to NHST). - E.g., what is the probability that the law parameter is < -0.05? .left-column[ `\(P(\beta<-0.05 | \theta)=0.04\)` or < -0.03? `\(P(\beta<-0.03 | \theta)=0.87\)` These statements are impossible with frequentist inference. ] .right-column[ .center[ ![:scale 65%](images/posterior-brms.png) ]] --- class: center, top, inverse # .orange[**Outline**] .left[ ## .gray[**Orientation**] ## .gray[**Background**] ## .gray[**Methods**] ## .gray[**Results**] ## .orange[**Discussion**] ] --- class: center, middle # Given our empirical priors, model, assumptions, and data, we can revise our inference. --- ## Interpretation: Impact on MVC deaths per billion VMT .pull-left[ ### .black[Before seeing the data:] #### .white[x] ### We were 95% certain true effect was in the interval (-1.7, 0.9) ] .pull-right[ ### .black[After seeing the data:] #### .white[x] ### Now we are 95% certain the true effect is in the interval (-0.89, 0.35). ] -- #### .white[x] .center[ ### .blue[Our estimate is now closer to the null, but with less uncertainty] ] --- ## Interpretation: Impact on proportion belted .pull-left[ ### .black[Before seeing the data:] #### .white[x] ### We were 95% certain true effect was in the interval (0.03, 0.23) ] .pull-right[ ### .black[After seeing the data:] #### .white[x] ### Now we are 95% certain the true effect is in the interval (0.04, 0.07) ] -- #### .white[x] .center[ ### .blue[Much more precision, despite weaker effect] ] --- .left-column[ ### Implications - Rates still declining. - Other mechanisms? ] .right-column[ ![](images/fig1.png) ] --- Some recent evidence for unintended consequences .center[ ![:scale 80%](images/riddell-2019.png) ] --- ## Conclusions .right-column[ ### Bayesian interpretation #### Newer evidence suggests weaker impact, despite priors. ### Other mechanisms #### Continued declines in MVC death rates due to improved road and vehicle technology, traffic calming measures, or other changes. ### Heterogeneity #### Recent converts to seat belt use (due to law upgrades) may be a subpopulation with different risks. ] --- ### Reproducible materials (including these slides) ![](images/osf-pic.png) --- background-image: url(images/mcgill-university.jpg) background-size: cover class: center, middle ####<EMAIL_ADDRESS>#### .white[@sbh4th]


{{#each ticks as tick}}
{{tick}}
{{/each}}
{{#each ticks as tick}}
{{tick}}
{{/each}}
x
2
+
y
2
= 1
While it may seem obvious now, it is remarkable that a geometric shape and an equation can represent the same underlying mathematical object.


C Interface¶
-
ccall
((symbol, library) or function_pointer, ReturnType, (ArgumentType1, ...), ArgumentValue1, ...)¶ Call function in C-exported shared library, specified by
(function name, library)
tuple, where each component is a string or symbol.Note that the argument type tuple must be a literal tuple, and not a tuple-valued variable or expression. Alternatively,
ccall
may also be used to call a function pointer, such as one returned bydlsym
.Each
ArgumentValue
to theccall
will be converted to the correspondingArgumentType
, by automatic insertion of calls tounsafe_convert(ArgumentType, cconvert(ArgumentType, ArgumentValue))
. (See also the documentation for each of these functions for further details.) In most cases, this simply results in a call toconvert(ArgumentType, ArgumentValue)
.
-
cglobal
((symbol, library)[, type=Void])¶ Obtain a pointer to a global variable in a C-exported shared library, specified exactly as in
ccall
. Returns aPtr{Type}
, defaulting toPtr{Void}
if no Type argument is supplied. The values can be read or written byunsafe_load
orunsafe_store!
, respectively.
-
cfunction
(function::Function, ReturnType::Type, (ArgumentTypes...))¶ Generate C-callable function pointer from Julia function. Type annotation of the return value in the callback function is a must for situations where Julia cannot infer the return type automatically.
For example:
function foo() # body retval::Float64 end bar = cfunction(foo, Float64, ())
-
unsafe_convert
(T, x)¶ Convert
x
to a value of typeT
In cases where
convert
would need to take a Julia object and turn it into aPtr
, this function should be used to define and perform that conversion.Be careful to ensure that a Julia reference to
x
exists as long as the result of this function will be used. Accordingly, the argumentx
to this function should never be an expression, only a variable name or field reference. For example,x=a.b.c
is acceptable, butx=[a,b,c]
is not.The
unsafe
prefix on this function indicates that using the result of this function after thex
argument to this function is no longer accessible to the program may cause undefined behavior, including program corruption or segfaults, at any later time.
-
cconvert
(T, x)¶ Convert
x
to a value of typeT
, typically by callingconvert(T,x)
In cases where
x
cannot be safely converted toT
, unlikeconvert
,cconvert
may return an object of a type different fromT
, which however is suitable forunsafe_convert
to handle.Neither
convert
norcconvert
should take a Julia object and turn it into aPtr
.
-
unsafe_load
(p::Ptr{T}[, i::Integer=1])¶ Load a value of type
T
from the address of the ith element (1-indexed) starting atp
. This is equivalent to the C expressionp[i-1]
.The
unsafe
prefix on this function indicates that no validation is performed on the pointerp
to ensure that it is valid. Incorrect usage may segfault your program or return garbage answers, in the same manner as C.
-
unsafe_store!
(p::Ptr{T}, x[, i::Integer=1])¶ Store a value of type
T
to the address of the ith element (1-indexed) starting atp
. This is equivalent to the C expressionp[i-1] = x
.The
unsafe
prefix on this function indicates that no validation is performed on the pointerp
to ensure that it is valid. Incorrect usage may corrupt or segfault your program, in the same manner as C.
-
unsafe_copy!
(dest::Ptr{T}, src::Ptr{T}, N)¶ Copy
N
elements from a source pointer to a destination, with no checking. The size of an element is determined by the type of the pointers.The
unsafe
prefix on this function indicates that no validation is performed on the pointersdest
andsrc
to ensure that they are valid. Incorrect usage may corrupt or segfault your program, in the same manner as C.
-
unsafe_copy!
(dest::Array, do, src::Array, so, N) Copy
N
elements from a source array to a destination, starting at offsetso
in the source anddo
in the destination (1-indexed).The
unsafe
prefix on this function indicates that no validation is performed to ensure that N is inbounds on either array. Incorrect usage may corrupt or segfault your program, in the same manner as C.
-
copy!
(dest, src)¶ Copy all elements from collection
src
to arraydest
. Returnsdest
.
-
copy!
(dest, do, src, so, N) Copy
N
elements from collectionsrc
starting at offsetso
, to arraydest
starting at offsetdo
. Returnsdest
.
-
pointer
(array[, index])¶ Get the native address of an array or string element. Be careful to ensure that a Julia reference to
a
exists as long as this pointer will be used. This function is “unsafe” likeunsafe_convert
.Calling
Ref(array[, index])
is generally preferable to this function.
-
unsafe_wrap
(Array, pointer::Ptr{T}, dims, own=false)¶ Wrap a Julia
Array
object around the data at the address given bypointer
, without making a copy. The pointer element typeT
determines the array element type.dims
is either an integer (for a 1d array) or a tuple of the array dimensions.own
optionally specifies whether Julia should take ownership of the memory, callingfree
on the pointer when the array is no longer referenced.This function is labelled “unsafe” because it will crash if
pointer
is not a valid memory address to data of the requested length.
-
pointer_from_objref
(object_instance)¶ Get the memory address of a Julia object as a
Ptr
. The existence of the resultingPtr
will not protect the object from garbage collection, so you must ensure that the object remains referenced for the whole time that thePtr
will be used.
-
unsafe_pointer_to_objref
(p::Ptr)¶ Convert a
Ptr
to an object reference. Assumes the pointer refers to a valid heap-allocated Julia object. If this is not the case, undefined behavior results, hence this function is considered “unsafe” and should be used with care.
-
disable_sigint
(f::Function)¶ Disable Ctrl-C handler during execution of a function on the current task, for calling external code that may call julia code that is not interrupt safe. Intended to be called using
do
block syntax as follows:disable_sigint() do # interrupt-unsafe code ... end
This is not needed on worker threads (
Threads.threadid() != 1
) since theInterruptException
will only be delivered to the master thread. External functions that do not call julia code or julia runtime automatically disable sigint during their execution.
-
reenable_sigint
(f::Function)¶ Re-enable Ctrl-C handler during execution of a function. Temporarily reverses the effect of
disable_sigint
.
-
systemerror
(sysfunc, iftrue)¶ Raises a
SystemError
forerrno
with the descriptive stringsysfunc
ififtrue
istrue
-
Ptr{T}
¶ A memory address referring to data of type
T
. However, there is no guarantee that the memory is actually valid, or that it actually represents data of the specified type.
-
Ref{T}
¶ An object that safely references data of type
T
. This type is guaranteed to point to valid, Julia-allocated memory of the correct type. The underlying data is protected from freeing by the garbage collector as long as theRef
itself is referenced.When passed as a
ccall
argument (either as aPtr
orRef
type), aRef
object will be converted to a native pointer to the data it references.There is no invalid (NULL)
Ref
.
-
Cchar
¶ Equivalent to the native
char
c-type.
-
Cuchar
¶ Equivalent to the native
unsigned char
c-type (UInt8
).
-
Cshort
¶ Equivalent to the native
signed short
c-type (Int16
).
-
Cushort
¶ Equivalent to the native
unsigned short
c-type (UInt16
).
-
Cint
¶ Equivalent to the native
signed int
c-type (Int32
).
-
Cuint
¶ Equivalent to the native
unsigned int
c-type (UInt32
).
-
Clong
¶ Equivalent to the native
signed long
c-type.
-
Culong
¶ Equivalent to the native
unsigned long
c-type.
-
Clonglong
¶ Equivalent to the native
signed long long
c-type (Int64
).
-
Culonglong
¶ Equivalent to the native
unsigned long long
c-type (UInt64
).
-
Cintmax_t
¶ Equivalent to the native
intmax_t
c-type (Int64
).
-
Cuintmax_t
¶ Equivalent to the native
uintmax_t
c-type (UInt64
).
-
Csize_t
¶ Equivalent to the native
size_t
c-type (UInt
).
-
Cssize_t
¶ Equivalent to the native
ssize_t
c-type.
-
Cptrdiff_t
¶ Equivalent to the native
ptrdiff_t
c-type (Int
).
-
Cwchar_t
¶ Equivalent to the native
wchar_t
c-type (Int32
).
-
Cfloat
¶ Equivalent to the native
float
c-type (Float32
).
-
Cdouble
¶ Equivalent to the native
double
c-type (Float64
).
LLVM Interface¶
-
llvmcall
(IR::String, ReturnType, (ArgumentType1, ...), ArgumentValue1, ...)¶ -
llvmcall
((declarations::String, IR::String), ReturnType, (ArgumentType1, ...), ArgumentValue1, ...) Call LLVM IR string in the first argument. Similar to an LLVM function
define
block, arguments are available as consecutive unnamed SSA variables (%0, %1, etc.).The optional declarations string contains external functions declarations that are necessary for llvm to compile the IR string. Multiple declarations can be passed in by separating them with line breaks.
Note that the argument type tuple must be a literal tuple, and not a tuple-valued variable or expression.
Each
ArgumentValue
tollvmcall
will be converted to the correspondingArgumentType
, by automatic insertion of calls tounsafe_convert(ArgumentType, cconvert(ArgumentType, ArgumentValue))
. (see also the documentation for each of these functions for further details). In most cases, this simply results in a call toconvert(ArgumentType, ArgumentValue)
.See
test/llvmcall.jl
for usage examples.


Create CheckBoxes using the CreateCheckBox method of the app object:
Use the SetOnTouch method of the CheckBox object to set the name of a function you want to be called when the CheckBox is touched. You can read the 'isChecked' parameter in your callback function to get the state of the CheckBox
See 'CreateCheckBox for more informations and a complete function list


Simple TMX Library documentation¶
This library reads and writes the Tiled TMX format in a simple way. This is useful for map editors or generic level editors, and it’s also useful for using a map editor or generic level editor like Tiled to edit your game’s levels.
To load a TMX file, use tmx.TileMap.load()
. You can then read the
attributes of the returned tmx.TileMap
object, modify the
attributes to your liking, and save your changes with
tmx.TileMap.save()
. That’s it! Simple, isn’t it?
At the request of the developer of Tiled, this documentation does not explain in detail what each attribute means. For that, please see the TMX format specification, found here:
http://doc.mapeditor.org/reference/tmx-map-format/
tmx.TileMap¶
-
class
tmx.
TileMap
¶ This class loads, stores, and saves TMX files.
-
version
¶ The TMX format version.
-
orientation
¶ Map orientation. Can be “orthogonal”, “isometric”, “staggered”, or “hexagonal”.
-
renderorder
¶ The order in which tiles are rendered. Can be
"right-down"
,"right-up"
,"left-down"
, or"left-up"
. Default is"right-down"
.
-
width
¶ The width of the map in tiles.
-
height
¶ The height of the map in tiles.
-
tilewidth
¶ The width of a tile.
-
tileheight
¶ The height of a tile.
-
staggeraxis
¶ Determines which axis is staggered. Can be “x” or “y”. Set to
None
to not set it. Only meaningful for staggered and hexagonal maps.
-
staggerindex
¶ Determines what indexes along the staggered axis are shifted. Can be “even” or “odd”. Set to
None
to not set it. Only meaningful for staggered and hexagonal maps.
-
hexsidelength
¶ Side length of the hexagon in hexagonal tiles. Set to
None
to not set it. Only meaningful for hexagonal maps.
-
backgroundcolor
¶ The background color of the map as a hex string (e.g.
"FF0000"
or"#00FF00"
), orNone
if no background color is defined.
-
nextobjectid
¶ The next available ID for new objects. Set to
None
to not set it.
-
layers
¶ A list of
Layer
,ObjectGroup
, andImageLayer
objects indicating the map’s tile layers, object groups, and image layers, respectively. Those that appear in this list first are rendered first (i.e. furthest in the back).
-
-
classmethod
TileMap.
load
(fname)¶ Load the TMX file with the indicated name and return a
TileMap
object representing it.
-
TileMap.
save
(fname, data_encoding='base64', data_compression=True)¶ Save the object to the file with the indicated name.
Arguments:
data_encoding
– The encoding to use for layers. Can be"base64"
or"csv"
. Set toNone
for the default encoding (currently"base64"
).data_compression
– Whether or not compression should be used on layers if possible (currently only possible for base64-encoded data).
Other Classes¶
-
class
tmx.
Image
(format_=None, source=None, trans=None, width=None, height=None, data=None)¶ -
format
¶ Indicates the format of image data if embedded. Should be an extension like
"png"
,"gif"
,"jpg"
, or"bmp"
. Set toNone
to not specify the format.
-
source
¶ The location of the image file referenced. If set to
None
, the image data is embedded.
-
trans
¶ The transparent color of the image as a hex string (e.g.
"FF0000"
or"#00FF00"
), orNone
if no color is treated as transparent.
-
width
¶ The width of the image in pixels; used for tile index correction when the image changes. If set to
None
, the image width is not explicitly specified.
-
height
¶ The height of the image in pixels; used for tile index correction when the image changes. If set to
None
, the image height is not explicitly specified.
-
data
¶ The image data if embedded, or
None
if an external image is referenced.
-
-
class
tmx.
ImageLayer
(name, offsetx, offsety, opacity=1, visible=True, properties=None, image=None)¶ -
name
¶ The name of the image layer.
-
offsetx
¶ The x position of the image layer in pixels.
-
offsety
¶ The y position of the image layer in pixels.
-
opacity
¶ The opacity of the image layer as a value from 0 to 1.
-
visible
¶ Whether or not the image layer is visible.
-
-
class
tmx.
Layer
(name, opacity=1, visible=True, offsetx=0, offsety=0, properties=None, tiles=None)¶ -
name
¶ The name of the layer.
-
opacity
¶ The opacity of the layer as a value from 0 to 1.
-
visible
¶ Whether or not the layer is visible.
-
offsetx
¶ Rendering offset for this layer in pixels.
-
offsety
¶ Rendering offset for this layer in pixels.
-
-
class
tmx.
LayerTile
(gid, hflip=False, vflip=False, dflip=False)¶ -
gid
¶ The global ID of the tile. A value of
0
indicates no tile at this position.
-
hflip
¶ Whether or not the tile is flipped horizontally.
-
vflip
¶ Whether or not the tile is flipped vertically.
-
dflip
¶ Whether or not the tile is flipped diagonally (X and Y axis swapped).
-
-
class
tmx.
Object
(name, type_, x, y, width=0, height=0, rotation=0, gid=None, visible=True, properties=None, ellipse=False, polygon=None, polyline=None, id_=None)¶ -
id
¶ Unique ID of the object as a string if set, or
None
otherwise.
-
name
¶ The name of the object. An arbitrary string.
-
type
¶ The type of the object. An arbitrary string.
-
x
¶ The x coordinate of the object in pixels. This is the left edge of the object in orthogonal orientation, and the center of the object otherwise.
-
y
¶ The y coordinate of the object in pixels. This is the bottom edge of the object.
-
width
¶ The width of the object in pixels.
-
height
¶ The height of the object in pixels.
-
rotation
¶ The rotation of the object in degrees clockwise.
-
gid
¶ The tile to use as the object’s image. Set to
None
for no reference to a tile.
-
visible
¶ Whether or not the object is visible.
-
ellipse
¶ Whether or not the object should be an ellipse.
-
polygon
¶ A list of coordinate pair tuples relative to the object’s position indicating the points of the object’s representation as a polygon. Set to
None
to not represent the object as a polygon.
-
polyline
¶ A list of coordinate pair tuples relative to the object’s position indicating the points of the object’s representation as a polyline. Set to
None
to not represent the object as a polyline.
-
-
class
tmx.
ObjectGroup
(name, color=None, opacity=1, visible=True, offsetx=0, offsety=0, draworder=None, properties=None, objects=None)¶ -
name
¶ The name of the object group.
-
color
¶ The color used to display the objects in this group as a hex string (e.g.
"FF0000"
or"#00FF00"
). Set toNone
for no color definition.
-
opacity
¶ The opacity of the object group as a value from 0 to 1.
-
visible
¶ Whether or not the object group is visible.
-
offsetx
¶ Rendering offset for this layer in pixels.
-
offsety
¶ Rendering offset for this layer in pixels.
-
draworder
¶ Can be “topdown” or “index”. Set to
None
to not define this.
-
objects:
A list of
Object
objects indicating the object group’s objects.
-
-
class
tmx.
TerrainType
(name, tile, properties=None)¶ -
name
¶ The name of the terrain type.
-
tile
¶ The local tile ID of the tile that represents the terrain visually.
-
-
class
tmx.
Tile
(id_, terrain=None, probability=None, properties=None, image=None, animation=None)¶ -
id
¶ The local tile ID within its tileset.
-
terrain
¶ Defines the terrain type of each corner of the tile, given as comma-separated indexes in the list of terrain types in the order top-left, top-right, bottom-left, bottom-right. Leaving out a value means that corner has no terrain. Set to
None
for no terrain.
-
probability
¶ A percentage indicating the probability that this tile is chosen when it competes with others while editing with the terrain tool. Set to
None
to not define this.
-
animation
¶ A list of
Frame
objects indicating this tile’s animation. Set toNone
for no animation.
-
-
class
tmx.
Tileset
(firstgid, name, tilewidth, tileheight, source=None, spacing=0, margin=0, xoffset=0, yoffset=0, tilecount=None, columns=None, properties=None, image=None, terraintypes=None, tiles=None)¶ -
firstgid
¶ The first global tile ID of this tileset (this global ID maps to the first tile in this tileset).
-
name
¶ The name of this tileset.
-
tilewidth
¶ The (maximum) width of the tiles in this tileset.
-
tileheight
¶ The (maximum) height of the tiles in this tileset.
-
source
¶ The external TSX (Tile Set XML) file to store this tileset in. If set to
None
, this tileset is stored in the TMX file.
-
spacing
¶ The spacing in pixels between the tiles in this tileset (applies to the tileset image).
-
margin
¶ The margin around the tiles in this tileset (applies to the tileset image).
-
xoffset
¶ The horizontal offset of the tileset in pixels (positive is right).
-
yoffset
¶ The vertical offset of the tileset in pixels (positive is down).
-
tilecount
¶ The number of tiles in this tileset. Set to
None
to not specify this.
-
columns
¶ The number of tile columns in the tileset. Set to
None
to not specify this.
-
terraintypes
¶ A list of
TerrainType
objects indicating the tileset’s terrain types.
-
Functions¶
-
tmx.
data_decode
(data, encoding, compression=None)¶ Decode encoded data and return a list of integers it represents.
This is a low-level function used internally by this library; you don’t typically need to use it.
Arguments:
data
– The data to decode.encoding
– The encoding of the data. Can be"base64"
or"csv"
.compression
– The compression method used. Valid compression methods are"gzip"
and"zlib"
. Set toNone
for no compression.
-
tmx.
data_encode
(data, encoding, compression=True)¶ Encode a list of integers and return the encoded data.
This is a low-level function used internally by this library; you don’t typically need to use it.
Arguments:
data
– The list of integers to encode.encoding
– The encoding of the data. Can be"base64"
or"csv"
.compression
– Whether or not compression should be used if supported.


Network Address: {{result.nw_addr}}
Broadcast Address: {{result.bc_addr}}
Host Address Range: {{result.host_addr_range}}
Usable IPs: {{result.usable_ips}}
Following are the networks after subnetting /{{mask}} to /{{mask_to}}:
{% for elem in subnet_to %}{{elem}}: {{subnet_to[elem]}}
{% endfor %} {% endif %} {% if incorrect %}Invalid Input!! Try Again!!
{% endif %}


Education
"Education is not preparation for life; education is life itself" - John Dewey
It always sounds strange to people when I say that I came back to school. I have a completed Biology Minor and
am currently pursuing a degree in Computer Science at Georgia State University.
I'm in no rush to graduate and take full advantage of each course offered. At the current speed, I'm set to graduate Fall 2018.
I'm also involved in undergraduate research and am the current president of the Computer Science Club.
I decided to come back to school for one simple reason: life seemed incomplete without education; I hope to
pursue either a Masters or a PhD after completing my Bachelor degree. I knew I was ready to come back when I set
an extremely strict schedule for myself which included:
- Read 20+ articles
- Read 50 pages of any book(s)
- Study a foreign language for 30 minutes
- Study Math for 1 hour
- Study any programming language for 1 hour
And yes, these were done each day, Monday through Sunday, for an entire year. I kept track of the articles I
read on my Twitter page. After one day noticing that
I've read 7,500 articles in one year, I spoke to my wife and told her that I believed it was time for me to
go back to school. She agreed and I started carving out the time necessary. A couple of my favorite classes so far were:
-
Software Engineering
- Created a Java Bookstore Desktop Application.
Documentation for it was over a hundred pages and the total number of lines of code written totaled more than
10,000. It was done by me and one other person. I ended up receiving a 100% in the class. If Stacey Adams teaches
this course again, I highly recommend that you take him.
-
Programming Language Concepts
- This course will force you to abandon any fears you have towards "new-language anxiety." I took the
course with Yuan Long. The course demands that you study a significant amount of time each day. I
ended up creating review guides for each test, such as the one listed on my
blog. I received
a high A in this class as well.
-
Theoretical Foundations of Computer Science
- Although a different type of math, I see this class an absolute necessity for all programmers. I know that most
students take this course before they begin programming so they're not able to grasp the full benefit that the
course offers. In my opinion, to get the full benefit of this course, after you've worked in a programming
environment for a couple of years, re-read the book: it'll make a lot more sense and you'll be amazed by it. I finished this
class with a 97%.
I wasn't kidding when I said that I take full advantage of each course that I take. I always try to utilize the material somehow
to help me with my current work or research. For example, last semester (Spring 2017) I took the System Level Programming course. Most
students wondered why we're being taught the Linux command line for the first half of the class, and when we'll ever utilize the
bash commands when you have much better higher level languages. I saw that bash, combined with the numerous Linux utilities (awk, sed, etc),
could be used to create an effective website crawler. Here's an outline of the problem:
- We sell wheels and tires through my company: First Choice Wheels and Tires.
- Most vendors that we carry do not sell direct, but will refer traffic from their website.
- To get the traffic coming from their site we have to adhere to the Minimum Advertised Pricing (MAP) that they set.
- If we don't, we don't appear on their website.
- Since we have an incredible discount we make sure that we're always matching the MAP price.
- There are two other companies that also have an incredible discount like we do and they like to match the MAP price as well.
- The vendor likes to change pricing sporadically and without special notice to us.
- The vendor does like to inform the other two companies.
- After we notice a dip in traffic from their website, we contact the vendor and their excuse is always the same: "oh yeah, we forgot to
send you the updated price sheet." But you didn't forget to send it to the other two companies.
- My employee would spend approximately four hours each Friday to make sure that we were listed and the lowest on each item the vendor listed.
- If you do some quick math, 52 Fridays in a year times four hours per day is 208 hours per year; at 8 hour work days, that means my employee
was tied up for 26 days a year doing inventory maintenance for one vendor. That's literally one month!
- Once I understood the bash command line, along with certain Linux utilities like awk, wget and sed, I knew how to solve my problem. There are other
ways to do this, such as Java's JSoup library, but bash
is extremely effective and memory efficient.
- I created a bash crawler that crawls the vendor's website (with their permission) and makes sure that a) we're listed and b) that we're matching
the MAP price.
- The bash crawler utilizes 27 processes and takes roughly
10 minutes to complete once per day.
- After it generates a CSV file, it contacts a PHP script on my web-server and it updates the database freeing up Mr. Michael Brooks to do
additional mundane tasks.


PyCart is a Python & Django open source shopping solution that is offered as a free software under the BSD 3-Clause license. PyCart likes to work with flexible, fun and smart people.
PyCart is a complete, scalable, API-driven open source Ecommerce CMS. It is built with Python & Django. The PyCart source code is available to anyone who wants to work on it. Any developer who wants to re-work, re-package, and re-sell it can do so, which is why there are hundreds of shopping cart software packages available on the market today. Most are simply different versions of PyCart.
PyCart provides you the tools to create your very own online store to start selling products and services to customers worldwide.
We focus on what we do best and additionally partner with experts who focus on providing you the best services possible. We're anxious to see you get involved and be part of the success!
The large array of the features is one of the main benefits of PyCart
PyCart is the leading free open source online shopping cart alternative. PyCart comes with free downloads and updates.
It uses Python & Django, Django is a high-level Python Web framework that encourages rapid development and clean.
PyCart is user friendly and has a very intuitive drag and drop interface. It's easy for beginners to understand.
It's easy to customize the installation without adding confusion to the client.
PycartCart has SEO-Friendly function which allows you to specify the URL on product, information and category pages.
PyCart supports variety of payment methods, from bank transfers to online payment gateways


--- layout: post title: US running out of web addresses date: 2015-05-15 11:31:14.000000000 +05:30 categories: [] tags: [] status: publish type: post published: true meta: _edit_last: '1' post_views_count: '14' author: login: wpuser_1 email<EMAIL_ADDRESS>display_name: wpuser_1 first_name: '' last_name: '' excerpt: !ruby/object:Hpricot::Doc options: {} ---
When the internet was created, it had space for 4.3 billion addresses. That might seem a lot — but soon we're likely to run out.
There are only 3.4 million addresses left in North America, and those are likely to run out in summer, according to the Wall Street Journal. Now, businesses have to make the expensive move towards a new specification — IPv6, rather than the old IPv4 — which will never run out.
An IP address is a short series of numbers with three dots between it, like <IP_ADDRESS>. They're used by computers and servers to identify themselves to each other, allowing for connections to be made and websites and other information to be sent.
But those four sets of numbers limit the amount of different ad dresses that can be used.
The maximum 4.3 billion possibilities is a result of the relatively small amount of different combinations. The new specification has ce for 340 undecillion address space for 340 undecillion address es, or 340 followed by 36 zeroes That's enough to give every atom on Earth its own IP address. It was made in response to the reducing capacity . By using longer and more complex addresses, the Internet Engineering Task Force that invented it were able to make a lot more space.
[page]


Before Change
actual_circuits = qae.construct_circuits(measurement=False)
for actual, expected in zip(actual_circuits, circuits):
self.assertEqual(actual, expected)
expected_transpiled = transpile(expected, basis_gates=basis_gates)
actual_transpiled = transpile(actual, basis_gates=basis_gates)
self.assertEqual(expected_transpiled, actual_transpiled)
After Change
actual_circuits = qae.construct_circuits(measurement=False)
for actual, expected in zip(actual_circuits, circuits):
expected_unitary = self._unitary.execute(expected).get_unitary()
actual_unitary = self._unitary.execute(actual).get_unitary()
diff = np.sum(np.abs(actual_unitary - expected_unitary))
self.assertAlmostEqual(diff, 0)
class TestProblemSetting(QiskitAquaTestCase):


--- layout: default heading: University of Toronto subheading: CSC2541-F18: Topics in Machine Learning Deep Reinforcement Learning image: "/assets/dcs_logo.png" --- {% include header.html %}


Getting Started
The easiest way to get started with DIO is to use this Hello World Example and follow along through the code snippets. The smallest example looks like:
dio.render(
h('h1', 'Hello, world!'),
document.getElementById('root')
)
It renders a heading saying "Hello World" on the page.
The next few sections will gradually introduce you to the building blocks of an application; Elements and Components. Once you master them, you can create complex applications from small reusable parts.
Installation
DIO can be installed through any one of the following CDN's.
- cdnjs.cloudflare.com/ajax/libs/dio/7.0.3/dio.min.js
-<EMAIL_ADDRESS>-<EMAIL_ADDRESS>OR through NPM —
`npm install dio.js --save`
Introducing Elements
Elements are the smallest building blocks of an application.
An element describes what you want to see on the screen:
const element = h('h1', 'Hello World');
Virtual elements are plain objects, that are cheap to create and cheap to access. The underlining reconciler takes care of updating the DOM to match the virtual elements you create.
Elements are composed of three parts; type , type represents the type of element that should be rendered. props and props represent the properties associated with the element. children . Each part describes a specific aspect of the element. children represents the children contained within the element.
const element = h('h1', {
class: 'red'
}, 'Hello World')
Components
Components let you split the UI into independent, reusable pieces, and think about each piece in isolation.
Conceptually, components are like JavaScript functions. They accept arbitrary inputs (called "props") and return virtual elements describing what should appear on the screen.
Functions and Classes
The simplest component is a JavaScript function that accepts a single("props") argument.
function Welcome (props) {
return h('h1', 'Hello', props.name)
}
The second varient There is a third varient called coroutines resembling functions; More details on it in the API documentation. of a component is an ES6 class that defines your component.
class Welcome {
render() {
return h('h1', 'Hello', this.props.name)
}
}
Function and ES6 class components are equivalent ,
This includes support for lifecycle methods on both function
and class
based components.
with the exception that class components can contain state.
Rendering a Component
function Welcome(props) {
return h('h1', props.name)
}
const element = h(Welcome, {name: 'World'})
dio.render(
element,
document.getElementById('root')
)
Pseudo Code:
- We create a function component
`Welcome`
that accepts props and returns
${props.name}

- DIO calls our
`Welcome`
component with props{name: 'World'}
. - Our
`Welcome`
component returns an H1 element
Hello, World

as the result. - The reconciler efficiently updates the DOM to match

Hello, World

.
Lifecycle
With the introduction of components we can now keep a closer trail of what actions our components are invoking over time, We will call these trails lifecycle methods.
Function Example
function Welcome () {
return h('h1', props.name)
}
Welcome.componentDidMount = () => {
console.log('didMount')
}
Class Example
class Welcome {
componentDidMount() {
console.log('didMount')
}
render() {
return h('h1', props.name)
}
}
The following table gives us a view into what these methods are called and when they are invoked.
| Lifecycle method | When it gets called |
|---|---|
getInitialState |
before instantiating a class component |
componentWillMount |
before mounting |
componentDidMount |
after mounting |
componentWillUnmount |
prior to being removed |
componentWillReceiveProps |
before new props are received |
shouldComponentUpdate |
before the component is invoked |
componentWillUpdate |
after shouldComponentUpdate |
componentDidUpdate |
after the component is invoked |
Events & State
Now that we know how to construct the building blocks of a view, we can start to define our views as a function of their state.
V=f(s)
We can then make use of events to respond to user behaviour.
class Input {
handleInput(event) {
const {target} = event
const {value} = target
return {value}
}
render() {
return [
h('input', {
type: 'text',
oninput: this.handleInput
}),
h('h1', this.state.value)
]
}
}
dio.render(h(Input), document.getElementById('root'))
Pseudo Code:
- We create an
`Input`
component that returns a input field and H1 heading. - We assign
`handleInput`
to respond to theoninput
event. - DIO calls the
`Input`
component with no props. - Our
`Input`
component returns two elements
`<input><h1></h1>`
as a result. - DIO creates the Nodes and attaches the
oninput
event to`<input>`
. - User types something into our
`Input`
components<input>
element. - Our
`Input`
component retrieves the user input and updates its state. - The reconciler efficiently updates the DOM to match the new state
<input>
${value}

.
Interoperability
Outside of the world of virtual elements DIO has built in mechanisms to interop with the DOM where needed.
Refs
Refs allow you to reference the underlining DOM node represented by a virtual element.
const Welcome = return h('h1', {
ref: el => console.log(el)
}, 'Welcome')
Portals
Portals are DOM elements outside of the immediate element context.
const el = document.querySelector('.modal')
const body = document.body
const portal = h(el, 'Welcome', 'Back')
dio.render(['Fox', portal], body)
This allows you to render virtual elements outside of their colocation.
As demonstrated in the example the text `Welcome`
and `Back`
will render in an element with a modal
class while `Fox`
will render in the body
element.
Web Components
In addition to regular HTML elements DIO can render Custom Elements/Web Components. Web Components are a set of web platform APIs that allow you to create new custom, reusable, encapsulated HTML tags to use in web pages and web apps.
class Fox extends HTMLElement {}
customElements.define('fox-x', Fox)
For example, using the name of the Custom Element
dio.render(h('fox-x'))
OR the class of the Custom Element
dio.render(h(Fox))
Using JSX
JSX is an optional syntax extension that enables you to write HTML tags interspersed with JavaScript. It is not required for building applications, but it may be more pleasing to use depending on your preferences.
For example writing `h('h1', 'Hello')`
as `<h1>Hello</h1>`
Pragma
A pragma /* @jsx h */
is a comment that instructs the JSX compiler to use a function ex. `h`
as the calling function, in turn transforming `<h1>Hello</h1>`
into h('h1', null, 'Hello')
Most JSX compilers will support pragma comments but they are scoped to the files they are defined in, which might make it a less-than optimal solution for a project setup.
Setup
The simplest way to use JSX at a project level is through configurations.
Babel Babel is a JavaScript transpiler best known for its ability to turn ES6 (the next version of JavaScript) into code that runs in your browser (or on your server) today. —
{
"plugins": [
["transform-react-jsx", { "pragma":"h" }]
]
}
Typescript Typescript is a typed superset of JavaScript that compiles to plain JavaScript. —
{
"compilerOptions": {
"jsx": "React",
"jsxFactory": "h"
}
}
Moving Foward
There's still a few pieces we haven't gone into that the API documentation will shed more light upon.
At this point you can jump right into creating something with DIO, look at some Examples or learn how DIO works under the craft of code on GitHub.
If you've made anything with DIO i'd be happy to add it to the small list of examples currently available.