Each time the page is loaded a new configuration is empty.The game can be stopped and re-started thanks to the buttons.
Some objects have been added to see some specific actions going in the game. The objects will be randomly postionned in the world.
You can click direcly on the board to change a status of a cell, but as the board updates every 150 ms, it is easier to do this once the games is stopped.


Answer the question belowe
What is your main role in the game?
Gender?
What is your favorite map?
What is your favorite weapon
(Check all that apply)
Any comments about yourself?


{% extends "base.html" %} {% block title %}Parsifal{% endblock %} {% load i18n %} {% block content %}
{% trans "An email has been sent to the given address. To continue the password reset process please follow the instructions in this email." %}
{% blocktrans trimmed %} If you have any problem with the password reset, please contact us at<EMAIL_ADDRESS>{% endblocktrans %}
{% endblock content %}


--- layout: page title: Computational Linguistics 2 sidebar_link: false order: 4 --- I teach LING 4434: Computational Linguistics 2. While LING 4424: Computational Linguistics 1 focuses on symbolic computational linguistics methods (n-gram smoothing, hidden markov modeling, probabilistic context-free grammars, etc), CL2 focuses on neural networks. Specifically, it focuses on techniques for inferring the linguistic knowledge encoded in neural network language models. This course is a work in progress, so any feedback/suggestions are appreciated.


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Army Corps of Engineers, Department of the Army, Department of Defense
This is a catalog of datasets organized and made more accessible from the Army Corps of Engineers, Department of the Army, Department of Defense, providing simple dowloadable CSV and JSON files, as...


In order to have some data to show in the graph, you can run on of the functional tests to populate the database. A good start is the AnomalyTest.php. It generates data for SID=1 and NID=1, and tries to add some anomalies in the data. These anomalies should show with red color in the state bar.


Watch: Bernie Sanders, AOC Introduce Green New Affordable Housing Deal
Rep. Alexandria Ocasio-Cortez (D-NY) and Democrat presidential candidate Sen. Bernie Sanders (I-VT) are holding a press conference on Thursday surrounded by climate change and affordable housing activists to introduce legislation aimed at putting a piece of the Green New Deal resolution into law.
by Penny Starr“Our team has been working on the Green New Deal for Public Housing for months, engaging residents & experts,” Ocasio-Cortez tweeted. “It takes principles of the GND & creates economic stimulus for working people to decarbonize & center frontline communities.”
Watch the press conference live at Breitbart News at 3 p.m. EST.
Follow Penny Starr on Twitter


Support for the Oracle database.
The following dialect/DBAPI options are available. Please refer to individual DBAPI sections for connect information.
The dialect supports several create_engine() arguments which affect the behavior of the dialect regardless of driver in use.
SQLAlchemy Table objects which include integer primary keys are usually assumed to have “autoincrementing” behavior, meaning they can generate their own primary key values upon INSERT. Since Oracle has no “autoincrement” feature, SQLAlchemy relies upon sequences to produce these values. With the Oracle dialect, a sequence must always be explicitly specified to enable autoincrement. This is divergent with the majority of documentation examples which assume the usage of an autoincrement-capable database. To specify sequences, use the sqlalchemy.schema.Sequence object which is passed to a Column construct:
t = Table('mytable', metadata,
Column('id', Integer, Sequence('id_seq'), primary_key=True),
Column(...), ...
)
This step is also required when using table reflection, i.e. autoload=True:
t = Table('mytable', metadata,
Column('id', Integer, Sequence('id_seq'), primary_key=True),
autoload=True
)
In Oracle, the data dictionary represents all case insensitive identifier names using UPPERCASE text. SQLAlchemy on the other hand considers an all-lower case identifier name to be case insensitive. The Oracle dialect converts all case insensitive identifiers to and from those two formats during schema level communication, such as reflection of tables and indexes. Using an UPPERCASE name on the SQLAlchemy side indicates a case sensitive identifier, and SQLAlchemy will quote the name - this will cause mismatches against data dictionary data received from Oracle, so unless identifier names have been truly created as case sensitive (i.e. using quoted names), all lowercase names should be used on the SQLAlchemy side.
Changed in version 0.6: SQLAlchemy uses the “native unicode” mode provided as of cx_oracle 5. cx_oracle 5.0.2 or greater is recommended for support of NCLOB. If not using cx_oracle 5, the NLS_LANG environment variable needs to be set in order for the oracle client library to use proper encoding, such as “AMERICAN_AMERICA.UTF8”.
Also note that Oracle supports unicode data through the NVARCHAR and NCLOB data types. When using the SQLAlchemy Unicode and UnicodeText types, these DDL types will be used within CREATE TABLE statements. Usage of VARCHAR2 and CLOB with unicode text still requires NLS_LANG to be set.
Oracle has no support for the LIMIT or OFFSET keywords. SQLAlchemy uses a wrapped subquery approach in conjunction with ROWNUM. The exact methodology is taken from http://www.oracle.com/technology/oramag/oracle/06-sep/o56asktom.html .
There are two options which affect its behavior:
Some users have reported better performance when the entirely different approach of a window query is used, i.e. ROW_NUMBER() OVER (ORDER BY), to provide LIMIT/OFFSET (note that the majority of users don’t observe this). To suit this case the method used for LIMIT/OFFSET can be replaced entirely. See the recipe at http://www.sqlalchemy.org/trac/wiki/UsageRecipes/WindowFunctionsByDefault which installs a select compiler that overrides the generation of limit/offset with a window function.
Oracle doesn’t have native ON UPDATE CASCADE functionality. A trigger based solution is available at http://asktom.oracle.com/tkyte/update_cascade/index.html .
When using the SQLAlchemy ORM, the ORM has limited ability to manually issue cascading updates - specify ForeignKey objects using the “deferrable=True, initially=’deferred’” keyword arguments, and specify “passive_updates=False” on each relationship().
When Oracle 8 is detected, the dialect internally configures itself to the following behaviors:
When using reflection with Table objects, the dialect can optionally search for tables indicated by synonyms that reference DBLINK-ed tables by passing the flag oracle_resolve_synonyms=True as a keyword argument to the Table construct. If DBLINK is not in use this flag should be left off.
As with all SQLAlchemy dialects, all UPPERCASE types that are known to be valid with Oracle are importable from the top level dialect, whether they originate from sqlalchemy.types or from the local dialect:
from sqlalchemy.dialects.oracle import \
BFILE, BLOB, CHAR, CLOB, DATE, DATETIME, \
DOUBLE_PRECISION, FLOAT, INTERVAL, LONG, NCLOB, \
NUMBER, NVARCHAR, NVARCHAR2, RAW, TIMESTAMP, VARCHAR, \
VARCHAR2
Types which are specific to Oracle, or have Oracle-specific construction arguments, are as follows:
Bases: sqlalchemy.types.LargeBinary
Construct a LargeBinary type.
| Parameters: | length – optional, a length for the column for use in DDL statements, for those BLOB types that accept a length (i.e. MySQL). It does not produce a small BINARY/VARBINARY type - use the BINARY/VARBINARY types specifically for those. May be safely omitted if no CREATE TABLE will be issued. Certain databases may require a length for use in DDL, and will raise an exception when the CREATE TABLE DDL is issued. |
|---|
Bases: sqlalchemy.types.Numeric
Bases: sqlalchemy.types.TypeEngine
Construct an INTERVAL.
Note that only DAY TO SECOND intervals are currently supported. This is due to a lack of support for YEAR TO MONTH intervals within available DBAPIs (cx_oracle and zxjdbc).
| Parameters: |
|
|---|
Bases: sqlalchemy.types.Text
Create a string-holding type.
| Parameters: |
|
|---|
Bases: sqlalchemy.types.Text
Create a string-holding type.
| Parameters: |
|
|---|
Bases: sqlalchemy.types._Binary
Support for the Oracle database via the cx-Oracle driver.
Documentation and download information (if applicable) for cx-Oracle is available at: http://cx-oracle.sourceforge.net/
When connecting with dbname present, the host, port, and dbname tokens are converted to a TNS name using the cx_oracle makedsn() function. Otherwise, the host token is taken directly as a TNS name.
Additional arguments which may be specified either as query string arguments on the URL, or as keyword arguments to create_engine() are:
allow_twophase - enable two-phase transactions. Defaults to True.
arraysize - set the cx_oracle.arraysize value on cursors, in SQLAlchemy it defaults to 50. See the section on “LOB Objects” below.
auto_convert_lobs - defaults to True, see the section on LOB objects.
auto_setinputsizes - the cx_oracle.setinputsizes() call is issued for all bind parameters. This is required for LOB datatypes but can be disabled to reduce overhead. Defaults to True. Specific types can be excluded from this process using the exclude_setinputsizes parameter.
exclude_setinputsizes - a tuple or list of string DBAPI type names to be excluded from the “auto setinputsizes” feature. The type names here must match DBAPI types that are found in the “cx_Oracle” module namespace, such as cx_Oracle.UNICODE, cx_Oracle.NCLOB, etc. Defaults to (STRING, UNICODE).
New in version 0.8: specific DBAPI types can be excluded from the auto_setinputsizes feature via the exclude_setinputsizes attribute.
mode - This is given the string value of SYSDBA or SYSOPER, or alternatively an integer value. This value is only available as a URL query string argument.
threaded - enable multithreaded access to cx_oracle connections. Defaults to True. Note that this is the opposite default of the cx_Oracle DBAPI itself.
cx_oracle 5 fully supports Python unicode objects. SQLAlchemy will pass all unicode strings directly to cx_oracle, and additionally uses an output handler so that all string based result values are returned as unicode as well. Generally, the NLS_LANG environment variable determines the nature of the encoding to be used.
Note that this behavior is disabled when Oracle 8 is detected, as it has been observed that issues remain when passing Python unicodes to cx_oracle with Oracle 8.
cx_oracle returns oracle LOBs using the cx_oracle.LOB object. SQLAlchemy converts these to strings so that the interface of the Binary type is consistent with that of other backends, and so that the linkage to a live cursor is not needed in scenarios like result.fetchmany() and result.fetchall(). This means that by default, LOB objects are fully fetched unconditionally by SQLAlchemy, and the linkage to a live cursor is broken.
To disable this processing, pass auto_convert_lobs=False to create_engine().
Two Phase transactions are implemented using XA transactions, and are known to work in a rudimental fashion with recent versions of cx_Oracle as of SQLAlchemy 0.8.0b2, 0.7.10. However, the mechanism is not yet considered to be robust and should still be regarded as experimental.
In particular, the cx_Oracle DBAPI as recently as 5.1.2 has a bug regarding two phase which prevents a particular DBAPI connection from being consistently usable in both prepared transactions as well as traditional DBAPI usage patterns; therefore once a particular connection is used via Connection.begin_prepared(), all subsequent usages of the underlying DBAPI connection must be within the context of prepared transactions.
The default behavior of Engine is to maintain a pool of DBAPI connections. Therefore, due to the above glitch, a DBAPI connection that has been used in a two-phase operation, and is then returned to the pool, will not be usable in a non-two-phase context. To avoid this situation, the application can make one of several choices:
Changed in version 0.8.0b2,0.7.10: Support for cx_oracle prepared transactions has been implemented and tested.
The SQLAlchemy dialect goes through a lot of steps to ensure that decimal numbers are sent and received with full accuracy. An “outputtypehandler” callable is associated with each cx_oracle connection object which detects numeric types and receives them as string values, instead of receiving a Python float directly, which is then passed to the Python Decimal constructor. The Numeric and Float types under the cx_oracle dialect are aware of this behavior, and will coerce the Decimal to float if the asdecimal flag is False (default on Float, optional on Numeric).
Because the handler coerces to Decimal in all cases first, the feature can detract significantly from performance. If precision numerics aren’t required, the decimal handling can be disabled by passing the flag coerce_to_decimal=False to create_engine():
engine = create_engine("oracle+cx_oracle://dsn",
coerce_to_decimal=False)
New in version 0.7.6: Add the coerce_to_decimal flag.
Another alternative to performance is to use the cdecimal library; see Numeric for additional notes.
The handler attempts to use the “precision” and “scale” attributes of the result set column to best determine if subsequent incoming values should be received as Decimal as opposed to int (in which case no processing is added). There are several scenarios where OCI does not provide unambiguous data as to the numeric type, including some situations where individual rows may return a combination of floating point and integer values. Certain values for “precision” and “scale” have been observed to determine this scenario. When it occurs, the outputtypehandler receives as string and then passes off to a processing function which detects, for each returned value, if a decimal point is present, and if so converts to Decimal, otherwise to int. The intention is that simple int-based statements like “SELECT my_seq.nextval() FROM DUAL” continue to return ints and not Decimal objects, and that any kind of floating point value is received as a string so that there is no floating point loss of precision.
The “decimal point is present” logic itself is also sensitive to locale. Under OCI, this is controlled by the NLS_LANG environment variable. Upon first connection, the dialect runs a test to determine the current “decimal” character, which can be a comma ”,” for european locales. From that point forward the outputtypehandler uses that character to represent a decimal point. Note that cx_oracle 5.0.3 or greater is required when dealing with numerics with locale settings that don’t use a period ”.” as the decimal character.
Changed in version 0.6.6: The outputtypehandler uses a comma ”,” character to represent a decimal point.
Support for the Oracle database via the zxJDBC for Jython driver.
Drivers for this database are available at: http://www.oracle.com/technology/software/tech/java/sqlj_jdbc/index.html.


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If the configuration variable daemonise
is set to true, Polipo
will run as a daemon: it will fork and detach from its controlling
terminal (if any). The variable daemonise
defaults to false.
When Polipo is run as a daemon, it can be useful to get it to
atomically write its pid to a file. If the variable
pidFile
is defined, it should be the name of a file where
Polipo will write its pid. If the file already exists when it
is started, Polipo will refuse to run.


What If Diesel is Put Into a Gas Car?
A local gas station recently had a delivery mix up causing diesel fuel to be dumped into the gasoline tanks that supply their fuel pumps. The station was quick to catch the mistake, but not before a few unlucky commuters had a nasty surprise.
While delivery mishaps are rare, it’s not unheard of for a driver to accidentally fill up their vehicle with the wrong fuel. Even though diesel pumps are different in color and size, and diesel gives off a different smell it still happens.
With that said, what exactly happens when a gasoline vehicle receives a hearty dose of diesel? Surprisingly nothing at first. With the gas tank located in the rear of the vehicle fuel must be transported to the engine via long fuel lines that traverse the entire length of the car. For the first few miles after the fill up the engine will still be running on the clean gasoline in the fuel lines.
However, the fuel pump is still doing it’s job of pumping fluid from the gas tank into the fuel lines and the bad fuel begins to make it’s way to the engine.
Once the diesel reaches the engine bay things get interesting. The first engine component it’ll encounter are the injectors. Injectors provide the engine with fuel by spraying small bursts of it into either the intake manifold, or directly into the cylinders if your vehicle has direct injection. The injectors won’t be too happy about pumping in the diesel/gas mixture but they’ll still function and manage to inject the mixture into the engine.
Sputter Out
Now, depending on how much diesel was put into the fuel tank things will vary. If a large amount of diesel was placed into the fuel tank then the mixture being injected into the engine will be more diesel than gas and the engine won’t be able to combust it. Diesel and gasoline are both petroleum based fuels but each combusts in a slightly different manner.
Diesel engines combust fuel by compressing it in a very high pressure environment. Gasoline engines on the other hand have 1 to 2 spark plugs per cylinder that emit an electrical ark to ignite the gasoline. Diesel cannot be ignited via a spark, and since gasoline engines have a compression ratio that is too low to ignite the diesel the motor will sputter out. This is the better of the two possibilities as the vehicle will only need a thorough cleansing of it’s fuel system before it can be driven again and there will be no lasting damage.
Catastrophic Failure
If a gasoline vehicle receives only a slight amount of diesel the outcome takes a turn for the worse. With only a small amount of diesel in the fuel the mixture will act similar to very very low grade gas. Gasoline is rated by it’s octane number and is the reason why you see the 87, 89, 91, and/or 93 buttons on the fuel pumps. The higher in octane the gas is, then the higher the compression the fuel can withstand before it ignites. When diesel is mixed into gasoline it causes the octane rating to drop severely.
Gasoline engines wait for roughly the moment the piston reaches TDC (top dead center) before igniting fuel via a small electrical charge from the spark plug. When fuel ignites before the piston reaches TDC it causes engine knock. A common cause of engine knock is gasoline with too low of an octane rating igniting prematurely due to high compression. This is the reason why high performance, or forced induction engines often require a higher octane fuel.
Engine knock causes serious wear on the engine’s internals as the engine is attempting to push the piston up, while at the same time the combustion forces are attempting to push the piston down. An engine experiencing knock will often have a sporadic idle, and lack of power.
If the driver is observant and notices the engine is running rough, lacking power, and probably flashing a CEL (check engine light), they’ll pull over and shut off the engine before it’s too late. If the engine is not shut off they’ll risk destroying it, and need a full rebuild.
There’s a good chance not all of the diesel will be combusted which can lead to deposits building up on the pistons, and valves, or worse cause the engine to hydrolock.
Some diesel may manage to seep past the piston rings allowing it to containment the oil that lubricates the lower half of the engine. The containment oil won’t be as lubricating which will cause further wear on the bearings of the engine that are already taking a beating from the repetitive misfires.
Diesel will also likely find it’s way into the exhaust system, and once it reaches the catalytic converter it will ignite destroying the fine honey comb structure within it. This will drop the efficiency of the cat, and eventually cause it to clog up. At this point the driver is looking at a very expensive repair bill.
In The End - Don’t Drive It
If you accidentally put diesel in your gasoline vehicle play it safe and don’t drive it. Call a tow truck and have it towed to a mechanic who can safely drain the fuel tank, and purge the lines of any remaining diesel.


TornadoFaces uses Foundation for Apps markup and styling, with a small override stylesheet.
Check out the default theme called Tornado from GitHub and use that as a base. TornadoFaces comes with a build system written in Gulp, so make your changes to settings.scss and run gulp
to get your custom theme.css.
You can either manually include your new theme as a css resource in your page template and disable the default theme (see below), or you can package your theme as a jar-file and configure your app to use it in web.xml
.
To create a reusable, pluggable theme for TornadoFaces, jar up the following directory structure, optionally adding in any image or other static resources relative to theme.css
.
In this example the theme name mytheme
was chosen. Include the jar in your project and change the theme as documented below.
Custom themese can be configured in a number of ways:
Define an EL expression in the theme argument web.xml:
The bean can have any scope. To disable theming completely, use the theme name none
.
You can disable the supplied JQuery version by setting
tornadofaces.SKIP_JQUERY
as either a view param or a web.xml init param.
You can disable all default TornadoFaces resources by setting
tornadofaces.DISABLE
as either a view param or a web.xml init param.


Just Enough To Be Dangerous
Stijn Masschelein
R
Angrist, Joshua D, and Jörn-Steffen Pischke. 2008. Mostly Harmless Econometrics: An Empiricist’s Companion. Princeton university press.
Cunningham, Scott. 2018. Causal Inference: The Mixtape.
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Page built: 2021-03-22


Vert.x is the framework for the next generation of asynchronous, effortlessly scalable, concurrent applications.
Vert.x is an event driven application framework that runs on the JVM - a run-time with real concurrency and unrivalled performance. Vert.x then exposes the API in Ruby, Java, Groovy, JavaScript and Python. So you choose what language you want to use. Scala and Clojure support is on the roadmap too.
Some of the key highlights include:
Polyglot. Write your application components in JavaScript, Ruby, Groovy, Java or Python. It's up to you. Or mix and match several programming languages in a single application.
Super simple concurrency model. Vert.x allows you to write all your code as single threaded, freeing you from the hassle of multi-threaded programming. (No more synchronized
, volatile
or explicit locking).
Unlike other popular event driven frameworks, Vert.x takes advantage of the JVM and scales seamlessly over available cores without having to manually fork multiple servers and handle inter process communication between them.
Vert.x has a simple, asynchronous programming model for writing truly scalable non-blocking applications.
Vert.x includes a distributed event bus that spans the client and server side so your applications components can communicate incredibly easily. The event bus even penetrates into in-browser JavaScript allowing you to create effortless so-called real-time web applications.
Vert.x provides real power and simplicity, without being simplistic. No more sprawling xml configuration files.
Vert.x includes a module system and public module repository, so you can easily re-use and share Vert.x modules with others.
Future applications will largely be running on mobile and embedded devices. These demand a platform that can scale with 10s, 100s or even millions of concurrent connections, and allow developers to write scalable, performant applications for them incredibly easily, in whatever language they prefer.
We believe Vert.x is that platform.
If you don't want the whole vert.x platform but just want to use HTTP, HTTPS, TCP, SSL, WebSockets, the event bus, or other vert.x functionality as a library in your own Java or Groovy application, then you can do this too.
Just use the jar vertx-core-<version>.jar
which is available in the lib
directory in the distribution.
You then have full access to the core vert.x API, in either Java or Groovy. If you use vert.x embedded you don't have to worry about verticles or any of the deployment related topics, and can just use the core API directly.
Here's an example of a simple embedded web server in Java:
Vertx vertx = Vertx.newVertx(); vertx.createHttpServer().requestHandler(new Handler<HttpServerRequest>() { public void handle(HttpServerRequest req) { String file = req.path.equals("/") ? "index.html" : req.path; req.response.sendFile("webroot/" + file); } }).listen(8080);
And here's the same server in Groovy:
def vertx = Vertx.newVertx() vertx.createHttpServer().requestHandler { req -> def file = req.uri == "/" ? "index.html" : req.uri req.response.sendFile "webroot/$file" }.listen(8080)
It's as simple as that!
The api documentation (JavaDoc and GroovyDoc) is here. Also please take a look at the core manual for Java or Groovy to see full documentation on how to use the API.
In this section I'll give an overview of the main concepts in vert.x. Many of these concepts will be discussed in more depth later on in this manual.
The unit of deployment in vert.x is called a verticle (think of a particle, for vert.x). Verticles can currently be written in JavaScript, Ruby, Java, or Groovy (we're looking to support Clojure, Scala and Python going ahead).
A verticle is defined by having a main which is just the script (or class in the case of Java) to run to start the verticle. A verticle can also contain other scripts which are referenced from the main. It can also contain any jars, and other resources that are used by the verticle.
When you write an application, you might write a single verticle, or your application could consist of a whole set of verticles communicating with each other using the event bus.
Verticles run inside a vert.x instance. A single vert.x instance runs inside its own JVM instance. There can be many verticles running inside a single vert.x instance at any one time. Vert.x makes sure each verticle is isolated by giving it its own classloader so they can't interact by sharing static members, global variables or other means.
There can be many vert.x instances running on the same host, or on different hosts on the network at the same time. The instances can be configured to cluster with each other forming a distributed event bus over which verticle instances can communicate.
We want you to be able to develop your verticles in a choice of programming languages. Never have developers had such a choice of great languages, and we want that to be reflected in the languages we support. Vert.x allows you to write verticles in JavaScript, Ruby, Java, and Groovy, and we aim to support Clojure, Scala and Python going ahead. These verticles can seamlessly interoperate with other verticles irrespective of what language they are written in.
A vert.x instance guarantees that a particular verticle instance is always executed by the exact same thread. This gives you a huge advantage as a developer, since you can program all your code as single threaded. Well, that won't be a big deal to you if you are coming from JavaScript where everything is single threaded, but if you're used to multi-threaded programming in Java, Scala, or even Ruby, this may come as a huge relief since you don't have to synchronize access to your state. This means a whole class of race conditions disappear, and OS thread deadlocks are a thing of the past.
Vert.x provides an event-based programming model, similar to frameworks such as node.js.
Most things you do in vert.x involve setting event handlers. For example, to receive data from a TCP socket you set a handler - the handler is then called when data arrives.
You also set handlers to receive messages from the event bus, to receive HTTP requests and responses, to be notified when a connection is closed, or to be notified when a timer fires. There are many examples throughout the vert.x api.
Any other operations in vert.x that don't involve handlers, e.g. writing some data to a socket are guaranteed never to block.
Let's imagine for a minute that the vert.x api allowed a blocking read on a TCP socket. When the code in a verticle called that blocking operation and no data arrived for, say, 1 minute, it means that thread cannot do anything else during that time - it can't do work for any other verticle.
For such a blocking model to work and the system to remain responsive, each verticle instance would need to be assigned its own thread. Now consider what happens when we have thousands, 10s of thousands, or 100s of thousands of verticles running. We clearly can't have that many threads - the overhead due to context switching and stack space would be horrendous. It's clear to see such a blocking model just doesn't scale.
Internally, a vert.x instance manages a small set of threads, normally matching the number of threads to the available cores on the server. We call these threads event loops, since they basically just loop around (well... they do actually go to sleep if there is nothing to do) seeing if there is any work to do, for example, handling some data that's been read from a socket, or executing a timer.
When a verticle instance is deployed, the server chooses an event loop which will be assigned to that instance. Any subsequent work to be done for that instance will always be dispatched using that thread. Of course, since there are potentially many thousands of verticles running at any one time, a single event loop is assigned to many verticles at the same time.
We call this the multi-reactor pattern. It's like the reactor pattern but there's more than one event loop.
Verticles can communicate with other verticles running in the same, or different vert.x instance using the event bus. If you think of each verticle as an actor, it in some ways resembles the actor model as popularised by the Erlang programming language.
By having many verticle instances in a vert.x server instance and allowing message passing allows the system to scale well over available cores without having to allow multi-threaded execution of any verticle code.
Message passing is extremely useful, but it's not always the best approach to concurrency for all types of applications.
An example would be an application that wishes to provide an in-memory web cache.
As requests for a resource arrive at a server, the server looks up the resource in the cache and returns it from there if the item is present, if the item is not present it loads it from disk and places it in the cache for the next time.
We want this application to scale across all available cores. Modelling this using message passing is problematic. At one end of the scale we could have a single verticle that manages the cache, but this means all requests to the cache will be serialized through a single threaded verticle instance. We could improve things by having multiple instances of the verticle managing different parts of the cache, but it quickly gets ugly and complicated.
Such a use case is better solved by providing a shared map structure that can be accessed directly by different verticle instances in the same vert.x instance. As requests come in, the data can be efficiently looked up in the cache with a single line of code and returned to the user.
It's fashionable these days to deride shared data. But shared data is only dangerous if the data that you share is mutable.
Vert.x provides a shared map and shared set facility which allows only immutable data to be shared between verticles.
Vert.x uses only a small number of event loop threads, and has to dispatch events to potentially thousands of verticles, and remain responsive.
When you consider that, it's pretty clear that those threads can't spend too much time in any particular verticle event handler- if they do it means that the event loop can't service events for other verticles, and the whole system can grind to a halt.
How do we define too much time? It's hard to put an exact figure on it, but one rule of thumb is anything that takes more than a few milliseconds of wall-clock time. (That's actual elapsed time, not CPU time).
That time includes the thread sleeping or blocking on a database operation (where few CPU cycles are involved), but it also involves busy waits such as a computationally intensive operation, e.g. factorising a prime number.
By default, verticle event handlers should not take a long time to execute, however there are cases where you can't avoid blocking, or you genuinely have computationally intensive operations to perform.
An example of the former would be calling a third-party blocking database API from a verticle. In this case you don't have control of the client library you are using so you just have to block until you get the result back.
Another example would be a worker verticle which needs to do an intensive calculation like calculating Fibonacci numbers. In such a case the calculation could be done a little at a time on different circuits around the event loop, but this is awkward, and just a little bit silly ;)
For cases like these, vert.x allows you to mark a particular verticle instance as a worker verticle. A worker verticle differs from a normal verticle in that it is not assigned a vert.x event loop thread, instead it executes on a thread from an internal thread pool called the background pool.
Worker verticles are never executed concurrently by more than one thread. Worker verticles are also not allowed to use TCP or HTTP clients or servers. Worker verticles normally communicate with other verticles using the vert.x event bus, e.g. receiving work to process.
Worker verticles should be kept to a minimum, since a blocking approach doesn't scale if you want to deal with many concurrent connections.
Vert.x functionality can be conceptually divided into two types: Core Services, and Modules.
Vert.x core provides a set of services which can be directly called from code in a verticle. The API for core is provided in each of the programming languages that vert.x supports.
The API will be described in detail in the Core API manual, but includes services such as:
Vert.x core is fairly static and is not envisaged that it will grow much over time. This is good since every service in core has to be provided in each of the languages we support - that's a lot of API adaptors to write.
Vert.x includes a module system and public module repository.
Vert.x applications or reusable resources can easily be packaged as modules. These modules can be written in any of the languages that Vert.x supports, and they communicate with each other over the event bus by sending and receiving JSON messages.
Since all communication is over the event bus, and not by direct calls like with vert.x core, it means it can be written once in any language, and any verticle in any other language can immediately use it without an API adaptor having to be written for each language.
Whilst vert.x core is fairly static, we envisage that a wide range of modules will be available in vert.x for performing common operations. The Vert.x repository already contains several modules including a persistor, a mailer and work queues.
We also encourage the community to create and contribute their own modules for others to use.
For more information on modules please see the modules manual.
The vertx
command is used to interact with vert.x from the command line. It's main use is to run vert.x verticles.
If you just type vertx
at a command line you can see the different options the command takes.
The command vertx run
is used to start a vert.x verticle.
At minimum vertx run
takes a single parameter - the name of the main or module to run.
If you're running a local verticle written in JavaScript, Ruby, or Groovy then it's just the name of the script, e.g. server.js
, server.rb
, or server.groovy
. (It doesn't have to be called server
, you can name it anything as long as it has the right extension). If the verticle is written in Java the name can either be the fully qualified class name of the Main class, or you can specify the Java Source file directly and Vert.x will compile it for you.
The vertx run
command can take a few optional parameters, they are:
-conf <config_file>
Provide some configuration to the verticle. config_file
is the name of a text file containing a JSON object that represents the configuration for the verticle. This is optional.
-cp <path>
The path on which to search for the main and any other resources used by the verticle. This defaults to .
(current directory). If your verticle references other scripts, classes or other resources (e.g. jar files) then make sure these are on this path. The path can contain multiple path entries separated by :
(colon). Each path entry can be an absolute or relative path to a directory containing scripts, or absolute or relative filenames for jar or zip files.
An example path might be -cp classes:lib/otherscripts:jars/myjar.jar:jars/otherjar.jar
Always use the path to reference any resources that your verticle requires. Please, do not put them on the system classpath as this can cause isolation issues between deployed verticles.
-instances <instances>
The number of instances of the verticle to instantiate in the vert.x server. Each verticle instance is strictly single threaded so to scale your application across available cores you might want to deploy more than one instance. If omitted a single instance will be deployed. We'll talk more about scaling later on in this user manual.
-includes <mod_list>
A comma separated list of module names to include in the classpath of this verticle.
For more information on what including a module means please see the modules manual.
-worker
This options determines whether the verticle is a worker verticle or not. Default is false (not a worker). This is discussed in detail later on in the manual.
-cluster
This option determines whether the vert.x server which is started will attempt to form a cluster with other vert.x instances on the network. Clustering vert.x instances allows vert.x to form a distributed event bus with other nodes. Default is false (not clustered).
-cluster-port
If the cluster
option has also been specified then this determines which port will be used for cluster communication with other vert.x instances. Default is 25500
. If you are running more than one vert.x instance on the same host and want to cluster them, then you'll need to make sure each instance has its own cluster port to avoid port conflicts.
-cluster-host
If the cluster
option has also been specified then this determines which host address will be used for cluster communication with other vert.x instances. By default it will try and pick one from the available interfaces. If you have more than one interface and you want to use a specific one, specify it here.
Here are some examples of vertx run
:
Run a JavaScript verticle server.js with default settings
vertx run server.js
Run 10 instances of a pre-compiled Java verticle specifying classpath
vertx run com.acme.MyVerticle -cp "classes:lib/myjar.jar" -instances 10
Run 10 instances of a Java verticle by source file
vertx run MyVerticle.java -instances 10
Run 20 instances of a ruby worker verticle
vertx run order_worker.rb -instances 20 -worker
Run two JavaScript verticles on the same machine and let them cluster together with each other and any other servers on the network
vertx run handler1.js -cluster vertx run handler1.js -cluster -cluster-port 25501
Run a Ruby verticle passing it some config
vertx run my_vert.rb -conf my_vert.conf
Where my_vert.conf
might contain something like:
{ "name": "foo", "num_widgets": 46 }
The config will be available inside the verticle via the core API.
While you can run a verticle directly from the command line using vertx run
this can be kind of fiddly since you have to specify the classpath every time.
An alternative is to package your application as a module. For detailed information on how to package your code as a module please see the modules manual.
Once you've packaged your module and installed it in the module directory, you can run a module directly from the command line in a similar way to vertx run
.
Instead of vertx run
you use vertx runmod <module name>
. This takes some of the same options as vertx run
. They are:
-conf <config_file>
-instances <instances>
-cluster
-cluster-port
-cluster-host
They have the exact same meanings as the corresponding options in vertx run
.
If you attempt to run a module and it hasn't been installed, Vert.x will attempt to install it from the module repository. See the modules manual for more on this.
Some examples of running modules directly:
Run an installed module called `com.acme.my-mod-v2.1` vertx run com.acme.my-mod-v2.1
Run an installed module called com.acme.other-mod-v1.0.beta1
specifying number of instances and some config
vertx run com.acme.other-mod-v1.0.beta1 -instances 10 -conf other-mod.conf
Run the module vertx.mongo-persistor-v1.0
vertx run vertx.mongo-persistor-v1.0
Each verticle gets its own logger which can be retrieved from inside the verticle. For information on how to get the logger please see the core guide for the language you are using.
The log files by default go in a file called vertx.log
in the system temp directory. On my Linux box this is \tmp
.
By default JUL logging is used. This can be configured using the file $VERTX_HOME\conf\logging.properties
. Where VERTX_HOME
is the directory in which you installed vert.x.
Advanced note: If you'd rather use a different logging framework, e.g. log4j you can do this by specifying a system property when running vert.x (edit the vertx.sh script), e.g.
-Dorg.vertx.logger-delegate-factory-class-name=org.vertx.java.core.logging.impl.Log4jLogDelegateFactory
or
-Dorg.vertx.logger-delegate-factory-class-name=org.vertx.java.core.logging.impl.SLF4JLogDelegateFactory
To configure clustering use the file conf/cluster.xml
in the distribution.
If you want to receive more info on cluster setup etc, then edit conf/logging.properties
to read com.hazelcast.level=INFO
In particular when running clustered, and you have more than one network interface to choose from, make sure Hazelcast is using the correct interface by editing the interfaces-enabled
element.
If you're creating a lot of connections to a Vert.x server in a short period of time, you need to tweak some settings in order to avoid the TCP accept queue getting full which can result in connections being refused or packets being dropped during the handshake which can cause the client to retry.
A classic symptom of this is if you see long connection times just over 3000ms at your client.#
In Linux you need to increase a couple of settings in the TCP / Net config (10000 is an arbitrarily large number)
sudo sysctl -w net.core.somaxconn=10000 sudo sysctl -w net.ipv4.tcp_max_syn_backlog=10000
And you also need to set the accept backlog in your server code, (e.g. in Java:)
HttpServer server = vertx.createHttpServer(); server.setAcceptBacklog(10000);
Vert.x uses the following amazing open source projects:


--- permalink: /foo/ --- This is a foo type of entry as a cheatsheet for me.
1. Here is how we embed math equations in the posts: $$mean = \frac{\displaystyle\sum_{i=1}^{n} x_{i}}{n}$$


A client for AWS Polly <http://aws.amazon.com/documentation/polly>, a speech synthesis service.
| Version: | 0.1.2 |
| Imports: | httr, jsonlite, aws.signature (≥ 0.2.6), tuneR |
| Published: | 2016-12-08 |
| Author: | Thomas J. Leeper [aut, cre] |
| Maintainer: | Thomas J. Leeper <thosjleeper at gmail.com> |
| BugReports: | https://github.com/cloudyr/aws.polly/issues |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | https://github.com/cloudyr/aws.polly |
| NeedsCompilation: | no |
| Citation: | aws.polly citation info |
| Materials: | README NEWS |
| In views: | WebTechnologies |
| CRAN checks: | aws.polly results |
| Reference manual: | aws.polly.pdf |
| Package source: | aws.polly_0.1.2.tar.gz |
| Windows binaries: | r-devel: aws.polly_0.1.2.zip, r-release: aws.polly_0.1.2.zip, r-oldrel: aws.polly_0.1.2.zip |
| OS X binaries: | r-release: aws.polly_0.1.2.tgz, r-oldrel: aws.polly_0.1.2.tgz |
| Reverse imports: | ari, awspack |
Please use the canonical form https://CRAN.R-project.org/package=aws.polly to link to this page.


I am going to use markdown in my posts.
This is so much fun – I can talk about a particular paper.
A banana is an edible fruit – botanically a berry – produced by several kinds of large herbaceous flowering plants in the genus Musa.


Affirmations
- May the force be with you.
- You are going to ace this interview fo sho.
- After today you are going to get a new gig that’s da bomb.
- Be chill on your technical interview. You’re gonna write some really far out code.
- Be yourself. Keep on truckin’ and keepin’ it real.
- Don’t forget to share that killer smile and get jiggy with it.
- Show pride in your mondo cool portfolio and Git hub projects.
- Your code samples and unit tests are out of sight and DYN-o-mite.
- Peace out. Do me righteous and rock your technical test human.


#include <ParticleUniverseLinearForceAffector.h>
Public Member Functions | |
| LinearForceAffector (void) | |
| virtual | ~LinearForceAffector (void) |
| virtual void | copyAttributesTo (ParticleAffector *affector) |
| virtual void | _preProcessParticles (ParticleTechnique *particleTechnique, Ogre::Real timeElapsed) |
| virtual void | _affect (ParticleTechnique *particleTechnique, Particle *particle, Ogre::Real timeElapsed) |
| ParticleUniverse::LinearForceAffector::LinearForceAffector | ( | void | ) | [inline] |
| virtual ParticleUniverse::LinearForceAffector::~LinearForceAffector | ( | void | ) | [inline, virtual] |
| void ParticleUniverse::LinearForceAffector::copyAttributesTo | ( | ParticleAffector * | affector | ) | [virtual] |
Copy attributes to another affector.
Reimplemented from ParticleUniverse::BaseForceAffector.
| void ParticleUniverse::LinearForceAffector::_preProcessParticles | ( | ParticleTechnique * | particleTechnique, | |
| Ogre::Real | timeElapsed | |||
| ) | [virtual] |
Perform activities before the individual particles are processed.
Reimplemented from ParticleUniverse::ParticleAffector.
| void ParticleUniverse::LinearForceAffector::_affect | ( | ParticleTechnique * | particleTechnique, | |
| Particle * | particle, | |||
| Ogre::Real | timeElapsed | |||
| ) | [virtual] |
Affect a particle.
| particle | Pointer to a ParticleTechnique to which the particle belongs. | |
| particle | Pointer to a Particle. | |
| timeElapsed | The number of seconds which have elapsed since the last call. |
Implements ParticleUniverse::ParticleAffector.


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Creative Web Development
Home
Github
LinkedIn
Bash-Me
Love Letters
Exquisite Corpse
Sprite Sheet
Sprite Animation
Boilerplate
Message to the Void
Faux Company
Parallax Narrative
Wordpress


MH17 probe links crash to Russian officials
An investigation into the downing of flight MH17 in 2014 has revealed that ties between high-ranking Russian officials and separatists in the region were “closer than thought”.
Investigators said they were making a “new witness appeal” based on “recorded telephone calls between the leaders of the DPR (Donetsk People’s Republic, a separatist group) and high-ranking Russian officials.”
“Ties between Russian officials and DPR leaders appear to have been much closer” than originally believed, Andy Kraag, the head of Dutch police’s Criminal Investigations Division, said in a video statement.
Investigators said in June that they were going to put three Russian nationals and one Ukrainian on trial in the Netherlands in March 2020 over the downing of the Malaysia Airlines jet, although they will likely be tried in absentia.
The Boeing passenger plane travelling from Amsterdam to Kuala Lumpur was shot down by a Russian-made BUK missile in 2014 over eastern Ukraine. All 298 people on board were killed.
Investigators said on Thursday they had now established that Russian officials and the Ukrainian rebels used secure communications that “appear to be provided by the Russian security service FSB”.
Heartbreaking video of ‘singed’ koala being rescued after fires
Shocking details of incest family
Bali responds to ‘unruly’ Jetstar family
The special phones were “used by Russian top officials in their contact with the fighters”, said David Nelson, senior investigations officer with the Australian Federal Police.
“We have released recorded conversations they had about administrative, financial and military business,” Nelson added.


File to C style array converter
v:
Select a file:
File info
This section contains basic information on the uploaded file.
If it is an image, the size in pixel and a preview will be shown
Name
Size
Type
Last modified
Preview
The grey border around the image is for visibility only.
If it is shorter than 32px, or taller than 100px, it will be resized. Again, for visibility reasons.
Conversion settings
Code format
The array declaration will contain bytes in this format.
So if you have choosen "Hex (0x00)", it will give you this output:
static const unsigned char awesomeimage[] = { 0x00, 0xff, 0xff };
Hex (0x00)
Hex (\x00)
Decimal (000)
Binary (B00000000)
Treat as binary
This tells the converter to treat the current file as a binary, eventhough it is an image.
Every image conversion option will be disabled.
Also will exclude the widht and height info from the output.
Palette mod
This controls the output palette format of the image
For more info on this, please see
the WikiPedia aricle on color depth
32 bit RGBA (4bytes/pixel)
32-bit BGRA (4bytes/pixel)
24bit RGB (3bytes/pixel)
16bit RRRRRGGGGGGBBBBB (2byte/pixel)
16bit BBBBBGGGGGGRRRRR (2byte/pixel)
15bit RRRRRGGGGGBBBBBA (2byte/pixel)
8bit RRRGGGBB (1byte/pixel)
8bit grayscale (1byte/pixel)
1bit line art (1bit/pixel)
Resize
Fill only one of the size boxes to resize the image maintaining the original spect ratio.
Keep both empty, if you do not want to change the size.
x
(Fill only one to maintain aspect ratio)
Multi line
The bytes in the array will be split into multiple lines, roughly matching the aspect ratio of the image.
Unselect this to have all the bytes in a single row.
Optimize for column read
For applications where you need to display the image one 8bit column at a time - as opposed to the normal mode where you plot the image by row.
Row read optimized:
Column read optimized:
This is only available in 1bit mode!
Separate bytes of pixels
If ticked every byte of every pixel will be added to the array separately. Otherwise one item in the array will represent all bytes of one pixel.
Endianness
Little-endian
Big-endian
static
const
unsigned
Data type
byte
char
int
word
short
long
int8_t
int16_t
int32_t
int64_t
uint8_t
uint16_t
uint32_t
uint64_t
PROGMEM
These control the signature generated.
Please note, not all of them make sense in all languages!
...
Result


Hello! I am Irfan Ahmed, React | React Native - Frontend Developer.
Self-motivated with strong organizational skills, I'm Quick learner and a passionate Computer Scientist having a interest in Web & Mobile Technologies, Quality Assurance, Database, Cloud Computing.
Nov 2020 - Present
I work on React Native CLI to create mobile applications front end. The project I have worked on are Savvy Mobile App. Savvy mobile project is a market place like Upwork and Fiverr. and MoveIT Mobile application this application is like Uber but its for trucks booking trucks to move your prducts, furniture and other stuff.
My Role:
- Create FrontEnd of Application on React Native.
- Installing requiried react native libaries.
- Integeration of API's and show data respectively.
- Fetching and posting data to API's.
- Implementing proper Application Navigation, Drawer and etc.
- Maintaining local storage.
- Maintaing proper application states.
- Building APK's
More:
- I have worked on facebook signin/ google signin.
- Used Camera functionality using React native application.
- Used Google Maps using React native application.
- Used Push Notifications.
- etc.
Sept 2020 - Oct 2020
Solving the different tasks related to the HTML, CSS, JS, and React-JS. Designing and Developing different projects Leaderboard, Dashboard, Quiz App..
Mar 2020 - Apr 2020
During the period of this internship, I was exposed to challenges related to: HTML, CSS, JS, PHP, WordPress. At the last this internship developed a WordPress (wooCommerce) website.
2015 - 2019
IQRA University
Some major courses studied during graduation are OOP, Database, Networking, Web programming, Mobile app development, Artificial intelligence, Machine learning, Digital image processing, Software quality assurance.


{% extends "_layouts/govuk-pay.html" %} {% set section = "index" %} {% block content %}
Find people and technology for digital projects


| 1 | package fr.sii.ogham.core.util; | |
| 2 | ||
| 3 | import static java.util.stream.Collectors.toList; | |
| 4 | ||
| 5 | import java.util.ArrayList; | |
| 6 | import java.util.List; | |
| 7 | ||
| 8 | /** | |
| 9 | * Helper class that registers objects with associated priority. Each registered | |
| 10 | * object is then returned as list ordered by priority. The higher priority | |
| 11 | * value comes first in the list. | |
| 12 | * | |
| 13 | * @author Aurélien Baudet | |
| 14 | * | |
| 15 | * @param <P> | |
| 16 | * the type of priorized objects | |
| 17 | */ | |
| 18 | public class PriorizedList<P> { | |
| 19 | private final List<WithPriority<P>> priorities; | |
| 20 | ||
| 21 | /** | |
| 22 | * Initializes with an empty list | |
| 23 | */ | |
| 24 | public PriorizedList() { | |
| 25 | this(new ArrayList<>()); | |
| 26 | } | |
| 27 | ||
| 28 | /** | |
| 29 | * Initializes with some priorized objects | |
| 30 | * | |
| 31 | * @param priorities | |
| 32 | * the priorized objects | |
| 33 | */ | |
| 34 | public PriorizedList(List<WithPriority<P>> priorities) { | |
| 35 | super(); | |
| 36 | this.priorities = priorities; | |
| 37 | } | |
| 38 | ||
| 39 | /** | |
| 40 | * Registers a new priorized object | |
| 41 | * | |
| 42 | * @param priorized | |
| 43 | * the wrapped object with its priority | |
| 44 | * @return this instance for fluent chaining | |
| 45 | */ | |
| 46 | public PriorizedList<P> register(WithPriority<P> priorized) { | |
| 47 | priorities.add(priorized); | |
| 48 |
1
1. register : replaced return value with null for fr/sii/ogham/core/util/PriorizedList::register → NO_COVERAGE |
return this; |
| 49 | } | |
| 50 | ||
| 51 | /** | |
| 52 | * Registers an object with its priority | |
| 53 | * | |
| 54 | * @param priorized | |
| 55 | * the object to register | |
| 56 | * @param priority | |
| 57 | * the associated priority | |
| 58 | * @return this instance for fluent chaining | |
| 59 | */ | |
| 60 | public PriorizedList<P> register(P priorized, int priority) { | |
| 61 | priorities.add(new WithPriority<>(priorized, priority)); | |
| 62 |
1
1. register : replaced return value with null for fr/sii/ogham/core/util/PriorizedList::register → RUN_ERROR |
return this; |
| 63 | } | |
| 64 | ||
| 65 | /** | |
| 66 | * Merge all priorities of another {@link PriorizedList} into this one. | |
| 67 | * | |
| 68 | * @param other | |
| 69 | * the priority list | |
| 70 | * @return this isntance for fluent chaining | |
| 71 | */ | |
| 72 | public PriorizedList<P> register(PriorizedList<P> other) { | |
| 73 | priorities.addAll(other.getPriorities()); | |
| 74 |
1
1. register : replaced return value with null for fr/sii/ogham/core/util/PriorizedList::register → NO_COVERAGE |
return this; |
| 75 | } | |
| 76 | ||
| 77 | /** | |
| 78 | * Returns true if this list contains no elements. | |
| 79 | * | |
| 80 | * @return if this list contains no elements | |
| 81 | */ | |
| 82 | public boolean isEmpty() { | |
| 83 |
2
1. isEmpty : replaced boolean return with false for fr/sii/ogham/core/util/PriorizedList::isEmpty → NO_COVERAGE 2. isEmpty : replaced boolean return with true for fr/sii/ogham/core/util/PriorizedList::isEmpty → NO_COVERAGE |
return priorities.isEmpty(); |
| 84 | } | |
| 85 | ||
| 86 | /** | |
| 87 | * Get the list of priorities ordered by priority | |
| 88 | * | |
| 89 | * @return ordered list of priorities | |
| 90 | */ | |
| 91 | public List<WithPriority<P>> getPriorities() { | |
| 92 |
1
1. getPriorities : replaced return value with Collections.emptyList for fr/sii/ogham/core/util/PriorizedList::getPriorities → NO_COVERAGE |
return sort(); |
| 93 | } | |
| 94 | ||
| 95 | /** | |
| 96 | * Get the list of priorized objects ordered by highest priority. | |
| 97 | * | |
| 98 | * @return list of objects ordered by highet priority | |
| 99 | */ | |
| 100 | public List<P> getOrdered() { | |
| 101 |
1
1. getOrdered : replaced return value with Collections.emptyList for fr/sii/ogham/core/util/PriorizedList::getOrdered → RUN_ERROR |
return sort().stream().map(WithPriority::getPriorized).collect(toList()); |
| 102 | } | |
| 103 | ||
| 104 | private List<WithPriority<P>> sort() { | |
| 105 |
1
1. sort : removed call to java/util/List::sort → RUN_ERROR |
priorities.sort(WithPriority.comparator()); |
| 106 |
1
1. sort : replaced return value with Collections.emptyList for fr/sii/ogham/core/util/PriorizedList::sort → RUN_ERROR |
return priorities; |
| 107 | } | |
| 108 | } | |
Mutations | ||
| 48 |
1.1 |
|
| 62 |
1.1 |
|
| 74 |
1.1 |
|
| 83 |
1.1 2.2 |
|
| 92 |
1.1 |
|
| 101 |
1.1 |
|
| 105 |
1.1 |
|
| 106 |
1.1 |


If you are not the admin you can't do that
CREATE NEW
POST
MAKE AN AMAZING SITE
SEND
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Native son of Southern California, Michael is a top-notch web developer ready for hire that has been called "the fastest learner I've ever taught". The greatest asset I bring to the table is a willingness and a facility of learning that you won't find elsewhere.
After receiving a full tuition academic scholarship, I graduated from Fordham University in New York City with a degree in Philosophy in 2013 and am excited to pursue a career that utilizes every day the logical training I had studying philosophy. My favorite philosopher is Nietzsche, with a special shout out to the little-known Boethius, whose book The Consolation of Philosophy changed my life.
I speak English, Spanish, and French. I have professional work experience in Spanish, when I interned at the Spanish Consulate in Boston the summer of 2012, and have a published article also written in Spanish.
I am a guitarist, bassist, composer, and songwriter. I have played shows around the greater Los Angeles area. Below is one of my songs. You can listen to more here.
I'm occasionally a serious runner. In high school I was a scoring member of the varsity cross country team, responsible for our 4th place finish in the California Division II State Championships. After college I started training again and in 2014 set a new personal best for the 5K (16:10) and raced my first 10K (34:52). My personal best in the mile still stands from high school (4:36). I've run more times than I can count a half-marathon distance (or greater) as part of my training for 5Ks, but have never raced one. I have not run a marathon yet either, but will one day soon!
Other things I enjoy and wish there as more time for include: juggling, slacklining, juggling on a slackline, body surfing, bouldering, craft beer, and dry Italian and Spanish wine.


--- date: '2020-03-17T13:14:33' draft: false linktitle: Lab 1 menu: MES623Econ: parent: Lab Material weight: 1 title: R Intro type: docs weight: 1 output: blogdown::html_page: toc: true ---
Determine which directory you are in:
getwd()
ProTip: Use Ctrl + Enter
(Windows) or Cmd + Enter
(Mac) to run the current line of code using a keyboard shortcut.
Not the right directory? That’s fine–let’s change it!
To change or set the directory in R use the setwd() function.
For example, to set the directory to a specific folder:
setwd("/Users/username/Desktop/MES623_Lab")
Note: In this character string, you can only use a “/” or “\\” between folder names.
To keep things tidy, create another folder specifically for today’s lab. This can be done by creating the object “todays.file” where you will change the name from day to day.
todays.file = "Lab_1"
dir.create(todays.file)
Set the directory to todays.file
to keep things saved in the right place.
setwd(todays.file)
ProTip: R does not like spaces. Use underscores instead.
Check to see if you are in the right place using getwd()
, then use the function dir()
to see what is in the folder.
ProTip: Messed up? Want to move up one directory? No problem! Run setwd("..")
."
Good to go? Let’s move on.
There are millions of R packages designed to do all kinds of data analyses. Two packages that I have grown fond of for manipulating and visualizing data are dplyr
and ggplot2
. Fortunately, somebody decided to bundle these packages and a few others in what is called the tidyverse
. We can install each package individually using the install.packages()
function or we can just install the tidyverse
package.
dplyr: a package for data manipulation
ggplot2: a package for creating graphics
tidyverse: the data analyses super package!
# Your install options
install.packages("ggplot2")
install.packages(c("ggplot2","dplyr"))
# All necessary packages can be installed using this line
install.packages("tidyverse")
Note: Installing the package only needs to be done once.
Load the package every time a new R session is started using the library()
function.

Download the mtcars data set and save it to the newly created lab folder as a .csv file:
Now read it in using the function read.csv()
and give it the object name data
:
data = read.csv("mtcars.csv")
Make sure the data was read in correctly using the function head()
.
head(data)
## X mpg cyl disp hp drat wt qsec vs am gear carb
## 1 Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## 2 Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## 3 Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
## 4 Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
## 5 Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
## 6 Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
ProTip: Not sure what a function does? Type ?head()
into the console and see what pops up in the help menu. Scroll down to the bottom–there are often examples of how to use the function that you can practice with.
Looks good to me!
What if we want to see the first eleven rows of the dataset?
head(data, n = 11)
## X mpg cyl disp hp drat wt qsec vs am gear carb
## 1 Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## 2 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## 3 Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## 4 Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## 5 Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## 6 Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## 7 Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## 8 Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## 9 Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## 10 Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## 11 Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
What if we want to see the bottom 5 rows of the dataset?
tail(data, n = 5)
## X mpg cyl disp hp drat wt qsec vs am gear carb
## 28 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.9 1 1 5 2
## 29 Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.5 0 1 5 4
## 30 Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.5 0 1 5 6
## 31 Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.6 0 1 5 8
## 32 Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.6 1 1 4 2
What are the column names?
names(data)
## [1] "X" "mpg" "cyl" "disp" "hp" "drat" "wt" "qsec" "vs" "am"
## [11] "gear" "carb"
What are the dimensions of the dataset, how many rows and columns?
dim(data)
## [1] 32 12
Last but not least, summerize the data using the function summary()
.
summary(data)
## X mpg cyl disp
## AMC Javelin : 1 Min. :10.40 Min. :4.000 Min. : 71.1
## Cadillac Fleetwood: 1 1st Qu.:15.43 1st Qu.:4.000 1st Qu.:120.8
## Camaro Z28 : 1 Median :19.20 Median :6.000 Median :196.3
## Chrysler Imperial : 1 Mean :20.09 Mean :6.188 Mean :230.7
## Datsun 710 : 1 3rd Qu.:22.80 3rd Qu.:8.000 3rd Qu.:326.0
## Dodge Challenger : 1 Max. :33.90 Max. :8.000 Max. :472.0
## (Other) :26
## hp drat wt qsec
## Min. : 52.0 Min. :2.760 Min. :1.513 Min. :14.50
## 1st Qu.: 96.5 1st Qu.:3.080 1st Qu.:2.581 1st Qu.:16.89
## Median :123.0 Median :3.695 Median :3.325 Median :17.71
## Mean :146.7 Mean :3.597 Mean :3.217 Mean :17.85
## 3rd Qu.:180.0 3rd Qu.:3.920 3rd Qu.:3.610 3rd Qu.:18.90
## Max. :335.0 Max. :4.930 Max. :5.424 Max. :22.90
##
## vs am gear carb
## Min. :0.0000 Min. :0.0000 Min. :3.000 Min. :1.000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:3.000 1st Qu.:2.000
## Median :0.0000 Median :0.0000 Median :4.000 Median :2.000
## Mean :0.4375 Mean :0.4062 Mean :3.688 Mean :2.812
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :1.0000 Max. :1.0000 Max. :5.000 Max. :8.000
##
Here we have information about the minimum and the maximum from each column.
We often just want to know about one variable. To grab a single variable in R (from a data frame or matrix), use the $. To grab the horsepower (hp) from the mtcars dataset type data$hp. To summarize the horsepower variable:
summary(data$hp)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 52.0 96.5 123.0 146.7 180.0 335.0
What if we only cared about the car name (X), the miles per gallon (mpg), the number of cylinders on the engine (cyl), what kind of transmission it was (am), the weight (wt), and the gross horsepower (hp)?
We could subset the data and create a new object using the select()
function in the package dplyr. I also show how to do this manipulation using base R (no package).
data_sub = select(data,X,mpg,cyl, hp, wt, am)
# Check the data
head(data_sub)
## X mpg cyl hp wt am
## 1 Mazda RX4 21.0 6 110 2.620 1
## 2 Mazda RX4 Wag 21.0 6 110 2.875 1
## 3 Datsun 710 22.8 4 93 2.320 1
## 4 Hornet 4 Drive 21.4 6 110 3.215 0
## 5 Hornet Sportabout 18.7 8 175 3.440 0
## 6 Valiant 18.1 6 105 3.460 0
# Using Base R
data_sub_Old = data[,c("X","mpg","cyl","hp","wt","am")]
# Check the data
head(data_sub_Old)
## X mpg cyl hp wt am
## 1 Mazda RX4 21.0 6 110 2.620 1
## 2 Mazda RX4 Wag 21.0 6 110 2.875 1
## 3 Datsun 710 22.8 4 93 2.320 1
## 4 Hornet 4 Drive 21.4 6 110 3.215 0
## 5 Hornet Sportabout 18.7 8 175 3.440 0
## 6 Valiant 18.1 6 105 3.460 0
We get the same answer!
Let’s arrange by mpg, smallest to largest (asending order). Here we use the arrange()
function in dplyr. Asending order is the default for arrange; if we wanted desending we would use the function desc()
.
Remember to save this arranged table to an object (e.g. data_sub_mpg)
data_mpg = arrange(data_sub,desc(mpg))
#check the data
head(data_mpg)
## X mpg cyl hp wt am
## 1 Toyota Corolla 33.9 4 65 1.835 1
## 2 Fiat 128 32.4 4 66 2.200 1
## 3 Honda Civic 30.4 4 52 1.615 1
## 4 Lotus Europa 30.4 4 113 1.513 1
## 5 Fiat X1-9 27.3 4 66 1.935 1
## 6 Porsche 914-2 26.0 4 91 2.140 1
# Using Base R
data_mpg_Old = data_sub_Old[order(-data_sub_Old$mpg),]
#check the data
head(data_mpg_Old)
## X mpg cyl hp wt am
## 20 Toyota Corolla 33.9 4 65 1.835 1
## 18 Fiat 128 32.4 4 66 2.200 1
## 19 Honda Civic 30.4 4 52 1.615 1
## 28 Lotus Europa 30.4 4 113 1.513 1
## 26 Fiat X1-9 27.3 4 66 1.935 1
## 27 Porsche 914-2 26.0 4 91 2.140 1
Looks like my Corolla beats everybody!
I can’t drive a manual so I only want an automatic transmission (am = 0
), but I still want a car that has a good mpg. This can be done using the filter()
function in dplyr on the data we just sorted.
auto_data = filter(data_mpg,am == "0" )
#check the data
head(auto_data)
## X mpg cyl hp wt am
## 1 Merc 240D 24.4 4 62 3.190 0
## 2 Merc 230 22.8 4 95 3.150 0
## 3 Toyota Corona 21.5 4 97 2.465 0
## 4 Hornet 4 Drive 21.4 6 110 3.215 0
## 5 Merc 280 19.2 6 123 3.440 0
## 6 Pontiac Firebird 19.2 8 175 3.845 0
# Using base R
# Think of this as rows where am is equal to "0"
auto_data_Old = data_mpg_Old[data_mpg_Old$am =="0",]
head(auto_data_Old)
## X mpg cyl hp wt am
## 8 Merc 240D 24.4 4 62 3.190 0
## 9 Merc 230 22.8 4 95 3.150 0
## 21 Toyota Corona 21.5 4 97 2.465 0
## 4 Hornet 4 Drive 21.4 6 110 3.215 0
## 10 Merc 280 19.2 6 123 3.440 0
## 25 Pontiac Firebird 19.2 8 175 3.845 0
Note: ==
is a logical operator. It poses the question – is something “exactly equal to” something else? We are looking for the rows where am
is “exactly equal to” 0.
All together now! Use the pipe operator %>%
from the magrittr package within the tidyverse package. This syntax leads to code that is easier to write and to read.USe the keyboard shortcut: Ctrl + Shift + M
(Windows) or Cmd + Shift + M
(Mac).
All_auto_data = data_sub %>% arrange(desc(mpg)) %>% filter(am == "0")
head(All_auto_data)
## X mpg cyl hp wt am
## 1 Merc 240D 24.4 4 62 3.190 0
## 2 Merc 230 22.8 4 95 3.150 0
## 3 Toyota Corona 21.5 4 97 2.465 0
## 4 Hornet 4 Drive 21.4 6 110 3.215 0
## 5 Merc 280 19.2 6 123 3.440 0
## 6 Pontiac Firebird 19.2 8 175 3.845 0
We want save this newly manipulated data to a csv file on our hard drive. This is done using the function write.csv()
write.csv(All_auto_data,"AutomaticTrans.csv", row.names = FALSE)
Note: Don’t forget the quotes around the file name and the .csv
in the file name. If you don’t put row.names = FALSE
you will have row numbers printed in your excel file.
I show how to do all visualizations in ggplot and base R.
Let’s look at our original data set and draw a scatter plot.
ggplot(data = data, mapping = aes(x = wt, y = mpg)) + geom_point()
# Base R
plot(data$wt, data$mpg, pch=19, ylab = "mpg", xlab ="wt")
Lets add a smooth line using stat_smooth()
.
ggplot(data = data, mapping = aes(x = wt, y = mpg)) + geom_point() +
stat_smooth(method = 'lm')
#Base R
plot(data$wt, data$mpg, pch=19, ylab = "mpg", xlab ="wt")
abline(lm(data$mpg~data$wt), col="slateblue")
Use the facet_wrap()
function to seperate graphs by the level of a certain factor–cyl in our case.
ggplot(data = data, mapping = aes(x = wt, y = mpg)) + geom_point() +
stat_smooth(method = 'lm') + facet_wrap(~ cyl)
# Base R
# 1st subset the data, change the graphical prameters, then plot the seperate graphs
cyl_4 = data %>% filter(cyl == "4")
cyl_6 = data %>% filter(cyl == "6")
cyl_8 = data %>% filter(cyl == "8")
par(mfrow = c(1,3))
plot(cyl_4$wt, cyl_4$mpg, pch=19, ylab = "mpg", xlab ="wt")
abline(lm(cyl_4$mpg~cyl_4$wt), col="slateblue")
plot(cyl_6$wt,cyl_6 $mpg, pch=19, ylab = "mpg", xlab ="wt")
abline(lm(cyl_6 $mpg~cyl_6 $wt), col="slateblue")
plot(cyl_8$wt, cyl_8$mpg, pch=19, ylab = "mpg", xlab ="wt")
abline(lm(cyl_8$mpg~cyl_8$wt), col="slateblue")
#Reset graphic parameters
par(mfrow = c(1,1))
# dev.off() # to start graphing in a totally new device
ProTip: Remember to use dev.off()
or reset the graphic parameters if not, all of your graphs will print in the (1, 3) format.
Note: The range of the X-axes are the same using ggplot2 across number of cylinders but not for base R. The ggplot2 graphic is a lot easier to understand quickly.
Attempt a box plot using the geom_boxplot()
function.
ggplot(data, aes(x=as.factor(cyl), y=mpg)) +
geom_boxplot(fill="slateblue", alpha=0.2) + xlab("cyl")
# Base R
boxplot(mpg~cyl,data=data,xlab="cyl", ylab="mpg")
Attempt a histogram of the data using the geom_histogram()
function. Change the binwidth to see what happens.
ggplot(data, aes(x = mpg)) + geom_histogram(binwidth = 1)
# Base R
hist(data$mpg, breaks=34, col="darkgrey", main = "", xlab="mpg")
head()
is not the same as Head()
.The flow and content included in this document were heavily inspired by Ed Rubin’s course notes.↩


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|
| def | __init__ (self, devCtrl, logger=None) |
| |
| def | __del__ (self) |
| |
| def | devMgrCB (self) |
| |
| def | readlineCB (self) |
| |
| def | setInputHook (self, hookFunc) |
| |
| def | shouldLoop (self, LoopCondition cond) |
| |
| def | runLoopUntil (self, LoopCondition cond) |
| |
| def | centralManagerDidUpdateState_ (self, manager) |
| |
| def | centralManager_didDiscoverPeripheral_advertisementData_RSSI_ (self, manager, peripheral, data, rssi) |
| |
| def | centralManager_didConnectPeripheral_ (self, manager, peripheral) |
| |
| def | centralManager_didFailToConnectPeripheral_error_ (self, manager, peripheral, error) |
| |
| def | centralManager_didDisconnectPeripheral_error_ (self, manager, peripheral, error) |
| |
| def | peripheral_didDiscoverServices_ (self, peripheral, services) |
| |
| def | peripheral_didDiscoverCharacteristicsForService_error_ (self, peripheral, service, error) |
| |
| def | peripheral_didWriteValueForCharacteristic_error_ (self, peripheral, characteristic, error) |
| |
| def | peripheral_didUpdateNotificationStateForCharacteristic_error_ (self, peripheral, characteristic, error) |
| |
| def | peripheral_didUpdateValueForCharacteristic_error_ (self, peripheral, characteristic, error) |
| |
| def | GetBleEvent (self) |
| |
| def | scan (self, line) |
| |
| def | bgScanStart (self, name) |
| |
| def | bgScanStop (self) |
| |
| def | ble_debug_log (self, line) |
| |
| def | CloseBle (self, connObj) |
| |
| def | updateCharacteristic (self, bytes, svcId, charId) |
| |
| def | runLoopUntil (self, should_tuple) |
| |
| def | Usage (self, cmd) |
| |
| def | ParseInputLine (self, line, cmd=None) |
| |
◆ __init__()
| def chip.ChipCoreBluetoothMgr.CoreBluetoothManager.__init__ |
( |
|
self, |
|
|
|
devCtrl, |
|
|
|
logger = None |
|
) |
| |
◆ __del__()
| def chip.ChipCoreBluetoothMgr.CoreBluetoothManager.__del__ |
( |
|
self | ) |
|
◆ bgScanStart()
| def chip.ChipCoreBluetoothMgr.CoreBluetoothManager.bgScanStart |
( |
|
self, |
|
|
|
name |
|
) |
| |
API to initiate background BLE scanning.
◆ bgScanStop()
| def chip.ChipCoreBluetoothMgr.CoreBluetoothManager.bgScanStop |
( |
|
self | ) |
|
API to stop background BLE scanning.
◆ ble_debug_log()
| def chip.ChipCoreBluetoothMgr.CoreBluetoothManager.ble_debug_log |
( |
|
self, |
|
|
|
line |
|
) |
| |
◆ centralManager_didConnectPeripheral_()
| def chip.ChipCoreBluetoothMgr.CoreBluetoothManager.centralManager_didConnectPeripheral_ |
( |
|
self, |
|
|
|
manager, |
|
|
|
peripheral |
|
) |
| |
Called by CoreBluetooth via runloop when a connection succeeds.
◆ centralManager_didDisconnectPeripheral_error_()
| def chip.ChipCoreBluetoothMgr.CoreBluetoothManager.centralManager_didDisconnectPeripheral_error_ |
( |
|
self, |
|
|
|
manager, |
|
|
|
peripheral, |
|
|
|
error |
|
) |
| |
Called by CoreBluetooth via runloop when a disconnect completes. error = None on success.
◆ centralManager_didDiscoverPeripheral_advertisementData_RSSI_()
| def chip.ChipCoreBluetoothMgr.CoreBluetoothManager.centralManager_didDiscoverPeripheral_advertisementData_RSSI_ |
( |
|
self, |
|
|
|
manager, |
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|
|
peripheral, |
|
|
|
data, |
|
|
|
rssi |
|
) |
| |
Called for each peripheral discovered during scan.
◆ centralManager_didFailToConnectPeripheral_error_()
| def chip.ChipCoreBluetoothMgr.CoreBluetoothManager.centralManager_didFailToConnectPeripheral_error_ |
( |
|
self, |
|
|
|
manager, |
|
|
|
peripheral, |
|
|
|
error |
|
) |
| |
Called by CoreBluetooth via runloop when a connection fails.
◆ centralManagerDidUpdateState_()
| def chip.ChipCoreBluetoothMgr.CoreBluetoothManager.centralManagerDidUpdateState_ |
( |
|
self, |
|
|
|
manager |
|
) |
| |
IO Bluetooth initialization is successful.
◆ CloseBle()
| def chip.ChipCoreBluetoothMgr.CoreBluetoothManager.CloseBle |
( |
|
self, |
|
|
|
connObj |
|
) |
| |
Called by Chip to close the BLE connection.
◆ devMgrCB()
| def chip.ChipCoreBluetoothMgr.CoreBluetoothManager.devMgrCB |
( |
|
self | ) |
|
A callback used by ChipDeviceCtrl.py to drive the OSX runloop while the
main thread waits for the Chip thread to complete its operation.
◆ GetBleEvent()
| def chip.ChipCoreBluetoothMgr.CoreBluetoothManager.GetBleEvent |
( |
|
self | ) |
|
Called by ChipDeviceMgr.py on behalf of Chip to retrieve a queued message.
◆ peripheral_didDiscoverCharacteristicsForService_error_()
| def chip.ChipCoreBluetoothMgr.CoreBluetoothManager.peripheral_didDiscoverCharacteristicsForService_error_ |
( |
|
self, |
|
|
|
peripheral, |
|
|
|
service, |
|
|
|
error |
|
) |
| |
Called by CoreBluetooth via runloop when a characteristic for a service is discovered.
◆ peripheral_didDiscoverServices_()
| def chip.ChipCoreBluetoothMgr.CoreBluetoothManager.peripheral_didDiscoverServices_ |
( |
|
self, |
|
|
|
peripheral, |
|
|
|
services |
|
) |
| |
Called by CoreBluetooth via runloop when peripheral services are discovered.
◆ peripheral_didUpdateNotificationStateForCharacteristic_error_()
| def chip.ChipCoreBluetoothMgr.CoreBluetoothManager.peripheral_didUpdateNotificationStateForCharacteristic_error_ |
( |
|
self, |
|
|
|
peripheral, |
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|
|
characteristic, |
|
|
|
error |
|
) |
| |
Called by CoreBluetooth via runloop when a subscribe for notification operation completes.
Error = None on success.
◆ peripheral_didUpdateValueForCharacteristic_error_()
| def chip.ChipCoreBluetoothMgr.CoreBluetoothManager.peripheral_didUpdateValueForCharacteristic_error_ |
( |
|
self, |
|
|
|
peripheral, |
|
|
|
characteristic, |
|
|
|
error |
|
) |
| |
Called by CoreBluetooth via runloop when a new characteristic value is received for a
characteristic to which this device has subscribed.
◆ peripheral_didWriteValueForCharacteristic_error_()
| def chip.ChipCoreBluetoothMgr.CoreBluetoothManager.peripheral_didWriteValueForCharacteristic_error_ |
( |
|
self, |
|
|
|
peripheral, |
|
|
|
characteristic, |
|
|
|
error |
|
) |
| |
Called by CoreBluetooth via runloop when a write to characteristic
operation completes. error = None on success.
◆ readlineCB()
| def chip.ChipCoreBluetoothMgr.CoreBluetoothManager.readlineCB |
( |
|
self | ) |
|
A callback used by readline to drive the OSX runloop while the main thread
waits for commandline input from the user.
◆ runLoopUntil()
| def chip.ChipCoreBluetoothMgr.CoreBluetoothManager.runLoopUntil |
( |
|
self, |
|
|
LoopCondition |
cond |
|
) |
| |
Helper function to drive OSX runloop until an expected event is received or
the timeout expires.
◆ scan()
| def chip.ChipCoreBluetoothMgr.CoreBluetoothManager.scan |
( |
|
self, |
|
|
|
line |
|
) |
| |
◆ setInputHook()
| def chip.ChipCoreBluetoothMgr.CoreBluetoothManager.setInputHook |
( |
|
self, |
|
|
|
hookFunc |
|
) |
| |
Set the PyOS_InputHook to call the specific function.
◆ shouldLoop()
| def chip.ChipCoreBluetoothMgr.CoreBluetoothManager.shouldLoop |
( |
|
self, |
|
|
LoopCondition |
cond |
|
) |
| |
Used by runLoopUntil to determine whether it should exit the runloop.
◆ updateCharacteristic()
| def chip.ChipCoreBluetoothMgr.CoreBluetoothManager.updateCharacteristic |
( |
|
self, |
|
|
|
bytes, |
|
|
|
svcId, |
|
|
|
charId |
|
) |
| |
◆ bg_peripheral_name
| chip.ChipCoreBluetoothMgr.CoreBluetoothManager.bg_peripheral_name |
◆ characteristics
| chip.ChipCoreBluetoothMgr.CoreBluetoothManager.characteristics |
◆ chip_queue
| chip.ChipCoreBluetoothMgr.CoreBluetoothManager.chip_queue |
◆ connect_state
| chip.ChipCoreBluetoothMgr.CoreBluetoothManager.connect_state |
◆ devCtrl
| chip.ChipCoreBluetoothMgr.CoreBluetoothManager.devCtrl |
◆ hookFuncPtr
| chip.ChipCoreBluetoothMgr.CoreBluetoothManager.hookFuncPtr |
◆ logger
| chip.ChipCoreBluetoothMgr.CoreBluetoothManager.logger |
◆ loop_condition
| chip.ChipCoreBluetoothMgr.CoreBluetoothManager.loop_condition |
◆ manager
| chip.ChipCoreBluetoothMgr.CoreBluetoothManager.manager |
◆ orig_input_hook
| chip.ChipCoreBluetoothMgr.CoreBluetoothManager.orig_input_hook |
◆ peripheral
| chip.ChipCoreBluetoothMgr.CoreBluetoothManager.peripheral |
◆ peripheral_adv_list
| chip.ChipCoreBluetoothMgr.CoreBluetoothManager.peripheral_adv_list |
◆ peripheral_list
| chip.ChipCoreBluetoothMgr.CoreBluetoothManager.peripheral_list |
◆ ready_condition
| chip.ChipCoreBluetoothMgr.CoreBluetoothManager.ready_condition |
◆ scan_quiet
| chip.ChipCoreBluetoothMgr.CoreBluetoothManager.scan_quiet |
◆ send_condition
| chip.ChipCoreBluetoothMgr.CoreBluetoothManager.send_condition |
◆ service
| chip.ChipCoreBluetoothMgr.CoreBluetoothManager.service |
◆ subscribe_condition
| chip.ChipCoreBluetoothMgr.CoreBluetoothManager.subscribe_condition |
The documentation for this class was generated from the following file:


The 5th Door Team
Mentor
Josephine Pramudia (RMIT)
Contributors
Ian Barnett (S8555204)
Ryan Kita (S3644493)
Tony Gorgioski (S3495512)
Deidre Ferreira (S3550192)
Story Authors
Ian Barnett (S8555204)
Tony Gorgioski (S3495512)
Deidre Ferreira (S3550192)
Story Artwork
Ryan Kita (S3644493)
Images
Pixabay
https://pixabay.com
Unsplash
https://unsplash.com
Vectors-free
http://all-free-download.com/free-vectors
Moving house
http://www.321howto.com/wp-content/uploads/2016/04/movinghouse.jpg
Shopping mall cafe
http://www.mrsfields.com.au/wp-content/uploads/2014/05/Joondalup-Website.jpg
Images
Class kid looking back
http://www.hhaircon.com.au/wp-content/uploads/2013/08/shutterstock_112388351.jpg
Moving van
http://crossfitsomerville.com/wp-content/uploads/2016/09/qf-move-trucks.png
Deserted street
http://img12.nnm.me/8/5/a/6/b/66dedd76ac86ca799a36a4c7893.jpg
First day of school
http://demo.meroschool.com/GalleryImg/GalleryAlbumId2156319166.jpg
Lunch break
http://www.dusa.org.au/getmedia/b96fe954-9a83-4100-bf26-0dd6f599d1f3/Home-Page-Banner.jpg.aspx?width=1332&height=502&ext=.jpg
Reading
The Sorcerer’s Maze Collection
(Blair Polly & D M Potter, Kindle)
https://www.amazon.com/Sorcerers-Maze-Collection-Three-Books-ebook/dp/B01JR3KHN0
How To Write A CYOA Story
(1st Ed Published 2016 Karen Woodward, Kindle)
https://www.amazon.com/How-Write-CYOA-Story-Thats-ebook/dp/B01M4MHVJX
Creating Character Arcs
(K.M. Weiland, Kindle)
https://www.amazon.com/Creating-Character-Arcs-Masterful-Development-ebook /dp/B01M6VC68U/ref=pd_sim_351_3?_encoding=UTF8&psc=1&refRID=C1AQNNK3CD4SVKX8CJR7
Choices App (Stories You Play)
PixelBerry Studios v1.8.0
https://itunes.apple.com/au/app/choices-stories-you-play/id1071310449?mt=8
Tools
Cloud9 IDE
https://ide.c9.io
Brackets Text Editor
http://brackets.io
Notepad++ Text Editor
https://notepad-plus-plus.org
Wireframes Editor - Axure
https://www.axure.com
Adobe CC
Illustrator, Animate, Photoshop, After-Effects
http://www.adobe.com
Licenses
JQuery 3.2.1
https://jquery.com | https://js.foundation/pdf/ip-policy.pdf
Adobe Creative Cloud Subscription
http://www.adobe.com/au/legal/licenses-terms.html


Kharon
Kharon is a python module (`pip install Kharon`) which will convert your Red-DiscordBot cogs' `info.json` files from v2 to v3.This was developed because I am lazy and didn't want to manually change all keys to `lower()`, change `authors` to be a list, or remap `DISABLED` to `hidden`.
To use the live version of Kharon:
- Provide Kharon with your `info.json` file:
- Provide Kharon your GitHub username, repository name, and cog name.
- Upload your `info.json` file from your local machine.
- Paste the contents of your `info.json` file in the `Preview` pane.
- Select which version you want to convert your `info.json` file.
- Convert your `info.json` file.
- Copy or export the results.
- ???
- PROFIT!


{% extends "layout_bsa.html" %} {% block page_title %} Social work bursary {% endblock %} {% block content %} Apply for Social Work Bursary Have you applied for any other financial help relating to this course? Have you applied for any other financial help relating to this course? For example: Grants, bursaries, retainers or support from other organisations Yes No Was your application approved? Yes No I haven't heard back yet Support type Grant Bursary Retainer Other support Name of organisation From To {% endblock %}
Apply for Social Work Bursary


Research and analysis
Provides an overview at national level of the findings from the FE choices employer satisfaction survey 2012 to 2013.
PDF , 714KB , 40 pages
If you use assistive technology (such as a screen reader) and need a version of this document in a more accessible format, please email<EMAIL_ADDRESS>. Please tell us what format you need. It will help us if you say what assistive technology you use.
PDF , 207KB , 3 pages
If you use assistive technology (such as a screen reader) and need a version of this document in a more accessible format, please email<EMAIL_ADDRESS>. Please tell us what format you need. It will help us if you say what assistive technology you use.
MS Excel Spreadsheet , 11.9KB
If you use assistive technology (such as a screen reader) and need a version of this document in a more accessible format, please email<EMAIL_ADDRESS>. Please tell us what format you need. It will help us if you say what assistive technology you use.
The purpose of FE choices is to provide consistent and comparable information for learners and employers to help them make informed choices about publicly funded education and training as well as providing consistent management information on key performance indicators.
FE choices comprises a set of performance indicators that cover aspects of a college or provider’s provision such as: success rates, learner destinations and satisfaction levels of learners and employers.
This page provides an overview, at national level, of the findings of the FE choices employer satisfaction survey 2012 to 2013. The survey asked employers to rate their providers on learning delivered in 2012 to 2013.
Published: 28 May 2015


Components
Components with local state
Component with state machine creator
Component with state chart service
App state
App state
Multiple stores
Persist store
Worker store


Here is one using dojox.mvc.Repeat as a mixin with no Group and dojo.parser instead of dojox.mobile.parser it is all good using mixin.
Here is one using MVC using Repeat no Group, with removeRepeatNode set true it is all good if Repeat sets select and onCheckStateChanged.
You can update the label or checked from here to update the model.
Model:


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| One-time throwaway offer! Hurry! |
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Araam Inc. is Canada's premier
manufacturer of both standard and
custom mattresses.
www.araam.ca
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Luxury & Comfort
mattress
The most comfortable bed you'll ever own.
Trusted since 1904, with royal
warrant from Queen Elizabeth II.
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Healthy & Natural
mattress
A mattress built for you, naturally.
Quality hand-crafted construction using 100% natural latex.
Free from foam & chemicals.
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Conventional & Contemporary
mattress
Machine-made standard mattresses for budget-conscious customers, looking for a contemporary look & feel.
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780.444.1388 |
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780.930.4295 |
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<EMAIL_ADDRESS>|
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Terms & Conditions
Spring Blowout Special program is valid beginning April 1st, 2018. Offer valid till stocks last. Promotion applies to all purchase orders for SOVA Monarch & Almoha mattresses only. Boxsprings and Accessories are NOT included in this promotion. This is a one-time special discount program for existing dealers/retailers of SOVA mattresses only, and cannot be combined with any other discounts. Discount is NOT applicable for floor model orders.
Any discrepancies arising from the “Spring Blowout Special 2018” program will be resolved at sole discretion of Araam. All questions pertaining to promotion must be directed to the appropriate Araam Sales Representative for your region. Araam reserves the right to change the terms & conditions of the campaign, with a 48 hour prior notice.
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| 11616 - 178 St Edmonton, AB T5S 2E6
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{% trans "City" %}/ {% trans "State" %}/ {% trans "Postal" %}
{% trans "Country" %}(ctrl/cmd {% trans "click to select multiple" %})
{% trans "Continental Region" %}(ctrl/cmd {% trans "click to select multiple" %})
{% if can_use_distance_filter %}
{% trans "Within Distance" %}
{% else %}
{% if not request.user.is_authenticated %}
{% trans "Additional filters are available to authenticated users, and you are currently not logged in." %}
{% elif not request.user.is_verified_user %}
{% trans "Additional filters are available to verified users, and you are currently not affiliated with any organizations." %}
{% endif %}


Role Description
We are looking for Machine Learning (ML) Engineers to help us create ground-breaking artificial intelligence-based solutions for the building design and construction industry. As part of our Machine Learning engineer team, you will participate in design, development and implementation of architectural deliverables to enable Data Science platforms to include components of the assessment and optimization of system design and review of user requirements.
Your ultimate goal will be to shape and build efficient self-learning applications that will play a key part in the development of our solutions.
This position will be based out of Irvine, CA but with a lot of flexibility to work from home.
Responsibilities
Design machine and deep learning systems
Research and implement appropriate ML algorithms and tools
Implementing appropriate ML Algorithms
Study and transform data science prototypes
Develop machine learning applications according to requirements
Select appropriate datasets and data representation methods
Run machine learning tests and experiments
Perform statistical analysis and fine-tuning using test results
Train and retrain systems when necessary
Extend existing ML libraries and frameworks
Keep abreast of developments in the field
Requirements
Demonstrable experience delivering and deploying a machine learning-based feature.
Understanding of data structures, data modeling and software architecture
Understanding of the all the stages of developing machine learning-based systems: labeling data, selecting algorithms, training, model development, visualization, regression, updates, debugging.
Mastery of at least one language from this list: Python, Java, R, Golang; or a well thought out argument for a language that is not on that list but should be.
Knowledge of machine learning frameworks like TensorFlow, Keras and PyTorch
Proven experience with machine learning tools, including one or more of: Scikit-learn, Numpy, SciPy, Pandas, Matplotlib, Jupyter Notebook, Spark.
Excellent communication skills
Ability to work in a team
Outstanding analytical and problem-solving skills
BSc in Computer Science, Applied Mathematics or similar field; Master’s degree or higher degree is a plus
Experience troubleshooting and debugging.
Strong communication skills
Nice to have
Have a strong understanding of the concept of building generative design
Have experience in any field of Architecture, Engineering or Construction
Understanding of the challenges surrounding applying machine learning to rich media, including automatic speech recognition (ASR), natural language processing (NLP), object detection and image recognition.
Hands-on experience Deep Learning, Artificial Intelligence and data science techniques and their frameworks
Knowledge of geometry and topology concepts, representations and data structures (such as NURBS, splines, level sets, surface and volumetric meshes, point clouds, analytical surfaces/solids, functional representations, subdivision surfaces, T-splines, etc.)
Understanding of computer vision applications with cognitive systems


[gecode-users] fzn-gecode -mode gist and variable names
Christian Schulte
cschulte at kth.se
Thu Aug 11 12:22:06 CEST 2016
Guido, how about adding a search annotation “free”? Would that work? Cheers Christian
--
Christian Schulte, www.gecode.org/~schulte
Professor of Computer Science, KTH, <mailto:cschulte at kth.se> cschulte at kth.se
Expert Researcher, SICS, <mailto:cschulte at sics.se> cschulte at sics.se
From: users-bounces at gecode.org [mailto:users-bounces at gecode.org] On Behalf Of Mats Carlsson
Sent: Thursday, August 11, 2016 09:28
To: Guido Tack
Cc: users at gecode.org
Subject: Re: [gecode-users] fzn-gecode -mode gist and variable names
Is there a way to _tell_ fzn-gecode to use free search and to ignore any search annotation?
On Thu, Aug 11, 2016 at 9:10 AM, Guido Tack <guido.tack at monash.edu> wrote:
The -f command line option is actually a no-op, it’s just there to maintain compatibility with the MiniZinc challenge (the specification is that you /may/ ignore the search annotation, not that you /must/…).
Cheers,
Guido
--
GUIDO TACK
Senior Lecturer
Information Technology
Monash University
Level 6, Room 6.40, Building H, Caulfield Campus
900 Dandenong Road
Caulfield East VIC 3145
Australia
T: +61 3 9903 1214 <tel:%2B61%203%209903%201214>
E: guido.tack at monash.edu
http://www.csse.monash.edu/~guidot/
On 11 Aug 2016, at 5:06 PM, Mats Carlsson <matsc at sics.se> wrote:
Thanks Guido. I need this in a context with free search, and by the way another unfortunate thing about fzn-gecode is that any search annotation seems to disable the -f flag.
So free search seems incompatible with Gist showing variable names... What a pity. I hope that you will return to these points eventually.
Keep up the good work,
On Thu, Aug 11, 2016 at 8:38 AM, Guido Tack <guido.tack at monash.edu> wrote:
Hi Mats,
this is a limitation in the current fzn-gecode interpreter, we can only get variable names for variables that are mentioned in a search annotation. So currently the only workaround is to include all variables in some search annotation (but of course I understand that that’s not necessarily what you want to do). I had a look at fixing this at some point, but it turned out to be more than just a few lines of code so I had to leave it for the time being.
Cheers,
Guido
--
GUIDO TACK
Senior Lecturer
Information Technology
Monash University
Level 6, Room 6.40, Building H, Caulfield Campus
900 Dandenong Road
Caulfield East VIC 3145
Australia
T: +61 3 9903 1214 <tel:%2B61%203%209903%201214>
E: guido.tack at monash.edu
http://www.csse.monash.edu/~guidot/
On 19 Jul 2016, at 6:39 PM, Mats Carlsson <matsc at sics.se> wrote:
Hi, I have a problem with fzn-gecode -mode gist, Gecode trunk 14847, MiniZinc 2.0.13.
Namely, anonymous variable names like var[123] get displayed instead of the variable names of the .fzn file. Admittedly, the latter names are a bit awkward, but at least they enable me to relate back to the names of the MiniZinc model. But I have no idea how to map the var[...] names to the names of the .fzn file. I have tried to edit the .fzn file in various ways to make Gist use its names, but it has not helped. Is there some work-around?
See the attachment for an example.
--
Mats Carlsson, SICS
www.sics.se/~matsc
<tree.pdf>_______________________________________________
Gecode users mailing list
users at gecode.org
https://www.gecode.org/mailman/listinfo/gecode-users
--
Mats Carlsson, SICS
www.sics.se/~matsc
_______________________________________________
Gecode users mailing list
users at gecode.org
https://www.gecode.org/mailman/listinfo/gecode-users
--
Mats Carlsson, SICS
www.sics.se/~matsc
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This inspection reports on any uses of the
caller
property in a JavaScript function. Use of this property to access the stack frame of the calling method can be extremely confusing, and result in subtle bugs.
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