The Charities Aid Foundation is the non-profit corporation of the Kansas State Council of the Knights of Columbus. The Foundation oversees the charitable fund raising, administration of funds collected, and distribution of these funds for the Kansas State Council of the Knights of Columbus.
The Foundation encourages the Kansas State Council to conduct statewide solicitations for the Commitment to Humanity Program, Pennies from Heaven Program, and the Religious Education Fund. The Foundation also encourages and accepts tax-deductible donations from wills and gifts for the purpose of the charitable work of the Knights of Columbus.
The Foundation also distributes monies earned from the Christopher Fund and Centennial Memorial Fund to support Catholic Education across the state.
The Emergency Disaster Aid Program (EDAP), funded by donations to the Pennies from Heaven Program, helps victims of natural disasters, regardless of race, creed, or nationality. Requests of up to $500 will be granted without any matching funds from the local council. The complete guidelines to request funds can be found on the inside back cover of the Official State Directory.
The Commitment to Humanity Program helps people with intellectual disabilities. Funds from this program are distributed to organizations that help these special people. The funds also are used to support the Kansas Special Olympics State Basketball Tournament. The Kansas State Council of the Knights of Columbus is proud to be the prime sponsor of this worthy event.
Monies raised for the Religious Education Fund help parishes, schools and CCD programs throughout the state. These funds are also used to help support the Catholic Campus Centers in Kansas, and underwrite special Religious Education projects submitted by the Bishops of Kansas, Catholic lay organizations and individuals.
Your support of the above programs is appreciated and needed to continue the work of this charitable foundation.
© Knights of Columbus - Kansas State Council
For comments or questions please email webmaster@kansas-kofc.org


MURDER, INC. (director: Burt Balaban/Stuart Rosenberg; screenwriters: Irve Tunick/Mel Barr/based on the book by Burton Turkus and Sid Feder "Murder, Incorporated"; cinematographer: Gayne Rescher; editor: Ralph Rosenblum; cast: Stuart Whitman (Joey Collins), May Britt (Eadie Collins), Henry Morgan (Burton Turkus), Peter Falk (Abe Reles), Simon Oakland (Lt. Tobin), Joseph Bernard (Mendy Weiss), Sarah Vaughan (Herself), Vincent Gardenia (Lazlo), Howard Smith (Albert Anastasia), David J. Stewart (Louis "Lepke" Buchalter), Morey Amsterdam (Walter Sage), Eli Mintz (Joe Rosen); Runtime: 103; 20th-Century Fox; 1960)
Reviewed by Dennis Schwartz
Based on the true-life book of lawman Burton Turkus, the mob story is played against the fictional account of a showbiz couple, Eadie (Britt) and Joey Collins (Whitman), being intimidated by the mob. This weary crime story is about a national crime syndicate, headed by Louie Lepke and Albert Anastia. It is set during the 1930s and tells about mobster Lepke (Stewart) hiring hit man Abe Reles (Falk), the leader of the Brownsville gang, to handle contract killings exclusively for him and the mob.
When nightclub singer Joey can't pay back the $300 loan to Abe Reles, he's forced into working for the mobster to pay the debt back. He fingers Walter Sage (Amsterdam), a Catskill nightclub owner and comedian, who is involved with illegal slot machines and has tried to shake-down the mob for a partnership. This is Reles' first contract for the mob, as he fulfills the contract by sticking an ice pick in the comic's guts.
The story is seen through the eyes of vicious hit man Reles, who says the reason we have hands is to take things. It is also seen through the eyes of the scared showbiz couple, Joey and Eadie, who can't get rid of Reles from their life -- no matter what they do. He sets them up in an apartment where the mob stashes stolen goods, and when Lepke needs a hiding place from Special Prosecutor Thomas Dewey, he stays with the couple. In this apartment he orders his trusted henchman, Mendy Weiss (Bernard), to rub out a key witness, Joe Rosen (Mintz). Lepke stole Rosen's trucking business from him, and he has info on him that could send him away for a long time. When Mendy tells Lepke that he shot the candy store owner in the back of the head as arranged, Joey overhears that. His testimony will be used to send the crime chief to the electric chair.
The crime story comes to a climax when tough Brownsville police Lt. Tobin (Oakland) and the new Special District Attorney Burton Turkus (Morgan), crack down on the Brownsville gang and get Reles to sing against the mob in a trade for immunity.
The best thing about the film was Falk's tough-guy performance. Otherwise, everything was routine.
REVIEWED ON 7/18/2001 GRADE: C
Dennis Schwartz: "Ozus' World Movie Reviews"
Dennis Schwartz: "Ozus' World Movie Reviews"
http://www.sover.net/~ozus
<EMAIL_ADDRESS>© ALL RIGHTS RESERVED DENNIS SCHWARTZ
========== X-RAMR-ID: 28914 X-Language: en X-RT-SourceID: 873 X-RT-AuthorID: 1315 X-RT-RatingText: C
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rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
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Related links: index of all rec.arts.movies.reviews reviews


--- prev_page: '/recipes/recipes_layout.html' next_page: '/examples/examples_intro.html' --- {% raw %}
# HIDDEN
# Clear previously defined variables
%reset -f
nbinteract also provides built-in methods to easily create multiple choice and short answer questions.
import nbinteract as nbi
nbinteract.multiple_choice
takes a question, a list of possible answer choices, and the correct answer. Clicking the buttons shows whether the choice was correct.
nbi.multiple_choice(question="What is 10 + 2 * 5?",
choices=['12', '60', '20'],
answers=2)
nbi.multiple_choice(question="Select all prime numbers.",
choices=['12', '3', '31'],
answers=[1, 2])
nbinteract.short_answer
takes a question and an answer.
The answer can either be a string, a list of strings, or a function that returns True
when called with the user's input. If the function errors, an error message will be displayed.
nbi.short_answer('What is 1+1?', answers='2', explanation='1+1 is 2')
nbi.short_answer('Enter the first name of a member of the Beatles.',
['John', 'Paul', 'George', 'Ringo'])
nbi.short_answer('Enter an even number.', lambda x: int(x) % 2 == 0)


This adds a drawing tool for symmetric round shapes to the Glyph window. It might take some getting used to.
Drag with the command key pressed to move the tangent points to make the flats longer and shorter.
Drag with the option key pressed to move the control points.
..to get some finer control over the position.
The horizontal and vertical values flip when the dragged rectangle goes from ▬ to ▐ and vice versa.
Version 1.0, May 2020


Status:
Waiting...
/
Connect
Send Message
New Message
Add Field
Save Message
Favourites
Sent
Received
string
int8
int16
int32
uint8
uint16
uint32


Space, the final frontier. These are the voyages of the Starship Enterprise. Its five-year mission: to explore strange new worlds, to seek out new life and new civilizations, to boldly go where no man has gone before. Many say exploration is part of our destiny, but it’s actually our duty to future generations and their quest to ensure the survival of the human species.
As a developer, you get to be the person that translates your passion into a functional website or web application to share with the world. A rapidly changing technological landscape means the industry continues to grow quickly, and is expected to grow by at least 13% in the next decade, according to the Bureau of Labor Statistics. While many of those jobs are located in major tech hubs like San Francisco and New York City, the widespread need for developers across all industries means there’s likely a job waiting for you wherever you want to live. And if you’ve got persistence, grit, curiosity, and a brain that likes to solve puzzles, full stack web development could be the career for you.
Read MoreThere’s a reason skilled software developers are in high demand. The industry is expected to grow by 21% over the next decade, according to the Bureau of Labor Statistics – and you could become the mobile developer who builds the next app that becomes part of everyday life for millions of iPhone users. If you’re analytical, logical, creative, and curious, iOS development could be the career for you.
Read MoreWith 26% annual growth, Data Science continues to top emerging job lists year after year. Building on centuries of statistics and mathematics, Data Science uses computational techniques to help the most innovative companies in the world scale. From self-driving cars to dynamic business insights for Fortune 500 Companies, Data Science is changing the world. If you enjoy mathematics and love using data to make decisions, a career in data science could be for you.
Read MoreIterative approaches to corporate strategy foster collaborative thinking to further the overall value proposition.
Read More


The opinions on this site are my own, and are not necessarily shared by my employer.
Upgrading OS X using a package
28 Sep 2015
It’s the time of year where we start to think about upgrading our machines to the latest version of OS X. There are several ways of doing this, but assuming your users are unable to perform the upgrade themselves via the App Store (if they’re running as a standard user or your policies prohibit the use of the App Store), you might be wondering how you can use your management tool to get your machines upgraded and make sure they stay enrolled in your management tool.
We’re fortunate that we have a standard packaging format on OS X that virtually all management tools can install, so this is the most universal way of distributing software. Greg Neagle wrote createOSXinstallPkg a few years ago that has several nice features for mac admins:
It wraps up an OS X Installer into a standard package.
It allows you to add in additional packages - perhaps you want to make sure your admin user is installed or make sure that a version of Munki that is compatible with the new OS is installed.
Yosemite introduced a nice undocumented requirement that all packages included in the OS X installer environment are distribution packages. This is in addition to the limited OS X Installer environment not having many of the command line tools you might expect to be there.
One solution to these issues is to use first-boot-pkg - a tool that will install a set of packages at first boot, and will wrap them in a distribution style package so it can be used with createOSXinstallPkg.
The first boot package
Our first job is to build the package that will be installed at first boot. I am only going to make sure that Munki is installed at first boot, but some other things you might want to put in include:
Your local admin user
Puppet and Facter
A payload free package to configure your SUS CatalogURL
Prep for the first boot package
First off we’re going to need the script to build a first boot package. Assuming you’re going to keep your code in ~/src:
You have two options for configuring the first boot package - you can pass it options on the command line or you can use a plist. We’re using a plist as it’s the most repeatable and sharable method. If you need further options, such as disabling the network check, see the project on Github.
The above is assuming you’ve saved your Munki package to ~/src/first-boot-pkg/munkitools-<IP_ADDRESS>5.pkg (i.e. in the same directory as your first-boot-config.plist).
Building the first boot package
Let’s make sure we’re in the right directory:
And let’s build the package:
Making the OS X upgrade package
As previously mentioned, we’re going to use createOSXinstallPkg, so let’s grab that:
And assuming your OS X Installer is saved to the usual place:
createOSXinstallPkg will let you know how it’s doing:
And voilla! You have a package that can be deployed by virtually any management tool (Munki in my case) that will make sure the latest version of Munki is also installed at the same time.


Establishments that are inspected can pass, pass with conditions or fail.
A fail is given if an establishment has serious violations that can't be fixed during the inspection and remain uncorrected during a re-inspection. See more below about serious violations. A fail also occurs when an establishment has critical violations that can't be corrected during inspection. In this situation, the establishment will have its license suspended until it passes a reinspection.
A pass with conditions is given if the establishment has serious or critical violations that are corrected during the inspection or the certified food service sanitation manager isn't present during the inspection. (The manager is someone who works for the restaurant and oversees the food-handling process, to make sure food is handled to prevent the occurrence of food-borne illness. The manager's certification must be prominently posted).
A pass is given if the establishment meets minimum city requirements and doesn't have serious or critical violations present during the inspection.
Out of business means the establishment was out of business at the time of the inspection.
Risk: Each establishment is categorized as to its risk of adversely affecting the public's health, with 1 being the highest and 3 the lowest. The frequency of inspection is tied to this risk, with risk 1 establishments inspected most frequently and risk 3 least frequently.
Violations: An establishment can receive one or more of 45 distinct violations (violation numbers 1-44 and 70). For each violation number listed for a given establishment, the requirement the establishment must meet in order for it to NOT receive a violation is noted, followed by a specific description of the findings that caused the violation to be issued.
Generally speaking, inspectors focus on food-handling practices, product temperatures, server hygiene, facility maintenance and pest control. Inspections usually occur every six months.
For more information, see:
Thanks to Everyblock and the City of Chicago for this info.
Food inspections are pretty interesting. It's also good to know what restaurants have failed. Plus, I like to make maps.
The data comes from Chicago's Department of Public Health's Food Protection Program inspections which can be found at:
This site is built with HTML, CSS, Javascript and Google's Fusion Tables and Maps API. The data is stored in Fusion Tables and is updated every night by a PHP script I wrote to import the data directly from the City of Chicago Data Portal.
Technologies used:
If you are interested, all the code for this project is up on github:
A project by Derek Eder


{%extends 'all-file/base.html'%} {% block content %} {{block.super}}
design:{{project.design}}
usability:{{project.usability}}
content:{{project.content}}
By {{project.user}} with {{rating}}


WWCommandSpeaker Class Reference
| Inherits from | WWCommand : WWComponent : WWObject : NSObject |
| Declared in | WWCommandSpeaker.h |
Overview
The WWCommandSpeaker
object instructs a WWRobot
object what sound file to play, if it exists.
The command is a no-op if the sound file does not exist on the robot.
Tasks
-
volume
property -
fileName
property -
directory
property -
– initWithSound:directory:volume:
-
– initWithSound:directory:
-
– initWithDefaultSound:
Properties
directory
Specifies the directory name where the sound file is located.
@property (nonatomic, strong) NSString *directory
Discussion
Specifies the directory name where the sound file is located.
Declared In
WWCommandSpeaker.h
Instance Methods
initWithDefaultSound:
Initializes the command with specified default sound fileName.
- (id)initWithDefaultSound:(NSString *)fileName
Parameters
- fileName
The default sound file to play.
Return Value
Returns a newly initialized WWCommandSpeaker
instance.
Discussion
Initializes the command with specified default sound fileName.
Volume will be set to WW_VOLUME_MAX, and directory will be set to WW_SOUND_SYSTEM_DIR.
Declared In
WWCommandSpeaker.h
initWithSound:directory:
- (id)initWithSound:(NSString *)fileName directory:(NSString *)directory
Return Value
Returns a newly initialized WWCommandSpeaker
instance.
Discussion
Initializes the command with specified fileName and directory.
Volume will be set to WW_VOLUME_MAX.
Declared In
WWCommandSpeaker.h
initWithSound:directory:volume:
- (id)initWithSound:(NSString *)fileName directory:(NSString *)directory volume:(double)volume
Parameters
- fileName
The sound file to play.
- directory
The directory that contains the sound file.
- volume
The volume level for the sound.
Return Value
Returns a newly initialized WWCommandSpeaker
instance.
Declared In
WWCommandSpeaker.h


Windows are at the heart of processing infinite streams. Windows split the stream into “buckets” of finite size, over which we can apply computations. This document focuses on how windowing is performed in Flink and how the programmer can benefit to the maximum from its offered functionality.
The general structure of a windowed Flink program is presented below. The first snippet refers to keyed streams,
while the second to non-keyed ones. As one can see, the only difference is the keyBy(...)
call for the keyed streams
and the window(...)
which becomes windowAll(...)
for non-keyed streams. This is also going to serve as a roadmap
for the rest of the page.
Keyed Windows
stream
.keyBy(...) <- keyed versus non-keyed windows
.window(...) <- required: "assigner"
[.trigger(...)] <- optional: "trigger" (else default trigger)
[.evictor(...)] <- optional: "evictor" (else no evictor)
[.allowedLateness(...)] <- optional: "lateness" (else zero)
[.sideOutputLateData(...)] <- optional: "output tag" (else no side output for late data)
.reduce/aggregate/fold/apply() <- required: "function"
[.getSideOutput(...)] <- optional: "output tag"
Non-Keyed Windows
stream
.windowAll(...) <- required: "assigner"
[.trigger(...)] <- optional: "trigger" (else default trigger)
[.evictor(...)] <- optional: "evictor" (else no evictor)
[.allowedLateness(...)] <- optional: "lateness" (else zero)
[.sideOutputLateData(...)] <- optional: "output tag" (else no side output for late data)
.reduce/aggregate/fold/apply() <- required: "function"
[.getSideOutput(...)] <- optional: "output tag"
In the above, the commands in square brackets ([…]) are optional. This reveals that Flink allows you to customize your windowing logic in many different ways so that it best fits your needs.
In a nutshell, a window is created as soon as the first element that should belong to this window arrives, and the
window is completely removed when the time (event or processing time) passes its end timestamp plus the user-specified
allowed lateness
(see Allowed Lateness). Flink guarantees removal only for time-based
windows and not for other types, e.g. global windows (see Window Assigners). For example, with an
event-time-based windowing strategy that creates non-overlapping (or tumbling) windows every 5 minutes and has an allowed
lateness of 1 min, Flink will create a new window for the interval between 12:00
and 12:05
when the first element with
a timestamp that falls into this interval arrives, and it will remove it when the watermark passes the 12:06
timestamp.
In addition, each window will have a Trigger
(see Triggers) and a function (ProcessWindowFunction
, ReduceFunction
,
AggregateFunction
or FoldFunction
) (see Window Functions) attached to it. The function will contain the computation to
be applied to the contents of the window, while the Trigger
specifies the conditions under which the window is
considered ready for the function to be applied. A triggering policy might be something like “when the number of elements
in the window is more than 4”, or “when the watermark passes the end of the window”. A trigger can also decide to
purge a window’s contents any time between its creation and removal. Purging in this case only refers to the elements
in the window, and not the window metadata. This means that new data can still be added to that window.
Apart from the above, you can specify an Evictor
(see Evictors) which will be able to remove
elements from the window after the trigger fires and before and/or after the function is applied.
In the following we go into more detail for each of the components above. We start with the required parts in the above snippet (see Keyed vs Non-Keyed Windows, Window Assigner, and Window Function) before moving to the optional ones.
The first thing to specify is whether your stream should be keyed or not. This has to be done before defining the window.
Using the keyBy(...)
will split your infinite stream into logical keyed streams. If keyBy(...)
is not called, your
stream is not keyed.
In the case of keyed streams, any attribute of your incoming events can be used as a key (more details here). Having a keyed stream will allow your windowed computation to be performed in parallel by multiple tasks, as each logical keyed stream can be processed independently from the rest. All elements referring to the same key will be sent to the same parallel task.
In case of non-keyed streams, your original stream will not be split into multiple logical streams and all the windowing logic will be performed by a single task, i.e. with parallelism of 1.
After specifying whether your stream is keyed or not, the next step is to define a window assigner.
The window assigner defines how elements are assigned to windows. This is done by specifying the WindowAssigner
of your choice in the window(...)
(for keyed streams) or the windowAll()
(for non-keyed streams) call.
A WindowAssigner
is responsible for assigning each incoming element to one or more windows. Flink comes
with pre-defined window assigners for the most common use cases, namely tumbling windows,
sliding windows, session windows and global windows. You can also implement a custom window assigner by
extending the WindowAssigner
class. All built-in window assigners (except the global
windows) assign elements to windows based on time, which can either be processing time or event
time. Please take a look at our section on event time to learn
about the difference between processing time and event time and how timestamps and watermarks are generated.
Time-based windows have a start timestamp (inclusive) and an end timestamp (exclusive)
that together describe the size of the window. In code, Flink uses TimeWindow
when working with
time-based windows which has methods for querying the start- and end-timestamp and also an
additional method maxTimestamp()
that returns the largest allowed timestamp for a given windows.
In the following, we show how Flink’s pre-defined window assigners work and how they are used in a DataStream program. The following figures visualize the workings of each assigner. The purple circles represent elements of the stream, which are partitioned by some key (in this case user 1, user 2 and user 3). The x-axis shows the progress of time.
A tumbling windows assigner assigns each element to a window of a specified window size. Tumbling windows have a fixed size and do not overlap. For example, if you specify a tumbling window with a size of 5 minutes, the current window will be evaluated and a new window will be started every five minutes as illustrated by the following figure.
The following code snippets show how to use tumbling windows.
Time intervals can be specified by using one of Time.milliseconds(x)
, Time.seconds(x)
,
Time.minutes(x)
, and so on.
As shown in the last example, tumbling window assigners also take an optional offset
parameter that can be used to change the alignment of windows. For example, without offsets
hourly tumbling windows are aligned with epoch, that is you will get windows such as
1:00:00.000 - 1:59:59.999
, 2:00:00.000 - 2:59:59.999
and so on. If you want to change
that you can give an offset. With an offset of 15 minutes you would, for example, get
1:15:00.000 - 2:14:59.999
, 2:15:00.000 - 3:14:59.999
etc.
An important use case for offsets is to adjust windows to timezones other than UTC-0.
For example, in China you would have to specify an offset of Time.hours(-8)
.
The sliding windows assigner assigns elements to windows of fixed length. Similar to a tumbling windows assigner, the size of the windows is configured by the window size parameter. An additional window slide parameter controls how frequently a sliding window is started. Hence, sliding windows can be overlapping if the slide is smaller than the window size. In this case elements are assigned to multiple windows.
For example, you could have windows of size 10 minutes that slides by 5 minutes. With this you get every 5 minutes a window that contains the events that arrived during the last 10 minutes as depicted by the following figure.
The following code snippets show how to use sliding windows.
Time intervals can be specified by using one of Time.milliseconds(x)
, Time.seconds(x)
,
Time.minutes(x)
, and so on.
As shown in the last example, sliding window assigners also take an optional offset
parameter
that can be used to change the alignment of windows. For example, without offsets hourly windows
sliding by 30 minutes are aligned with epoch, that is you will get windows such as
1:00:00.000 - 1:59:59.999
, 1:30:00.000 - 2:29:59.999
and so on. If you want to change that
you can give an offset. With an offset of 15 minutes you would, for example, get
1:15:00.000 - 2:14:59.999
, 1:45:00.000 - 2:44:59.999
etc.
An important use case for offsets is to adjust windows to timezones other than UTC-0.
For example, in China you would have to specify an offset of Time.hours(-8)
.
The session windows assigner groups elements by sessions of activity. Session windows do not overlap and do not have a fixed start and end time, in contrast to tumbling windows and sliding windows. Instead a session window closes when it does not receive elements for a certain period of time, i.e., when a gap of inactivity occurred. A session window assigner can be configured with either a static session gap or with a session gap extractor function which defines how long the period of inactivity is. When this period expires, the current session closes and subsequent elements are assigned to a new session window.
The following code snippets show how to use session windows.
Static gaps can be specified by using one of Time.milliseconds(x)
, Time.seconds(x)
,
Time.minutes(x)
, and so on.
Dynamic gaps are specified by implementing the SessionWindowTimeGapExtractor
interface.
Attention Since session windows do not have a fixed start and end,
they are evaluated differently than tumbling and sliding windows. Internally, a session window operator
creates a new window for each arriving record and merges windows together if their are closer to each other
than the defined gap.
In order to be mergeable, a session window operator requires a merging Trigger and a merging
Window Function, such as ReduceFunction
, AggregateFunction
, or ProcessWindowFunction
(FoldFunction
cannot merge.)
A global windows assigner assigns all elements with the same key to the same single global window. This windowing scheme is only useful if you also specify a custom trigger. Otherwise, no computation will be performed, as the global window does not have a natural end at which we could process the aggregated elements.
The following code snippets show how to use a global window.
After defining the window assigner, we need to specify the computation that we want to perform on each of these windows. This is the responsibility of the window function, which is used to process the elements of each (possibly keyed) window once the system determines that a window is ready for processing (see triggers for how Flink determines when a window is ready).
The window function can be one of ReduceFunction
, AggregateFunction
, FoldFunction
or ProcessWindowFunction
. The first
two can be executed more efficiently (see State Size section) because Flink can incrementally aggregate
the elements for each window as they arrive. A ProcessWindowFunction
gets an Iterable
for all the elements contained in a
window and additional meta information about the window to which the elements belong.
A windowed transformation with a ProcessWindowFunction
cannot be executed as efficiently as the other
cases because Flink has to buffer all elements for a window internally before invoking the function.
This can be mitigated by combining a ProcessWindowFunction
with a ReduceFunction
, AggregateFunction
, or FoldFunction
to
get both incremental aggregation of window elements and the additional window metadata that the
ProcessWindowFunction
receives. We will look at examples for each of these variants.
A ReduceFunction
specifies how two elements from the input are combined to produce
an output element of the same type. Flink uses a ReduceFunction
to incrementally aggregate
the elements of a window.
A ReduceFunction
can be defined and used like this:
The above example sums up the second fields of the tuples for all elements in a window.
An AggregateFunction
is a generalized version of a ReduceFunction
that has three types: an
input type (IN
), accumulator type (ACC
), and an output type (OUT
). The input type is the type
of elements in the input stream and the AggregateFunction
has a method for adding one input
element to an accumulator. The interface also has methods for creating an initial accumulator,
for merging two accumulators into one accumulator and for extracting an output (of type OUT
) from
an accumulator. We will see how this works in the example below.
Same as with ReduceFunction
, Flink will incrementally aggregate input elements of a window as they
arrive.
An AggregateFunction
can be defined and used like this:
The above example computes the average of the second field of the elements in the window.
A FoldFunction
specifies how an input element of the window is combined with an element of
the output type. The FoldFunction
is incrementally called for each element that is added
to the window and the current output value. The first element is combined with a pre-defined initial value of the output type.
A FoldFunction
can be defined and used like this:
The above example appends all input Long
values to an initially empty String
.
Attention fold()
cannot be used with session windows or other mergeable windows.
A ProcessWindowFunction gets an Iterable containing all the elements of the window, and a Context object with access to time and state information, which enables it to provide more flexibility than other window functions. This comes at the cost of performance and resource consumption, because elements cannot be incrementally aggregated but instead need to be buffered internally until the window is considered ready for processing.
The signature of ProcessWindowFunction
looks as follows:
Note The key
parameter is the key that is extracted
via the KeySelector
that was specified for the keyBy()
invocation. In case of tuple-index
keys or string-field references this key type is always Tuple
and you have to manually cast
it to a tuple of the correct size to extract the key fields.
A ProcessWindowFunction
can be defined and used like this:
The example shows a ProcessWindowFunction
that counts the elements in a window. In addition, the window function adds information about the window to the output.
Attention Note that using ProcessWindowFunction
for simple aggregates such as count is quite inefficient. The next section shows how a ReduceFunction
or AggregateFunction
can be combined with a ProcessWindowFunction
to get both incremental aggregation and the added information of a ProcessWindowFunction
.
A ProcessWindowFunction
can be combined with either a ReduceFunction
, an AggregateFunction
, or a FoldFunction
to
incrementally aggregate elements as they arrive in the window.
When the window is closed, the ProcessWindowFunction
will be provided with the aggregated result.
This allows it to incrementally compute windows while having access to the
additional window meta information of the ProcessWindowFunction
.
Note You can also use the legacy WindowFunction
instead of
ProcessWindowFunction
for incremental window aggregation.
The following example shows how an incremental ReduceFunction
can be combined with
a ProcessWindowFunction
to return the smallest event in a window along
with the start time of the window.
The following example shows how an incremental AggregateFunction
can be combined with
a ProcessWindowFunction
to compute the average and also emit the key and window along with
the average.
The following example shows how an incremental FoldFunction
can be combined with
a ProcessWindowFunction
to extract the number of events in the window and return also
the key and end time of the window.
In addition to accessing keyed state (as any rich function can) a ProcessWindowFunction
can
also use keyed state that is scoped to the window that the function is currently processing. In this
context it is important to understand what the window that per-window state is referring to is.
There are different “windows” involved:
Per-window state is tied to the latter of those two. Meaning that if we process events for 1000 different keys and events for all of them currently fall into the [12:00, 13:00) time window then there will be 1000 window instances that each have their own keyed per-window state.
There are two methods on the Context
object that a process()
invocation receives that allow
access to the two types of state:
globalState()
, which allows access to keyed state that is not scoped to a windowwindowState()
, which allows access to keyed state that is also scoped to the windowThis feature is helpful if you anticipate multiple firing for the same window, as can happen when you have late firings for data that arrives late or when you have a custom trigger that does speculative early firings. In such a case you would store information about previous firings or the number of firings in per-window state.
When using windowed state it is important to also clean up that state when a window is cleared. This
should happen in the clear()
method.
In some places where a ProcessWindowFunction
can be used you can also use a WindowFunction
. This
is an older version of ProcessWindowFunction
that provides less contextual information and does
not have some advances features, such as per-window keyed state. This interface will be deprecated
at some point.
The signature of a WindowFunction
looks as follows:
It can be used like this:
A Trigger
determines when a window (as formed by the window assigner) is ready to be
processed by the window function. Each WindowAssigner
comes with a default Trigger
.
If the default trigger does not fit your needs, you can specify a custom trigger using trigger(...)
.
The trigger interface has five methods that allow a Trigger
to react to different events:
onElement()
method is called for each element that is added to a window.onEventTime()
method is called when a registered event-time timer fires.onProcessingTime()
method is called when a registered processing-time timer fires.onMerge()
method is relevant for stateful triggers and merges the states of two triggers when their corresponding windows merge, e.g. when using session windows.clear()
method performs any action needed upon removal of the corresponding window.Two things to notice about the above methods are:
1) The first three decide how to act on their invocation event by returning a TriggerResult
. The action can be one of the following:
CONTINUE
: do nothing,FIRE
: trigger the computation,PURGE
: clear the elements in the window, andFIRE_AND_PURGE
: trigger the computation and clear the elements in the window afterwards.2) Any of these methods can be used to register processing- or event-time timers for future actions.
Once a trigger determines that a window is ready for processing, it fires, i.e., it returns FIRE
or FIRE_AND_PURGE
. This is the signal for the window operator
to emit the result of the current window. Given a window with a ProcessWindowFunction
all elements are passed to the ProcessWindowFunction
(possibly after passing them to an evictor).
Windows with ReduceFunction
, AggregateFunction
, or FoldFunction
simply emit their eagerly aggregated result.
When a trigger fires, it can either FIRE
or FIRE_AND_PURGE
. While FIRE
keeps the contents of the window, FIRE_AND_PURGE
removes its content.
By default, the pre-implemented triggers simply FIRE
without purging the window state.
Attention Purging will simply remove the contents of the window and will leave any potential meta-information about the window and any trigger state intact.
The default Trigger
of a WindowAssigner
is appropriate for many use cases. For example, all the event-time window assigners have an EventTimeTrigger
as
default trigger. This trigger simply fires once the watermark passes the end of a window.
Attention The default trigger of the GlobalWindow
is the NeverTrigger
which does never fire. Consequently, you always have to define a custom trigger when using a GlobalWindow
.
Attention By specifying a trigger using trigger()
you
are overwriting the default trigger of a WindowAssigner
. For example, if you specify a
CountTrigger
for TumblingEventTimeWindows
you will no longer get window firings based on the
progress of time but only by count. Right now, you have to write your own custom trigger if
you want to react based on both time and count.
Flink comes with a few built-in triggers.
EventTimeTrigger
fires based on the progress of event-time as measured by watermarks.ProcessingTimeTrigger
fires based on processing time.CountTrigger
fires once the number of elements in a window exceeds the given limit.PurgingTrigger
takes as argument another trigger and transforms it into a purging one.If you need to implement a custom trigger, you should check out the abstract Trigger class. Please note that the API is still evolving and might change in future versions of Flink.
Flink’s windowing model allows specifying an optional Evictor
in addition to the WindowAssigner
and the Trigger
.
This can be done using the evictor(...)
method (shown in the beginning of this document). The evictor has the ability
to remove elements from a window after the trigger fires and before and/or after the window function is applied.
To do so, the Evictor
interface has two methods:
/**
* Optionally evicts elements. Called before windowing function.
*
* @param elements The elements currently in the pane.
* @param size The current number of elements in the pane.
* @param window The {@link Window}
* @param evictorContext The context for the Evictor
*/
void evictBefore(Iterable<TimestampedValue<T>> elements, int size, W window, EvictorContext evictorContext);
/**
* Optionally evicts elements. Called after windowing function.
*
* @param elements The elements currently in the pane.
* @param size The current number of elements in the pane.
* @param window The {@link Window}
* @param evictorContext The context for the Evictor
*/
void evictAfter(Iterable<TimestampedValue<T>> elements, int size, W window, EvictorContext evictorContext);
The evictBefore()
contains the eviction logic to be applied before the window function, while the evictAfter()
contains the one to be applied after the window function. Elements evicted before the application of the window
function will not be processed by it.
Flink comes with three pre-implemented evictors. These are:
CountEvictor
: keeps up to a user-specified number of elements from the window and discards the remaining ones from
the beginning of the window buffer.DeltaEvictor
: takes a DeltaFunction
and a threshold
, computes the delta between the last element in the
window buffer and each of the remaining ones, and removes the ones with a delta greater or equal to the threshold.TimeEvictor
: takes as argument an interval
in milliseconds and for a given window, it finds the maximum
timestamp max_ts
among its elements and removes all the elements with timestamps smaller than max_ts - interval
.Default By default, all the pre-implemented evictors apply their logic before the window function.
Attention Specifying an evictor prevents any pre-aggregation, as all the elements of a window have to be passed to the evictor before applying the computation.
Attention Flink provides no guarantees about the order of the elements within a window. This implies that although an evictor may remove elements from the beginning of the window, these are not necessarily the ones that arrive first or last.
When working with event-time windowing, it can happen that elements arrive late, i.e. the watermark that Flink uses to keep track of the progress of event-time is already past the end timestamp of a window to which an element belongs. See event time and especially late elements for a more thorough discussion of how Flink deals with event time.
By default, late elements are dropped when the watermark is past the end of the window. However,
Flink allows to specify a maximum allowed lateness for window operators. Allowed lateness
specifies by how much time elements can be late before they are dropped, and its default value is 0.
Elements that arrive after the watermark has passed the end of the window but before it passes the end of
the window plus the allowed lateness, are still added to the window. Depending on the trigger used,
a late but not dropped element may cause the window to fire again. This is the case for the EventTimeTrigger
.
In order to make this work, Flink keeps the state of windows until their allowed lateness expires. Once this happens, Flink removes the window and deletes its state, as also described in the Window Lifecycle section.
Default By default, the allowed lateness is set to
0
. That is, elements that arrive behind the watermark will be dropped.
You can specify an allowed lateness like this:
Note When using the GlobalWindows
window assigner no
data is ever considered late because the end timestamp of the global window is Long.MAX_VALUE
.
Using Flink’s side output feature you can get a stream of the data that was discarded as late.
You first need to specify that you want to get late data using sideOutputLateData(OutputTag)
on
the windowed stream. Then, you can get the side-output stream on the result of the windowed
operation:
When specifying an allowed lateness greater than 0, the window along with its content is kept after the watermark passes
the end of the window. In these cases, when a late but not dropped element arrives, it could trigger another firing for the
window. These firings are called late firings
, as they are triggered by late events and in contrast to the main firing
which is the first firing of the window. In case of session windows, late firings can further lead to merging of windows,
as they may “bridge” the gap between two pre-existing, unmerged windows.
Attention You should be aware that the elements emitted by a late firing should be treated as updated results of a previous computation, i.e., your data stream will contain multiple results for the same computation. Depending on your application, you need to take these duplicated results into account or deduplicate them.
The result of a windowed operation is again a DataStream
, no information about the windowed
operations is retained in the result elements so if you want to keep meta-information about the
window you have to manually encode that information in the result elements in your
ProcessWindowFunction
. The only relevant information that is set on the result elements is the
element timestamp. This is set to the maximum allowed timestamp of the processed window, which
is end timestamp - 1, since the window-end timestamp is exclusive. Note that this is true for both
event-time windows and processing-time windows. i.e. after a windowed operations elements always
have a timestamp, but this can be an event-time timestamp or a processing-time timestamp. For
processing-time windows this has no special implications but for event-time windows this together
with how watermarks interact with windows enables
consecutive windowed operations with the same window sizes. We
will cover this after taking a look how watermarks interact with windows.
Before continuing in this section you might want to take a look at our section about event time and watermarks.
When watermarks arrive at the window operator this triggers two things:
Intuitively, a watermark “flushes” out any windows that would be considered late in downstream operations once they receive that watermark.
As mentioned before, the way the timestamp of windowed results is computed and how watermarks interact with windows allows stringing together consecutive windowed operations. This can be useful when you want to do two consecutive windowed operations where you want to use different keys but still want elements from the same upstream window to end up in the same downstream window. Consider this example:
In this example, the results for time window [0, 5)
from the first operation will also end up in
time window [0, 5)
in the subsequent windowed operation. This allows calculating a sum per key
and then calculating the top-k elements within the same window in the second operation.
Windows can be defined over long periods of time (such as days, weeks, or months) and therefore accumulate very large state. There are a couple of rules to keep in mind when estimating the storage requirements of your windowing computation:
Flink creates one copy of each element per window to which it belongs. Given this, tumbling windows keep one copy of each element (an element belongs to exactly one window unless it is dropped late). In contrast, sliding windows create several of each element, as explained in the Window Assigners section. Hence, a sliding window of size 1 day and slide 1 second might not be a good idea.
ReduceFunction
, AggregateFunction
, and FoldFunction
can significantly reduce the storage requirements, as they eagerly aggregate elements and store only one value per window. In contrast, just using a ProcessWindowFunction
requires accumulating all elements.
Using an Evictor
prevents any pre-aggregation, as all the elements of a window have to be passed through the evictor before applying the computation (see Evictors).


{% extends '_layouts/layout.html' %} {% block page_title %} {% endblock %} {% block body_css_classes %}nhs-redesign{% endblock %} {% block content %}
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Paper Talk: Sunday's football transfer rumours and gossip
by Sporting LifePSG are targeting Liverpool's Adam Lallana to replace MLS-bound Edinson Cavani according to today's back pages.
SUNDAY MIRROR
PSG are targeting Liverpool's Adam Lallana to replace MLS-bound Edinson Cavani.
Arsenal will offer Granit Xhaka as bait to land Borussia Monchengladbach's midfield sensation Denis Zakaria.
Moise Kean has reportedly been offered to AC Milan after a nightmare spell at Everton.
A bomb dating back to World War Two has reportedly been found just yards away from West Ham's London Stadium.
DAILY STAR SUNDAY
Liverpool have made Jadon Sancho their main summer target as the youngster suggests he is ready to leave Borussia Dortmund.
MAIL ON SUNDAY
Cristiano Ronaldo has given Juventus the green light to try and sign Paul Pogba in January.
Former Real Madrid boss Santiago Solari is plotting a move to the Premier League after a scouting mission to watch Chelsea.
Crystal Palace are set to miss out on £21m Genoa star Christian Kouame after he suffered a serious knee injury for Ivory Coast Under 23s.
Gareth Southgate is set to hand Burnley goalkeeper Nick Pope his full England debut against Kosovo on Sunday.
THE SUN ON SUNDAY
Inter Milan's sporting director was in London this week working on a deal to sign Chelsea striker Olivier Giroud, according to reports.
Manchester United and Arsenal are set to battle it out for Juventus centre-back Merih Demiral in the January transfer window.
Jody Morris, assistant to Chelsea manager Frank Lampard, has reportedly been told to stop social media posts about Jose Mourinho.
SUNDAY EXPRESS
PSG are ready to provide the biggest obstacle to Manchester United's bid to land £120m-rated Jadon Sancho.
Manchester United executive vice-chairman Ed Woodward has a list of eight players he may target in January, including Erling Haaland.
Wolves are closing in on a loan deal to bring Royal Antwerp's Aurelio Buta to Molineux in January.
SUNDAY TIMES
Saracens are abandoning their appeal against the £5.4m fine and 35-point penalty they were given for a breach of the salary cap.
SCOTTISH SUN
Former Rangers winger Gregg Wylde could be set to return to football with Morton after being out of the game since September.


Chris A. Trees
Sr. Program Manager & Research DeveloperExperiences
20182012
Research Developer
Mail Services, LLC Des Moines, IA (remote)- Researched NFC production process to leverage USPS commercialization incentives.
- Evaluated and integrated AGILE, CI/CD, QA testing and system virtualization.
- Evaluated Angular, Ember, React, Vue, Backbone and Polymer.
- Intro Team for Atlassian JIRA, Confluence, Bitbucket and Bamboo toolchains.
- Evaluated production site processes for refresh and maintenance sustainablity.
- Production site was a PHP frontend with restricted storage, network, and SQL database.
20122010
Sr. Software Engineer / Field Sales Engineer
OvalPath San Jose, California (and remote)- Social Mobile communications platform serving the commercial nuclear power market.
- Developed HTML5 map annotation for mobile field devices.
- Established QA Test Automation and build documentation.
- Worked as Sales Engineer with CEO to adopt the platform to specific needs of new customers.
- Acquired by CurtissWright.
20102002
Consultant
CAT Services Cedar Falls, Iowa (and remote)- Croft Utility Partners Cloud infrastructure design for small communities.
- Grasshorse Animation Studio "Proto-Motion" mixed media stop motion animation.
- Vianeta Communications Training materials for data center installation and testing.
20022001
Founder / Architect
Pantera Mountain View, California- Matrix grid network of distributed storage and compute engine for accelerated batch processing.
- Using COTS servers, a grid of indexed storage was used to maintain matrix reduce results in cache.
- Pantera IP was a matrix index tracking of distributed cache algorithm partials for performance.
20011985
Previous Millennium
Company & Project per line Various, California- Fusion One - Integration of Levient redundant high availability server infrastructure stack.
- Monggo - Remote desktop service using WAP devices (cell phones).
- Vertical Networks - Lead hardware & mechanical design for telephony product "Instant-Office".
- Rapid City Communications - Built high-speed-density backplane for VLAN switch.
- SkyStream - FPGA design for video streaming switch.
- Sun Microsystems - Netra group consultant for an embedded user control console.
- IKOS - Field Applications Engineer for ASIC simulation products.
- General Magic - Partner development systems. Created touch based magic cell phone prototype.
- National Semiconductor - Custom FPGA for ISO-Ethernet product development kit.
- Hughes LAN - ATM network switch hub chassis backplane and FPGA based control bus.
- The Engineering Dept (TED) - PC Transporter Apple II card used to run IBM Software.
- Lockeed MSC - P387 - Video display subsystem control board and test station.
Education
20182016
Courses (selective list)
- Docker and Kubernetes
- DevOps: Advanced Kubernetes
- DevOps: Automation with Terraform
- Ansible Automation
- Cumulus Linux and Ansible
- GNS3 Advanced Switching
- React, Redux & Firestore
- Flutter, Dart & Firebase
- Progressive Web Apps (PWA)
- Appium Mobile Automation Testing
- Machine Learing with Python & R
- Ethereum Developer
- Xamarin: Android and iOS shared C# Code
- Web Development with Go
- Active Directory Lab using Hyper-V
- Complete Blender Creator (3D Modelling)
19851980
B.S. Electrical Engineering
Iowa State University Ames, IowaMath Honors, Nuclear Engineering Studies


Pragati is a social innovation wing that works on the principles of serving the society. Our society is facing various economic, environmental and social problems. The wing believes that innovations are the key to tackle these problems to improve our communities. Based on these beliefs, the wing shall work on creating, developing and implementing practices as well as providing innovative solutions to some of the common problems that exist now.


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CITY HALL A film review by Christophe Boone Copyright 1996 Christophe Boone
Directed by Harold Becker Produced by Harold Becker, Ken Lipper, Charles Mulvehill, and Edward R. Pressman Written by Bo Goldman, Ken Lipper, Nicholas Pileggi, and Paul Schrader CAST: John Cusack, Al Pacino, Bridget Fonda, Danny Aiello, David Paymer, and Martin Landau
Opens nationally Friday, February 16, 1996
No mayor can ever be liked by everybody, especially the mayor of New York City. No matter what services he claims to have brought to the city, there is always something behind him to make him corrupt. CITY HALL tries to paint a portrait of the perfect mayor, but winds up falling short.
One rainy afternoon in Brooklyn, on the corner of Broadway and Marcy, a detective meets up with a mob screw up that he tried to put away two years ago. Something goes wrong, bullets fly, the cop and the mobster lie dead, and a six year old is caught in the crossfire. Whose bullet hits him is a little ambiguous, but watch closely and you'll know.
News in the city travels fast. Deputy Mayor Kevin Calhoun (John Cusack) learns from an aid of the incident and cuts the Mayor's (Al Pacino) speech short. Calhoun is down home boy from Louisiana spouting quotes from Huey Long and Harry S Truman, only trying to do what is right and find the truth. He idolizes the Mayor, John Pappas, Time cover man, and one of the finest mayors ever to grace this office with his presence. Together, they start a crusade to find out why these killings took place.
Along the way, Calhoun runs into Marybeth Cogan, a lawyer assigned to protect the widow and her chances of receiving her husband's pension. She believes that Calhoun is out to tarnish his name and expose him as corrupt. They make an unlikely pair that should have no romantic spark. Well, the writers couldn't resist at the end in one of the lamest conclusions I have seen in a while. Can you say reshoots after poor test showings? Together, these two become detectives doing police work that should be left to IA, but they seem to only have one moment of brilliance. The rest is left up to Calhoun and Cogan, which in and of itself is hard to swallow.
CITY HALL remains almost too ambiguous at times. For the normal American audience, many people are going to be confused as to why there are so many twists and turns, and where are they all leads. A central theme to this film is that when one doors closes, another one opens, or rather, the other way around. Unfortunately, from the beginning, the audience knows something is wrong, but each twist could result in a dead end at the answer. The film continues to plow through its own dead ends.
The biggest problem with this film is the screenplay. Cusack turns in a strong performance, much improved since his days as a high school slack in BETTER OFF DEAD and ONE CRAZY SUMMER. His potential from SAY ANYTHING is coming into fruition. Al Pacino plays Al Pacino, and one wonders if the audiences will ever tire of him. Bridget Fonda smiles nicely, and bobs her hair, that's about it. Upon closer examination, however, there is a reason why the credits do not precede this film. Four separate writers are credited with penning this script: newcomer Ken Lipper, Bo Goldman (SCENT OF A WOMAN), Nicholas Pileggi (GOODFELLAS, CASINO), and Paul Schrader (TAXI DRIVER, RAGING BULL). Three well known writers on a not-so-worthy script. If I had to guess, I'd say that Lipper wrote it, and the studio saw potential, so they passed it from one hand to the next. Their strategy did not pay off. This film was supposed to arrive in theatres in December, but the studio pulled out, exposing their lack of confidence in this film among the holiday competition. The plot lines do not weave a complete story, but run parallel instead. One character says it best, "The sweater unravels."
For Cusack's performance alone, it might be worth a look, but I have reservations. This is not Pacino's best, and Bridget Fonda has still not found her breakout role, if it ever comes. But, CITY HALL is a worthy rental, so save the $7, and spend the $3 in a few months.
-cbb
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class: center, middle, inverse, title-slide # Counting things ### Bas Jacobs ### 11/01/2019 --- background-image: url(card_overlaps_problem.png) background-size: contain --- class: inverse, center, middle # Simulating! --- ```python from numpy.random import choice ``` -- ```python n_experiments = 100000 overlaps = np.zeros(n_experiments) for i in range(n_experiments): shop_1 = choice(2000, size = 100, replace = False) shop_2 = choice(2000, size = 100, replace = False) overlap = len(set(shop_1) & set(shop_2)) overlaps[i] = overlap ``` -- ```python plt.hist(overlaps, bins = np.unique(overlaps)) ``` ![hist](card_overlaps_hist.png) --- class: inverse, center, middle # But what if we want an exact answer? --- class: center, middle # Combinatorics _Combinatorics is the branch of mathematics studying the **enumeration**, **combination**, and **permutation** of sets of elements and the mathematical relations that characterize their properties._1 .footnote[ [1] http://mathworld.wolfram.com/Combinatorics.html ] --- # Today - Some kind of _Combinatorics 101_ (but not with balls and urns) - Problem examples and approaches to solve them --- # Shuffling cards - In how many ways can the letters `cards` be ordered? - `cards`, `carsd`, ..., `arscd` --- # Shuffling cards `_ _ _ _ _` -- `a _ _ _ _` `\(\qquad 5\)` -- `a d _ _ _` `\(\qquad 5\cdot 4\)` -- `a d c _ _` `\(\qquad 5\cdot 4\cdot 3\)` -- `a d c r _` `\(\qquad 5\cdot 4\cdot 3 \cdot 2\)` -- `a d c r s` `\(\qquad 5\cdot 4\cdot 3 \cdot 2 \cdot 1\)` --- # Permutations `\(n\)` items can be reordered in `\(n! = n\cdot(n-1)\cdot(n-2)\cdot\ldots \cdot 2\cdot 1\)` ways --- # Permutations grow fast Stirling's approximation1 $$ n! \sim \sqrt{2\pi n} \left(\frac{n}{e}\right)^n$$ ![](stirling.png) .footnote[ [1] https://en.wikipedia.org/wiki/Stirling's_approximation ] --- class: inverse, center, middle background-image: url(lidl_factorial.jpg) background-size: contain # Permutations grow fast # 362 880 --- # Choosing cards for one store In how many ways can we draw 100 unique cards from 2000 possible cards? -- `$$2000\cdot 1999\cdot 1998\cdot \ldots \cdot 1901$$` -- `$$= \frac{2000!}{1900!}$$` -- But the order does not matter! -- How many reorderings of these 100 cards are there? -- `\(100!\)` -- $$ \frac{2000!}{1900!\cdot100!} = {2000 \choose 100} $$ --- class: inverse # Combinations Choosing `\(k\)` objects from a set of `\(n\)` objects without repetition, order does not matter $$ {n \choose k} = \frac{n!}{k!\cdot (n-k)!}$$ --- # Example Counting paths on a 10x5 grid ``` ----------+ |.......... --+........ ..........| |.......... ..+-+...... ..........| +--+....... ....+-+.... ..........| ...|....... ......+-+.. ..........| ...|....... ........|.. ..........| ...+------- ........+-- ``` -- `\({15\choose 5}\)` -- .footnote[ Source: Peter Norvig's _pytudes_ - _How To Count Things_ ] --- background-image: url(safe_image.jpg) background-size: contain # Example --- # Calculating overlap -- 1) Let shop A choose 100 cards: `\({2000 \choose 100}\)` -- 2a) Overlap of 0: `\({1900 \choose 100}\)` -- 2b) Overlap of 1: `\({100\choose 1}\cdot{1900 \choose 99}\)` -- 2c) Overlap of `\(n\)`: `\({100\choose n}\cdot{1900\choose 100-n}\)` -- 3) Number of ways of `\(n\)` overlap: `\({2000 \choose 100}\cdot{100\choose n}\cdot{1900 \choose 100-n}\)` -- 4) Probability of `\(n\)` overlap: `$$\frac{{2000 \choose 100}{100\choose n}{1900 \choose 100-n} }{ {2000 \choose 100}{2000 \choose 100} }$$` --- ```python from math import factorial def choose(n, k): return factorial(n) // factorial(n-k) // factorial(k) ``` -- ```python probs = [] for overlap in range(100): probs.append( choose(100, overlap) * choose(1900, 100-overlap) / choose(2000, 100) ) ``` ![](card_overlaps_fact_hist.png) --- class: inverse, center, middle # Hypergeometric distribution1 .footnote[ [1] https://en.wikipedia.org/wiki/Hypergeometric_distribution ] --- # Sending cards - 10 identical cards - 3 friends to send cards to - Every friend should receive at least 1 card - In how many ways can we send cards to friends? --- # Stars and bars - Denote every card with a star ★ -- - Put them in a row ★ ★ ★ ★ ★ ★ ★ ★ ★ ★ -- - Place two bars between the stars -- ★ ★ ★|★ ★ ★ ★ ★|★ ★ -- `\({9 \choose 2} = 36\)` ways of sending cards --- # Bootstrapping In how many different ways can we select **with replacement** `\(n\)` observations from a dataset of `\(n\)` observations, where **order doesn't matter**? --- # Bootstrapping: example In how many different ways can we select **with replacement** 10 observations from a dataset of 10 observations, where **order doesn't matter**? -- | ★ ★ | | | ★ ★ ★ | ★ | ★ ★ | | | ★ ★ -- [0, 2, 0, 0, 3, 1, 2, 0, 0, 2] -- `$${19\choose 10} = 92378$$` --- # Bootstrapping: in general - `\(n\)` stars (observations) - `\(n-1\)` bars (we draw `\(n\)` observations) `$${n + (n-1) \choose n} = {2n-1 \choose n}$$` --- # Python ```python from itertools import combinations_with_replacement ``` -- ```python %time len(list(combinations_with_replacement(range(10), 10))) ``` ``` ## CPU times: user 23.8 ms, sys: 679 µs, total: 24.5 ms ## Wall time: 25.6 ms ## 92378 ``` -- ```python %time choose(2*10-1, 10) ``` ``` ## CPU times: user 19 µs, sys: 4 µs, total: 23 µs ## Wall time: 33.4 µs ## 92378 ``` --- # Python ```python %time len(list(combinations_with_replacement(range(15), 15))) ``` ``` ## CPU times: user 12.4 s, sys: 4.3 s, total: 16.7 s ## Wall time: 17.3 s ## 77558760 ``` -- ```python %time choose(2*15-1, 15) ``` ``` ## CPU times: user 14 µs, sys: 5 µs, total: 19 µs ## Wall time: 25.3 µs ## 77558760 ``` --- class: center, middle # Thank you!


As if talking to a Non-technical friend, discuss:
• An analogy to describe the differences between HTML and CSS
Hasn’t this been done before? HTML is the actor and CSS is the makeup artist.
• Explain control flow and loops using an example process from everyday life, for example 'waking up' or 'brushing your teeth'. (But not those ones).
You have to have things run and move forward. Even *.run did that and nothing has changed. The difference is new names and outputs. The looping is needed to make sure the code runs, continues and stops when criteria has been met. Like the encore for a band.
• Describe what the DOM is and an example of how you might interact with it.
It is what the site is. Not all the coding but what it is.
• Explain the difference between accessing data from arrays and objects.
Arrays are objects that have an array of values or information while the object is almost everything in JavaScript, sans a primitive unless they have been defined as an object.
• Explain what functions are and why they are useful.
Functions are the worker bees of the hives. People only pay attention to the honey but the bee is there making things run when called to do so by the queen or the user of the interface.


Reflecting on the Website
January 10, 2015
Creating this website has been an extremeley enjoyable project. It is the first website I have created from scratch, using my own design and aesthetic, my own code and blog posts. At times it was challenging, but it was largely satisfying. And I never stopped learning, which was the best part of building this website and providing content.
As far as things that I liked the most, I think transforming my conception of the website into actual code and a visible product was my favorite. Not only was it incredibly satisfying to be so creative and fulfilling to design a working product, but it was also challenging and pushed me to learn more and more. It was gratifying that my curiosity and desire to create a viable product drove me to learn and teach myself so much.
What did I dislike? Not much. Even the hardships with errors that I encountered or the hours of research that I did were, in the end, worth it. I learned so much, and I cannot wait to continue posting more, evolving the website, and pushing myself to new levels.


Small British manufacturing firms are their most pessimistic since just after the Brexit referendum in 2016 as they face political uncertainty at home and trade wars abroad, according to a survey published on Wednesday.
The Confederation of British Industry's gauge of optimism for small and medium-sized manufacturers fell to minus-32 percent in the three months to the end of October, down from minus-28 percent in the three months to July.
More:
The figures represent the difference between the proportions of respondents who said they were optimistic against those who expressed pessimism about the future.
Companies reported weak new orders - especially from clients in Britain - and little enthusiasm to raise investment spending.
"Firms are caught between the perfect storm of perennial Brexit uncertainty at home, and sluggish growth in the global economy," Alpesh Paleja, the CBI's lead economist, said.
"As well as hitting output, orders and hiring, these issues are depressing investment plans across the board."
Whichever political party wins Britain's December 12 national election must ensure the country leaves the European Union with a transition deal and then push ahead with negotiations for a new, long-term trade relationship with the bloc, he said.
Britain's economy shrank in the second quarter of 2019 but is widely expected to have shown some growth in the July-September period, thus avoiding a recession.
As well as Brexit, British manufacturers are facing up to a slowdown in the global economy caused by trade tensions between the United States and China.
The CBI's survey showed almost two-thirds of the 240 companies which took part cited political or economic conditions abroad as likely to limit export orders, a survey record high.


This vignette offers some guidance (with working example) on how to collect bot estimates (and, optionally, Twitter timeline data) for a large number of users.
## load package
library(tweetbotornot2)
For example purposes, this vignette retrieves user IDs from accounts appearing on a Twitter list of 2020 Democratic presidential candidates.
## get accounts from the twitter list: twitter.com/kearneymw/lists/dems-pres-2020
d20 <- rtweet::lists_members(owner_user = "kearneymw", slug = "dems-pres-2020")
## preview data
head(d20[, c("screen_name", "description")])
## store user IDs as 'users' vector
users <- d20$user_id
To get estimates for hundreds, thousands, or even millions of users, it is wise to use a for-loop and wait for the rate limit reset by sleeping between calls. This can be done in a number of ways, but the functions chunk_users()
and chunk_users_data()
are provided to make this easier. The code below should work for either user or bearer (rtweet) token. It also provides a safety oops
counter and prints out rate limit and percent complete information.
## convert users vector (user IDs or screen names) a list of 10-user chunks
users <- chunk_users(users, n = 10)
## initialize output vector
output <- vector("list", length(users))
for (i in seq_along(output)) {
## set oops counter
oops <- 0L
## check rate limit- if < 10 calls remain, sleep until reset
print(rl <- rtweet::rate_limit(query = "get_timelines"))
while (rl[["remaining"]] < 10L) {
## prevent infinite loop
oops <- oops + 1L
if (oops > 3L) {
stop("rate_limit() isn't returning rate limit data", call. = FALSE)
}
cat("Sleeping for", round(max(as.numeric(rl[["reset"]], "mins"), 0.5), 1), "minutes...")
Sys.sleep(max(as.numeric(rl[["reset"]], "secs"), 30))
rl <- rtweet::rate_limit(query = "get_timelines")
}
## get bot estimates
output[[i]] <- predict_bot(users[[i]])
## print iteration update
cat(sprintf("%d / %d (%.f%%)\n", i, length(output), i / length(output) * 100))
}
## merge into single data table
output <- do.call("rbind", output)
## sort by bot probability
output[order(-prob_bot), ]
If you’d also like to keep the raw (timeline) Twitter data, the above for loop can be modified to collect the Twitter data prior to generating estimates with predict_bot()
. This is demonstrated in the code below.
## convert users vector (user IDs or screen names) a list of 10-user chunks
users <- chunk_users(users, n = 10)
## initialize output vectors
output <- vector("list", length(users))
usrtml <- vector("list", length(users))
for (i in seq_along(output)) {
## set oops counter
oops <- 0L
## check rate limit- if < 10 calls remain, sleep until reset
print(rl <- rtweet::rate_limit(query = "get_timelines"))
while (rl[["remaining"]] < 10L) {
## prevent infinite loop
oops <- oops + 1L
if (oops > 3L) {
stop("rate_limit() isn't returning rate limit data", call. = FALSE)
}
cat("Sleeping for", round(max(as.numeric(rl[["reset"]], "mins"), 0.5), 1), "minutes...")
Sys.sleep(max(as.numeric(rl[["reset"]], "secs"), 30))
rl <- rtweet::rate_limit(query = "get_timelines")
}
## get user timeline data (set check=FALSE to avoid excessive rate-limit calls)
usrtml[[i]] <- rtweet::get_timelines(users[[i]], n = 200, check = FALSE)
## use the timeline data to get bot estimates
output[[i]] <- predict_bot(users[[i]])
## print iteration update
cat(sprintf("%d / %d (%.f%%)\n", i, length(output), i / length(output) * 100))
}
## merge both data lists into single data tables
output <- do.call("rbind", output)
usrtml <- do.call("rbind", usrtml)
## sort by bot probability
output[order(-prob_bot), ]
## preview timeline data
usrtml[1:10, ]
If you’ve already collected lots of user timeline data, the processing of timeline data into numeric feature data can still be computationally intensive. To deal with this, the feature engineering function, preprocess_bot()
, includes a batch_size
argument, which allows you to specify how many users should be processed at a time. You can adjust the size to optimize to your machine.
preprocess_bot(usrtml, batch_size = 200)
1 Unless you’ve already collected user timeline data, {tweetbotornot2}
relies on {rtweet}
to pull data from Twitter’s REST API.
2 The first time you make a request [from your computer] to Twitter’s API’s, a browser should pop-up, asking you to authorize the app. After that, the authorization token is stored and remembered behind the scenes (you will only have to reauthorize the app if your Twitter settings or token credentials get reset or if you use a different machine).


A collaboration between The Coleman Foundation, JPMorgan Chase Foundation, Robert R. McCormick Foundation and The Chicago Community Trust.
Black and Latinx-owned businesses comprise just 8% of the total number of small businesses combined.
Despite their vision, tenacity and commitment, entrepreneurs of color face significant barriers to success.
Black and Latinx-owned businesses comprise just 8% of the total number of small businesses combined.
Small businesses in Englewood (94% Black) are operating with less than a week of cash reserves in their deposit accounts, compared to 17 days for small businesses in Buena Park (Uptown, 54% White)—three times the cash liquidity.
There are more than 135 business support organizations positioned throughout the city, offering a wide range of services for entrepreneurs, and yet the barriers for entrepreneurs of color persist.
What is lacking is coordination among these organizations to provide targeted support to address the issues faced specifically by entrepreneurs of color.
Our theory is that if business servicing organizations are connected to one another and given the tools and technical assistance to expand partnerships with other providers, they will be able to offer more strategic and impactful services for entrepreneurs of color.
This will build stronger businesses of color throughout the Chicago area that will in turn begin to grow more sustainable wealth for communities of color.
Over the course of the program, we anticipate an increase in:


Your site is avaible in HTTPS with certificate issued by Let's Encrypt.
If you did not turn on "PRODUCTION" flag in HTTPS-PORTAL config, you are using a test certificate issued by Let's Encrypt staging server which might not be trusted by your browser.
Documents are available at https://github.com/SteveLTN/https-portal.


Authentication source
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Provided groups


{% extends "base.html" %} {% block main %} {% if not resend %}
We have sent you an email. If you do not receive it within a few minutes, try resending.
We have resent the password email.


46 cinema
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Infinity War
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Joker
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Ludo
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Ajeeb Dastaan
330.00
Black Panther
100.00
Roohi
120.00
Badla
90.00
Shada
180.00
Jaat and Juliet
120.00
Gudiyan Patole
130.00
Qismat
160.00
Highend yaariyan
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Jai Mummy Di
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FATAL BEAUTY A film review by Jeff Meyer Copyright 1987 Jeff Meyer
I try to avoid hyperbole when I review anything, and most of the time, it's pretty easy to do. Most of the films I go to see are fairly close to the median thumbs-up/thumbs-down line, and then a few are memorably good.   But those are very rare.
They happen, though. I *HATED* FATAL BEAUTY. The term "mindless violence" is a cliche that is used far too often when reviewing action films, but it is totally appropriate here. A undercover narcotics agent tracks down a couple of psychotic thugs passing out quick-killing cocaine. Lots of people get shot. A mall gets destroyed. MTV music in the background. Well, let's see, black comedi{an,enne} + cop + violence + rock music = BEVERLY HILLS COP! Gosh, I love a film where the marketing strategy is almost displayed in the opening credits.
Of course, other films have risen above pedestrian plots. They have good characters. They have a good script. They have good actors. Well, FATAL BEAUTY has a very good actress, but I only know that because I saw THE COLOR PURPLE and JUMPIN' JACK FLASH (where she showed through some pedestrian material). There is not a character in this film that I could *stand*, much less like, much less care about. Goldberg's fellow cops are crude, cruel Neanderthal bumpkins who appear to be there so that Whoopi can one-up them with similar humorless comebacks. Really, this is thirteen-year-old "dirty humor." The plot is transparent. The criminals are crazy, but their familiar psychos. And people change character like an on-off switch.  Then, her son slits his wrists, and, *bang*, concerned mother. Whoopi says, after the mother has supplied the name of the cocaine dealer who gave her the coke she gave her kid, "you're beautiful." Right. Got that. Blech.
Script -- everything was telegraphed at least an hour ahead of time. Acting should be changed to over-acting, even Whoopi -- her tale of why she hunts down drug dealers with such fervor is tacked into the last third to provide her 3 minutes of acting potential, but to no avail; I was so numbed by that time to notice even a glimmer of talent. The clumsiness of the plot dominated everything.
And, of course, the pity is that Whoopi Goldberg can play interesting characters; she can keep a lame script going. She is one of the few actresses who seems to be given heroic action roles. Great! Now could someone **PLEASE** give said role a script that hasn't been produced by some PR research dweeb! But she has to take some of the blame herself; I cannot imagine how a film this bad could look good in script form.
This is, by far and away, the WORST film I have seen this year. Even worse than that Australian horse picture I saw at the festival. I would have walked out, but it was the first part of a double feature.
Oh, the following film *seemed* to be better; but FATAL BEAUTY could make GILLIGAN'S ISLAND look promising in comparison. It was SOMEONE TO WATCH OVER ME, a 30's style love/thrill story that Ridley Scott has reanimated wonderfully -- he is perhaps the only person alive who can still make New York look glamorous. Good acting, solid script and characters, FANTASTIC music and cinematography. A simple plot with a MAJOR stumbling block of a plot element, something too large to enjoy; but worth $3.
Moriarty, aka Jeff Meyer INTERNET<EMAIL_ADDRESS>Manual UUCP: {uw-beaver, sun, allegra, hplsla, lbl-csam}!fluke!moriarty
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
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Related links: index of all rec.arts.movies.reviews reviews


Unifty.io
UNIFTY is a new hub for NFT projects to create custom farms, NFT collections with just a few clicks! No coding or difficult contract interactions required while you are fully owning your contracts. Use our tools to market your work!


Wang, X..
"{{CHAPTER}}" In
Fast and robust equalization: A case study,
Proceedings of the World Multiconference on Systemics, Cybernetics and Informatics,
{{SCHOOL}},
{{INSTITUTION}},
{{JOURNAL}}.
398—403.
, {{EDITION}} ed.
Chp. {{VOLUME}}.
Vol. {{VOLUME}}.
{{NUMBER}}.
{{EDITOR}}.
FL, USA,
International Institute of Informatics and Systemics
{{MONTH}},
2000.
Note: {{NOTE}}.
Annotation: {{ANNOTE}}.
{{HOWPUBLISHED}}.
{{ORGANIZATION}}.


{% block head %} {% endblock %} {% block body %}
To view this video please enable JavaScript, and consider upgrading to a web browser that supports HTML5 video


This extension highlights the Proxy history to differentiate requests made by different browsers. The plugin can highlight the requests automatically based on the User-Agent or specified by specific HTTP headers.
During pentesting, I often have two or more different browsers opened to test issues such as role matrix, as well as to show how requests in one client might affect another. It is however hard to visualize which requests were made by which browser within the proxy histroy. Hence this plug-in was created to help visualize how different requests interleave with one another.
When enabled, the plugin acts automatically by assigning a color per browser User-Agent (compatible with autochrome). You can also set the color and comment with the "X-Pentest" header by separating the color and comments with a semicolon (;). Available colors are: red, blue, pink, green, magenta, cyan, orange, gray, yellow
Example:
It is designed to be non-intrusive, so highlighting is disabled by default. Turn it on in the Proxy context menu only when you need it.


{{ stories?.length }}
Top Stories
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The shortcuts will be listed below which personally need to use at most while using Eclipse.
CTRL+Page Up and CTRL+Page Down:
By using these shortcuts, you can access JAVA classes which are already opened as tabs in Eclipse.
F3
Accesses the declaration of the method where cursor is.
CTRL + Left Click
Accesses the declaration of the method where user clicks.
CTRL + Shift + G
Shows all callers of the method where cursor is.
CTRL + Shift + F
Formats JAVA classes in a fast way and regulates the code to be read easily.
CTRL + Shift + T
Searchs for a Type in all workspace.
CTRL + Shift + R
Searchs for a Resource in all workspace. The difference between Resource and Type can be explained this way. When we refer Resource , it means JAVA classes which are included in workspace and written by users. However Type doesnt only show classes written by users, but also includes the JAVA classes which are contained in JDK such as java.lang.String class.
CTRL + ALT + Up Arrow or CTRL + ALT + Down Arrow
The all line which the cursor is on is copied to upper line or lower line
Alt + Shift + T
Opens the Refactor panel.
Alt + Up Arrow or Alt + Down Arrow
When getting around JAVA classes and methods, it accesses the previous or next method which were on our path.
CTRL + 1
Gives some advices about the problem related to the line which the cursor is on.
CTRL + Space
Auto-complete shortcut. It gives some suggestions about what you can write after the current cursor.For instance; if you use this shortcut after an instance's name, it shows all public methods and attribute.


Exception system for Axxel Discovery Protocol. More...
#include <axxel_discovery_protokoll.h>
Public Member Functions | |
| Exception (int, std::string) | |
| void | saveInLog () |
| void | dispose () |
| dispose errors More... | |
Exception system for Axxel Discovery Protocol.
Show the Fault Message and save them in Syslog
Definition at line 77 of file axxel_discovery_protokoll.h.
| Exception::Exception | ( | int | iFaultCode, |
| std::string | sFaultMessage | ||
| ) |
Exception.cpp Project: Axxel Discovery Protocol Created on: 2012-8-4 Author: Li, Yuan
Exception system for Axxel Discovery Protocol Show the Fault Messages and save them in Syslog Constructor
Definition at line 17 of file Exception.cpp.
| void Exception::dispose | ( | ) |
dispose errors
iFaultCode = -1# : gsoap errors
iFaultCode = 1 : daemon errors
iFaultCode = 2 : open files errors
iFaultCode = 3 : get time errors
iFaultCode = 4 : get ip address errors
iFaultCode = 5 : get default interface errors
iFaultCode = 6 : get host name or domain name errors
iFaultCode = 7 : iterator errors
iFaultCode = 8 : records warnings
iFaultCode = 9 : unexpected errors
Definition at line 46 of file Exception.cpp.
| void Exception::saveInLog | ( | ) |
save faultmessages in Log file
Definition at line 26 of file Exception.cpp.


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#include <db_cxx.h>u_int32_t DbTxn::id();
The DbTxn::id method returns the unique transaction id associated with the specified transaction. Locking calls made on behalf of this transaction should use the value returned from DbTxn::id as the locker parameter to the DbLockTab::get or DbLockTab::vec calls.


Contact: Address: 1234 Non Streat, Philadelphia, PA 10101 Home Phone:<PHONE_NUMBER> Hand Phone:<PHONE_NUMBER>


Victor Lazzarini, Edward Costello, Steven Yi, John ffitch
Member of the Music-N family of languages
Originally created by Barry Vercoe, MIT 1986, goes LGPL in 2002, and now is a community-driven project, with a small group of core developers.
;; ORC Code
instr 1
asig = vco2(0.5, cpspch(p4))
kenv = adsr(0.05, 0.05, 0.9, 0.1) * ampdbfs(p5)
asig = moogladder(asig, 4000, 0.25) * kenv
out(asig)
endin
schedule(1, 0.0, 1.5, 8.00, -12)
;; ORC Code
instr 1
asig = vco2(0.5, cpspch(p4))
kenv = adsr(0.05, 0.05, 0.9, 0.1) * ampdbfs(p5)
asig = moogladder(asig, 4000, 0.25) * kenv
out(asig)
endin
;; SCO Events
i1 0.0 1.5 8.00 -12
i1 1.0 1.0 9.00 -12
Cross-platform Compiler => Cross-platform Library (API)
Making Music => Making Music Software
Csound has been employed in a variety of ways, on a variety of platforms...
Basic mesasurements have been taken with a classic Csound example (Boulanger, Trapped in Convert , 4m50s):
[OSX10, Intel Core i7 2.8GHZ (quad), Chrome]


 We use all natural and pure essential oils and plant butters, herbs, and spices to make natural soaps that cleanse, moisturize and soothe your skin. Our soaps are lovely, good and safe for you and your family, and the environment.
 We use all natural and pure essential oils and plant butters, herbs, and spices to make natural soaps that cleanse, moisturize and soothe your skin. Our soaps are lovely, good and safe for you and your family, and the environment.
We Love Gifts! Do You? We love our clients! And we like to make them extra happy! So when you shop in our online store, besides all the great products you choose, with your order you also get:FREE gift with all orders FREE bar soap when you spend over €40 FREE shipping on all orders over €50 NO COUPONS NEEDED!


Dear {{ first_name }}{% if last_name %} {{ last_name}}{% endif %},
Please click on the following link to set your new password:
{{ link }}
Thank you, {{ user_fullname }}


You call the destructor of the object pointed to by pBase. That object has type Derived.  It would be an error to not call it.
The destructor is virtual. That means that instead of resolving call statically (calling whichever function correspond to pointer type) it resolves it dinamically: though hidden member in class it selects function corresponding to real type of class pointer is pointing to.
So ~Derived is executed. Destructors of the derived classes call destructors of base class after they finished. So you will see
so you are saying that here the virtual concept is working
so it invoke to drive class destructor first
but as we know that always that drive class destructor invoke first then base class destructor invoke so base destructor exicute second
so here two concepts work together
i m right or wrong ?
Yes. two concepts at work:
1) Destructors are called from most derived to base
2) Virtual calls call functions corresponding to real type of object, not type of pointer/reference.
You might be mixing two things. The virtual function system does select during runtime which function is called, but what that function does is an another matter.
Here is a simple case to show what the function does:
#include <iostream>
using std::cout;
class Base
{
public:
Base() { cout << "Calling Base()\n"; }
/* virtual */ ~Base() { cout << "Calling ~Base()\n"; }
};
class Derived : public Base
{
public:
Derived() { cout << "Calling Derived()\n"; }
~Derived() { cout << "Calling ~Derived()\n"; }
};
class Bar : public Derived
{
public:
Bar() { cout << "Calling Bar()\n"; }
~Bar() { cout << "Calling ~Bar()\n"; }
};
int main()
{
Base* foo = new Bar;
cout << "Hello\n";
delete foo;
return 0;
}
Notice in line 27 I made foo a pointer to the base class, but instantiated an object of the derived class "Bar". By changing the destructor in line 8 from virtual to non-virtual, the behavior of the delete in line 29 changes. With a virtual destructor, the destructor chain beginning at Bar is called. Without the virtual destructor, only the "Base" constructor is called.
The base class would be RoofedStructure. It's constructor puts up a roof.
The intermediate class would be House. A House is a RoofedStructure, and its constructor first calls the RoofedStructure constructor and then adds walls.
The bottom class would be FurnishedHouse. A FurnishedHouse is a House that contains furniture. The constructor first calls the House constructor and then adds furniture.
That being said, your conclusion is CORRECT. This is why it is critical to make destructors virtual in classes that are intended to be base classes. If the base class has a non-virtual destructor and the derived classes have resources that need to be cleaned up, if you delete a pointer to the base class, the derived class resources will not get cleaned up properly.


The Akan people of Ghana frequently name their children after the day of the week they were born and the order in which they were born. These "day names" have further meanings concerning the soul and character of the person
This naming tradition is shared throughout West Africa and the African diaspora.
Feed in the information as required to get yours. Names are based on your gender and the date you were born.


Lattices For Rigging
Level Pixel Level writes:
This is a video series that explores how to use lattices in Blender. The series starts with the very basics and work towards advanced concepts for adding lattices to a mesh and combine them with a modifier and a rig.
Beginner/Introdution:
Intermediate Techniques:
Advanced Technigues:


|
libsir 2.2.2
Standard Incident Reporter
|
Functions | |
| PLUGIN_EXPORT bool | sir_plugin_query (sir_plugininfo *info) |
| Called by libsir after the plugin library object is loaded, but before any other functions are probed or called. | |
| PLUGIN_EXPORT bool | sir_plugin_init (void) |
| Called by libsir after the plugin is queried and successfully validated. | |
| PLUGIN_EXPORT bool | sir_plugin_write (sir_level level, const char *message) |
Called by libsir when a message is dispatched on any level whose bit was set in the levels bitmask of the sir_plugininfo structure when sir_plugin_query was called. | |
| PLUGIN_EXPORT bool | sir_plugin_cleanup (void) |
| Called by libsir when the plugin is about to be unloaded. | |
Using plugins, it is possible to extend libsir's reach when dispatching log messages. A simple example of this would be a plugin that only registers for emergency-level messages. Whenever it receives a message, it posts the message to a REST API endpoint which results in a push notification being sent to a mobile device.
Use your imagination; essentially anything is possible. There are some caveats, though: until a thread pool and job queue mechanism can be implemented in libsir (#121), plugins must either only perform operations that are guaranteed to complete quickly, or perform those operations asynchronously (i.e., on another thread).
libsir's plugin interface will be versioned; the functions appearing on this page comprise the plugin interface v1. If/when a new function export is added (or one is modified), the version number will be bumped.
When plugins are compiled, their interface version is hard-coded in. This means that as libsir continues to evolve (and the version number increases), it can still communicate with older plugins via backwards-compatible versioned interfaces.
The following steps should be taken in order to write your own fully- functioning libsir plugin:
plugins/sample
directory (within plugins
).cd
back into the root directory of the repository and run make clean plugins
. If everything goes smoothly, your shiny new plugin should now be located in build/lib
. If you named your directory 'foo', you should see a plugin_foo.[so/dll]
.If you encounter any problems, rebuild with env SIR_SELFLOG=1 SIR_DEBUG=1 make clean plugins
. This will enable diagnostic output from libsir that should aid in the diagnosis of any issues you encounter. If you're still stuck, reach out and somebody will assist you within a reasonable amount of time.
| PLUGIN_EXPORT bool sir_plugin_query | ( | sir_plugininfo * | info | ) |
Called by libsir after the plugin library object is loaded, but before any other functions are probed or called.
The plugin must fill out the information in the info
structure in a way satisfactory to libsir, or it will immediately be unloaded:
iface_ver
must be set to SIR_PLUGIN_VCURRENT
.char*
members must be set to valid strings, and must point at static memory that will not go out of scope when the function exits. Do not allocate heap memory for these properties; they will be leaked.levels
must be a valid sir_level bitmask.opts
must be a valid sir_option bitmask.| info | Pointer to a sir_plugininfo structure to be initialized by the plugin. |
true
if the info
structure was successfully initialized, false
otherwise. If false
, the plugin will be unloaded. | PLUGIN_EXPORT bool sir_plugin_init | ( | void | ) |
Called by libsir after the plugin is queried and successfully validated.
The plugin should only at the time of this call begin allocating resources and initializing its internal state.
true
if the plugin successfully initialized its internal state, false
otherwise. | PLUGIN_EXPORT bool sir_plugin_write | ( | sir_level | level, |
| const char * | message | ||
| ) |
Called by libsir when a message is dispatched on any level whose bit was set in the levels
bitmask of the sir_plugininfo structure when sir_plugin_query was called.
The message
string will be pre-formatted according to the sir_option set in the opts
bitmask of the sir_plugininfo structure.
| level | The sir_level of the message being dispatched. |
| message | A string containing the pre-formatted message being dispatched. |
true
if the message was successfully processed, false
otherwise. | PLUGIN_EXPORT bool sir_plugin_cleanup | ( | void | ) |
Called by libsir when the plugin is about to be unloaded.
The plugin should immediately begin releasing allocated resources and resetting its internal state.
true
if the plugin cleaned up successfully, false
otherwise.


This page is an example of how to register an observer object on a Spry region to allow some additional processing after the region is updated.
In this sample, we generate the accordion markup from a dataset. When that is done, we fire a function to activate the accordion javascript.