Living Out Loud
[3.0/4.0]
It is possible to be surrounded by people and yet still be alone. That is what happens to Judith (Holly Hunter). Since divorcing her unfaithful husband, she is isolated in her New York high rise apartment. Her usual social group was shared by her ex-husband, and because of that link, she avoids contact with those people. The only outside activity she engages in is attending a nightclub to hear her favorite Jazz singer, Liz Baily (Queen Latifah). Judith's divorce has been devastating, but it also offers her a new beginning. Whereas she had been putting all of her energy into her troubled marriage, she now can focus solely on herself. She even thinks about pursuing the medical career that she had abandonded for her husband. However, she is mostly lost and unsure of where she is and where she is headed.
The doorman in her Judith's building, Pat (Danny DeVito), is also a kindred spirit. His adolescent daughter has just died from cancer, and he has been long since estranged from his wife. She threw him out because of his restlessness. He has racked up large gambling debts, and he does not settle into a meaningful job (like working for his brother who inherited a bar) because he feels he destined for bigger things. He is also blindly groping his way through a lonely life.
The film is about the two characters, and their relationship with each other. He is attracted to her, but obviously she will not initially be attracted to the older, short, balding, rotund man. If this were a stereotypical story, the focus of the film would be about how the underdog fights to win the romantic attention of the object of his affection. However, this is an intelligent drama with much more depth. (Their relationship has similarities to that of Esmeralda and Quasimodo, from "The Hunchback of Notre Dame".) Although it is not completely successful in drawing a total coherent picture, it is engaging and avoids predictability.
DeVito gives a typically superb performance as the confident but vulnerable Pat. Although the character of Liz Bailey is underwritten and could have been better utilized, Queen Latifah does a commendable job with the role. Hunter has a difficult job in playing such a nuanced character. Judith is at different times devastated, terrified, emboldened, and introspective. Although the scripted evolution of the character is sometimes awkward, Hunter is able to keep the character believable.
The film has its share of flaws. Judith's furtive attempt at a new romance is rather nonsensical. Her friendship with Liz seems improbable. Pat's suggested background does not match the character represented on screen. That is, it is not understandable why his wife would have thrown him out. Judith's husband is unilaterally sinister, but fortunately has minimal screen time. Also, the epilogue is superfluous because it presents the characters in exactly the state in which the viewer would have guessed. The film utilizes an interesting technique of presenting Judith's mental projections against her reality, so that the viewer can contrast she how dreams her life to be and how she actually lives it, but this is initially overused and then abruptly abandoned.
Despite its problems, the story is still enjoyable. Judith undergoes a rebirth and she reclaims her independence, and the transformance is realistically achieved in small steps. There are no melodramatic climaxes or twists that try to overstate the problems and solutions of the characters. The story always remains subdued and focused.
Recommended. Although it cannot seamlessly join the different parts into a fully coherent result, the film laudably attempts to avoid formulas and present an intelligent examination of kindred spirits.
(c) 1999 Murali Krishnan The Art House Squatter http://ArtHouseSquatter.com
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iOS readability tools
iOS tools to test readability and comprehension difficulty for contemporary English text.
See included example application.
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The ImageMover Applet can be arranged in one of three directions, and you can adjust its speed:
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CrimPapers
Articles published on 2 February 2019
On 2 February 2019, 5 new articles were published in 5 journals.
An Outcome Evaluation of A Substance Abuse Program For Probationers: Findings From A Quasi-Experimental Design
Settings Matter: Examining Protection’s Influence on the Illicit Drug Trade in Convergence Settings in the Paso Del Norte Metropolitan Area
On the Origin of Concept of “Intersectionality” in Criminology: the Civil Rights Movement and the Rise of “Scholarship of Confrontation” by Reza Barmaki
“Nullification Through Armed Civil Disobedience”: A Case Study of Strategic Framing in the Patriot/militia Movement by Sam Jackson
Editor Acknowledgements and New Co-Editor by Robert Geffner


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Sincerely,


I'm a 15 year old who likes K-Pop and Graphic Design.
As I said before I'm A 15 year old who likes to listen to Kpop and Graphic Design, but I also enjoy Read/Writing and I also like talking with my internet friends. There are all kinds of things that I can say that I like but that will take forever. But What I will do Is say why I am interested In Graphic Design and Coding. I don't exactly remember when I fell in love with the thought of being a game creator but it just hit me one day. Now before all of that I wanted to be a Vet, Writer, and I even wanted to become a Detective, but eventually I didn't really know what I wanted to do. That was when I got my first Phone and watched my first youtube video, which was a gaming video. The Game I watched was Shadow of the Colossus, and what I liked about it was the amazing graphics and I believe that was when I wanted to become a Game Creator.


Your organisation has invested to ensure Customer Service is provided. To lose a customer is costly and the need to 'win back' a customer is expensive.
The TM ViSight Reports structured in this section are specially designed for Customer Service and Call Centre Managers in order to help in day to day operations and process within your call centre environment. The information will help you manage staffing resources based on caller demand, plan for staff coverage during peak and off-peak periods, and monitor unusual calling behaviours.
Top Views
The TM ViSight Top View Report identifies the most frequent callers, longest call duration and most expensive calls. You may select a specific period by selecting the date range. The report will assist you in monitoring the call activities and ensure staff are spending time servicing calls coming into the call centre and KPI's are achieved. (Example: Why is this customer calling us so often? Is it necessary for these calls to be taking so long? Is additional staff training required?).
Answerpoint Summary
To ensure customer expectations are meet, TM TollFree subscribers are using the network features transferring calls to another answerpoint on busy, no answer, by region, or by percentage. This TM ViSight Report provides you a complete view of the number of calls being transferred according to the features subscribed.
If a TollFree number is being answered in more than one location, the answering location (Answerpoint) will be listed in this report with the complete and incomplete call information. The report enables you to manage resources in multiple locations and monitor each location performance by comparing average call duration and call volumes. .
Missed Calls
The Missed Calls Report shows the number of customers/prospects unable to reach your organisation. This information will assist you in identify the cause and allow you to take immediate action. Utilising this Management Tool will reduce the missed calls and result in better performance in your call centre, will enhance your customer service levels and ensure a sales opportunity is never lost.


{% extends "layout.html" %} {% block page_title %} {% if serviceName %} {{ serviceName }} {% endif %} {% endblock %} {% block content %} {% include "includes/phase_banner.html" %} Prototype Index Home Your train journey Add the details of your train journey From For example, Euston Departure time For example, 10am To For example, Birmingham New Street Is this a return journey? Yes What time is your return train home? For example, 4pm No {% endblock %}
Add the details of your train journey
For example, Euston
For example, 10am
For example, Birmingham New Street
For example, 4pm


Strictly's Oti Mabuse in tears over Kelvin Fletcher's touching Blackpool surprise
He brought her mum and godmother to Blackpool
by Rebecca CarterOti Mabuse was left in tears over her Strictly Come Dancing partner Kelvin Fletcher's touching Blackpool surprise.
The pair scored an incredible 39 points out of 40 for their Jive to Jailhouse Rock from Smokey Joe’s Cafe at the iconic Blackpool Tower ballroom on Saturday night.
After their performance, Oti took to Instagram to gush over "thoughtful Kelvin" as she revealed he had brought her mum and God mum to Blackpool.
Read more: Strictly viewers slam judges' scoring as they claim Karim Zeroual was 'robbed' of perfect 40
The dancer shared a photo of Kelvin smiling alongside a sweet caption.
She wrote: "Wow! I don’t even know where to start.

"The last we were in Blackpool she was training me in the Blackpool championships so today was THE BEST DAY OF MY LIFE!!!"
She added: "Not just that but it’s his nan saw him dance for the first time too.... I’m so grateful to be dancing with such thoughtful man and lastly but not least that jive.
"I will never forget this day for the rest of my life."
Fans gushed over Kelvin's touching gesture.
One person commented: "He is an angel. So happy for you to have such an amazing partner," while another wrote: "What a wonderful thing to do."
A third said: "Wow just wow... I'm speechless... how lovely."
Read more: Strictly Come Dancing viewers hail Blackpool show a triumph
During last night's show, Kelvin and Oti topped the leaderboard along with Karim Zeroual and Amy Dowden.
Elsewhere in the programme, professional dancer Anton Due Beke scored his first ever 10s in the history of his time on Strictly with his American smooth to Let's Face the Music and Dance with EastEnders' Emma Barton.
- Strictly Come Dancing airs tonight on BBC One at 7.15pm
Leave us a comment on our Facebook page @EntertainmentDailyFix and let us know what you think of this story.


Typically we think about an argument as a fight between people. However, a mathematical argument is different. A mathematical argument is a collection of statements called premises and a conclusion. Below is an example of an argument:
My state made it illegal to get an abortion after 6 weeks gestation. I just found out that I am pregnant and I am 8 weeks pregnant. Therefore, I cannot legally get an abortion in my state.in-context


Tsukushinbo Traditional Japanese Cuisine
Home
Menu
List Menu
Sushi
Sashimi/Nigiri
Sake
Location
Order Online
Aji
Spanish Mackeral
Albacore
Tuna
<
Ama Ebi
Sweet Shrimp
Ebi
Cooked Shrimp
Mutsu
Super White Tuna
Toro
Fatty Tuna
Kani
Crab
Sake
Salmon
Maguro
Tuna
Hamachi
Yellowtail Tuna
Hirame
Fluke
Suzuki
Stripe Sea Bass
Unagi
Eel
Tai
Red Snapper
Smoked Sake
Smoked Salmon
Hotate
Scallop
Ikura
Salmon Roe
Tobiko
Flying Fish Roe
Tamago
Egg Custard
Uni
Sea Urchin
Kabashira
Cooked Spicy Scallop
Hokkigai
Surf Clam
Ika
Squid
Tako
Octopus


Last Name
First Name
Address
City
State
ZIP/Postal Code
Country
Bulgaria
Phone(country code, area code, number)
(+
)
-
E-mail
Birth date
Month
Day
Year(4 digit)
Gender
Male
Female
Starting date
Spring 2006
Summer 2006
Comments/Questions


A cool lead sentence
One nice long paragraph. There are fixed points throughout time where things must stay exactly the way they are. This is not one of them.
This is an opportunity! Whatever happens here will create its own timeline, its own reality, a temporal tipping point.


3. Thin-plate spline module (symmetrize.tps)¶
-
symmetrize.tps.
tpsWarping
(from_points, to_points, images=None, axis=None, interpolation_order=1, approximate_grid=1, **kwds)[source]¶ Calculate the thin-plate spline (TPS) warping transform that from the from_points to the to_points, and then warp the given images by that transform. This transform is described in the paper: “Principal Warps: Thin-Plate Splines and the Decomposition of Deformations” by F.L. Bookstein.
Parameters: - from_points, to_points : 2D array, 2D array (dim = n x 2)
Correspondence point sets containing n 2D landmarks from the distorted and ideal images. The coordinates are in the (row, column) convention.
- images : 3D array | None
3D image stack to warp with the calculated thin-plate spline transform.
- axis : int | None
Image stacking axis in 3D image. Specify None to mean 2D image.
- interpolation_order : int | 1
If 1, then use linear interpolation; if 0 then use nearest-neighbor. See
scipy.ndimage.map_coordinates()
.- approximate_grid : int | 1
Use the approximate grid (if set > 1) for the transform. The approximate grid is smaller than the output image region, and then the transform is bilinearly interpolated to the larger region. This is fairly accurate for values up to 10 or so.
- kwds : keyword arguments
output_region: tuple | (0, 0, # of columns in image, # of rows in image) The (xmin, ymin, xmax, ymax) region of the output image that should be produced. (Note: The region is inclusive, i.e. xmin <= x <= xmax).
ret: str | ‘all’ Function return specification.
'image'
: return the transformed image.'deform'
: return the deformation field.'all'
: return both the transformed images and deformation field.
Returns: - images_tf : nD array
Transformed image stack.
- transform : list
Deformation field along x and y axes.


About Me
I am a Computer Science Student looking for internships for the upcoming 2022 year. I have experience in Python, Java and C++ but I'm always excited to learn new technologies! In addition, I have worked in a lab environment with the QCC Physics department.
Particle Physics Research Assistant
I assisted Professor Raul Armendariz in astro-particle physics research as a software developer. I built a Raspberry Pi application to graph the frequency of muon detections using Python and the libraries, Matplotlib, PyQt5 and NumPy.
Google Mentorship Development Program
Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Vitae justo eget magna fermentum iaculis eu. Ut tellus elementum sagittis vitae et leo duis ut. Eget magna fermentum iaculis eu non diam. Scelerisque


// "tldextract==1.2.2"
// "jieba"
requires = [
"lxml",
"requests",
"nltk",
"Pillow",
"cssselect",
"BeautifulSoup4"
]
setup(
name="newspaper",
version="0.0.8",
if sys.argv[-1] == "publish":
os.system("python setup.py sdist bdist_wininst upload")
sys.exit()
with open("requirements.txt") as f:


Contact details for Student Finance England including complaints, appeals, the Student Finance Services Non UK Team and the Advanced Learner Loan.
Parents and partners need the student’s consent before Student Finance England can give them information - the student can give consent when they apply or at a later date.
These addresses are post collection points only - not offices.
Student Finance England
Twitter
Facebook
Telephone: 0300 100 0607
Monday to Friday, 8am to 8pm
Saturday, 9am to 4pm
Textphone: 0300 100 0622
Monday to Friday, 8am to 4pm
Find out about call charges
Student Finance England
PO Box 210
Darlington DL1 9HJ
Student Finance Services Non UK Team
Twitter
Facebook
Telephone: 0141 243 3570
From outside the UK: +44 (0)141 243 3570
Monday to Friday, 9am to 5:30pm
Find out about call charges
Student Finance Services Non UK Team
Student Finance England
PO Box 89
Darlington DL1 9AZ
Telephone: 0300 100 0607
Monday to Friday, 8am to 8pm
Saturday, 9am to 4pm
Textphone: 0300 100 0622
Monday to Friday, 8am to 4pm
Find out about call charges
European Economic Area (EEA) Migrant Worker Applications
Student Finance England
PO Box 89
Darlington DL1 9AZ
Telephone: 0300 100 0031
Monday to Friday, 8am to 6pm
Find out about call charges
Student Finance England
PO Box 210
Darlington DL1 9HJ
Telephone: 0300 100 0619
Monday to Friday, 8am to 8pm
Saturday, 9am to 4pm
Find out about call charges
Advanced Learner Loan
PO Box 302
Darlington DL1 9NQ
Contact the Student Loans Company if you have a complaint.
Student Loans Company
<EMAIL_ADDRESS>Telephone: 0300 100 0601
Find out about call charges
An appeal is a formal request to Student Finance England asking them to review a decision about whether you can get financial help and if so how much.
Read the guidance and complete the application form.
Download ‘Student Finance England - how to appeal’ (PDF, 98KB)
Download ‘Student Finance England - appeals form’ (PDF, 42KB)
Send the completed form by email or post to Student Finance England.
Student Finance England
<EMAIL_ADDRESS>Formal Appeals
Student Finance England
Memphis Building
Lingfield Point
PO Box 226
Darlington DL1 9GA


Thrown when a query fails to analyze, usually because the query itself is invalid.
Thrown when a query fails to analyze, usually because the query itself is invalid.
1.3.0
A column that will be computed based on the data in a DataFrame
.
A column that will be computed based on the data in a DataFrame
.
A new column can be constructed based on the input columns present in a DataFrame:
df("columnName") // On a specific `df` DataFrame. col("columnName") // A generic column no yet associated with a DataFrame. col("columnName.field") // Extracting a struct field col("`a.column.with.dots`") // Escape `.` in column names. $"columnName" // Scala short hand for a named column.
Column objects can be composed to form complex expressions:
$"a" + 1 $"a" === $"b"
1.3.0
The internal Catalyst expression can be accessed via expr, but this method is for debugging purposes only and can change in any future Spark releases.
A convenient class used for constructing schema.
A convenient class used for constructing schema.
1.3.0
Functionality for working with missing data in DataFrame
s.
Functionality for working with missing data in DataFrame
s.
1.3.1
Interface used to load a Dataset from external storage systems (e.g.
Interface used to load a Dataset from external storage systems (e.g. file systems,
key-value stores, etc). Use SparkSession.read
to access this.
1.4.0
Statistic functions for DataFrame
s.
Statistic functions for DataFrame
s.
1.4.0
Interface used to write a Dataset to external storage systems (e.g.
Interface used to write a Dataset to external storage systems (e.g. file systems,
key-value stores, etc). Use Dataset.write
to access this.
1.4.0
A Dataset is a strongly typed collection of domain-specific objects that can be transformed in parallel using functional or relational operations.
A Dataset is a strongly typed collection of domain-specific objects that can be transformed
in parallel using functional or relational operations. Each Dataset also has an untyped view
called a DataFrame
, which is a Dataset of Row.
Operations available on Datasets are divided into transformations and actions. Transformations
are the ones that produce new Datasets, and actions are the ones that trigger computation and
return results. Example transformations include map, filter, select, and aggregate (groupBy
).
Example actions count, show, or writing data out to file systems.
Datasets are "lazy", i.e. computations are only triggered when an action is invoked. Internally,
a Dataset represents a logical plan that describes the computation required to produce the data.
When an action is invoked, Spark's query optimizer optimizes the logical plan and generates a
physical plan for efficient execution in a parallel and distributed manner. To explore the
logical plan as well as optimized physical plan, use the explain
function.
To efficiently support domain-specific objects, an Encoder is required. The encoder maps
the domain specific type T
to Spark's internal type system. For example, given a class Person
with two fields, name
(string) and age
(int), an encoder is used to tell Spark to generate
code at runtime to serialize the Person
object into a binary structure. This binary structure
often has much lower memory footprint as well as are optimized for efficiency in data processing
(e.g. in a columnar format). To understand the internal binary representation for data, use the
schema
function.
There are typically two ways to create a Dataset. The most common way is by pointing Spark
to some files on storage systems, using the read
function available on a SparkSession
.
val people = spark.read.parquet("...").as[Person] // Scala Dataset<Person> people = spark.read().parquet("...").as(Encoders.bean(Person.class)); // Java
Datasets can also be created through transformations available on existing Datasets. For example, the following creates a new Dataset by applying a filter on the existing one:
val names = people.map(_.name) // in Scala; names is a Dataset[String] Dataset<String> names = people.map((Person p) -> p.name, Encoders.STRING));
Dataset operations can also be untyped, through various domain-specific-language (DSL) functions defined in: Dataset (this class), Column, and functions. These operations are very similar to the operations available in the data frame abstraction in R or Python.
To select a column from the Dataset, use apply
method in Scala and col
in Java.
val ageCol = people("age") // in Scala Column ageCol = people.col("age"); // in Java
Note that the Column type can also be manipulated through its various functions.
// The following creates a new column that increases everybody's age by 10. people("age") + 10 // in Scala people.col("age").plus(10); // in Java
A more concrete example in Scala:
// To create Dataset[Row] using SparkSession val people = spark.read.parquet("...") val department = spark.read.parquet("...") people.filter("age > 30") .join(department, people("deptId") === department("id")) .groupBy(department("name"), people("gender")) .agg(avg(people("salary")), max(people("age")))
and in Java:
// To create Datasetusing SparkSession
Dataset<Row> people = spark.read().parquet("..."); Dataset<Row> department = spark.read().parquet("..."); people.filter(people.col("age").gt(30)) .join(department, people.col("deptId").equalTo(department.col("id"))) .groupBy(department.col("name"), people.col("gender")) .agg(avg(people.col("salary")), max(people.col("age")));
1.6.0
A container for a Dataset, used for implicit conversions in Scala.
A container for a Dataset, used for implicit conversions in Scala.
To use this, import implicit conversions in SQL:
val spark: SparkSession = ... import spark.implicits._
1.6.0
:: Experimental ::
Used to convert a JVM object of type T
to and from the internal Spark SQL representation.
:: Experimental ::
Used to convert a JVM object of type T
to and from the internal Spark SQL representation.
Encoders are generally created automatically through implicits from a SparkSession
, or can be
explicitly created by calling static methods on Encoders.
import spark.implicits._ val ds = Seq(1, 2, 3).toDS() // implicitly provided (spark.implicits.newIntEncoder)
Encoders are specified by calling static methods on Encoders.
List<String> data = Arrays.asList("abc", "abc", "xyz"); Dataset<String> ds = context.createDataset(data, Encoders.STRING());
Encoders can be composed into tuples:
Encoder<Tuple2<Integer, String>> encoder2 = Encoders.tuple(Encoders.INT(), Encoders.STRING()); List<Tuple2<Integer, String>> data2 = Arrays.asList(new scala.Tuple2(1, "a"); Dataset<Tuple2<Integer, String>> ds2 = context.createDataset(data2, encoder2);
Or constructed from Java Beans:
Encoders.bean(MyClass.class);
1.6.0
:: Experimental :: Holder for experimental methods for the bravest.
:: Experimental :: Holder for experimental methods for the bravest. We make NO guarantee about the stability regarding binary compatibility and source compatibility of methods here.
spark.experimental.extraStrategies += ...
1.3.0
A class to consume data generated by a StreamingQuery
.
A class to consume data generated by a StreamingQuery
. Typically this is used to send the
generated data to external systems. Each partition will use a new deserialized instance, so you
usually should do all the initialization (e.g. opening a connection or initiating a transaction)
in the open
method.
Scala example:
datasetOfString.writeStream.foreach(new ForeachWriter[String] { def open(partitionId: Long, version: Long): Boolean = { // open connection } def process(record: String) = { // write string to connection } def close(errorOrNull: Throwable): Unit = { // close the connection } })
Java example:
datasetOfString.writeStream().foreach(new ForeachWriter<String>() { @Override public boolean open(long partitionId, long version) { // open connection } @Override public void process(String value) { // write string to connection } @Override public void close(Throwable errorOrNull) { // close the connection } });
2.0.0
:: Experimental :: A Dataset has been logically grouped by a user specified grouping key.
:: Experimental ::
A Dataset has been logically grouped by a user specified grouping key. Users should not
construct a KeyValueGroupedDataset directly, but should instead call groupByKey
on
an existing Dataset.
2.0.0
Lower priority implicit methods for converting Scala objects into Datasets.
Lower priority implicit methods for converting Scala objects into Datasets. Conflicting implicits are placed here to disambiguate resolution.
Reasons for including specific implicits:
newProductEncoder - to disambiguate for List
s which are both Seq
and Product
A set of methods for aggregations on a DataFrame
, created by Dataset.groupBy
.
A set of methods for aggregations on a DataFrame
, created by Dataset.groupBy
.
The main method is the agg function, which has multiple variants. This class also contains convenience some first order statistics such as mean, sum for convenience.
This class was named GroupedData
in Spark 1.x.
2.0.0
Represents one row of output from a relational operator.
Represents one row of output from a relational operator. Allows both generic access by ordinal, which will incur boxing overhead for primitives, as well as native primitive access.
It is invalid to use the native primitive interface to retrieve a value that is null, instead a
user must check isNullAt
before attempting to retrieve a value that might be null.
To create a new Row, use RowFactory.create()
in Java or Row.apply()
in Scala.
A Row object can be constructed by providing field values. Example:
import org.apache.spark.sql._ // Create a Row from values. Row(value1, value2, value3, ...) // Create a Row from a Seq of values. Row.fromSeq(Seq(value1, value2, ...))
A value of a row can be accessed through both generic access by ordinal, which will incur boxing overhead for primitives, as well as native primitive access. An example of generic access by ordinal:
import org.apache.spark.sql._ val row = Row(1, true, "a string", null) // row: Row = [1,true,a string,null] val firstValue = row(0) // firstValue: Any = 1 val fourthValue = row(3) // fourthValue: Any = null
For native primitive access, it is invalid to use the native primitive interface to retrieve
a value that is null, instead a user must check isNullAt
before attempting to retrieve a
value that might be null.
An example of native primitive access:
// using the row from the previous example. val firstValue = row.getInt(0) // firstValue: Int = 1 val isNull = row.isNullAt(3) // isNull: Boolean = true
In Scala, fields in a Row object can be extracted in a pattern match. Example:
import org.apache.spark.sql._ val pairs = sql("SELECT key, value FROM src").rdd.map { case Row(key: Int, value: String) => key -> value }
1.3.0
Runtime configuration interface for Spark.
Runtime configuration interface for Spark. To access this, use SparkSession.conf
.
Options set here are automatically propagated to the Hadoop configuration during I/O.
2.0.0
The entry point for working with structured data (rows and columns) in Spark 1.x.
The entry point for working with structured data (rows and columns) in Spark 1.x.
As of Spark 2.0, this is replaced by SparkSession. However, we are keeping the class here for backward compatibility.
1.0.0
A collection of implicit methods for converting common Scala objects into Datasets.
A collection of implicit methods for converting common Scala objects into Datasets.
1.6.0
The entry point to programming Spark with the Dataset and DataFrame API.
The entry point to programming Spark with the Dataset and DataFrame API.
In environments that this has been created upfront (e.g. REPL, notebooks), use the builder to get an existing session:
SparkSession.builder().getOrCreate()
The builder can also be used to create a new session:
SparkSession.builder .master("local") .appName("Word Count") .config("spark.some.config.option", "some-value") .getOrCreate()
:: Experimental :: Holder for injection points to the SparkSession.
:: Experimental :: Holder for injection points to the SparkSession. We make NO guarantee about the stability regarding binary compatibility and source compatibility of methods here.
This current provides the following extension points: - Analyzer Rules. - Check Analysis Rules - Optimizer Rules. - Planning Strategies. - Customized Parser. - (External) Catalog listeners.
The extensions can be used by calling withExtension on the SparkSession.Builder, for example:
SparkSession.builder() .master("...") .conf("...", true) .withExtensions { extensions => extensions.injectResolutionRule { session => ... } extensions.injectParser { (session, parser) => ... } } .getOrCreate()
Note that none of the injected builders should assume that the SparkSession is fully initialized and should not touch the session's internals (e.g. the SessionState).
Converts a logical plan into zero or more SparkPlans.
Converts a logical plan into zero or more SparkPlans. This API is exposed for experimenting with the query planner and is not designed to be stable across spark releases. Developers writing libraries should instead consider using the stable APIs provided in org.apache.spark.sql.sources
A Column where an Encoder has been given for the expected input and return type.
A Column where an Encoder has been given for the expected input and return type.
To create a TypedColumn, use the as
function on a Column.
The input type expected for this expression. Can be Any
if the expression is type
checked by the analyzer instead of the compiler (i.e. expr("sum(...)")
).
The output type of this column.
1.6.0
Functions for registering user-defined functions.
Functions for registering user-defined functions. Use SparkSession.udf
to access this:
spark.udf
1.3.0
The user-defined functions must be deterministic.
:: Experimental :: Methods for creating an Encoder.
:: Experimental :: Methods for creating an Encoder.
1.6.0
1.3.0
This SQLContext object contains utility functions to create a singleton SQLContext instance, or to get the created SQLContext instance.
This SQLContext object contains utility functions to create a singleton SQLContext instance, or to get the created SQLContext instance.
It also provides utility functions to support preference for threads in multiple sessions scenario, setActive could set a SQLContext for current thread, which will be returned by getOrCreate instead of the global one.
Contains API classes that are specific to a single language (i.e.
Contains API classes that are specific to a single language (i.e. Java).
Functions available for DataFrame operations.
Functions available for DataFrame operations.
1.3.0
Support for running Spark SQL queries using functionality from Apache Hive (does not require an existing Hive installation).
Support for running Spark SQL queries using functionality from Apache Hive (does not require an existing Hive installation). Supported Hive features include:
Users that would like access to this functionality should create a HiveContext instead of a SQLContext.
A set of APIs for adding data sources to Spark SQL.
Contains a type system for attributes produced by relations, including complex types like structs, arrays and maps.
Allows the execution of relational queries, including those expressed in SQL using Spark.


Instead of '[ true ]', just use 'true'.
if [ true ]
then
echo "always triggers"
fi
if true
then
echo "always triggers"
fi
This is a stylistic suggestion to use true
instead of [ true ]
.
[ true ]
seems to suggest that the value "true" is somehow relevant to the statement. This is not the case, it doesn't matter. You can replace it with [ false ]
or [ wombat ]
, and it will still always be true:
| String | In brackets | Outside brackets |
|---|---|---|
| true | true | true |
| false | true | false |
| wombat | true | unknown command |
It's therefore better to use it without brackets, so that the "true" actually matters.
None.


Chapters, Club Managers, Students, and Faculty all benefit from grants and scholarships from The Club Foundation. Your donation supports the professional development of club managers through four signature scholarships: Willmoore H. Kendall Scholarship, LaRocca Family Executive Scholarship, Sally Burns Rambo Scholarship, and James B. Singerling Scholarship. Undergraduate students receive funding through the annual Joe Perdue Scholarship.
The Club Foundation funds research that provides lasting benefit for the club industry. CMAA’s 2016 Millennial/Generational Insights Research was funded in part by The Club Foundation, providing valuable insight into what potential club members are seeking and uncovering generational attitudes about club memberships, specifically the Millennial demographic.
Caddy For a Cure, Nextgengolf, and Tee It Up For The Troops are the three charities that The Club Foundation supports on an annual basis. In addition, The Club Foundation also supported Hurricane Relief efforts Florida, Texas, and the Carolinas as well as for disaster relief for Napa and Sonoma and a donation to GLIDE in San Francisco, site of the 2018 World Conference.
Statistics based on the 2016 Economic Impact Report, conducted in conjunction with Club Benchmarking.
In the early years, the Foundation began by funding student scholarships to a small group of individuals seeking to expand their practical knowledge of the club industry. The Foundation’s leaders realized that they needed additional funding to maintain a long-term program for the professional development of club managers.
With the 1990’s came the next step, a formalized campaign to generate the dollars needed to become a truly sustainable organization. To better align with the strategic direction of the organization, the name was officially changed in 1991 to The Club Foundation. From 1992 through 1998, the Campaign to Promote Excellence raised $3.5 million from CMAA members, CMAA Chapters, clubs, and the corporate community. This helped in two areas:
As the next century dawned, the need for education continued to increase, and The Foundation recognized the need for a more formal funding strategy. The Club Foundation developed the five key funding areas:
Student Scholarships, named for BMI architect and creator Joe Perdue, CCM, CHE, have had a substantial impact on hospitality students’ financial scholastic needs. CMAA also added specific and in-depth business management courses to round out the essential core programs already in place. With the completion of these, club managers were able to obtain certification from CMAA. In 2001, The Foundation created the Willmoore H. Kendall Scholarship in honor of CMAA Past President Bill Kendall to financially assist club managers pursuing their Certified Club Manager (CCM) designation. In addition, The Club Foundation focused its energies on the industry at large with grants to organizations that would help drive the industry forward.
With the increased demand for education and programs to support the five targeted funding areas, it became necessary for the Foundation to launch a second fundraising campaign. The Campaign to Promote Excellence…Yesterday, Today and Tomorrow raised another $4.5 million. Through this campaign, The Club Foundation increased not only the amount of dollars contributed, but the number of scholarship and grants given.
2006
The Foundation introduced the Corporate Alliance Program in conjunction with CMAA which helped to organize and cement both long term corporate contributors as well as cultivate new corporate donors. This new funding stream combined with new CMAA member contributors, long-term contributors, and other CF fundraising events have continued to positively impact the club industry.
2010
Recognizing how much The Club Foundation has impacted their careers, the LaRocca Family stepped forward to give back to the industry by making a generous contribution to the Foundation. The LaRocca Family Executive Scholarship was created to both honor their commitment to professional development and to recognize their continued support of The Club Foundation. The purpose of this scholarship is to provide financial support to club managers interested in both furthering their own professional development, as well as helping others achieve their career goals.
2012
The Club Foundation launched the Sally Burns Rambo Scholarship with a start-up grant from the Texas Lone Star Chapter of CMAA. As a mentor and role model, Sally Burns Rambo, CCM, was a pioneer who paved the way for the success of countless club industry leaders. To honor her memory and the legacy that she created, this scholarship provides financial support to female club managers interested in furthering their professional development.
2015
The James B. Singerling Scholarship was created in honor of CMAA’s long-time CEO, Jim Singerling, CCM. The purpose of this scholarship is to make it possible for recipients to attend the CMAA World Conference on Club Management and Club Business Expo for the first time as a club industry professional.


Barcelona chief Eric Abidal responds to Ivan Rakitic’s comments on alleged first-team snub
by HarigovindBarcelona’s Sporting Director Eric Abidal has finally responded to Ivan Rakitic’s comments on his alleged first-team snub, saying that the decision was taken due to a “sporting reason” which the Croatian will be made aware of soon.
“Rakitic has been warned. There’s a sporting decision that he must understand,” Abidal said, before adding:
“I will speak with him to explain the plan.”
In case you missed it, the 31-year-old midfielder had been very outspoken about his lack of playing time for Barcelona this season.
“I’ve given a lot in the five and a half years I’ve been here, and I want to continue to enjoy it,” Rakitic said, in a recent interview.
He further added: “I trust my football, I’m 31 and not 38, I feel at my best, but I understand and respect the decisions made by a coach. I think last season was the best of my career, I’m working harder than ever and I’m crazy about details, I love football.”
Rakitic has played in only ten of Barcelona’s matches so far in the ongoing 2019-20 season – seven in the La Liga and three in the Champions League.
There are also rumours that the Catalans may offload him during January, provided they receive a suitable offer.
Quotes via Marca.


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Hack 15 Manipulate Files with sed
If you've ever had to change the formatting of a file, you know that it can be a time-consuming process. Why waste your time making manual changes to files when Unix systems come with many tools that can very quickly make the changes for you? 2.4.1 Removing Blank LinesSuppose you need to remove the blank lines from a file. This invocation of grep will do the job: % grep -v '^$' letter1.txt > tmp ; mv tmp letter1.txt The pattern ^$ anchors to both the start and the end of a line with no intervening characters—the regexp definition of a blank line. The -v option reverses the search, printing all nonblank lines, which are then written to a temporary file, and the temporary file is moved back to the original.
You can rewrite the preceding example in sed as: % sed '/^$/d' letter1.txt > tmp ; mv tmp letter1.txt '/^$/d' is actually a sed script. sed's normal mode of operation is to read each line of input, process it according to the script, and then write the processed line to standard output. In this example, the expression '/^$/ is a regular expression matching a blank line, and the trailing d' is a sed function that deletes the line. Blank lines are deleted and all other lines are printed. Again, the results are redirected to a temporary file, which is then copied back to the original file. 2.4.2 Searching with sedsed can also do the work of grep: % sed -n '/$USER/p' * This command will yield the same results as: % grep '$USER' * The -n (no-print, perhaps) option prevents sed from outputting each line. The pattern /$USER/ matches lines containing $USER, and the p function prints matched lines to standard output, overriding -n. 2.4.3 Replacing Existing TextOne of the most common uses for sed is to perform a search and replace on a given string. For example, to change all occurrences of 2003 into 2004 in a file called date, include the two search strings in the format 's/oldstring/newstring/', like so: % sed 's/2003/2004/' date Copyright 2004 ... This was written in 2004, but it is no longer 2003. ... Almost! Noticed that that last 2003 remains unchanged. This is because without the g (global) flag, sed will change only the first occurrence on each line. This command will give the desired result: % sed 's/2003/2004/g' date Search and replace takes other flags too. To output only changed lines, use: % sed -n 's/2003/2004/gp' date Note the use of the -n flag to suppress normal output and the p flag to print changed lines. 2.4.4 Multiple TransformationsPerhaps you need to perform two or more transformations on a file. You can do this in a single run by specifying a script with multiple commands: % sed 's/2003/2004/g;/^$/d' date This performs both substitution and blank line deletion. Use a semicolon to separate the two commands. Here is a more complex example that translates HTML tags of the form <font> into PHP bulletin board tags of the form [font]: % cat index.html <title>hello </title> % sed 's/<\(.*\)>/[\1]/g' index.html [title]hello [/title] How did this work? The script searched for an HTML tag using the pattern '<.*>'. Angle brackets match literally. In a regular expression, a dot (.) represents any character and an asterisk (*) means zero or more of the previous item. Escaped parentheses, \( and \), capture the matched pattern laying between them and place it in a numbered buffer. In the replace string, \1 refers to the contents of the first buffer. Thus the text between the angle brackets in the search string is captured into the first buffer and written back inside square brackets in the replace string. sed takes full advantage of the power of regular expressions to copy text from the pattern to its replacement. % cat index1.html <title>hello</title> % sed 's/<\(.*\)>/[\1]/g' index1.html [title>hello</title] This time the same command fails because the pattern .* is greedy and grabs as much as it can, matching up to the second >. To prevent this behavior, we need to match zero or more of any character except <. Recall that [...] is a regular expression that lists characters to match, but if the first character is the caret (^), the match is reversed. Thus the regular expression [^<] matches any single character other than <. I can modify the previous example as follows: % sed 's/<\([^<]*\)>/[\1]/g' index1.html [title]hello[/title] Remember, grep will perform a case-insensitive search if you provide the -i flag. sed, unfortunately, does not have such an option. To search for title in a case-insensitive manner, form regular expressions using [...], each listing a character of the word in both upper- and lowercase forms: % sed 's/[Tt][Ii][Tt][Ll][Ee]/title/g' title.html 2.4.5 See Also
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