introduction
my name is emad I m 32 years old I graduated from Al-Zaytoonah University in 2013 with a major in accounting. I am very interested in technology and programming. My dream is to work in this field.
my education
- Bachelor’s Degree in Accounting
my Experience summary
- Senior Accountant
- junior accountant
my top ten favorite places in jordan
- irbed
- dead sea
- um qais
- al aqaba
- petra
- jarash
- irbed
- al karak
- Jabal Amman
- Jabal al wabda


Visual Analysis of Spatio-temporal Phenomena with 1D Projections
This is a live version of the prototype produced for the publication Visual Analysis of Spatio-temporal Phenomena with 1D Projections
by Max Franke, Henry Martin, Steffen Koch, and Kuno Kurzhals;
submitted to and presented at EuroVis 2021 held virtually in Zurich, Switzerland, on June 14–18, 2021.
The code for this prototype is available under the Apache-2.0
license on GitHub.
The COVID-19 data for the COVID-19 Cases
was obtained from Covidatlas and is in the public domain.
The COVID-19 data for the RKI COVID-19 Cases Germany
was obtained from arcgis.com and were provided by the Robert Koch Institute under the Data license Germany – attribution – version 2.0 (Robert Koch-Institut (RKI), dl-de/by-2-0).
The Australian wildfire data was obtained from MODIS Collection 6 NRT Hotspot / Active Fire Detections MCD14DL distributed from NASA FIRMS (DOI: 10.5067/firms/modis/mcd14dl.nrt.006)
COVID-19 Cases
The first dataset is a timeseries of confirmed COVID-19 cases. The data is hierarchically aggregated based on geopolitical entities: countries, states or provinces, counties, and on the lowest level cities.
The data originates from multiple sources, which have been aggregated by
https://covidatlas.com/
.
The data repository has unfortunately been removed, so only data until April 18, 2021, is included.
RKI COVID-19 Cases Germany
The second dataset is a timeseries of confirmed COVID-19 cases in the counties of Germany. The data has been provided by the Robert Koch Institute (RKI) under the Data license Germany – attribution – version 2.0 (Robert Koch-Institut (RKI), dl-de/by-2-0).
Australian Wildfires
Satellite imagery measured Fire Radiative Power (FRP) over Australia, indicating wildfires. These are visualized on a map and in a 1D sequence over time. The data is hierarchized based on grid squares.


Mitigation for developers
For software developers there are a number of actions they can take
- Use SetEnvironmentVariable(TEXT(“PATH”),NULL) API which removes the path environment variable from its search order
- Change default installation folder to C:\Program Files
- Use fully qualified path when loading DLLs, i.e. LoadLibrary(“C:\\program files\\MyApp\\mylibrary.dll”);
- Use SetDllDirectory(“”) API removing the current working directory from the search order
Mitigation for IT professionals
For IT support professionals there are also a number of actions that can be taken
- When packaging and deploying software via deployment tools such as Marimba, Landesk, etc. or manually installing software change the installation folder to C:\Program Files
- If software needs to be installed on the root check there are no binaries needing SYSTEM privileges
- If SYSTEM privileges are required then change the ACL’s of the folder
- Remove the path entry from the SYSTEM path variable if not needed


Chiken
While not specifying borders and colors according to the requirements does not hurt your grade so much, not doing so will make it much harder for your classmates to peer grade the rest of your assignment, possibly resulting in a much lower grade.
Sushi
While not specifying borders and colors according to the requirements does not hurt your grade so much, not doing so will make it much harder for your classmates to peer grade the rest of your assignment, possibly resulting in a much lower grade.
Beef
While not specifying borders and colors according to the requirements does not hurt your grade so much, not doing so will make it much harder for your classmates to peer grade the rest of your assignment, possibly resulting in a much lower grade.


To install React, follow the instructions on GitHub.
If you can see this, React is not working right.
If you checked out the source from GitHub make sure to run grunt
.
This example demonstrates WebComponent/ReactComponent interoperability by rendering a ReactComponent, which renders a WebComponent, which renders another ReactComponent in the WebComponent's shadow DOM.
Learn more about React at facebook.github.io/react.


This topic describes the syntax of the Java Network Launch Protocol (JNLP) file for rich Internet applications (RIAs).
The following code snippet shows a sample JNLP file for a Java Web Start application:
<?xml version="1.0" encoding="UTF-8"?> <jnlp spec="1.0+" codebase="" href=""> <information> <title>Dynamic Tree Demo</title> <vendor>Dynamic Team</vendor> <icon href="sometree-icon.jpg"/> <offline-allowed/> </information> <resources> <!-- Application Resources --> <j2se version="1.6+" href="http://java.sun.com/products/autodl/j2se"/> <jar href="DynamicTreeDemo.jar" main="true" /> </resources> <application-desc name="Dynamic Tree Demo Application" main-class="webstartComponentArch.DynamicTreeApplication" width="300" height="300"> </application-desc> <update check="background"/> </jnlp>
The following table describes the elements and attributes commonly used in JNLP files. Click the parent link to view an element's parent.
|
Element |
Attributes |
Description |
Since |
Required |
|---|---|---|---|---|
|
|
The topmost xml element for a JNLP file. |
1.0 |
Yes |
|
|
|
spec |
Value of the attribute can be 1.0, 1.5, or 6.0, or can use wildcards such as 1.0+. It denotes the minimum version of the JNLP Specification that this JNLP file can work with. |
1.0 |
|
|
|
codebase |
The base location for all
relative URLs specified in |
1.0 |
|
|
|
href |
The URL of the JNLP file itself. |
1.0 |
|
|
|
version |
The version of the RIA being launched, as well as the version of the JNLP file itself. |
1.0 |
|
|
information parent |
|
Contains other elements that describe the RIA and its source. |
1.0 |
Yes |
|
|
os |
The operating system for which this information element should be considered. |
1.5.0 |
|
|
|
arch |
The architecture for which this information element should be considered. |
1.5.0 |
|
|
|
platform |
The platform for which this information element should be considered. |
1.5.0 |
|
|
|
locale |
The locale for which this information element should be considered. |
1.5.0 |
|
|
title parent |
|
The title of the RIA. |
1.0 |
Yes |
|
vendor parent |
|
The provider of the RIA. |
1.0 |
Yes |
|
homepage parent |
|
The homepage of the RIA. |
1.0 |
|
|
|
href |
A URL pointing to where more information about this RIA can be found. |
1.0 |
Yes |
|
description parent |
|
A short statement describing the RIA. |
1.0 |
|
|
|
kind |
An indicator as to the type of description. Legal values are one-line, short, and tooltip. |
1.0 |
|
|
icon parent |
|
An icon that can be used to identify the RIA to the user. |
1.0 |
|
|
|
href |
A URL pointing to the icon file. Can be in one of the following formats: gif, jpg, png, ico. |
1.0 |
Yes |
|
|
kind |
Indicates the suggested use of the icon, can be: default, selected, disabled, rollover, splash, or shortcut. |
1.0 |
|
|
|
width |
Can be used to indicate the resolution of the image. |
1.0 |
|
|
|
height |
Can be used to indicate the resolution of the image. |
1.0 |
|
|
|
depth |
Can be used to indicate the resolution of the image. |
1.0 |
|
|
offline-allowed parent |
|
Indicates that this RIA can operate when the client system is disconnected from the network. |
1.0 |
|
|
shortcut parent |
|
Can be used to indicate the RIA's preferences for desktop integration. |
1.5.0 |
|
|
|
online |
Can be used to describe the RIA's preference for creating a shortcut to run online or offline. |
1.5.0 |
|
|
desktop parent |
|
Can be used to indicate the RIA's preference for putting a shortcut on the user's desktop. |
1.5.0 |
|
|
menu parent |
|
Can be used to indicate the RIA's preference for putting a menu item in the user's start menus. |
1.5.0 |
|
|
|
sub-menu |
Can be used to indicate the RIA's preference for where to place the menu item. |
1.5.0 |
|
|
association parent |
|
Can be used to hint to the JNLP client that the RIA wants to be registered with the operating system as the primary handler of certain extensions and a certain mime-type. |
1.5.0 |
|
|
|
extensions |
A list of file extensions (separated by spaces) that the RIA requests it be registered to handle. |
1.5.0 |
|
|
|
mime-type |
The mime-type that the RIA requests it be registered to handle. |
1.5.0 |
|
|
related-content parent |
|
An additional piece of related content that can be integrated with the RIA. |
1.5.0 |
|
|
|
href |
A URL pointing to the related content. |
1.5.0 |
Yes |
|
update parent |
|
The preferences for how RIA updates should be handled by the JNLP client. |
1.6.0 |
|
|
|
check |
The preference for when the JNLP client should check for updates. Value can be always, timeout, or background.. |
1.6.0 |
|
|
|
policy |
The preference for how the JNLP client should handle a RIA update when a new version is available before the RIA is launched. Values can be always, prompt-update, or prompt-run. |
1.6.0 |
|
|
|
|
|
1.0 |
|
|
security parent |
|
Can be used to request enhanced permissions. |
1.0 |
|
|
all-permissions parent |
|
Requests that the RIA be run with all permissions. |
1.0 |
|
|
j2ee-application-client-permissions parent |
|
Requests that the RIA be run with a permission set that meets the security specifications of the J2EE application client environment. |
1.0 |
|
|
resources parent |
|
Describes all the resources that are needed for the RIA. |
1.0 |
Yes |
|
|
os |
The operating system for which the resources element should be considered. |
1.0 |
|
|
|
arch |
The architecture for which the resources element should be considered. |
1.0 |
|
|
|
locale |
The locales for which the resources element should be considered. |
|
|
|
java or j2se parent |
|
Versions of Java software to run the RIA with. |
1.6.0 (java) |
|
|
|
version |
Ordered list of version ranges to use. |
1.0 |
Yes |
|
|
href |
The URL denoting the supplier of this version of Java software, and from where it can be downloaded. |
1.0 |
|
|
|
java-vm-args |
An additional set of standard and non-standard virtual machine arguments that the RIA would prefer the JNLP client use when launching the JRE software. |
1.0 |
|
|
|
initial-heap-size |
The initial size of the Java heap. |
1.0 |
|
|
|
max-heap-size |
The maximum size of the Java heap. |
1.0 |
|
|
jar parent |
|
A JAR file that is part of the RIA's classpath. |
1.0 |
Yes |
|
|
href |
The URL of the JAR file. |
1.0 |
Yes |
|
|
version |
The requested version of the JAR file. Requires using the version-based download protocol |
1.0 |
|
|
|
main |
Indicates if this JAR file contains the class containing the
|
1.0 |
|
|
|
download |
Indicates that this JAR file can be downloaded lazily, or when needed. |
1.0 |
|
|
|
size |
The downloadable size of the JAR file in bytes. |
1.0 |
|
|
|
part |
Can be used to group resources together so that they are downloaded at the same time. |
1.0 |
|
|
nativelib parent |
|
A JAR file that contains native libraries in its root directory. |
1.0 |
|
|
|
href |
The URL of the JAR file. |
1.0 |
Yes |
|
|
version |
The requested version of the JAR file. Requires using the version-based download protocol |
1.0 |
|
|
|
download |
Can be used to indicate this JAR file can be downloaded lazily. |
1.0 |
|
|
|
size |
The downloadable size of the JAR file in bytes. |
1.0 |
|
|
|
part |
Can be used to group resources together so they will be downloaded at the same time. |
1.0 |
|
|
extension parent |
|
A pointer to an additional component-desc or installer-desc to be used with this RIA. |
1.0 |
|
|
|
href |
The URL to the additional extension JNLP file. |
1.0 |
Yes |
|
|
version |
The version of the additional extension JNLP file. |
1.0 |
|
|
|
name |
The name of the additional extension JNLP file |
1.0 |
|
|
ext-download parent |
|
Can be used in an extension element to denote the parts contained in a component-extension. |
1.0 |
|
|
|
ext-part |
The name of a part that can be expected to be found in the extension. |
1.0 |
Yes |
|
|
download |
Can be used to indicate this extension can be downloaded eagerly or lazily. |
1.0 |
|
|
|
part |
Denotes the name of a part in this JNLP file in which to include the extension. |
1.0 |
|
|
package parent |
|
Can be used to indicate to the JNLP client which packages are implemented in which JAR files. |
1.0 |
|
|
|
name |
Package name contained in the JAR files of the given part. |
1.0 |
Yes |
|
|
part |
Part name containing the JAR files that include the given package name. |
1.0 |
Yes |
|
|
recursive |
Can be used to indicate that all package names, beginning with the given name, can be found in the given part. |
1.0 |
|
|
property parent |
|
Defines a system property that will be available through
the |
1.0 |
|
|
|
name |
Name of the system property. |
1.0 |
Yes |
|
|
value |
Value of the system property. |
1.0 |
Yes |
|
|
|
Note: A JNLP file must contain one of the following: application-desc, applet-desc, component-desc, or installer-desc. |
1.0 |
Yes |
|
application-desc parent |
|
Denotes this is the JNLP file for an application. |
1.0 |
|
|
|
main-class |
The name of the class containing the |
1.0 |
Yes |
|
argument parent |
|
Each argument contains (in order) an additional argument
to be passed to the |
1.0 |
|
|
applet-desc parent |
|
Denotes this is the JNLP file for an applet. |
1.0 |
|
|
|
main-class |
The name of the main applet class. |
1.0 |
Yes |
|
|
documentbase |
The document base for the applet as a URL. |
1.0 |
|
|
|
name |
Name of the applet. |
1.0 |
Yes |
|
|
width |
The width of the applet in pixels. |
1.0 |
Yes |
|
|
height |
The height of the applet in pixels. |
1.0 |
Yes |
|
param parent |
|
A set of parameters that can be passed to the applet. |
1.0 |
|
|
|
name |
The name of this parameter. |
1.0 |
Yes |
|
|
value |
The value of this parameter. |
1.0 |
Yes |
|
component-desc parent |
|
Denotes this is the JNLP file for a component extension. |
1.0 |
|
|
installer-desc parent |
|
Denotes this is the JNLP file for an installed extension. |
1.0 |
|
|
|
main-class |
The name of the class containing the |
1.0 |
Yes |
<?xml version="1.0" encoding="utf-16"?>


DEVIL IN A BLUE DRESS A film review by Steve Rhodes Copyright 1995 Steve Rhodes
"It was the summer of 1948, and I needed money," begins Easy Rawlins (Denzel Washington) in DEVIL IN A BLUE DRESS. In a loving and lush recreation of black Los Angles of that era by set designer Gary Frutkoff, this film noir starts. Think of any of the Raymond Chandler mysteries. Now switch the characters so that most are black, and voila, DEVIL IN A BLUE DRESS.
The sets and the sepia toned images by famous cinematographer Tak Fujimoto (PHILADELPHIA, SILENCE OF THE LAMBS, and SOMETHING WILD) form a picture that is a delight to your eyes. Easy owns a home in a black middle class suburb of LA that Frutkoff has created with wide streets, tall cool palm trees, and green lawns. Although Easy has a nice house and a long, new Pontiac, all is not easy for him as he has just lost the lucrative job in the aircraft industry that he migrated years ago from Texas to get. He was fired because of the color of his skin. Racism interlaces the story as a subtext, but it is a mystery more than a morality play.
While he is down on his luck and two mortgage payments behind, a strange white figure called DeWitt Albright (Tom Sizemore) makes him an offer too good to pass up. It seems that in the midst of the LA mayoral campaign, mayor Todd Carter (Terry Kinney from that great old TV series "thirysomething") has dropped out of the race claiming he is despondent over the mysteriously disappearance of his girlfriend Daphne Monet (Jennifer Beals). DeWitt says that the mayor has hire him to find her.
The complication is that Daphne is white, but likes to hang around with black men so DeWitt wants to pay Easy $100 upfront to locate her. He says that Easy could be more inconspicuous in black nightclubs than he would be. Easy has no idea why he is picked but needs the money so he takes the job. Along the way he runs into Coretta James (Lisa Nicole Carson) who claims to know Daphne. Soon however people start getting killed and the police want to pin the murders on Easy. The mystery is who is doing it and why. Is it Todd Carter? Is it Daphne Monet? How about the other mayoral candidate Matthew Terrell (Maury Chaykin from UNSTRUNG HEROES)? Perhaps it is Easy after all? Or could it be DeWitt? A police frame-up? A mysterious stranger? Who?
I thought the script by the director Carl Franklin, based on the novel of the same name by Walter Mosely, was too much of a period and mood piece and too little of a mystery. Although I did not guess the outcome, I did not try nor care much. I liked the show because of Denzel Washington's acting and not the totally forgettable mystery. There were several memorable lines. One of my favorites was Easy's philosophy: "A man once told me that you step out of your door in the morning, and you are already in trouble. The only question is 'are you on top of that trouble or not?'" The dialog as delivered, on the other hand, was frequently so thick that you almost needed subtitles to understand it.
As much as I liked Easy, there is a minor character named Mouse (Don Cheadle) that I liked even more.  He is a firecracker with a short fuse that may explode at any moment, and yet he is extremely funny. One of my favorite lines of his is: "You said don't shot him, right? Well I didn't; I strangled him. If you didn't want me to kill him, why did you leave me alone with him?"
The director Carl Franklin also did ONE FALSE MOVE which was much more innovative and daring. In comparison, DEVIL IN A BLUE DRESS is a nice little formula picture. It has some excellent performances in it by Cheadle and Washington, but the script rarely draws in the audience and the less said about the pathetic non-acting by Jennifer Beals, the better. Perhaps Beals should just sign up to do remakes of FLASHDANCE for the rest of her life since that is the only decent movie she ever made. Oh yes, the music by that old movie music master Elmer Berstein is effective as always. Of the over one hundred and fifty movies he has scored, my favorite of his remains THE TEN COMMANDMENTS.
DEVIL IN A BLUE DRESS runs 1:42 and is in need of better editing by Carol Kravetz. Kravetz seems to have fallen asleep at the cutting board as she lets too many scenes drag on. The movie is rated R for violence, profanity, and sex, but no nudity. All of these are handled quite tastefully, and I would have no problem in letting teenagers see the film. I give the movie a thumbs up and award it ** 1/2.
**** = One of the top few films of this or any year. A must see film. *** = Excellent show. Look for it. ** = Average movie. Kind of enjoyable. * = Poor show.  Totally unbearable.
REVIEWED WRITTEN ON: September 20, 1995
Opinions expressed are mine and not meant to reflect my employer's.
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.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


--- layout: post title: "The Third Sunday of Lent" date: 2021-03-07 09:00:00 categories: Sunday tags: featured image: /assets/article_images/2020-04-26-Third-Sunday-Easter/scripture.jpg image2: /assets/article_images/2020-04-26-Third-Sunday-Easter/scripture-mobile.jpg ---
Holy Mass is at the very core of our worship as a Catholic community. Nothing can ever be substituted for the celebration of the Eucharist. But in this exceptional time of difficulty for our communities, our nation, and the whole world, this order of prayer can be offered by individuals and families and engages us in our Sunday celebration of the Word of God. We can pray this prayer in our homes—the “domestic churches” which help build up the Body of Christ on earth—and thus be connected to the entire Church at prayer.
During this time of prayer, we include the opportunity to make a Spiritual Communion. Making a Spiritual Communion is a worthy practice, recommended by innumerable saints throughout the ages, which unites our whole selves to God and his Church.
Taken from the Mass and the Liturgy of the Hours
Leader: O God, ✠ come to my assistance.
Response: O Lord, make haste to help me.
Leader: Glory be to the Father, and to the Son, and to the Holy Spirit:
Response: as it was in the beginning, is now, and ever shall be, world without end. Amen.
Lord, who throughout these forty days
For us did fast and pray,
Teach us to overcome our sins,
And close by you to stay.
As you with Satan did contend
And did the vict’ry win,
O give us strength in you to fight,
In you to conquer sin.
As you did hunger and did thirst,
So teach us, gracious Lord,
To die to self, and only live
By your most holy word.
And through these days of penitence,
And through your Passiontide,
For evermore, in life and death,
O Lord, with us abide.
Abide with us that, when this life
Of suffering is past,
An Easter of unending joy
We may attain at last!
Leader: Let us acknowledge our sins, and seek the Lord’s pardon and peace.
Together: I confess to almighty God
and to you, my brothers and sisters,
that I have greatly sinned,
in my thoughts and in my words,
in what I have done and in what I have failed to do,
through my fault, through my fault,
through my most grievous fault;
therefore I ask blessed Mary ever-Virgin,
all the Angels and Saints,
and you, my brothers and sisters,
to pray for me to the Lord our God.
Leader: O God, author of every mercy and of all goodness,
who in fasting, prayer and almsgiving
have shown us a remedy for sin,
look graciously on this confession of our lowliness,
that we, who are bowed down by our conscience,
may always be lifted up by your mercy.
Through our Lord Jesus Christ, your Son,
who lives and reigns with you in the unity of the Holy Spirit,
God, for ever and ever.
Together: Amen.
USCCB Audio Recording of Sunday Readings
In those days, God delivered all these commandments: “I, the LORD, am your God, who brought you out of the land of Egypt, that place of slavery. You shall not have other gods besides me. You shall not carve idols for yourselves in the shape of anything in the sky above or on the earth below or in the waters beneath the earth; you shall not bow down before them or worship them.
“You shall not take the name of the LORD, your God, in vain. For the LORD will not leave unpunished the one who takes his name in vain. “Remember to keep holy the sabbath day. Six days you may labor and do all your work, but the seventh day is the sabbath of the LORD, your God. No work may be done then either by you, or your son or daughter, or your male or female slave, or your beast, or by the alien who lives with you. In six days the LORD made the heavens and the earth, the sea and all that is in them; but on the seventh day he rested. That is why the LORD has blessed the sabbath day and made it holy.
“Honor your father and your mother, that you may have a long life in the land which the LORD, your God, is giving you. You shall not kill.  You shall not steal. You shall not bear false witness against your neighbor. You shall not covet your neighbor’s house.
The Word of the Lord.
―Thanks be to God.
Reader: Lord, you have the words of everlasting life.
Together: Lord, you have the words of everlasting life.
Reader: The law of the LORD is perfect,
refreshing the soul;
The decree of the LORD is trustworthy,
giving wisdom to the simple.
Together: Lord, you have the words of everlasting life.
Reader: The precepts of the LORD are right,
rejoicing the heart;
the command of the LORD is clear,
enlightening the eye.
Together: Lord, you have the words of everlasting life.
Reader: The fear of the LORD is pure,
enduring forever;
the ordinances of the LORD are true,
all of them just.
Together: Lord, you have the words of everlasting life.
Reader: They are more precious than gold,
than a heap of purest gold;
sweeter also than syrup
or honey from the comb.
Together: Lord, you have the words of everlasting life.
Brothers and sisters: Jews demand signs and Greeks look for wisdom, but we proclaim Christ crucified, a stumbling block to Jews and foolishness to Gentiles, but to those who are called, Jews and Greeks alike, Christ the power of God and the wisdom of God. For the foolishness of God is wiser than human wisdom, and the weakness of God is stronger than human strength.
The word of the Lord.
―Thanks be to God.
Since the Passover of the Jews was near, Jesus went up to Jerusalem. He found in the temple area those who sold oxen, sheep, and doves, as well as the money changers seated there. He made a whip out of cords and drove them all out of the temple area, with the sheep and oxen, and spilled the coins of the money changers and overturned their tables, and to those who sold doves he said, “Take these out of here, and stop making my Father’s house a marketplace.” His disciples recalled the words of Scripture, Zeal for your house will consume me. At this the Jews answered and said to him, “What sign can you show us for doing this?” Jesus answered and said to them, “Destroy this temple and in three days I will raise it up.” The Jews said, “This temple has been under construction for forty-six years, and you will raise it up in three days?” But he was speaking about the temple of his body. Therefore, when he was raised from the dead, his disciples remembered that he had said this, and they came to believe the Scripture and the word Jesus had spoken.
While he was in Jerusalem for the feast of Passover, many began to believe in his name when they saw the signs he was doing. But Jesus would not trust himself to them because he knew them all, and did not need anyone to testify about human nature. He himself understood it well.
The Gospel of the Lord.
―Praise to you, Lord Jesus Christ
Leader: Christ, Son of the living God, have mercy on us.
Response: Christ, Son of the living God, have mercy on us.
Leader: You were wounded for our offenses,
Response: have mercy on us.
Leader: Glory to the Father, and to the Son and to the Holy Spirit.
Response: Christ, Son of the living God, have mercy on us.
Leader: Let us praise our loving Redeemer, who gained for us this season of grace,
and pray to him, saying:
Response: Lord, create a new spirit in us.

Response: Lord, create a new spirit in us.
Leader: Lord, you have brought blessings to all mankind, bring us to share your
concern for the good of all.
Response: Lord, create a new spirit in us.
Leader: May we work together to build up the earthly city, with our eyes fixed on
the city that lasts for ever.
Response: Lord, create a new spirit in us.
Leader: Healer of body and soul, cure the sickness of our spirit, so that we may
grow in holiness through your constant care.
Response: Lord, create a new spirit in us.
Leader: In your mercy, continue to sanctify all who are afflicted from the present pandemic, and draw us all closer to you,
and to one another, in this time of suffering.
Response: Lord, create a new spirit in us.
Leader: Let us pray together as Jesus taught us:
Together: Our Father, who art in heaven …
Leader: You have given us bread from heaven,
Response: Containing in itself all delight
Together: My Jesus,
I believe that you are present
in the Most Holy Sacrament of the Altar.
I love you above all things,
and I desire to receive you into my soul.
Since I cannot at this moment receive you sacramentally,
come at least spiritually into my heart.
I embrace you as if you were already there
and unite myself wholly to you.
Never permit me to be separated from you,
my Lord and my God!
Amen.
Canticle of Zechariah (Luke 1:68-79)
Together: ✠ Blessed be the Lord, the God of Israel;
he has come to his people and set them free.
He has raised up for us a mighty savior,
born of the house of his servant David.
Through his holy prophets he promised of old
that he would save us from our enemies,
from the hands of all who hate us.
He promised to show mercy to our fathers
and to remember his holy covenant.
This was the oath he swore to our father Abraham:
to set us free from the hands of our enemies,
free to worship him without fear,
holy and righteous in his sight
all the days of our life.
You, my child, shall be called the prophet of the Most High;
for you will go before the Lord to prepare his way,
to give his people knowledge of salvation
by the forgiveness of their sins.
In the tender compassion of our God
the dawn from on high shall break upon us,
to shine on those who dwell in darkness and the shadow of death,
and to guide our feet into the way of peace.
Glory be to the Father, and to the Son,
and to the Holy Spirit:
as it was in the beginning, is now, and ever shall be,
world without end. Amen.
Leader: ✠ May the Lord bless us, protect us from all evil,
and bring us to everlasting life.
Together: Amen.
Hymn - “Lord, Who Throughout These Forty Days” Tune: ST FLAVIAN, arranged by Tony Alonso. Published by GIA Publications, copyright 2017. All rights reserved. Recorded by Dan Klocke. Music printed with permission granted by OneLicense.net #A-705112.
Excerpts from The Roman Missal © 2010, ICEL; excerpts from The Liturgy of the Hours © 1994, ICEL. All rights reserved.
Lectionary for Mass for Use in the Dioceses of the United States, second typical edition, Copyright © 2001, 1998, 1997, 1986, 1970 Confraternity of Christian Doctrine; Psalm refrain © 1968, 1981, 1997, International Committee on English in the Liturgy, Inc. All rights reserved. Neither this work nor any part of it may be reproduced, distributed, performed or displayed in any medium, including electronic or digital, without permission in writing from the copyright owner.


| | |
- sm.eod.EODError
-
- EngineOutputDataFileError
- SerialPortError
- SocketError
- CompressingFileWriter
- DecompressingFileReader
- EngineOutputDataFileReader
- EngineOutputDataFileWriter
- FileReader
- FileWriter
class CompressingFileWriter |
| |
Allows writing compressed data to a log file. |
| |
Methods defined here:
- __init__(self, file_path=None)
- Constructs and optionally opens a log file for writing.
This method may raise the following exceptions:
- SerializableError An error occured while trying to open the file.
- close(self)
- Close the file if open.
- isOpen(self)
- Returns True if file is currently opened.
- write(self)
- Writes the object to the file.
This method may raise the following exceptions:
- SerializableError An error occured while writing to file.
close() will have been automatically called.
|
class DecompressingFileReader |
| |
Allows sequential access to a log file containing compressed objects. |
| |
Methods defined here:
- __init__(self, arg=None)
- Initializes the reader
The arguments are:
- arg If None then the reader is un-opened.
If a string then the file is opened and if
that fails a SerializationError is raised.
If it is a SerializableCompressorDecoderFactory object
then the reader is un-opened but will use the decoder factory
for deserialization.
This method may raise the following exceptions:
- SerializableError Failed to open file for reading.
- atEnd(self)
- Returns True if there is no more data to be read.
- close(self)
- Close the file if open.
- isOpen(self)
- Returns True if file is currently opened.
- read(self)
- Reads the next Serializable object from the file.
This method may raise the following exceptions:
- SerializableError An error occured while reading from the file.
close() will have been automatically called.
- setDecoderFactory(self, decoder_factory)
- Sets the decoder factory to use for decompressing data.
The arguments are:
- decoder_factory The decoder factory to use for decompresing.
This method may raise the following exceptions:
- invalid_argument isOpen() is not False.
|
class EngineOutputDataFileReader |
| |
Reads log files. |
| |
Methods defined here:
- __init__(self)
- Initializes the reader
- areFilesOpen(self)
- Returns True if files are currently opened.
- closeFiles(self)
- Closes any open files.
- endOfData(self)
- Returns True if there is more data to be read.
- openFiles(self, objects_to_read_or_dir, dir)
- Opens all the '.fll' files in the specified directory for reading.
The arguments are:
- objects_to_read_or_dir A VectorInt8 containing the object IDs of the objects
to read, or a directory path to read all object from.
- dir The directory to read log files from if objects_to_read_or_dir
is a VectorInt8 instance.
This method may raise the following exceptions:
- EngineOutputDataFileError No log files in dir, dir is not a directory,
or a log file could not be opened for reading.
- readData(self)
- Assemble an EngineOutputData object from the log files.
Reads an EngineOutputData from each file, and attaches any sub-objects from it that
have a corresponding entry in the _objects_to_read vector to a new
EngineOutputData.
If the ends of the files have ben reached, None is returned.
This method may raise the following exceptions:
- EngineOutputDataFileError A log file did not contain an EngineOutputData
top level object. closeFiles() will have been
automatically called.
- SerializableError An error occured while reading from the files,
or, duplicate object IDs were found in different
log files. closeFiles() will have been automatically
called.
|
class EngineOutputDataFileWriter |
| |
Writes log files. |
| |
Methods defined here:
- __init__(self)
- Initializes the writer.
- areFilesOpen(self)
- Returns True if files are currently opened.
- closeFiles(self)
- Closes any open files.
- newFiles(self, log_file_path, file_buffer_size=32768)
- Open a new logfile for writing.
The arguments are:
- log_file_path The path to a file to write to.
- file_buffer_size The size of the file buffer.
This method may raise the following exceptions:
- EngineOutputDataFileError An error occurred while opening the files.
- objectIDToFileName(object_id)
- Returns the filename that represents the data specified by object_id.
The arguments are:
- object_id The object ID to get the filename for.
The currently known object IDs are:
- TimingOutputData "Timing"
- HeadOutputData "Head"
- HeadOutputDataV2 "HeadV2"
- EyeOutputData "Eye"
- FeatureSetsByCamera "Features"
- FaceOutputData "Face"
- FaceOutputDataV2 "FaceV2"
- WorldOutputData "World"
- WorldOutputDataV2 "WorldV2"
- EngineStateOutputData "EngineState"
- InertiaOutputData "Inertia"
- CustomEventOutputData "Custom_Events"
- GPSOutputData "GPS"
- GPSOutputDataV2 "GPSV2"
This method may raise the following exceptions:
- EngineOutputDataFileError The object ID is not recognized.
- writeData(self, label)
- Writes data to the open logfiles.
The arguments are:
- data The EngineOutputData instance to write.
Each top-level sub object that has serializeEnabled() == True
and corresponds to an open logfile is extracted
from data and written to file.
|
class FileReader |
| |
Reads log files. |
| |
Methods defined here:
- __init__(self, file_path=None)
- Initializes the reader
- atEnd(self)
- Returns True if there is no more data to be read.
- close(self)
- Close the file if open.
- isOpen(self)
- Returns True if file is currently opened.
- read(self)
- Reads the next Serializable object from the file.
Returns a 2-tuple of (Serialiable,int)
|
class FileWriter |
| |
Reads log files. |
| |
Methods defined here:
- __init__(self, file_path=None)
- Initializes the writer
- close(self)
- Close the file if open.
- isOpen(self)
- Returns True if file is currently opened.
- write(self)
- Writes the object to file if serializableEnabled()
This method may raise the following exceptions:
- SerializableError An error occured while writing to file.
close() will have been automatically called.
- writeNoThrow(self)
- Writes the object to file if serializableEnabled()
The arguments are:
- object The Serializable derived object to write.
Returns True if no error occurred. If False is returned
the writer is automatically closed.
|
class SerialPortError(sm.eod.EODError) |
| |
Raised when an error occurs dealing with the serial port. |
| |
Methods defined here:
- __init__(self, message, timeout=False)
- Initializes the error object with a message and timeout flag.
The arguments are:
- message Message describing error.
- timeout Flag indicating error is due to a timeout.
- timeout(self)
- Returns true if the error was due to timeout on read or write
Methods inherited from sm.eod.EODError:
- className(self)
- Returns the name of the class as a string.
|
|


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Select any file which has links (a href) in it.
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//demo input
st=>start: Entry:>http://www.bing.com[blank]
e=>end:>http://www.bing.com
op1=>operation: My Operation
sub1=>subroutine: My Subroutine
cond=>condition: Yes
or No?:>http://www.bing.com
io=>inputoutput: catch something...
para=>parallel: parallel tasks
st->op1->cond
cond(yes)->io->e
cond(no)->para
para(path1, bottom)->sub1(right)->op1
para(path2, top)->op1
|
This should be a diagram! If you do not see it you need Javascript enabled |


About RustSec
The RustSec Advisory Database is a repository of security advisories filed against Rust crates published via https://crates.io maintained by the Rust Secure Code Working Group.
{% extends "base.html" %}
{% block title %}About RustSec{% endblock %}
{% block content %}
The RustSec Advisory Database
is a repository of security advisories filed against Rust crates published
via https://crates.io maintained by
the Rust Secure Code Working Group.About RustSec
Cargo.lock
files for crates with security vulnerabilities.
To report a new vulnerability for a Rust crate, open a pull request against the RustSec Advisory Database.
{% endblock %}


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Exponential advances in throughput of single cell genomics technologies have led to a watershed moment in developmental biology. We can now feasibly and economically sequence millions of cells from thousands of specimens, which allows us to interrogate the effects of diverse genetic, chemical, and environmental perturbations on embryonic development. In effect, we can study gene regulation across individuals at whole-embryo scale and single-cell resolution to see how a perturbation impacts every gene, in every cell type, at every stage of its development.
We are working to systematically perturb zebrafish embryonic development and read out the effects using massively multiplexed single-cell molecular profiling. These experiments will not only facilitate the mapping of the gene networks that control vertebrate development, but also will shed light on how those networks buffer variability and confer robustness. Such a map would dramatically accelerate efforts to understand the molecular mechanisms that determine severity in a wide range of human developmental disorders.


Now that you have and idea of how different elements combine to create a plot, I’ll walk you through the steps to “layer” these elements together to create these two example plots.
First, you’ll need to bring the example data into your R session. If you followed the steps in the “Prerequisites” section, you should have a comma-separated file8 comma-separated file format. A flat file format (try opening the file in a plain text editor—unlike a binary file format like Excel, you should be able to read all the content) where each column entry is separated by a comma. This is a common file format for tabular data that can be read into and written from most statistical programs (including R, Excel, and SAS). in a “data” subdirectory of the R Project you downloaded. Also, if you followed the steps in the “Prerequisites” section, you should have installed all the necessary R packages for this example.
While there are functions in base R that import comma-separated files, I think the
functions in the readr package (Wickham, Hester, and Francois 2018Wickham, Hadley, Jim Hester, and Romain Francois. 2018. Readr: Read Rectangular Text Data. https://CRAN.R-project.org/package=readr.), a package in the tidyverse,
has some nicer defaults. To use functions in this package, you first need to load
it into your R session using the library
function:
library("readr")
The library
function must specify the name of the package you’d like to load (in
this case, “readr”). If you have forgotten to install the “readr” package before you
run this function, you’ll get the message:
Error in library(readr) : there is no package called ‘readr’
If you get that message, go back and re-read the “Prerequisites” to make sure you’ve
installed all the required packages.9 There is also a small chance, especially if
you’re using a computer for which you don’t have superuser privileges, that something
else is going on. Double-check that you installed the package you’re trying to load,
and if loading the package still doesn’t work, talk with your IT team about
installing and loading R packages on your computer. For some company- or
government-issued computers, the computer might have been set up to restrict
installing new software, or to save installed software somewhere other than the
default locations where R searches with library
.
Now that you’ve loaded readr
, you can use the package’s read_csv
function10 For
this and any other R function, you can open a help file in RStudio to give you
more information about the function, usually including some examples of how to
use if. Just type ?
and then the function name—for this example, ?read_csv
.
to read in the data. The one argument this function requires is the
file path11 file path. The directions for where to find a file within your
computer’s directory structure. If the file you want is in your current working
directory, this will just be the filename of the file. If it’s not, but in a directory
close-by, you can use a relative pathname to give the directions from the current
working directory. Since the file you want to read in is in a subdirectory called
“data” of the current working directory, the filepath here is
“data/daily_fatalities.csv”.
to the data file.
daily_fatalities <- read_csv("data/daily_fatalities.csv")
## Parsed with column specification:
## cols(
## date = col_date(format = ""),
## week = col_double(),
## weekday = col_character(),
## fatals = col_double()
## )
Don’t be alarmed by the message that’s printed out after you run the function!
In data frames in R, each columns can have one of several different classes
(some examples: character: “Florida”, date: 2017-09-10, integer: 17,
double: 17.0). The read_csv
function looks at the values in each column in the
data and tries to guess what class each column should be, and this message
tells you what it guessed, so you can check. In this case, the number of
fatalities will always be a whole number, so the “integer” class would have
also worked well, but there should be no problem with the column having a
“double” class for the plotting we’ll be doing, so everything looks fine.
The previous code used a gets arrow12 gets arrow. The function
<-
, which allows you to save the output from running the code on the right-hand
side of the arrow to an object with the name on the left of the arrow. This is a
funny kind of function called an infix function, which goes between two
function arguments instead of putting the arguments inside parentheses. If you
want to look up the helpfile for an infix function, wrap it in backticks:
?`<-`
. to save the data you read in to an R object called daily_fatalities
.
Now, anytime you want to use this data, you can reference it with the name
daily_fatalities
instead of needing to read it in again. For example, you
can print out the start of the data by calling the object name by itself:
daily_fatalities
## # A tibble: 28 x 4
## date week weekday fatals
## <date> <dbl> <chr> <dbl>
## 1 2017-08-27 35 Sunday 4
## 2 2017-08-28 35 Monday 5
## 3 2017-08-29 35 Tuesday 6
## 4 2017-08-30 35 Wednesday 6
## 5 2017-08-31 35 Thursday 6
## 6 2017-09-01 35 Friday 9
## 7 2017-09-02 35 Saturday 8
## 8 2017-09-03 36 Sunday 15
## 9 2017-09-04 36 Monday 7
## 10 2017-09-05 36 Tuesday 8
## # … with 18 more rows
By default, read_csv
reads the data into a structure called a tibble,13 tibble.
A slightly fancier dataframe, which is a tabular data format in R, where there are
rows and columns, with each column having data with the same class (e.g., character,
integer, date) and all columns having the same length. Values in the columns should
“line up” across the rows—for example, in this case, the second value in the
fatals
column should be the number of fatalities for the date given by the second
value in the date
column. Compared to a dataframe, a tibble prints out more nicely
when you call the object name alone, among some other advantages. and you
can see that the top of the print-out notes this. It also gives the dimensions
(28 rows and 4 columns) and, under each column name, the class of the column.
When you start plotting this data, you’ll use the dataframe’s object name
(daily_fatalities
) to reference the full dataset and each column name
(e.g., date
for the date of the observations, fatals
for the number of
fatalities observed on that date) to reference specific elements of the data.
To create a data visualization using ggplot2
, we’ll add up “layers” for each of
the plot elements described earlier. In this section, I’ll step through this process.
First, we’ll create a ggplot object using the ggplot
call, and then we’ll add
layers to it with +
. To use these functions, you’ll need to load the ggplot2
package:
library("ggplot2")
For the first step, create the ggplot
object. When you create this object, specify
the dataframe with the data you’d like to plot using the data
parameter:
ggplot(data = daily_fatalities)
Figure 2.3 shows the output with this single, initial layer. We haven’t added any geoms yet, so the plot isn’t showing anything. Since we’ve specified the
data, however, we’ll be able to add geoms where we map aesthetics to columns in the
dataset. Let’s do that next, and add a layer with a line (geom_line
) for the number
of fatalities per day. We’ll use the aes
function inside the geom_line
call
to specify that we want the x-axis to show the value in the date
column and the
y-axis to show the value in the fatals
column:
ggplot(data = daily_fatalities) +
geom_line(aes(x = date, y = fatals))
The result is in Figure 2.4. You can see that you now have a line showing
the number of fatalities per day. Next, we can add a layer on top of the line with a
point (geom_point
) for each date showing the number of fatalities. Two of the
aesthetics for this geom (x and y) will be the same as for the line. However, we
also want to map color to day of the week. Since day of the week is in a column
called weekday
, we can specify this aesthetic as color = weekday
within the aes
call. Try running the following code to add this layer:
ggplot(data = daily_fatalities) +
geom_line(aes(x = date, y = fatals)) +
geom_point(aes(x = date, y = fatals, color = weekday))
You should get the plot shown in Figure 2.5. You may have noticed that the set of colors that is used for weekdays is different than in the plot in Figure 2.1. This set of colors is actually specified by the “scale” element of the plot, so we’ll change that in a different layer.
So far, we’ve customized some of the plot aesthetics by mapping them so that their values are based on observations in the data. However, sometimes you’ll want to change an aesthetic to a constant, where the aesthetic changes for the aesthetic for all of the observations from the data, but in the same way.
For example, for this plot, we’d like the line to be dark gray for all observations,
and we’d like the points to be a little bigger. To specify a constant aesthetic,
move it outside of the aes
call. For colors, you can set a constant value to
one of the many named “R Colors”14 See this website for examples and names
(make sure you put the color name inside parentheses—otherwise, R will think you’re
refering to an R object). Constant point size values can be specified using numbers,
where larger numbers will make bigger points and the default value is somewhere
around 1.15 For
an in-depth look at aesthetic specifications, see
the ggplot2 specs vignette Try numbers bigger than 1 (e.g., 1.5, 2) for bigger points and smaller
values (e.g., 0.8, 0.5) for smaller points.
The following code will set the line to be dark gray and the points to be a bit larger (result in Figure 2.6):
ggplot(data = daily_fatalities) +
geom_line(aes(x = date, y = fatals), color = "darkgray") +
geom_point(aes(x = date, y = fatals, color = weekday), size = 2)
We’re using the default coordinate system for this plot, so we don’t need to add
a layer for the coordinate system. For plots where you need to change from this
default coordinate system, you’ll add a layer that starts with coord_
. For example,
in the “Map” section, you’ll see how to use a geographic coordinate system using
coord_map
. If you want to flip your x- and y-axis (there are a few examples where
this is useful), you can add a layer with coord_flip
.
While we’re also using the default scales for the x aesthetic, we aren’t for
the y or color aesthetic. The change to the y scale is very minimal: we’re just
expanding it to include 0. This can be done by adding the layer expand_limits
with the y
parameter set to 0.
To change the color scale, you need to add a layer to specify the alternative color scale. We’ll use a color scale called “viridis”. This is good for discrete data (like here, where we’re showing day of the week rather than a continuous number), it really shines when you’re plotting continuous values. The order of the scale is clear to those who are colorblind, and it’s also clear when printed out on a black-and-white printer.
To change the color scale to use this scale, add a layer with scale_color_viridis_d
.
The “d” here is for “discrete”; if you were using color to show a continuous
value in the data (e.g., a column with an integer or double class), you’d
add a layer called scale_color_viridis_c
instead. The final result of these
scale customizations is shown in Figure 2.7.
ggplot(data = daily_fatalities) +
geom_line(aes(x = date, y = fatals), color = "darkgray") +
geom_point(aes(x = date, y = fatals, color = weekday), size = 2) +
expand_limits(y = 0) +
scale_color_viridis_d()
The function for this layer has a few options for customization. For example, try
changing it to scale_color_viridis_d(option = "A")
or
scale_color_viridis_d(direction = -1)
. To see all its options, check its
helpfile with ?scale_color_viridis_d
.
Next, we’ll change the scale labels. By default, the label for each scale is the
name of the column in the data that the aesthetic was mapped to (x: “date”,
y: “fatals”, color: “weekday”). You can add a labs
layer to change these
to labels that are easier to understand. The other
labeling we’d like to do is to add a title and subtitle, which we can do with
a ggtitle
layer (the subtitle is added with the sub
parameter of this layer).
The final result of adding these layers is shown in Figure 2.8.
ggplot(data = daily_fatalities) +
geom_line(aes(x = date, y = fatals), color = "darkgray") +
geom_point(aes(x = date, y = fatals, color = weekday), size = 2) +
expand_limits(y = 0) +
scale_color_viridis_d() +
labs(x = "Date", y = "# of fatalities", color = "Day of week") +
ggtitle("Motor vehicle fatalities in Florida",
subtitle = "Late summer / early fall of 2019")
The last layer we need to add is a theme layer. While we’re using most of the
elements from the default theme (theme_gray
), we do want to change the position
of the legend. For a time series plot like this, the change can be helpful, as it
lets us create a plot that’s much wider than it is tall. You can move the legend
using the theme
layer with an argument specified for legend.position
. With
the theme
function, you can customize almost any of the background elements of a
plot.16 See the helpfile for “theme” with ?theme
for a full listing.
However, you’ll usually only want to do that for a few elements—if you want
to change a lot of elements, there is a set of functions that start theme_
that
will let you change to one of several “themes” that change many elements at once
(as I’ll show in the next example).
If you find you’re often using theme
to specify lots of elements by hand, you
can create your own theme_*
function (fill in *
with the name of your choice!).
ggplot(data = daily_fatalities) +
geom_line(aes(x = date, y = fatals), color = "darkgray") +
geom_point(aes(x = date, y = fatals, color = weekday), size = 2) +
expand_limits(y = 0) +
scale_color_viridis_d() +
labs(x = "Date", y = "# of fatalities", color = "Day of week") +
ggtitle("Motor vehicle fatalities in Florida",
subtitle = "Late summer / early fall of 2019") +
theme(legend.position = "bottom")
As one final detail, if you were looking very closely, you may have noticed that the order of the days of week are different in the original plot from our final version here. That’s because, by default, discrete character-class values are ordered alphabetically. We can change this order, and should, to be the order that week days occur. To do this, we need to change the column class to a factor class17 factor class. An R class for values that take character names, but that describe categories, for which you expect values to show up more than once in your data. In this data, the day of week is an example, since it is expressed as a character variable (“Monday”, “Tuesday”, etc.), but we expect there to be multiple observations with, e.g., “Monday”. If you have a varible that you expect to be unique (e.g., name of study subjects, unique ID number), the column should be in a character, not a factor, class. In R, each possible catogory for a factor is called a level. You can change the order of the levels of a factor, and this will change the order they’re shown on a plot. and then change the order of that factor’s levels.
To do this, we’ll need to load a few additional R packages (you’ll also use these
in the “Tidy” section, which has much more on manipulating datasets). The
forcats package (Wickham 2019Wickham, Hadley. 2019. Forcats: Tools for Working with Categorical Variables (Factors). https://CRAN.R-project.org/package=forcats.) has functions for working with factors, including
a function we can use to reorder the factor levels. The magrittr package
(Bache and Wickham 2014Bache, Stefan Milton, and Hadley Wickham. 2014. Magrittr: A Forward-Pipe Operator for R. https://CRAN.R-project.org/package=magrittr.) includes two infix functions we’ll use to make the code
cleaner: the pipe operator (%>%
) and the compound assignment pipe operator (%<>%
).
The dplyr package (Wickham, François, et al. 2019Wickham, Hadley, Romain François, Lionel Henry, and Kirill Müller. 2019. Dplyr: A Grammar of Data Manipulation. https://CRAN.R-project.org/package=dplyr.) includes a number of simple but powerful functions
for manipulating tibbles.
The resources listed in the “Learn More” part of the Tidy" section go into detail for how
to use all these functions. Briefly, the mutate
function used twice to change the
values in the “weekday” column:
first, to convert the class of the column to a factor (as_factor
) and second to
change the order of those levels (fct_relevel
) by hand, to start with Monday and
go in order through Sunday. The compound assignment pipe operator (%<>%
) allows
us to perform all those operations to columns inside the “daily_fatalities” tibble,
and then to save the result back to the same R object (overwriting the earlier
version of the tibble).
library("forcats")
library("magrittr")
library("dplyr")
daily_fatalities %<>%
mutate(weekday = as_factor(weekday)) %>%
mutate(weekday = fct_relevel(weekday,
"Sunday", "Monday", "Tuesday", "Wednesday",
"Thursday", "Friday", "Saturday"))
Now when we run the same ggplot
code, you can see that the order of the days of
week in the color legend, and the order the colors are assigned to week day, have
changed (Figure 2.10).
ggplot(data = daily_fatalities) +
geom_line(aes(x = date, y = fatals), color = "darkgray") +
geom_point(aes(x = date, y = fatals, color = weekday), size = 2) +
expand_limits(y = 0) +
scale_color_viridis_d() +
labs(x = "Date", y = "# of fatalities", color = "Day of week") +
ggtitle("Motor vehicle fatalities in Florida",
subtitle = "Late summer / early fall of 2019") +
theme(legend.position = "bottom")
The second example plot we can go through much more quickly. There are two
elements I’d like to point out: the layer for faceting and the layer for the theme.
The facet_wrap
function separates the plot into small multiples
based on the values in a column of the data. By default, all the scales (e.g., x-axis,
y-axis) will be the same across all the small multiples, allowing for an easier
comparison across the plots. The other layers in this code should now be somewhat
familiar to you: the ggplot
call initializes a ggplot
object with the
“daily_fatalities” dataset, just like in the last plot, while the geom_col
layer
adds a column geom where the x aesthetic is mapped to the value in the “weekday”
column and the y aesthetic is mapped to the value in the “fatals” column.
To change to the theme_classic
, all you need to do is add a theme_classic
layer
(Figure 2.12).
Most of the rest of the layers for the plot are very similar to the first plot.
They include changing the fill18 fill aesthetic. While all geoms have
a color aesthetic, some also have a fill aesthetic. These include columns,
bars, polygons, and points with certain shapes. In these cases, ‘color’ will specify
the outline of the geom while ‘fill’ will specify the inside. Each geom has a set of
required aesthetics and a set of possible aesthetics. To find each set for a geom,
go to its helpfile (e.g., ?geom_col
) and scroll down to the “Aesthetics” section.
of the geom by mapping it to a constant fill
aesthetic (color = "skyblue"
in the geom_col
call), customizing the labels with
a lab
layer, and adding a title with ggtitle
. The result of adding these layers
is shown in Figure 2.13
ggplot(data = daily_fatalities) +
geom_col(aes(x = weekday, y = fatals), fill = "skyblue") +
facet_wrap(~ week, ncol = 1) +
theme_classic() +
labs(x = "", y = "# of fatalities") +
ggtitle("Motor vehicle fatalities in Florida by weekday, 2019")
As with the first graph, to take the plot to its final stage, you’ll need to
change the input data a bit. In this case, this step is to create clearer labels for
the facets. The week number (e.g., 35) won’t mean much to most viewers. A label giving
the date of the first day in the week (“Week of August 27”) will be more helpful.
To create these new labels, you can use group_by
, mutate
, and first
functions
from the dplyr
package (which you have already loaded if you’ve worked through
the examples in order) to create a column called ‘first_day’ with the date of the
first day in each week. Then you can use mutate
(also from dplyr
) to create the
‘week_label’ column from the existing columns of the tibble. This code uses the
month
and day
functions from the lubridate
package (very useful for
working with dates) (Spinu, Grolemund, and Wickham 2018Spinu, Vitalie, Garrett Grolemund, and Hadley Wickham. 2018. Lubridate: Make Dealing with Dates a Little Easier. https://CRAN.R-project.org/package=lubridate.) to extract the month and day from the ‘first_day’
dates, and then paste
(from base R) pastes these together with “Week of”.
Finally, the as_factor
and fct_reorder
calls (both from forcats
, which you
also should have loaded if you’ve followed the examples in order) get this new
column in the right order, so that the facets show up in temporal order rather
than alphabetical.
Once you’ve made these changes to the ‘daily_fatalities’ tibble, you should get the final version of the plot when you re-run the plotting code used in the last step, getting Figure 2.14.
library("lubridate")
daily_fatalities %<>%
group_by(week) %>%
mutate(first_day = first(date)) %>%
ungroup() %>%
mutate(week_label = paste("Week of",
month(first_day, label = TRUE, abbr = FALSE),
day(first_day))) %>%
mutate(week_label = as_factor(week_label),
week_label = fct_reorder(week_label, week, .fun = min))
ggplot(data = daily_fatalities) +
geom_col(aes(x = weekday, y = fatals), fill = "skyblue") +
facet_wrap(~ week_label, ncol = 1) +
labs(x = "", y = "# of fatalities") +
theme_classic() +
ggtitle("Motor vehicle fatalities in Florida by weekday, 2019")


|
Figure 1
|
Unit cell of B20 (FeSi-type) crystal structure. Red and gree ... |
|
Figure 2
|
Band structure of CrSi, MnSi, FeSi, and CoSi with B20 (P2 1 ... |
|
Figure 3
|
Band structure of TcSi, RuSi, and RhSi with B20 (P2 1 3, #19 ... |
|
Figure 4
|
Band structure of ReSi and OsSi with B20 (P2 1 3, #198) crys ... |
|
Figure 5
|
Total and projected density of states in FeSi, RuSi, and OsS ... |
|
Figure 6
|
Band structure and surface Fermi arcs in CoSi. |
|
Figure 7
|
Band structure and surface Fermi arcs in RhSi. |
|
Figure 8
|
Band structure and surface Fermi arcs in CoGe. |
|
Figure 9
|
Band structure and surface Fermi arcs in RhGe. |
|
Figure 10
|
Band structure and surface Fermi arcs in RhSn. |
|
Figure 11
|
Constant-energy surfaces of valence ( left) and conduction b ... |
|
Figure 12
|
Room temperature Seebeck coefficients of considered transiti ... |


--- layout: post title: Mashing up a Rails app with Google Maps categories: - launches author: admin published: true date: 2005-05-17 18:44:26.000000000 +01:00 ---
ListSomething.com can help you find a place to live and does so by running Ruby on Rails with a hook into Google Maps. It’s pretty neat. Remixing it, baby.


Eye-tracking data must be transformed into fixations and saccades before it can be analyzed. This package provides a non-parametric speed-based approach to do this on a trial basis. The method is especially useful when there are large differences in data quality, as the thresholds are adjusted accordingly. The same pre-processing procedure can be applied to all participants, while accounting for individual differences in data quality.
| Version: | 1.2 |
| Depends: | R (≥ 2.10) |
| Imports: | SDMTools, jpeg, zoo, scales, shiny |
| Published: | 2017-03-13 |
| Author: | Daan van Renswoude & Ingmar Visser |
| Maintainer: | Daan van Renswoude <D.R.vanRenswoude at uva.nl> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| CRAN checks: | gazepath results |
| Reference manual: | gazepath.pdf |
| Package source: | gazepath_1.2.tar.gz |
| Windows binaries: | r-devel: gazepath_1.2.zip, r-release: gazepath_1.2.zip, r-oldrel: gazepath_1.2.zip |
| OS X binaries: | r-release: gazepath_1.2.tgz, r-oldrel: gazepath_1.2.tgz |
| Old sources: | gazepath archive |
Please use the canonical form https://CRAN.R-project.org/package=gazepath to link to this page.


--- layout: post date: 2009-01-30 time: "09:00:00" authors: ["Greg Wilson"] title: 'Communicate First, Standardize Second' tags: ["Noticed"] ---
That quote from Jean-Claude Bradley is on slide 34 of Cameron Neylon's presentation "Open Access, Open Data. Open Research?" Very worthwhile...


MCQ Mock Exam
These additional quizzes and crib sheets are provided to aid with revision of important topics.


{% load i18n %}
{% trans "The information on this page come from the pharma companies in Switzerland that signed the Pharma Cooperation Codex. This codex determine that the companies have to publish their payments on their websites." %}


About
Who Created CaffeeCoin?
CaffeeCoin is created by the same person who created this website ;)
CaffeeCoin is created by N.L.C
How does the CaffeeCoin Blockchain Work?
CaffeeCoin basically is a cryptocurrency like LiteCoin and DogeCoin as we also use the Scrypt Algorithim and the proof of work algorithim and blockcahin technology
Click here for more details
What is the purpose of CaffeeCoin?
It is to connect everyone in a playful way and also allow everyone to access the currency and is not only limited to certain people,
which also wants to promote equality and fairness. We want CaffeeCoin to be the fun currency of the internet!
We also want CaffeeCoin to
connect people around the world and allow everyone to access the currency.
One of the main goals of CaffeeCoin is to donate money to the needy and the earth, we want to donate money to preserve the earth and try
to help fight global warming. We also want to fight for equality as we want to donate to people who are not as fortunate as others, as we think
that people all around the world deserve to have internet connection and basic necessities.


Obtain Information About a File
Tags: Foundation, file
Class: FileManager
See Code Snippet.
Also see the Apple Developer Docs for FileAttributeKey.
Link: Apple Developer Docs
Sample Code:
let fileManager = FileManager.default
do {
let attributes = try fileManager.attributesOfItem(atPath: noteURL.path)
let creationDate = attributes[FileAttributeKey.creationDate]
let lastModDate = attributes[FileAttributeKey.modificationDate]
print("Creation Date = \(creationDate)")
print("Last Mod Date = \(lastModDate)")
}
catch let error as NSError {
print("Ooops! Something went wrong: \(error)")
}
Contributed by Herb Bowie on 21 Oct 2019


--- layout: post title: 'Daily Poem 10: On Gene Wolfe' date: 2014-10-14 08:00:02.000000000 -05:00 categories: - Poetry tags: - gene wolfe - poem - poetry status: publish type: post published: true author: ---
What you thought you read,
I have not written. You
are writing in the blanks.
(Based on Gene Wolfe's comments about his short stories in the anthology "Again, Dangerous Visions.")


Select File
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Error Conditions
This is a list of common, named error conditions returned by Spark SQL.
Also see SQLSTATE Codes.
AMBIGUOUS_COLUMN_OR_FIELD
Column or field <name>
is ambiguous and has <n>
matches.
AMBIGUOUS_LATERAL_COLUMN_ALIAS
Lateral column alias <name>
is ambiguous and has <n>
matches.
AMBIGUOUS_REFERENCE
Reference <name>
is ambiguous, could be: <referenceNames>
.
AMBIGUOUS_REFERENCE_TO_FIELDS
Ambiguous reference to the field <field>
. It appears <count>
times in the schema.
ARITHMETIC_OVERFLOW
<message>
.<alternative>
If necessary set <config>
to “false” to bypass this error.
CANNOT_CAST_DATATYPE
Cannot cast <sourceType>
to <targetType>
.
CANNOT_CONSTRUCT_PROTOBUF_DESCRIPTOR
SQLSTATE: none assigned
Error constructing FileDescriptor for <descFilePath>
.
CANNOT_CONVERT_PROTOBUF_FIELD_TYPE_TO_SQL_TYPE
SQLSTATE: none assigned
Cannot convert Protobuf <protobufColumn>
to SQL <sqlColumn>
because schema is incompatible (protobufType = <protobufType>
, sqlType = <sqlType>
).
CANNOT_CONVERT_PROTOBUF_MESSAGE_TYPE_TO_SQL_TYPE
SQLSTATE: none assigned
Unable to convert <protobufType>
of Protobuf to SQL type <toType>
.
CANNOT_CONVERT_SQL_TYPE_TO_PROTOBUF_ENUM_TYPE
SQLSTATE: none assigned
Cannot convert SQL <sqlColumn>
to Protobuf <protobufColumn>
because <data>
cannot be written since it’s not defined in ENUM <enumString>
.
CANNOT_CONVERT_SQL_TYPE_TO_PROTOBUF_FIELD_TYPE
SQLSTATE: none assigned
Cannot convert SQL <sqlColumn>
to Protobuf <protobufColumn>
because schema is incompatible (protobufType = <protobufType>
, sqlType = <sqlType>
).
CANNOT_DECODE_URL
Cannot decode url : <url>
.
CANNOT_LOAD_FUNCTION_CLASS
SQLSTATE: none assigned
Cannot load class <className>
when registering the function <functionName>
, please make sure it is on the classpath.
CANNOT_LOAD_PROTOBUF_CLASS
SQLSTATE: none assigned
Could not load Protobuf class with name <protobufClassName>
. <explanation>
.
CANNOT_MERGE_INCOMPATIBLE_DATA_TYPE
Failed to merge incompatible data types <left>
and <right>
.
CANNOT_MODIFY_CONFIG
Cannot modify the value of the Spark config: <key>
.
See also ‘<docroot>
/sql-migration-guide.html#ddl-statements’.
CANNOT_PARSE_DECIMAL
Cannot parse decimal.
CANNOT_PARSE_JSON_FIELD
Cannot parse the field name <fieldName>
and the value <fieldValue>
of the JSON token type <jsonType>
to target Spark data type <dataType>
.
CANNOT_PARSE_PROTOBUF_DESCRIPTOR
SQLSTATE: none assigned
Error parsing file <descFilePath>
descriptor byte[] into Descriptor object.
CANNOT_PARSE_TIMESTAMP
<message>
. If necessary set <ansiConfig>
to “false” to bypass this error.
CANNOT_READ_FILE_FOOTER
SQLSTATE: none assigned
Could not read footer for file: <file>
.
CANNOT_RECOGNIZE_HIVE_TYPE
Cannot recognize hive type string: <fieldType>
, column: <fieldName>
.
CANNOT_RESTORE_PERMISSIONS_FOR_PATH
SQLSTATE: none assigned
Failed to set permissions on created path <path>
back to <permission>
.
CANNOT_UP_CAST_DATATYPE
SQLSTATE: none assigned
Cannot up cast <expression>
from <sourceType>
to <targetType>
.
<details>
CAST_INVALID_INPUT
The value <expression>
of the type <sourceType>
cannot be cast to <targetType>
because it is malformed. Correct the value as per the syntax, or change its target type. Use try_cast
to tolerate malformed input and return NULL instead. If necessary set <ansiConfig>
to “false” to bypass this error.
CAST_OVERFLOW
The value <value>
of the type <sourceType>
cannot be cast to <targetType>
due to an overflow. Use try_cast
to tolerate overflow and return NULL instead. If necessary set <ansiConfig>
to “false” to bypass this error.
CAST_OVERFLOW_IN_TABLE_INSERT
Fail to insert a value of <sourceType>
type into the <targetType>
type column <columnName>
due to an overflow. Use try_cast
on the input value to tolerate overflow and return NULL instead.
COLUMN_ALREADY_EXISTS
The column <columnName>
already exists. Consider to choose another name or rename the existing column.
COLUMN_NOT_FOUND
The column <colName>
cannot be found. Verify the spelling and correctness of the column name according to the SQL config <caseSensitiveConfig>
.
COMPARATOR_RETURNS_NULL
SQLSTATE: none assigned
The comparator has returned a NULL for a comparison between <firstValue>
and <secondValue>
. It should return a positive integer for “greater than”, 0 for “equal” and a negative integer for “less than”. To revert to deprecated behavior where NULL is treated as 0 (equal), you must set “spark.sql.legacy.allowNullComparisonResultInArraySort” to “true”.
CONCURRENT_QUERY
SQLSTATE: none assigned
Another instance of this query was just started by a concurrent session.
CONNECT
SQLSTATE: none assigned
Generic Spark Connect error.
For more details see CONNECT
CONVERSION_INVALID_INPUT
The value <str>
(<fmt>
) cannot be converted to <targetType>
because it is malformed. Correct the value as per the syntax, or change its format. Use <suggestion>
to tolerate malformed input and return NULL instead.
CREATE_TABLE_COLUMN_OPTION_DUPLICATE
CREATE TABLE column <columnName>
specifies option “<optionName>
” more than once, which is invalid.
DATATYPE_MISMATCH
Cannot resolve <sqlExpr>
due to data type mismatch:
For more details see DATATYPE_MISMATCH
DATATYPE_MISSING_SIZE
DataType <type>
requires a length parameter, for example <type>
(10). Please specify the length.
DATA_SOURCE_NOT_FOUND
Failed to find the data source: <provider>
. Please find packages at https://spark.apache.org/third-party-projects.html
.
DATETIME_OVERFLOW
Datetime operation overflow: <operation>
.
DECIMAL_PRECISION_EXCEEDS_MAX_PRECISION
Decimal precision <precision>
exceeds max precision <maxPrecision>
.
DEFAULT_DATABASE_NOT_EXISTS
Default database <defaultDatabase>
does not exist, please create it first or change default database to <defaultDatabase>
.
DIVIDE_BY_ZERO
Division by zero. Use try_divide
to tolerate divisor being 0 and return NULL instead. If necessary set <config>
to “false” to bypass this error.
DUPLICATED_MAP_KEY
Duplicate map key <key>
was found, please check the input data. If you want to remove the duplicated keys, you can set <mapKeyDedupPolicy>
to “LAST_WIN” so that the key inserted at last takes precedence.
DUPLICATE_KEY
Found duplicate keys <keyColumn>
.
EMPTY_JSON_FIELD_VALUE
Failed to parse an empty string for data type <dataType>
.
ENCODER_NOT_FOUND
SQLSTATE: none assigned
Not found an encoder of the type <typeName>
to Spark SQL internal representation. Consider to change the input type to one of supported at ‘<docroot>
/sql-ref-datatypes.html’.
FAILED_EXECUTE_UDF
Failed to execute user defined function (<functionName>
: (<signature>
) => <result>
).
FAILED_FUNCTION_CALL
Failed preparing of the function <funcName>
for call. Please, double check function’s arguments.
FAILED_RENAME_PATH
Failed to rename <sourcePath>
to <targetPath>
as destination already exists.
FIELD_NOT_FOUND
No such struct field <fieldName>
in <fields>
.
FORBIDDEN_OPERATION
The operation <statement>
is not allowed on the <objectType>
: <objectName>
.
GENERATED_COLUMN_WITH_DEFAULT_VALUE
SQLSTATE: none assigned
A column cannot have both a default value and a generation expression but column <colName>
has default value: (<defaultValue>
) and generation expression: (<genExpr>
).
GRAPHITE_SINK_INVALID_PROTOCOL
SQLSTATE: none assigned
Invalid Graphite protocol: <protocol>
.
GRAPHITE_SINK_PROPERTY_MISSING
SQLSTATE: none assigned
Graphite sink requires ‘<property>
’ property.
GROUPING_COLUMN_MISMATCH
Column of grouping (<grouping>
) can’t be found in grouping columns <groupingColumns>
.
GROUPING_ID_COLUMN_MISMATCH
Columns of grouping_id (<groupingIdColumn>
) does not match grouping columns (<groupByColumns>
).
GROUPING_SIZE_LIMIT_EXCEEDED
Grouping sets size cannot be greater than <maxSize>
.
GROUP_BY_AGGREGATE
Aggregate functions are not allowed in GROUP BY, but found <sqlExpr>
.
GROUP_BY_POS_AGGREGATE
GROUP BY <index>
refers to an expression <aggExpr>
that contains an aggregate function. Aggregate functions are not allowed in GROUP BY.
GROUP_BY_POS_OUT_OF_RANGE
GROUP BY position <index>
is not in select list (valid range is [1, <size>
]).
IDENTIFIER_TOO_MANY_NAME_PARTS
<identifier>
is not a valid identifier as it has more than 2 name parts.
INCOMPARABLE_PIVOT_COLUMN
Invalid pivot column <columnName>
. Pivot columns must be comparable.
INCOMPATIBLE_COLUMN_TYPE
<operator>
can only be performed on tables with compatible column types. The <columnOrdinalNumber>
column of the <tableOrdinalNumber>
table is
INCOMPATIBLE_DATASOURCE_REGISTER
SQLSTATE: none assigned
Detected an incompatible DataSourceRegister. Please remove the incompatible library from classpath or upgrade it. Error: <message>
INCOMPATIBLE_JOIN_TYPES
The join types
INCOMPATIBLE_VIEW_SCHEMA_CHANGE
SQLSTATE: none assigned
The SQL query of view <viewName>
has an incompatible schema change and column <colName>
cannot be resolved. Expected <expectedNum>
columns named <colName>
but got <actualCols>
.
Please try to re-create the view by running: <suggestion>
.
INCOMPLETE_TYPE_DEFINITION
Incomplete complex type:
For more details see INCOMPLETE_TYPE_DEFINITION
INCONSISTENT_BEHAVIOR_CROSS_VERSION
You may get a different result due to the upgrading to
For more details see INCONSISTENT_BEHAVIOR_CROSS_VERSION
INCORRECT_END_OFFSET
Max offset with <rowsPerSecond>
rowsPerSecond is <maxSeconds>
, but it’s <endSeconds>
now.
INCORRECT_RAMP_UP_RATE
Max offset with <rowsPerSecond>
rowsPerSecond is <maxSeconds>
, but ‘rampUpTimeSeconds’ is <rampUpTimeSeconds>
.
INDEX_ALREADY_EXISTS
Cannot create the index <indexName>
on table <tableName>
because it already exists.
INDEX_NOT_FOUND
Cannot find the index <indexName>
on table <tableName>
.
INTERNAL_ERROR
<message>
INTERVAL_ARITHMETIC_OVERFLOW
<message>
.<alternative>
INTERVAL_DIVIDED_BY_ZERO
Division by zero. Use try_divide
to tolerate divisor being 0 and return NULL instead.
INVALID_ARRAY_INDEX
The index <indexValue>
is out of bounds. The array has <arraySize>
elements. Use the SQL function get()
to tolerate accessing element at invalid index and return NULL instead. If necessary set <ansiConfig>
to “false” to bypass this error.
INVALID_ARRAY_INDEX_IN_ELEMENT_AT
The index <indexValue>
is out of bounds. The array has <arraySize>
elements. Use try_element_at
to tolerate accessing element at invalid index and return NULL instead. If necessary set <ansiConfig>
to “false” to bypass this error.
INVALID_BUCKET_FILE
SQLSTATE: none assigned
Invalid bucket file: <path>
.
INVALID_BYTE_STRING
SQLSTATE: none assigned
The expected format is ByteString, but was <unsupported>
(<class>
).
INVALID_COLUMN_NAME_AS_PATH
The datasource <datasource>
cannot save the column <columnName>
because its name contains some characters that are not allowed in file paths. Please, use an alias to rename it.
INVALID_COLUMN_OR_FIELD_DATA_TYPE
Column or field <name>
is of type <type>
while it’s required to be <expectedType>
.
INVALID_EMPTY_LOCATION
The location name cannot be empty string, but <location>
was given.
INVALID_EXTRACT_BASE_FIELD_TYPE
Can’t extract a value from <base>
. Need a complex type [STRUCT, ARRAY, MAP] but got <other>
.
INVALID_EXTRACT_FIELD
Cannot extract <field>
from <expr>
.
INVALID_EXTRACT_FIELD_TYPE
Field name should be a non-null string literal, but it’s <extraction>
.
INVALID_FIELD_NAME
Field name <fieldName>
is invalid: <path>
is not a struct.
INVALID_FORMAT
The format is invalid: <format>
.
For more details see INVALID_FORMAT
INVALID_FRACTION_OF_SECOND
The fraction of sec must be zero. Valid range is [0, 60]. If necessary set <ansiConfig>
to “false” to bypass this error.
INVALID_IDENTIFIER
The identifier <ident>
is invalid. Please, consider quoting it with back-quotes as <ident>
.
INVALID_INDEX_OF_ZERO
The index 0 is invalid. An index shall be either < 0 or > 0 (the first element has index 1).
INVALID_JSON_ROOT_FIELD
Cannot convert JSON root field to target Spark type.
INVALID_JSON_SCHEMA_MAP_TYPE
Input schema <jsonSchema>
can only contain STRING as a key type for a MAP.
INVALID_LATERAL_JOIN_TYPE
The <joinType>
JOIN with LATERAL correlation is not allowed because an OUTER subquery cannot correlate to its join partner. Remove the LATERAL correlation or use an INNER JOIN, or LEFT OUTER JOIN instead.
INVALID_OPTIONS
Invalid options:
For more details see INVALID_OPTIONS
INVALID_PANDAS_UDF_PLACEMENT
The group aggregate pandas UDF <functionList>
cannot be invoked together with as other, non-pandas aggregate functions.
INVALID_PARAMETER_VALUE
The value of parameter(s) <parameter>
in <functionName>
is invalid:
For more details see INVALID_PARAMETER_VALUE
INVALID_PROPERTY_KEY
<key>
is an invalid property key, please use quotes, e.g. SET <key>
=<value>
.
INVALID_PROPERTY_VALUE
<value>
is an invalid property value, please use quotes, e.g. SET <key>
=<value>
INVALID_SCHEMA
The input schema <inputSchema>
is not a valid schema string.
For more details see INVALID_SCHEMA
INVALID_SET_SYNTAX
Expected format is ‘SET’, ‘SET key’, or ‘SET key=value’. If you want to include special characters in key, or include semicolon in value, please use backquotes, e.g., SET key
=value
.
INVALID_SQL_ARG
SQLSTATE: none assigned
The argument <name>
of sql()
is invalid. Consider to replace it by a SQL literal.
INVALID_SQL_SYNTAX
Invalid SQL syntax: <inputString>
.
INVALID_SUBQUERY_EXPRESSION
Invalid subquery:
For more details see INVALID_SUBQUERY_EXPRESSION
INVALID_TEMP_OBJ_REFERENCE
SQLSTATE: none assigned
Cannot create the persistent object <objName>
of the type <obj>
because it references to the temporary object <tempObjName>
of the type <tempObj>
. Please make the temporary object <tempObjName>
persistent, or make the persistent object <objName>
temporary.
INVALID_TYPED_LITERAL
The value of the typed literal <valueType>
is invalid: <value>
.
INVALID_WHERE_CONDITION
The WHERE condition <condition>
contains invalid expressions: <expressionList>
.
Rewrite the query to avoid window functions, aggregate functions, and generator functions in the WHERE clause.
LOCATION_ALREADY_EXISTS
Cannot name the managed table as <identifier>
, as its associated location <location>
already exists. Please pick a different table name, or remove the existing location first.
MALFORMED_CSV_RECORD
SQLSTATE: none assigned
Malformed CSV record: <badRecord>
MALFORMED_PROTOBUF_MESSAGE
SQLSTATE: none assigned
Malformed Protobuf messages are detected in message deserialization. Parse Mode: <failFastMode>
. To process malformed protobuf message as null result, try setting the option ‘mode’ as ‘PERMISSIVE’.
MALFORMED_RECORD_IN_PARSING
SQLSTATE: none assigned
Malformed records are detected in record parsing: <badRecord>
.
Parse Mode: <failFastMode>
. To process malformed records as null result, try setting the option ‘mode’ as ‘PERMISSIVE’.
MISSING_AGGREGATION
The non-aggregating expression <expression>
is based on columns which are not participating in the GROUP BY clause.
Add the columns or the expression to the GROUP BY, aggregate the expression, or use <expressionAnyValue>
if you do not care which of the values within a group is returned.
MISSING_GROUP_BY
The query does not include a GROUP BY clause. Add GROUP BY or turn it into the window functions using OVER clauses.
MULTI_UDF_INTERFACE_ERROR
SQLSTATE: none assigned
Not allowed to implement multiple UDF interfaces, UDF class <className>
.
NESTED_AGGREGATE_FUNCTION
It is not allowed to use an aggregate function in the argument of another aggregate function. Please use the inner aggregate function in a sub-query.
NON_LAST_MATCHED_CLAUSE_OMIT_CONDITION
When there are more than one MATCHED clauses in a MERGE statement, only the last MATCHED clause can omit the condition.
NON_LAST_NOT_MATCHED_BY_SOURCE_CLAUSE_OMIT_CONDITION
When there are more than one NOT MATCHED BY SOURCE clauses in a MERGE statement, only the last NOT MATCHED BY SOURCE clause can omit the condition.
NON_LAST_NOT_MATCHED_BY_TARGET_CLAUSE_OMIT_CONDITION
When there are more than one NOT MATCHED [BY TARGET] clauses in a MERGE statement, only the last NOT MATCHED [BY TARGET] clause can omit the condition.
NON_LITERAL_PIVOT_VALUES
Literal expressions required for pivot values, found <expression>
.
NON_PARTITION_COLUMN
PARTITION clause cannot contain the non-partition column: <columnName>
.
NOT_A_PARTITIONED_TABLE
SQLSTATE: none assigned
Operation <operation>
is not allowed for <tableIdentWithDB>
because it is not a partitioned table.
NOT_NULL_CONSTRAINT_VIOLATION
Assigning a NULL is not allowed here.
For more details see NOT_NULL_CONSTRAINT_VIOLATION
NO_HANDLER_FOR_UDAF
SQLSTATE: none assigned
No handler for UDAF ‘<functionName>
’. Use sparkSession.udf.register(…) instead.
NO_SQL_TYPE_IN_PROTOBUF_SCHEMA
SQLSTATE: none assigned
Cannot find <catalystFieldPath>
in Protobuf schema.
NO_UDF_INTERFACE
SQLSTATE: none assigned
UDF class <className>
doesn’t implement any UDF interface.
NULLABLE_COLUMN_OR_FIELD
Column or field <name>
is nullable while it’s required to be non-nullable.
NULLABLE_ROW_ID_ATTRIBUTES
Row ID attributes cannot be nullable: <nullableRowIdAttrs>
.
NULL_MAP_KEY
Cannot use null as map key.
NUMERIC_OUT_OF_SUPPORTED_RANGE
The value <value>
cannot be interpreted as a numeric since it has more than 38 digits.
NUMERIC_VALUE_OUT_OF_RANGE
<value>
cannot be represented as Decimal(<precision>
, <scale>
). If necessary set <config>
to “false” to bypass this error, and return NULL instead.
NUM_COLUMNS_MISMATCH
<operator>
can only be performed on inputs with the same number of columns, but the first input has <firstNumColumns>
columns and the <invalidOrdinalNum>
input has <invalidNumColumns>
columns.
ORDER_BY_POS_OUT_OF_RANGE
ORDER BY position <index>
is not in select list (valid range is [1, <size>
]).
PARSE_EMPTY_STATEMENT
Syntax error, unexpected empty statement.
PARSE_SYNTAX_ERROR
Syntax error at or near <error>
<hint>
.
PARTITIONS_ALREADY_EXIST
Cannot ADD or RENAME TO partition(s) <partitionList>
in table <tableName>
because they already exist.
Choose a different name, drop the existing partition, or add the IF NOT EXISTS clause to tolerate a pre-existing partition.
PARTITIONS_NOT_FOUND
The partition(s) <partitionList>
cannot be found in table <tableName>
.
Verify the partition specification and table name.
To tolerate the error on drop use ALTER TABLE … DROP IF EXISTS PARTITION.
PATH_ALREADY_EXISTS
Path <outputPath>
already exists. Set mode as “overwrite” to overwrite the existing path.
PATH_NOT_FOUND
Path does not exist: <path>
.
PIVOT_VALUE_DATA_TYPE_MISMATCH
Invalid pivot value ‘<value>
’: value data type <valueType>
does not match pivot column data type <pivotType>
.
PLAN_VALIDATION_FAILED_RULE_EXECUTOR
SQLSTATE: none assigned
The input plan of <ruleExecutor>
is invalid: <reason>
PLAN_VALIDATION_FAILED_RULE_IN_BATCH
SQLSTATE: none assigned
Rule <rule>
in batch <batch>
generated an invalid plan: <reason>
PROTOBUF_DEPENDENCY_NOT_FOUND
SQLSTATE: none assigned
Could not find dependency: <dependencyName>
.
PROTOBUF_DESCRIPTOR_FILE_NOT_FOUND
SQLSTATE: none assigned
Error reading Protobuf descriptor file at path: <filePath>
.
PROTOBUF_FIELD_MISSING
SQLSTATE: none assigned
Searching for <field>
in Protobuf schema at <protobufSchema>
gave <matchSize>
matches. Candidates: <matches>
.
PROTOBUF_FIELD_MISSING_IN_SQL_SCHEMA
SQLSTATE: none assigned
Found <field>
in Protobuf schema but there is no match in the SQL schema.
PROTOBUF_FIELD_TYPE_MISMATCH
SQLSTATE: none assigned
Type mismatch encountered for field: <field>
.
PROTOBUF_MESSAGE_NOT_FOUND
SQLSTATE: none assigned
Unable to locate Message <messageName>
in Descriptor.
PROTOBUF_TYPE_NOT_SUPPORT
SQLSTATE: none assigned
Protobuf type not yet supported: <protobufType>
.
RECURSIVE_PROTOBUF_SCHEMA
SQLSTATE: none assigned
Found recursive reference in Protobuf schema, which can not be processed by Spark by default: <fieldDescriptor>
. try setting the option recursive.fields.max.depth
0 to 10. Going beyond 10 levels of recursion is not allowed.
RENAME_SRC_PATH_NOT_FOUND
Failed to rename as <sourcePath>
was not found.
REPEATED_CLAUSE
The <clause>
clause may be used at most once per <operation>
operation.
REQUIRES_SINGLE_PART_NAMESPACE
<sessionCatalog>
requires a single-part namespace, but got <namespace>
.
ROUTINE_ALREADY_EXISTS
Cannot create the function <routineName>
because it already exists.
Choose a different name, drop or replace the existing function, or add the IF NOT EXISTS clause to tolerate a pre-existing function.
ROUTINE_NOT_FOUND
The function <routineName>
cannot be found. Verify the spelling and correctness of the schema and catalog.
If you did not qualify the name with a schema and catalog, verify the current_schema() output, or qualify the name with the correct schema and catalog.
To tolerate the error on drop use DROP FUNCTION IF EXISTS.
SCALAR_SUBQUERY_TOO_MANY_ROWS
More than one row returned by a subquery used as an expression.
SCHEMA_ALREADY_EXISTS
Cannot create schema <schemaName>
because it already exists.
Choose a different name, drop the existing schema, or add the IF NOT EXISTS clause to tolerate pre-existing schema.
SCHEMA_NOT_EMPTY
Cannot drop a schema <schemaName>
because it contains objects.
Use DROP SCHEMA … CASCADE to drop the schema and all its objects.
SCHEMA_NOT_FOUND
The schema <schemaName>
cannot be found. Verify the spelling and correctness of the schema and catalog.
If you did not qualify the name with a catalog, verify the current_schema() output, or qualify the name with the correct catalog.
To tolerate the error on drop use DROP SCHEMA IF EXISTS.
SECOND_FUNCTION_ARGUMENT_NOT_INTEGER
The second argument of <functionName>
function needs to be an integer.
SORT_BY_WITHOUT_BUCKETING
SQLSTATE: none assigned
sortBy must be used together with bucketBy.
STAR_GROUP_BY_POS
Star (*) is not allowed in a select list when GROUP BY an ordinal position is used.
STATIC_PARTITION_COLUMN_IN_INSERT_COLUMN_LIST
SQLSTATE: none assigned
Static partition column <staticName>
is also specified in the column list.
STREAM_FAILED
SQLSTATE: none assigned
Query [id = <id>
, runId = <runId>
] terminated with exception: <message>
TABLE_OR_VIEW_ALREADY_EXISTS
Cannot create table or view <relationName>
because it already exists.
Choose a different name, drop or replace the existing object, or add the IF NOT EXISTS clause to tolerate pre-existing objects.
TABLE_OR_VIEW_NOT_FOUND
The table or view <relationName>
cannot be found. Verify the spelling and correctness of the schema and catalog.
If you did not qualify the name with a schema, verify the current_schema() output, or qualify the name with the correct schema and catalog.
To tolerate the error on drop use DROP VIEW IF EXISTS or DROP TABLE IF EXISTS.
TASK_WRITE_FAILED
SQLSTATE: none assigned
Task failed while writing rows to <path>
.
TEMP_TABLE_OR_VIEW_ALREADY_EXISTS
Cannot create the temporary view <relationName>
because it already exists.
Choose a different name, drop or replace the existing view, or add the IF NOT EXISTS clause to tolerate pre-existing views.
TEMP_VIEW_NAME_TOO_MANY_NAME_PARTS
CREATE TEMPORARY VIEW or the corresponding Dataset APIs only accept single-part view names, but got: <actualName>
.
TOO_MANY_ARRAY_ELEMENTS
Cannot initialize array with <numElements>
elements of size <size>
.
UNABLE_TO_ACQUIRE_MEMORY
Unable to acquire <requestedBytes>
bytes of memory, got <receivedBytes>
.
UNABLE_TO_CONVERT_TO_PROTOBUF_MESSAGE_TYPE
SQLSTATE: none assigned
Unable to convert SQL type <toType>
to Protobuf type <protobufType>
.
UNABLE_TO_INFER_SCHEMA
Unable to infer schema for <format>
. It must be specified manually.
UNBOUND_SQL_PARAMETER
Found the unbound parameter: <name>
. Please, fix args
and provide a mapping of the parameter to a SQL literal.
UNCLOSED_BRACKETED_COMMENT
Found an unclosed bracketed comment. Please, append */ at the end of the comment.
UNEXPECTED_INPUT_TYPE
Parameter <paramIndex>
of function <functionName>
requires the <requiredType>
type, however <inputSql>
has the type <inputType>
.
UNKNOWN_PROTOBUF_MESSAGE_TYPE
SQLSTATE: none assigned
Attempting to treat <descriptorName>
as a Message, but it was <containingType>
.
UNPIVOT_REQUIRES_ATTRIBUTES
UNPIVOT requires all given <given>
expressions to be columns when no <empty>
expressions are given. These are not columns: [<expressions>
].
UNPIVOT_REQUIRES_VALUE_COLUMNS
At least one value column needs to be specified for UNPIVOT, all columns specified as ids.
UNPIVOT_VALUE_DATA_TYPE_MISMATCH
Unpivot value columns must share a least common type, some types do not: [<types>
].
UNPIVOT_VALUE_SIZE_MISMATCH
All unpivot value columns must have the same size as there are value column names (<names>
).
UNRECOGNIZED_SQL_TYPE
Unrecognized SQL type <typeName>
.
UNRESOLVED_ALL_IN_GROUP_BY
Cannot infer grouping columns for GROUP BY ALL based on the select clause. Please explicitly specify the grouping columns.
UNRESOLVED_COLUMN
A column or function parameter with name <objectName>
cannot be resolved.
For more details see UNRESOLVED_COLUMN
UNRESOLVED_FIELD
A field with name <fieldName>
cannot be resolved with the struct-type column <columnPath>
.
For more details see UNRESOLVED_FIELD
UNRESOLVED_MAP_KEY
Cannot resolve column <objectName>
as a map key. If the key is a string literal, add the single quotes ‘’ around it.
For more details see UNRESOLVED_MAP_KEY
UNRESOLVED_ROUTINE
Cannot resolve function <routineName>
on search path <searchPath>
.
UNRESOLVED_USING_COLUMN_FOR_JOIN
USING column <colName>
cannot be resolved on the <side>
side of the join. The <side>
-side columns: [<suggestion>
].
UNSUPPORTED_ARROWTYPE
Unsupported arrow type <typeName>
.
UNSUPPORTED_DATASOURCE_FOR_DIRECT_QUERY
SQLSTATE: none assigned
Unsupported data source type for direct query on files: <dataSourceType>
UNSUPPORTED_DATATYPE
Unsupported data type <typeName>
.
UNSUPPORTED_DESERIALIZER
The deserializer is not supported:
For more details see UNSUPPORTED_DESERIALIZER
UNSUPPORTED_EXPRESSION_GENERATED_COLUMN
SQLSTATE: none assigned
Cannot create generated column <fieldName>
with generation expression <expressionStr>
because <reason>
.
UNSUPPORTED_EXPR_FOR_OPERATOR
SQLSTATE: none assigned
A query operator contains one or more unsupported expressions. Consider to rewrite it to avoid window functions, aggregate functions, and generator functions in the WHERE clause.
Invalid expressions: [<invalidExprSqls>
]
UNSUPPORTED_EXPR_FOR_WINDOW
Expression <sqlExpr>
not supported within a window function.
UNSUPPORTED_FEATURE
The feature is not supported:
For more details see UNSUPPORTED_FEATURE
UNSUPPORTED_GENERATOR
The generator is not supported:
For more details see UNSUPPORTED_GENERATOR
UNSUPPORTED_GROUPING_EXPRESSION
SQLSTATE: none assigned
grouping()/grouping_id() can only be used with GroupingSets/Cube/Rollup.
UNSUPPORTED_SAVE_MODE
SQLSTATE: none assigned
The save mode <saveMode>
is not supported for:
For more details see UNSUPPORTED_SAVE_MODE
UNSUPPORTED_SUBQUERY_EXPRESSION_CATEGORY
Unsupported subquery expression:
For more details see UNSUPPORTED_SUBQUERY_EXPRESSION_CATEGORY
UNSUPPORTED_TYPED_LITERAL
Literals of the type <unsupportedType>
are not supported. Supported types are <supportedTypes>
.
UNTYPED_SCALA_UDF
SQLSTATE: none assigned
You’re using untyped Scala UDF, which does not have the input type information. Spark may blindly pass null to the Scala closure with primitive-type argument, and the closure will see the default value of the Java type for the null argument, e.g. udf((x: Int) => x, IntegerType)
, the result is 0 for null input. To get rid of this error, you could:
-
use typed Scala UDF APIs(without return type parameter), e.g.
udf((x: Int) => x)
. -
use Java UDF APIs, e.g.
udf(new UDF1[String, Integer] { override def call(s: String): Integer = s.length() }, IntegerType)
, if input types are all non primitive. -
set “spark.sql.legacy.allowUntypedScalaUDF” to “true” and use this API with caution.
VIEW_ALREADY_EXISTS
Cannot create view <relationName>
because it already exists.
Choose a different name, drop or replace the existing object, or add the IF NOT EXISTS clause to tolerate pre-existing objects.
VIEW_NOT_FOUND
The view <relationName>
cannot be found. Verify the spelling and correctness of the schema and catalog.
If you did not qualify the name with a schema, verify the current_schema() output, or qualify the name with the correct schema and catalog.
To tolerate the error on drop use DROP VIEW IF EXISTS.
WRITE_STREAM_NOT_ALLOWED
SQLSTATE: none assigned
writeStream
can be called only on streaming Dataset/DataFrame.
WRONG_COMMAND_FOR_OBJECT_TYPE
SQLSTATE: none assigned
The operation <operation>
requires a <requiredType>
. But <objectName>
is a <foundType>
. Use <alternative>
instead.
WRONG_NUM_ARGS
The <functionName>
requires <expectedNum>
parameters but the actual number is <actualNum>
.
For more details see WRONG_NUM_ARGS


Pathanamthitta is a municipality situated in the Central Travancore region in the state of Kerala, India, spread over an area of 23.50 km2. It is the administrative capital of Pathanamthitta district. The town has a population of 37,538. The Hindu pilgrim centre Sabarimala is situated in the Pathanamthitta district; as the main transport hub to Sabarimala, the town is known as the 'Pilgrim Capital of Kerala'.[3] Pathanamthitta District, the thirteenth revenue district of the State of Kerala, was formed with effect from 1 November 1982 vide G.O. (M.S) No.1026/82/RD dated 29 October 1982, with headquarters at Pathanamthitta. Forest covers more than half (1396.95 km2.) of the total area of the District. Pathanamthitta District ranks the 7th in area (2652 sq. km.) in the State.
Aranmula is a place mainly known for its expert craftsmanship and snake boat race organized on the final day of Onam Festival. The most renowned art of metal mirror of Aranmula Kannadi has its roots originated from this place
ReadmoreA natural waterfall with captivating surroundings is situated on the banks of the River Pamba. The fall is 60 to 100 ft deep which pours down on a rocky bed. The place is a favourite picnic spot among locals as well as tourists.
ReadmoreA natural waterfall with captivating surroundings is situated on the banks of the River Pamba. The fall is 60 to 100 ft deep which pours down on a rocky bed. The place is a favourite picnic spot among locals as well as tourists.
Readmore


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BYOP (Bring Your Own Project)
Project Nights are all about jumping in, getting help and giving back. Be that working on an upstream change to Ember itself or a side project using Ember. The sessions are informal, friendly and agenda-less.
Mix with your fellow Embereños and get hacking!
Refreshments provided by our awesome host(s) and sponsor(s).
We strive to ensure everyone feels comfortable and included and has a great time at our events.
See the code of conduct for more detail