This year’s International Symposium on Computer Architecture (ISCA 19) hosted the very first
iteration of the uArch Workshop. As the name suggests, this workshop is meant to be a mentoring
workshop for late stage undergraduates and new master’s students, who are aiming to pursue graduate
studies in Computer Architecture.
The uArch workshop is a fantastic opportunity to interact with the biggest names in computer
architecture, network with academics and industry workers, and to understand what a career in
Computer Architecture research entails. The workshop provides funding to all selected
students. This funding includes all conference fees, meals, accommodation, and travel funding
up to 500 USD.
As part of the program, the students not only get to attend the one full day dedicated to the
workshop itself, but also take part in the main ISCA program.
My experience visiting ISCA this year was beyond fantastic.
The conference took place at the Phoenix Convention Center, in Phoenix, Arizona, USA. The
workshop was partly sponsored by Microsoft, and one day before the event, Microsoft invited
all the students to an informal networking dinner where we were introduced to various people
in Architecture, including the ISCA general chair Dr. Srilatha Mane, and the workshop chairs Dr.
Lena Olson and Dr. Newsha Ardalani. Throughout my time at ISCA, the organizers were
extremely helpful and they were the most important resource when it came to interacting with
new people. If I was too hesitant to approach someone, Lena or Newsha always helped in
introducing me to them.
The workshop had 6 sessions in total followed by a networking event.
The first session focused on networking and we got some great tips on interacting with people
at conferences. I really came to appreciate the value of this session by the end of the
conference because I realized that the conference was more about networking and discussions
about individual research rather than the presentations for the accepted papers in ISCA 19.
The second session was a talk titled “What I wish I knew my first few years of grad school,” by
Prof. Jason Mars from the University of Michigan. This talk was by far one of the most engaging
talks I have encountered in my life. Jason communicated his ideas very effectively. Although he
did present some controversial opinions such as – Working on a different topic after submitting
a paper, or submitting to top tier conferences only, he also gave some solid advice about what
it takes to publish a paper at a top conference.
Prof. Mary Hall from the University of Utah was the third speaker and she spoke on the topic
“How to be a great researcher.” She talked about the importance of team work and
collaborations, and identified curiosity and persistence as the key traits in order to be a
successful researcher.
This was followed by a panel discussion on the topic “How you made decisions about your
career?” I believe this was probably the most helpful session out of all the others when it comes
to understanding what one must expect out of graduate school or a job in the industry. There
were some very good tips given to us about applying to PhD programs and there was in-depth
discussion about the admission process, the PhD Journey, and the life after. One great thing
about this panel was that it had an equal representation from both academia and industry.
Moreover, the panelists were also at different points in their career paths which helped us gain
both – insights that come with experience, and about contemporary practices.
The next session was a talk on the history of Computer Architecture research by Turing Award
Winner, Prof. David Patterson. Prof. Patterson is the co-author of the most widely used
Computer Systems textbooks and it was very inspiring to see him there in person.
The last session of the uArch workshop was by Dr. Karin Strauss from Microsoft Research, and
her presentation was about interdisciplinary research in Computer Architecture. She talked
about a research project that Microsoft is currently working on with the University of
Washington which involves working with DNA sequences to use them as storage.
The workshop ended there, and we got to interact with various faculty after it as part of the
informal networking session.
One important part of the ISCA experience was the “Meet a Senior Architect” Program where
we got to meet a mentor in a one on one session. I was really fortunate to have been assigned
Prof. Daniel Jimenez, who came up with the original perceptron-based branch predictor and is
an eminent personality in Computer Architecture. I greatly appreciate the time he gave me and
I got some very good tips regarding research in general, and grad school admissions from him.
I consider myself really fortunate for attending ISCA this year, especially due to
the fact that it was organized as part of the ACM FCRC. The FCRC is an event that occurs
once in every 4 years (I found out only after attending!) and since multiple workshops happen
parallelly as part of FCRC, the scale of the event was even bigger than it usually is. This unique
opportunity allowed us to attend the FCRC Plenary sessions – which are talks by eminent
personalities in Computer Science on a diverse set of topics.
I attended a total of four Plenary Talks.
The first one was the Turing Lecture by Dr. Yann LeCun and Dr. Geoffrey Hinton. This plenary
talk was very humorous and Dr. Hinton gave us a piece of advice – Friends may come and go,
but enemies accumulate. This was immediately followed by a slide on how (and why) his papers
were rejected by conference reviewers as late as 2009 and some of the reasons were quite
hilarious.
Another great plenary talk was the one by Prof. Shriram Krishnamurthy from Brown University.
The talk revolved around how Computer Scientists can make computer science education more
accessible to students. What was really amazing about the talk, was how effectively he communicated
his ideas. It also made me realize the importance of good slides. The presentation was so well done that
it felt like a performance – every step carefully choreographed.
Amidst the various main sessions of ISCA and lots of networking, there were some other events
of interest – such as the business meeting by SIGARCH and the ISCA Awards Luncheon. The
business meeting was to discuss logistical matters – such as the amount of money
spent/earned, the criteria for paper selection, etc. The Awards Luncheon was an event where
key contributions to Computer Architecture were recognized.
This year’s Eckert-Mauchly Award was presented to Prof. Mark Hill from University of
Wisconsin-Madison. Prof. Mark Hill is famously known for coming up with the 3Cs model of
cache misses. His acceptance speech was one of the most inspiring talks for ISCA. Not only did
he give great advice regarding research, but the way he talked about his career reflected his
greatness and I am sure that it motivated a lot of young researchers.
It has been rightly said that “All work and no play makes Jack a dull boy.” The last event for ISCA
was an excursion to the Musical Instrument Museum in Phoenix and with that, the 4-day event
came to an end.
Attending ISCA 2019 turned out to be a life changing experience for me. I had a lot of fun, got
to network with lots of really great people, met some of the biggest personalities in Computer
Architecture, and got a lot of tips regarding grad school and a career in this field. The workshop
accomplished the goals it set out to achieve, and I have much more clarity about my future
plans.
I am very thankful to the organizers for starting this great initiative and executing it in a spot-on
manner. I would also like to acknowledge the contributions of my Professor, Dr. Manu Awasthi
who was instrumental in getting me interested in computer architecture.


<pie-chart> and <progress-circle> examples
Load
<script src="https://pie-meister.github.io/PieMeister.min.js"></script>
OR
<script src="https://pie-meister.github.io/PieMeister-with-Progress.min.js"></script>
Use
Then ALL HTML required to display a pie-chart is:
The <pie-chart> Web Component processes all innerHTML <slice> elements and creates an SVG
Element in shadowDOM
The
- documentation
Get 6 colors free
The SVG has a default 1000 x 1000 units viewBox
If a custom label is declared, the word '$size' is replaced.
An empty <slice> auto-completes a percentage pie
labels can be pulled with
See F12 console: There are plenty of extra attributes available to play with
The SVG <circle>, <text> and <style> elements become part of the created <svg> element
As the <pie-chart> element copies all SVG, you can apply all SVG patterns,masks,animations you can find online.
Note: in SVG, styles have higher Specificity than SVG element attributes
Thus an inline style is used to override the stroke color.
The
If you know SVG: all <slice> attributes are copied to the SVG <path> element
And/or color a slice
The slice
<pie-chart> Challenge: Radiation
Now knowing <pie-chart> syntax; how would you create this?
- How many slices do you see?
- How many circles do you see?
- Hint: The first slice starts at the top
Progress Circle
Use SVG
See the <progress-circle> below for a advanced version
Recreating Google Analytics multiple percentages
By adding extra slices, that always start at
and clever
CSS translating the label locations
this chart with 4 percentages can be created.
The <progress-circle> Web Component (below) creates this <pie-chart> code in 6 HTML lines
<progress-circle> Web Component
Is built on top of the PieMeister Base Class and converts innerHTML to <pie-chart> notation
The previous 30 lines HTML/CSS code can now be written as:
A single <progress-circle> Element
can be configured with Attributes:
- width
- fill
- stroke
- color
- edge
- Todo
- document size=360 , or set fixed pathLength=100
- Document SVG <tspan>
- Document gap
- Document slice.point( x , y , z)
- Fix .createSlice
- Notes
- (contrary to HTML) <style> definitions overrule attributes on SVG elements
- <circle /> self-closing tags do not work
-
A <slice> is converted to a SVG <path>, it can only contain SVG Elements not HTML elements
<slize size="10"><span> HTML here breaks the SVG </span></slice>
- Fun fact:
does not use any Pi calculations!
GZip analysis
PieMeister code is an optimized balance between function, size and performance
Blue is best compression, Red is worst compression
Can you squeeze out more bytes?
PieMeister:
PieMeister with Progress:
- Experiments
Maybe create multiple SVGs, for slices and labels, and stack them, like done in the ChessMeister Chessboard experiment


BIRTHDAY Happy Birthday America Birthday KIDZ BOP 1 (20th Birthday Edition) Birthday Happy Birthday Kodak Birthday Bitch Thank You Happy Birthday Birthday (feat. Monty) Birthday Ouu


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| DOSE: |
|
insufflated |
Cocaine |
(powder / crystals) |
| |
repeated |
oral |
Alcohol |
(liquid) |
| |
repeated |
smoked |
Cannabis |
(plant material) |
This actually happened in both 2004 and 2005, as it took place New Year's Eve night. I have been a long time drinker, coke sniffer, and pot smoker for only being 16. I've been doing coke since elementary school, and alcohol and pot since middle school, and I'm now currently almost finished with my Junior year.
I went up to Tampa with my mom dad and little brother who is 14 to see my older brother who is 20 at his apartment where he goes to college (USF). He lives with his girlfriend, and me and my little brother were staying several nights there while my parents got a hotel so that they could spend time together and we could party. We got a couple cases of St. Pauli girl since all three of us brothers love German beer and drank with my mom into the ball falling down in New York. My dad is a recovering alcoholic so he doesn't drink obviously.
After we drank into the night my mom and dad went to their hotel and I went into my older brothers sorda game room/guest room with my little brother and had him lock the door. His girlfriend has no problem with us smoking Marijuana in the room, but I didn't even wanna ask her about coke so I decided to just not bring it up. My plan was to quit coke for the new years, and in doing such, go out with a bang. I only had a little bit of powder left, but when I emptied it out, it became much more than it seemed in the bag. I had over 20 lines, which is plenty for myself to have some fun. After 3-4 lines I got the body buzz I usually expect, and hit a few more lines and got the emotion buzz I always look for when sniffing coke.
100x worse than even when drunk, I become unreasonably more talkative and I want to right my wrongs, take back horrible thing's Iíve done to people, and just everything positive, the kinda feeling like I could love or forgive anybody. By now the alcohol was completely drained of my system, at least enough that I wasn't distorted and any other body type buzz was completely drained out from rail after rail of expensive pure powder.
Spacing the rest of the lines over an hour to make him feel better, we finally went outside into the living room to talk to my older brother and his girlfriend. I made sure I had no coke residue and although I know the difference between a coked up eye and a stoned eye, I think she just assumed I was stoned because she never once mentioned powder. Being extremely friendly and generous with kindness to her we talked while my drunk older brother was calling one of his college friends and we all stood around him. We were supposed to be playing cards but it was delayed for several minutes standing their talking, just because anything in the world is enjoyable on coke.
I guess the higher I get the faster it wears off, because after those short 5-10 minutes of talking to her (although alot of the effects happened in the room which makes sense) I almost instantly became violently depressed, unhappy and ashamed. I felt as though I was tainted by the devil, and I was dirty and didn't deserve to talk to the people around me. Being a long time coke user, I knew coming down happens and it's never fun once I get 4-5 lines into me, but I had never felt this horrible coming off a drug in all my life, which surprises me because Iíve done more coke than I did that night.
I felt so bad, I knew I was hurting alot of my friends and relatives when they knew I did powder, and I was so ashamed that I did all those lines just earlier.  I felt as though Satan were holding my hand, not in a trippy way, in a metaphorical way, and it was at that point I decided I was never gonna sniff coke again. I did end up quitting for good now, I did slip a few times early this month though. I smoked a black and mild cigarillo to help calm the come down. I smoke any tobacco like a cigarette, pipe tobacco, cigars, I donít care. My lungs are really unhealthy.
It took many, many bowls to even get the sharpness of the come down out of my mind. Even late into 4:20 AM where we continued to smoke, I still felt the bad feelings left behind from cocaine, and it took many hours of sleep to finally get rid of it. My older brother was glad that I quit, and told me to 'Stick to weed, man'. I hope I can continue to do what I am doing, because for all the evil and regret and depression coke causes, it will always tempt me with it's initial positive effects.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


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- Rearranging Angular Application
{% include 'main/section-3.1.html' %}
- Project Root:
{% include 'main/section-3.2.html' %}
- Configuring Typescript Compilation
{% include 'main/section-3.3.html' %}
- Configuring Angular CLI
{% include 'main/section-3.4.html' %}
- Building All Together
{% include 'main/section-3.5.html' %}


| PWG LA Meeting |
1394 Printing: December 13 & 14
PWG Plenary: December 15 AM
IPP: December 15 & 16
UPDF: December 17
| Meeting Location: Manhattan Beach Marriott Hotel 1400 Parkview Ave Manhattan Beach, CA 90266 Phone:<PHONE_NUMBER> Contact: Jennifer Swallow,<PHONE_NUMBER> |
The hotel rate is $109 per night.
Ask for the PWG rate at this hotel.
Deadline for reservations is November 30, 1999.
All attendees are responsible for making their own hotel reservations.
There will be a meeting charge in the range of $35 - $45; participants are responsible for their own lunch.


Bicyclist fatally struck by driver who left scene in East Harlem
by Eyewitness NewsEAST HARLEM, Manhattan (WABC) -- Police are searching for the driver who fatally struck a bicyclist in East Harlem early Saturday.
The incident happened at about 3 a.m. at East 125th Street and First Avenue.
The 25-year-old bicyclist was found in the middle of an intersection and rushed to Harlem Hospital, where he was pronounced dead.
He was identified by family members as Matthew Travis Palacios.
Investigators determined a Kenworth dump truck traveling southbound on First Avenue made an illegal left turn onto East 125th Street attempting to enter the Willis Avenue Bridge and struck Palacios as he traveled southbound on a bike path coming off the bridge.
The truck kept on going and was last seen heading north on the Willis Avenue Bridge. It is not yet clear whether the driver realized that someone had been hit.
Police are reviewing video of the crash. So far there have been no arrests.
27 bicyclists have now been killed in New York City this year.


Unfortunately I don't have much to put here yet. Hopefully I wlll change that soon
In the meanwhile, here is my Github Account.
Unfortunately I don't have much to put here yet. Hopefully I wlll change that soon
In the meanwhile, here is my Github Account.


US Scientists: Data at a Glance
Numbers, location, and economic impact
The following data visualization story draws from The
Nobel Prize organization and The National
Center
for Science and Engineering Statistics within the National Science Foudnation
Organizations with Nobel Winners, 1901-2020
Science & Engineering Occupations, 2008-2017
Research & Development, 2008-2017


Utilities to style box elements
Besides the text, you can also style the box elements.
Margin and pading
Padding is blank space between the border of an element and its content. Margin is a blank space outside the border of an element, where no other elements can be. In Co-Design, the utilities work identically for margins and paddings, therefore, we only explain everything once for paddings.
You are able to override the padding to a multiple of the padding unit. The padding unit can be modified in the "_child.scss" and defaults to 4px.
We support the following multiples:
| class | resulting padding |
|---|---|
| .has-padding-0 | 0px |
| .has-padding-1 | 4px |
| .has-padding-2 | 8px |
| .has-padding-4 | 16px |
| .has-padding-6 | 24px |
| .has-padding-8 | 32px |
| .has-padding-12 | 48px |
| .has-padding-16 | 64px |
| .has-padding-18 | 72px |
| .has-padding-24 | 96px |
| .has-padding-32 | 128px |
| .has-padding-36 | 144px |
The class .has-padding-X
can also be replaced with any of the following classes, which only target some of the sides:
.has-padding-top-X
for only the top padding.has-padding-right-X
for only the right padding.has-padding-bottom-X
for only the bottom padding.has-padding-left-X
for only the left padding.has-padding-horizontal-X
for only the left and right padding.has-padding-vertical-X
for only the top and bottom padding
Example

padding: 4

padding: 2 vertical, margin 2 vertical

padding: 16 horizontal, margin: 32 vertical

Border
An element's border can have different styles, widths and radii and can be colored. These properties can be overridden by utilities.
Border width
You can set the border width to any integer value between 0 (no border) and 10. The number is converted to pixels. You can set the border for all sides, or for any of the four sides top, right, bottom or left:
.has-border-width-X
for all four border widths.has-border-top-width-X
for only the top border width.has-border-right-width-X
for only the right border width.has-border-bottom-width-X
for only the bottom border width.has-border-left-width-X
for only the left border width
Example

box with different border widths

box with different border widths
Border style
You can set the border style to any of "solid", "none", "dotted", "dashed", "double". You can set the border for all sides, or for any of the four sides top, right, bottom or left:
.has-border-style-X
for all four border styles.has-border-top-style-X
for only the top border style.has-border-right-style-X
for only the right border style.has-border-bottom-style-X
for only the bottom border style.has-border-left-style-X
for only the left border style
Example

box with different border styles

box with different border styles
Border color
To set the border color, you need the color name and possibly its shade number. Then you can use the pattern .has-border-color-NAME-SHADE
or alternatively .has-border-color-NAME
for the default shade (400).
See the page related to colors in order to see a list of color names and shades.
Border radius
You can set the border radius to any integer value between 0 (no border radius) and 10. The number is converted to pixels. You can set the border for all corners, or for any of the four corners top-left, top-right, bottom-left or bottom-right:
.has-border-radius-X
for all four border radii.has-border-top-left-style-X
for only the top-left corner border radius.has-border-top-right-style-X
for only the top-right corner border radius.has-border-bottom-left-style-X
for only the bottom-left corner border radius.has-border-bottom-right-style-X
for only the bottom-right corner border radius
Example

r0

r3

r6

r9

Box shadow
Box shadows can be given to elements. You have to give them the class .has-shadow-X
for this, where X is a value between 1 and 10. You can use .has-shadow-0
to reset and remove the shadow.
Example

shadow: 0

shadow: 1

shadow: 4

shadow: 7

shadow: 10


{{ post.content }}
{% endfor %}
--- layout: page title: "Blog" description: A blog that is used as a resource for web development and game development tips and tricks. As well as a log of any recent projects. ---
{{ post.date | date: "%-d %B %Y" }}


Constructor
new BodyPart(name, description)
Parameters:
| Name |
Type |
Description |
name |
string
|
The body parts name. |
description |
string
|
The body parts description. |
- Source:
Classes
- BodyPart
Members
changedEvent
Event that is emitted when the body part changes.
- Source:
Methods
addCharacteristic(characteristic)
Parameters:
| Name |
Type |
Description |
characteristic |
Characteristic
|
A characteristic to add to the body part. |
- Source:
clearCharacteristic()
Removes all characteristics from the body part.
- Source:
removeCharacteristic(characteristic)
Parameters:
| Name |
Type |
Description |
characteristic |
Characteristic
|
A characteristic to remove from the body part. |
- Source:


Python
Python is one of the fastest growing programming languages today. With the ability to make concepts into prototypes very quickly, Python is a great first programming language to learn. There's a large community supporting Python too, ensuring it will be valuable for a long time.


In order to carry on the the work from last week on the laser sensor, it was necessary to determine the most suitable equation to converte the output signal of the sensor into a distance. To do so I had to convert the current output signal into a voltage output signal with a 220Ω resistor, and also use a low-pass filter to remove the noise. This filter only allows low frequency signals from 0Hz to its cut-off frequency (ƒc) point to pass while blocking those any higher. The cuf-off frequency is calculated with the following equation:
\[ƒc = {1 \over 2\pi RC}.\]
The first circuit used to convert the output signal, and its schematics are represented below.
While installing the sensor in the robot, which can be seen in the next pictures, I decided to limit the sensor measurements to a maximum and a minimum. Since the sensor will probably not be so far from the object, as shown in the picture, when measuring with precision the distance to its grasping point, I set the minimum to 100mm and the maximum to 900mm. This limits can be changed in the future if necessary.
After gathering some analysis with this filter which as a cut-off frequency of 1592Hz, I obtained the following curve with the corresponding equation.
After changing the Arduino code and tested this equation I got satisfactory results, but not perfect when considering that I want to have as much precision as possible. Therefore I probably have to change my circuit in order to have a lower cut-off frequency and redo all the process and the tests.
Processing the point cloudIn this past two weeks I also calibrated the intrinsic parameters of the Kinect and created a launch file that runs all the necessary programs to process and visualize the point cloud acquired by the Kinect live. This launch file already includes the respective .yaml files to calibrate the rgb and the depth of the sensor.
Using the recorded rosbag I continued to process the point cloud and was able to determine the centroid of a simple box and its normal. To do so I started by calculating all the normals of the entire surface and determine the center of it. Afterwards it was necessary to define which of the points of the point cloud was closer to the center of the surface and determine its corresponding normal. The visualization of the centroid and its normal is shown below.


Game of the Week: Skyline High School at Eastlake High School
March 13, 2013 | 5:00pm
High School baseball is an excellent opportunity for students to take part in America's sport. They have the chance to practice after school and play against other high schools as they learn and improve on how to play baseball, whether they are just starting or have been playing for years.
Game of the Week: Eastside Catholic at Juanita High School
March 13, 2013 | 3:00pm
Fastpitch, or Softball, is a quickly growing sport in America, with many girls wanting to partake in an experience similar to baseball. Any girl from grades 9 to 12 can join one of three softball teams: Varsity, Junior Varsity, and Sophomore Team, with a large emphasis being placed on fun on all of the teams.
Game of the Week: Garfield High School at Redmond High School
March 12, 2013 | 6:00pm
High school soccer is an excellent opportunity for soccer players, new and experienced, to represent their high school on the field. Player's practice after school on weekdays, with two games per week, so this is a seasonal commitment. The kids have the chance to compete with other high schools around the state, which increases the level of competition as compared to the local youth soccer, which only play against the regional teams. There are three teams in high school soccer: Varsity, Junior Varsity and Sophomore team.
Game of the Week: Regional Invitation (Bellevue)
March 15, 2013 | 5:00pm
Track and Field is one of the most popular High Schools sports simply because of the plethora of options that one can participate in while on the Track and Field Team. From the 100 meter dash to the mile to pole vaulting, there are options for any level of athlete, making track and field the most ideal sport to join.


|
XNLO - XNLO
1.1.0
eXtreme NonLinear Optics
|
#include <Schrodinger_atom_1D.hpp>
Public Member Functions | |
| Schrodinger_atom_1D (grid_tw &tw_, double alpha_, int output_wavefunction_) | |
| void | set_GS (int N_it_) |
| ArrayXd | get_acceleration (int N_it_, double dt_, ArrayXd E_) |
| ArrayXd | solve_TDSE_PS (int N_it_, std::complex< double > dt_, ArrayXd E_, int e_) |
Public Attributes | |
| grid_tw | tw |
| grid_xkx | xkx |
| double | alpha |
| ArrayXd | V_model |
| ArrayXcd | wfn_GS |
| ArrayXcd | wfn |
| double | energy |
| int | output_wavefunction |
| ArrayXXcd | wfn_output |
Modified by Patrick Anderson on 07/05/2015. "Schrodinger_atom_1D" encapsulates the interaction of an isolated atom with a strong laser field. The interaction is restricted to a single active electron and spatial dimension.
| XNLO::Schrodinger_atom_1D::Schrodinger_atom_1D | ( | grid_tw & | tw_, |
| double | alpha_, | ||
| int | output_wavefunction_ | ||
| ) |
Class constructor.
| ArrayXd XNLO::Schrodinger_atom_1D::get_acceleration | ( | int | N_it_, |
| double | dt_, | ||
| ArrayXd | E_ | ||
| ) |
Find the electron acceleration generated by an arbitary field. This is achieved by solving the TDSE using the pseudo-spectral method and then using the wavefunction in conjunction with the Ehrefest theorem to obtain the electron acceleration by
M. B. Gaarde and K. J. Schafer. Theory of attosecond pulse generation. Springer series in Optical Sciences, 177:11-31, 2013.
| void XNLO::Schrodinger_atom_1D::set_GS | ( | int | N_it_ | ) |
Find the GS wavefunction by imaginary time propagation, also display the energy. The time-dependent Schrodinger equation (TDSE) in atomic units is given as
Transforming to imaginary time causes the TDSE to become a diffusion equation,
As time increases, the wavefunction then converges to the ground state,
P. Bader et al. Solving the Schrodinger eigenvalue problem by imaginary time propagation techniques using splitting methods with complex coefficients, J. Chem. Phys., 139, 2013
The energy expectation value is given by
| ArrayXd XNLO::Schrodinger_atom_1D::solve_TDSE_PS | ( | int | N_it_, |
| std::complex< double > | dt_, | ||
| ArrayXd | E_, | ||
| int | e_ | ||
| ) |
TDSE solver (pseudo-spectral method). The time propagator of the pseudo-spectral method is given as
P. L. DeVries and J. Hasbun. A first course in computational physics. Jones and Bartlett, 2nd edition, 2010.
| double XNLO::Schrodinger_atom_1D::alpha |
The parameter is used to scale the Coulomb potential to match the ground state energy of the atom.
| double XNLO::Schrodinger_atom_1D::energy |
The energy expectation value of the elctron.
| int XNLO::Schrodinger_atom_1D::output_wavefunction |
A switch to toggle outputting and saving the electron wavefunction, for every position and time step.
| grid_tw XNLO::Schrodinger_atom_1D::tw |
The linear temporal grid.
| ArrayXd XNLO::Schrodinger_atom_1D::V_model |
The soft Coulomb potential,
| ArrayXcd XNLO::Schrodinger_atom_1D::wfn |
The wavefunction of the electron at a time and as a function of position.
| ArrayXcd XNLO::Schrodinger_atom_1D::wfn_GS |
The ground state wavefunction of the electron at the initial time and as a function of position.
| ArrayXXcd XNLO::Schrodinger_atom_1D::wfn_output |
The electron wavefunction at every position and time step.
| grid_xkx XNLO::Schrodinger_atom_1D::xkx |
The linear spatial grid.


Soothing climate attracts tourists to Kodagu
by Adithya K AAs the mountain mist begins to surround Kodagu with the onset of winter, a notable change is witnessed in the tourism activities in the district.
Tourism in the district, which was affected by back-to-back floods and landslides, has been showing signs of bouncing back to normalcy as visitors have begun thronging the tourist spots in significant numbers.
The owners of homestays, hotels, resorts, tourist taxis, cars and jeeps are hoping for an increase in their business.
An increase in the number of tourists was found in the first week of November.
Even though there were large scale landslides in 32 villages of Madikeri and Somwarpet in 2018, no such incidents of such massive scale was repeated in 2019, which is one of the reasons for a good turn up of tourists.
The tourist spots such as Raja Seat, Dubare, Abbi Falls, Bhagamandala and coffee and tea plantations are again buzzing with tourists after two years.
A favoured destination
Following incessant rain, the tourists were prohibited from visiting Mandalpatti, as a precautionary measure.
Mandalpatti has been the most favourite destination of tourists. As the rain has receded, the gates to Mandalpatti have been thrown open to the tourists again.
Rental jeeps have begun plying to the location after a long gap of three months. Jeeps are preferred by the tourists and the local people to visit such places on hilly terrain.
A huge crowd of tourists was also seen in Bhagamandala and Talacauvery.
Ajay, a software engineer from Bengaluru, said that he visits Kodagu once in a while. The lush greenery in most of the tourist spots has been providing a very good experience.
Although it is sad to see the historic Old Fort Palace in such a pitiable condition, he lamented.
Another visitor, Poorvika, a Bengaluru based lecturer, said that it is impossible to describe the beauty of Mandalpatti in Kodagu.
The view of the sunset at Raja Seat, was memorable, she stressed.
According to the officials from the tourism department, the number of tourists drastically fell in the past two years. Earlier, 18 lakh people used to annually visit
Kodagu.
Homestay owners opine that the number of tourists may still increase by the end of November and in December as the weather will be cool.


Deletes an existing activity, if the access controls allow it.
list(userId, collection, pageToken=None, hl=None, maxResults=None)
Retrieves a list of activities.
list_next(previous_request, previous_response)
Retrieves the next page of results.
delete(activityId)
Deletes an existing activity, if the access controls allow it. Args: activityId: string, ID of the activity to remove. (required)
list(userId, collection, pageToken=None, hl=None, maxResults=None)
Retrieves a list of activities. Args: userId: string, The ID of the user whose activities will be listed. Can be me to refer to the viewer (i.e. the authenticated user). (required) collection: string, The collection of activities to list. (required) Allowed values all - All activities created by the specified user that the authenticated user is authorized to view. scraps - The specified user's scrapbook. stream - The specified user's stream feed, intended for consumption. This includes activities posted by people that the user is following, and activities in which the user has been mentioned. pageToken: string, A continuation token that allows pagination. hl: string, Specifies the interface language (host language) of your user interface. maxResults: integer, The maximum number of activities to include in the response. Returns: An object of the form: { "nextPageToken": "A String", # The value of pageToken query parameter in activities.list request to get the next page, if there are more to retrieve. "items": [ # List of activities retrieved. { "kind": "orkut#activity", # The kind of activity. Always orkut#activity. "links": [ # Links to resources related to this activity. { # Links to resources related to the parent object. "href": "A String", # URL of the link. "type": "A String", # Media type of the link. "rel": "A String", # Relation between the resource and the parent object. "title": "A String", # Title of the link. }, ], "title": "A String", # Title of the activity. "object": { # The activity's object. "content": "A String", # The HTML-formatted content, suitable for display. When updating an activity's content, post the changes to this property, using the value of originalContent as a starting point. If the update is successful, the server adds HTML formatting and responds with this formatted content. "items": [ # The list of additional items. { "displayName": "A String", # The title of the object. "links": [ # Links to other resources related to this object. { # Links to resources related to the parent object. "href": "A String", # URL of the link. "type": "A String", # Media type of the link. "rel": "A String", # Relation between the resource and the parent object. "title": "A String", # Title of the link. }, ], "community": { # The community which is related with this activity, e.g. a joined community. "category": "A String", # The category of the community. "kind": "orkut#community", # Identifies this resource as a community. Value: "orkut#community" "member_count": 42, # The number of users who are part of the community. This number may be approximate, so do not rely on it for iteration. "description": "A String", # The description of the community. "language": "A String", # The official language of the community. "links": [ # List of resources for the community. { # Links to resources related to the parent object. "href": "A String", # URL of the link. "type": "A String", # Media type of the link. "rel": "A String", # Relation between the resource and the parent object. "title": "A String", # Title of the link. }, ], "creation_date": "A String", # The time the community was created, in RFC 3339 format. "owner": { # The person who owns the community. "url": "A String", # The URL of the author who posted the comment [not yet implemented] "image": { # Image data about the author. "url": "A String", # A URL that points to a thumbnail photo of the author. }, "displayName": "A String", # The name of the author, suitable for display. "id": "A String", # Unique identifier of the person who posted the comment. This is the person's OpenSocial ID. }, "moderators": [ # The list of moderators of the community. { "url": "A String", # The URL of the author who posted the comment [not yet implemented] "image": { # Image data about the author. "url": "A String", # A URL that points to a thumbnail photo of the author. }, "displayName": "A String", # The name of the author, suitable for display. "id": "A String", # Unique identifier of the person who posted the comment. This is the person's OpenSocial ID. }, ], "location": "A String", # The location of the community. "co_owners": [ # The co-owners of the community. { "url": "A String", # The URL of the author who posted the comment [not yet implemented] "image": { # Image data about the author. "url": "A String", # A URL that points to a thumbnail photo of the author. }, "displayName": "A String", # The name of the author, suitable for display. "id": "A String", # Unique identifier of the person who posted the comment. This is the person's OpenSocial ID. }, ], "photo_url": "A String", # The photo of the community. "id": 42, # The id of the community. "name": "A String", # The name of the community. }, "content": "A String", # The HTML-formatted content, suitable for display. When updating an activity's content, post the changes to this property, using the value of originalContent as a starting point. If the update is successful, the server adds HTML formatting and responds with this formatted content. "person": { # The person who is related with this activity, e.g. an Added User. "name": { # An object that encapsulates the individual components of a person's name. "givenName": "A String", # The given name (first name) of this person. "familyName": "A String", # The family name (last name) of this person. }, "url": "A String", # The person's profile url. This is adapted from Google+ and was originaly introduced as extra OpenSocial convenience fields. "gender": "A String", # The person's gender. Values include "male", "female", and "other". "image": { # The person's profile photo. This is adapted from Google+ and was originaly introduced as extra OpenSocial convenience fields. "url": "A String", # The URL of the person's profile photo. }, "birthday": "A String", # The person's date of birth, represented as YYYY-MM-DD. "id": "A String", # The person's opensocial ID. }, "id": "A String", # The ID for the object. "objectType": "A String", # The object type. }, ], "replies": { # Comments in reply to this activity. "totalItems": "A String", # Total number of comments. "items": [ # The list of comments. { "inReplyTo": { # Link to the original activity where this comment was posted. "type": "A String", # Type of the post on activity stream being commented. Always text/html. "href": "A String", # Link to the post on activity stream being commented. "ref": "A String", # Unique identifier of the post on activity stream being commented. "rel": "inReplyTo", # Relationship between the comment and the post on activity stream being commented. Always inReplyTo. }, "kind": "orkut#comment", # Identifies this resource as a comment. Value: "orkut#comment" "links": [ # List of resources for the comment. { # Links to resources related to the parent object. "href": "A String", # URL of the link. "type": "A String", # Media type of the link. "rel": "A String", # Relation between the resource and the parent object. "title": "A String", # Title of the link. }, ], "actor": { # The person who posted the comment. "url": "A String", # The URL of the author who posted the comment [not yet implemented] "image": { # Image data about the author. "url": "A String", # A URL that points to a thumbnail photo of the author. }, "displayName": "A String", # The name of the author, suitable for display. "id": "A String", # Unique identifier of the person who posted the comment. This is the person's OpenSocial ID. }, "content": "A String", # The content of the comment in text/html "published": "A String", # The time the comment was initially published, in RFC 3339 format. "id": "A String", # The unique ID for the comment. }, ], "url": "A String", # URL for the collection of comments in reply to this activity. }, "objectType": "A String", # The type of the object affected by the activity. Clients can use this information to style the rendered activity object differently depending on the content. }, "updated": "A String", # The time at which the activity was last updated. "actor": { # The person who performed the activity. "url": "A String", # The URL of the author who posted the comment [not yet implemented] "image": { # Image data about the author. "url": "A String", # A URL that points to a thumbnail photo of the author. }, "displayName": "A String", # The name of the author, suitable for display. "id": "A String", # Unique identifier of the person who posted the comment. This is the person's OpenSocial ID. }, "access": { # Identifies who has access to see this activity. "items": [ # The list of ACL entries. { "type": "A String", # The type of entity to whom access is granted. "id": "A String", # The ID of the entity. For entities of type "person" or "circle", this is the ID of the resource. For other types, this will be unset. }, ], "kind": "orkut#acl", # Identifies this resource as an access control list. Value: "orkut#acl" "description": "A String", # Human readable description of the access granted. "totalParticipants": 42, # The total count of participants of the parent resource. }, "verb": "A String", # This activity's verb, indicating what action was performed. Possible values are: # - add - User added new content to profile or album, e.g. video, photo. # - post - User publish content to the stream, e.g. status, scrap. # - update - User commented on an activity. # - make-friend - User added a new friend. # - birthday - User has a birthday. "published": "A String", # The time at which the activity was initially published. "id": "A String", # The ID for the activity. }, ], "kind": "orkut#activityList", # Identifies this resource as a collection of activities. Value: "orkut#activityList" }
list_next(previous_request, previous_response)
Retrieves the next page of results. Args: previous_request: The request for the previous page. (required) previous_response: The response from the request for the previous page. (required) Returns: A request object that you can call 'execute()' on to request the next page. Returns None if there are no more items in the collection.


Dan Lewis
Clemson University
"Web accessibility means that people with disabilities can use the Web."
"More specifically, Web accessibility means that people with disabilities can perceive, understand, navigate, and interact with the Web, and that they can contribute to the Web."
Many countries have laws for accessibility
"To enable persons with disabilities to live independently and participate fully in all aspects of life"
"To ensure that persons with disabilities can exercise the right to freedom of expression and opinion"
"We're all just temporarily abled."
Cindy Li
Design for the Future
As developers, we have a certain type of brain...
A person wearing a hat is sitting in a chair and begins speaking to a bystander.
Person in Hat: There's a certain type of brain that's easily disabled.
If you show it an interesting problem, it involuntariliy drops everything else to work on it.
This has led me to invent a new sport: nerd sniping.
See that physicist crossing the road?
Hey!
The Person wearing the hat holds up a sign that reads: On this infinite grid of ideal one-ohm resistors, what's the equivalent resistance between the two marked nodes?
Physicist: It's... hmm. Interesting. Maybe if you start with... No, wait. Hmm... You could...
(A truck zooms by.)
Bystander: I will have no part in this.
Person in Hat: C'mon, make a sign. It's fun! Physicists are two points, mathematicians three.


perovskite perovskites Zhang Chem. PbI Wang Mater. (BA) (PEA) emission −Pb Copyright LEDs Figure Liu Reproduced halide Chen Lett. structure organic reported inorganic different charge energy Adv. Yang bandgap (MA) Phys. light solar exciton layers permission. solution ACS binding film (CH EQE high obtained American Energy Soc. (Figure Am. Chemical PbBr Society. efficiency films group layer .[qv: lead optoelectronic quantum surface {border-style Nano cations quasi‐D band crystal number perovskites, perovskites. px;} single #ffffff;} Commun. recombination Kim PEA Science Stoumpos absorption metal nonradiative peak spectra stability Kanatzidis Sun confinement crystals device efficient large orientation phase used Gao Huang Yan Zhao form hybrid Nat. PCE Wei achieved cation good highest layered permission.[qv: properties shown shows transfer Cao However, Sci. based broad increase introduction white LEDs. Lee PLQY Shi achieve blue cells current defects electrons holes improved larger materials method mixed organic–inorganic precursor radiative solvent temperature Recently, State Tsai Zhu carrier devices growth important lattice low lower material mobility optical order spin synthesized thickness (FA) Appl. Mitzi [qv: cations, compared crystallization process range ratio results series (NMA) /Au Fang Guo Jilin Mao Mathews Nie Peng RPP Rev. Shen SnI Sum TPC Table Yin Zou caused change defect density devices. dielectric effect electron excellent excitons fluorescence higher layers. methods px; smaller state synthesis uniform value visible {font-family (EDBE)PbBr )Pb Compared Development Ishihara MAPbI MSE Mhaisalkar Nanoscale Nature Nature. N‐MEDA)[PbBr PbCl PbX Ruddlesden–Popper SnBr Snaith Springer University Zhou addition amine cells. changes containing corresponding deposition diffraction effective film, font-size force greatly iodide light‐emitting liquid‐phase method. morphology recombination, regulation small stability. strong structures time valence (C) (quasi‐) Angew. Cheng China CsPbBr D/D Different Ding Han ITO/PEIE–ZnO/EM/TFB/MoO Jin Karunadasa Key LEDs, Miao Mohite N‐MEDA)PbBr Program Qiu So, Tan Therefore, Wiley‐VCH. addition, affected b,] color conduction control decreases demonstrated der distribution double; excitation excited films. formed green images improve injection longer luminescence materials, monolayer near‐infrared orbitals perovskite–based prepared rate reaction reduce region room self‐trapping stoichiometric structure. transition tunable wide (OAm) )PbI .Br Bai Blancon Colvin Deleporte Electronic Fan GeI Interfaces Jiang John Katan LPC LSM Laboratory Lauret Liang MAPbBr National Niu Opt. Optical Park Photonics Properties Sargent Soci Solid Synthesis TPC[qv: Then, Voznyy Wasielewski Wiley adjusting alkyl ammonium appropriate aromatic bands border-color border-width chain coating colloidal composed concentration constant continuous controlled cooling corrugated design diammonium distortion energy, evaporation films, formula free grain group. hole including increases insulating known lead‐free ligands make maximum mechanism nm. parallel performance perovskite, prepare properties. proportion recombination. reduced relatively research resulted resulting similar simple solid; solvents substrate summarized synthesize transport trap van vapor {font-style {font-weight (NH (PhE) .%. AFM According Amassian Bruno CsPb Dai EQE) Environ. FWHM Heremans ITO/PEDOT:PSS/EM/TPBi/Ca/Al Kepenekian Kondo LED Lin Ltd., Moreover, NFPI PEABr Perovskite Qin Quan Rogach Sadhanala Schematic Seo Shang Smith Song Sons, Takahashi Tian Waals Xing Yantara Yuan Zeng Zhai Zhang's Zheng Zhong [], a). a,] a], acts adding ambient application applications applications. applied b). ball balls bandgap, biexciton bold bond brightness broadband c). carbon cell cells, charges chemical composite compounds confirmed conventional corner‐sharing coupling courier; crystallization. decay decrease density. devices, dissolved dropped eV. easy effectively efficiency. efforts em; emission, energies film. formation great greater ground highly hot importantly, improved. impurity increases, influence intensity introduced large‐sized length located main mainly meV min moisture molar multiphase nanoplatelets narrow need obtained. peaks permanent photographs process. promotes proved pt;} quality recent red reduces role seen separation sheet sheets single‐crystal size solutions stable state, states studied temperature, thicknesses unique voltage wider


Documentation¶
This is the pyglet documentation, generated with sphinx.
Details:
Note
See the Sphinx warnings log file for errors.
Writing documentation¶
pyglet uses reStructuredText markup language for both the Programming Guide and the docstrings embedded in the code.
Literature¶
It is divided into several files, which are organized by toctree directives .
The entry point for all the documentation is pyglet/doc/index.txt, which calls to:
- pyglet/doc/programming_guide: The first page of the programming guide.
- pyglet/doc/internal: Documentation for those working on Pyglet itself, such as how to make a release or how the ctypes library is used.
See also
Source code¶
The API documentation is generated from the source code docstrings.
| Example: | class Class1():
"""Short description.
Detailed explanation, formatted as reST.
Can be as detailed as it is needed.
:Ivariables:
`arg1`
description
:since: pyglet 1.2
"""
attribute1 = None
"""This is an attribute.
More details.
"""
#: This is another attribute.
attribute2 = None
def __init__(self):
"""Constructor
:parameters:
`arg1` : type
description
"""
self.instance_attribute = None
"""This is an instance attribute.
"""
def method(self):
"""Short description.
:returns: return description
:rtype: returned type
"""
def _get_property1(self):
"""Getter Method
:return: property1 value
:rtype: property1 type
"""
def _set_property1(self, value):
"""Setter Method
:param value: property1 value # This is the ReST style
:type value: property1 type # But you can use :parameters:
"""
property1 = property(_get_property1, _set_property1,
doc="""Short description
This is another attribute.
:type: type
:see: something else
""")
|
|---|
Pyglet has some special roles.
Source Shows :deprecated: Do not use Warning
Deprecated. Do not use
:since: 1.2 Note
Since 1.2
Internal references¶
To cross-reference to any documented API member, use the following roles:
Source Reference :mod:`pyglet.app` The pyglet.app module :func:`~pyglet.app.run` The run() function :class:`~pyglet.window.Window` The Window class :meth:`~pyglet.window.Window.close` The close() method :attr:`~pyglet.window.Window.fullscreen` The fullscreen attribute
Note
Use ~ to show only the last part.
You can link to arbitrary locations in any document using :ref:, but pyglet has a special role :guide: for this guide.
A section header of the guide can have an anchor like this:
.. _guide_doc_ref:
Internal references
===================
It is also possible to put an anchor anywhere:
.. _my_anchor:
My anchor.
And to insert a reference to an anchor:
Source Shows :guide:`doc_ref` See also
Programming Guide - Internal references
:ref:`My Anchor<my_anchor>` My Anchor
Generation¶
The complete documentation can be generated using sphinx.
Requirements
- sphinx: Documentation builder.
- graphviz: If you want to generate graphs.
To build the documentation, execute:
./make.py docs
If the build succeeds, the web pages are in _build/html
Details


Concurrency control refers to the various techniques that are used to preserve the integrity of the database when multiple users are updating rows at the same time. Incorrect concurrency can lead to problems such as dirty reads, phantom reads, and non-repeatable reads. The Microsoft SQL Server JDBC Driver provides interfaces to all the concurrency techniques used by SQL Server to resolve these issues.
The JDBC driver supports the following concurrency types:
| Concurrency Type | Characteristics | Row Locks | Description |
|---|---|---|---|
|
CONCUR_READ_ONLY |
Read Only |
No |
Updates through the cursor are not allowed, and no locks are held on the rows that make up the result set. |
|
CONCUR_UPDATABLE |
Optimistic Read Write |
No |
Database assumes row contention is unlikely, but possible. Row integrity is checked with a timestamp comparison. |
|
CONCUR_SS_SCROLL_LOCKS |
Pessimistic Read Write |
Yes |
Database assumes row contention is likely. Row integrity is ensured with row locking. |
|
CONCUR_SS_OPTIMISTIC_CC |
Optimistic Read Write |
No |
Database assumes row contention is unlikely, but possible. Row integrity is verified with a timestamp comparison. For SQL Server 2005 and later, the server will change this to CONCUR_SS_OPTIMISTIC_CCVAL if the table does not contain a timestamp column. For SQL Server 2000, if the underlying table has a timestamp column, OPTIMISTIC WITH ROW VERSIONING is used even if OPTIMISTIC WITH VALUES is specified. If OPTIMISTIC WITH ROW VERSIONING is specified and the table does not have timestamps, OPTIMISTIC WITH VALUES is used. |
|
CONCUR_SS_OPTIMISTIC_CCVAL |
Optimistic Read Write |
No |
Database assumes row contention is unlikely, but possible. Row integrity is checked with a row data comparison. |
Result Sets That Are Not Updateable
An updatable result set is a result set in which rows can be inserted, updated, and deleted. In the following cases, SQL Server cannot create an updatable cursor. The exception generated is, "Result set is not updatable."
| Cause | Description | Remedy |
|---|---|---|
|
Statement is not created by using JDBC 2.0 (or later) syntax |
JDBC 2.0 introduced new methods to create statements. If JDBC 1.0 syntax is used, the result set defaults to read-only. |
Specify result set type and concurrency when creating the statement. |
|
Statement is created by using TYPE_SCROLL_INSENSITIVE |
SQL Server creates a static snapshot cursor. This is disconnected from the underlying table rows to help protect the cursor from row updates by other users. |
Use TYPE_SCROLL_SENSITIVE, TYPE_SS_SCROLL_KEYSET, TYPE_SS_SCROLL_DYNAMIC, or TYPE_FORWARD_ONLY with CONCUR_UPDATABLE to avoid creating a static cursor. |
|
Table design precludes a KEYSET or DYNAMIC cursor |
The underlying table does not have unique keys to enable SQL Server to uniquely identify a row. |
Add unique keys to the table to provide unique identification of each row. |


WaDE [WaDE2.0_schema_star.xml]
| PK | Name | Data Type | NULL | Auto | Default | Comment | FK |
|
| OrganizationUUID | NVarChar(250) | unique identifier to Organization | ||||
| AllocationNativeID | NVarChar(250) | ||||||
| AllocationOwner | NVarChar(250) | ||||||
| AllocationBasisCV | NVarChar(250) |
| |||||
| AllocationLegalStatusCV | NVarChar(250) | ||||||
| AllocationTypeCV | NVarChar(250) |
| |||||
| AllocationPriorityDate | Date | ||||||
| AllocationExpirationDate | Date |
| |||||
| AllocationChangeApplicationIndicator | NVarChar(250) |
| |||||
| LegacyAllocationIDs | NVarChar(250) |
| |||||
| SiteUUID | NVarChar(250) | unique identifier to site | |||||
| VariableSpecificUUID | NVarChar(250) | unique identifier to variable specific | |||||
| BeneficialUseCategoryCV | NVarChar(250) | coma seperated beneficial uses | |||||
| PrimaryUseCategoryCV | NVarChar(250) |
| If multiple beneficial uses are specified, add primary use category | ||||
| WaterSourceUUID | NVarChar(250) | foreign key unique identifier to water source | |||||
| MethodUUID | NVarChar(250) | ||||||
| TimeframeStartDate | Date | Start date for the allocation each year in MM/DD/YYYY | |||||
| TimeframeEndDate | Date | End date for the allocation each year in MM/DD/YYYY | |||||
| AllocationCropDutyAmount | Float |
| Duty amount per acre in feet, if available | ||||
| AllocationAmount | Float |
| The allocation amount | ||||
| AllocationMaximum | Float |
| Amount of the allocation maximum or cap | ||||
| PopulationServed | Float |
| Population served by the aggregated variable amount, If municipal or community water supply is a specified beneficial use | ||||
| PowerGeneratedGWh | Float |
| GWh to be generated by the aggregated variable amount, if thermoelectric is a specified beneficial use | ||||
| IrrigatedAcreage | Float |
| Number of acres irrigated by the aggregated variable amount, if irrigation or agriculture is a specified beneficial use | ||||
| AllocationCommunityWaterSupplySystem | NVarChar(250) |
| If the use is municipal, the data provider can add the SDWIS identifier for the CWS. Only for site specific data. Leave it null for aggregate data | ||||
| CustomerTypeCV | NVarChar(100) |
| Residential, Commercial, Industrial, Institutional, Combined, Unspecified | ||||
| AllocationSDWISIdentifierCV | NVarChar(250) |
| If the use is municipal, the data provider can add the SDWIS identifier for the CWS | ||||
| Geometry | Geometry |
| Well-Known Text (WKT): The GIS objects supported by PostGIS are a superset of the "Simple Features" defined by the OpenGIS Consortium (OGC). PostGIS supports all the objects and functions specified in the OGC "Simple Features for SQL" specification. | ||||
| DataPublicationDOI | NVarChar(250) |
|
(none)
(none)
(none)
| Diagram Name |
| 6_InputTables |


HTML is the bones and DOM is the heirarchy that connects all the bones in a meaningful way so that the body can function effectively. CSS is all the layers on top such as the skin, muscles, eyes etc that affect the look.
Boxifying is the process of dividing the elements of a web page design into boxes so that the DOM structure and the layout of elements within a page can be outlined. This process is usually done before jumping into HTML and CSS. It can also be used to understand the layout and DOM structure of already exsting web pages.
All HTML elements can be considered as boxes. In CSS, the term "box model" is used when talking about design and layout. The CSS box model is essentially a box that wraps around every HTML element. It consists of: margins, borders, padding, and the actual content.


Try the development version¶
Note
git is the name of a source code management system. It is used to keep track of changes made to code and to manage contributions coming from several different people. If you want to read more about git right now take a look at Git Basics.
If you have never used git before, allow one hour the first time you do this. You will not need to do this every time you want to contribute; most of it is one-time setup. You can count on one hand the number of git commands you will need to regularly use to keep your local copy of Astropy up to date. If you find this taking more than an hour email the astropy developers list for help
Trying out the development version of Astropy is useful in three ways:
- More users testing new features helps uncover bugs before the feature is released.
- A bug in the most recent stable release might have been fixed in the development version. Knowing whether that is the case can make your bug reports more useful.
- You will need to go through all of these steps before contributing any code to Astropy. Practicing now will save you time later if you plan to contribute.
Overview¶
Conceptually, there are several steps to getting a working copy of the latest version of Astropy on your computer:
- Make your own copy of Astropy on GitHub; this copy is called a fork (if you don’t have an account on github yet, go there now and make one).
- Make sure git is installed and configured on your computer
- Copy your fork of Astropy from GitHub to your computer; this is called making a clone of the repository.
- Tell git where to look for changes in the development version of Astropy
- Create your own private workspace; this is called making a branch.
- “Activate” the development version of astropy
- Test your development copy
- Try out the development version
- “Deactivate” the development version
Step-by-step instructions¶
Make your own copy of Astropy on GitHub¶
In the language of GitHub, making a copy of someone’s code is called making a fork. A fork is a complete copy of the code and all of its revision history.
Log into your GitHub account.
Go to the Astropy GitHub home page.
Click on the fork button:
After a short pause and an animation of Octocat scanning a book on a flatbed scanner, you should find yourself at the home page for your own forked copy of Astropy.
Make sure git is installed and configured on your computer¶
Check that git is installed:
Check by typing, in a terminal:
$ git --version
# if git is installed, will get something like: git version 1.8.4
If git is not installed, get it.
Basic git configuration:
Follow the instructions at Set Up Git at GitHub to take care of two essential items:
- Set your user name and email in your copy of git
- Set up authentication so you don’t have to type your github password every time you need to access github from the command line. The default method at Set Up Git at GitHub may require administrative privleges; if that is a problem, set up authentication using SSH keys instead
We also recommend setting up git so that when you copy changes from your computer to GitHub only the copy (called a branch) of Astropy that you are working on gets pushed up to GitHub. If your version of git is 1.7.11 or, greater, you can do that with:
git config --global push.default simple
If you skip this step now it is not a problem; git will remind you to do it in those cases when it is relevant. If your version of git is less than 1.7.11, you can still continue without this, but it may lead to confusion later, as you might push up branches you do not intend to push.
Note
Make sure you make a note of which authentication method you set up because it affects the command you use to copy your GitHub fork to your computer.
If you set up password caching (the default method) the URLs will look like
https://github.com/your-user-name/astropy.git
.
If you set up SSH keys the URLs you use for making copies will look
something like git@github.com:your-user-name/astropy.git
.
Copy your fork of Astropy from GitHub to your computer¶
One of the commands below will make a complete copy of your GitHub fork
of Astropy in a directory called astropy
; which form you use depends
on what kind of authentication you set up in the previous step:
# Use this form if you setup SSH keys...
$ git clone git@github.com:your-user-name/astropy.git
# ...otherwise use this form:
$ git clone https://github.com/your-user-name/astropy.git
If there is an error at this stage it is probably an error in setting up authentication.
Tell git where to look for changes in the development version of Astropy¶
Right now your local copy of Astropy doesn’t know where the development version of Astropy is. There is no easy way to keep your local copy up to date. In git the name for another location of the same repository is a remote. The repository that contains the latest “official” development version is traditionally called the upstream remote, but here we use a more meaningful name for the remote: astropy.
Change into the astropy
directory you created in the previous step and
let git know about about the astropy remote:
cd astropy
git remote add astropy git://github.com/astropy/astropy.git
You can check that everything is set up properly so far by asking git to
show you all of the remotes it knows about for your local repository of
Astropy with git remote -v
, which should display something like:
astropy git://github.com/astropy/astropy.git (fetch)
astropy git://github.com/astropy/astropy.git (push)
origin git@github.com:your-user-name/astropy.git (fetch)
origin git@github.com:your-user-name/astropy.git (push)
Note that git already knew about one remote, called origin; that is your fork of Astropy on GitHub.
To make more explicit that origin is really your fork of Astropy, rename that remote to your GitHub user name:
git remote rename origin your-user-name
Create your own private workspace¶
One of the nice things about git is that it is easy to make what is essentially your own private workspace to try out coding ideas. git calls these workspaces branches.
Your repository already has several branches; see them if you want by running
git branch -a
. Most of them are on remotes/origin
; in other words,
they exist on your remote copy of Astropy on GitHub.
There is one special branch, called master. Right now it is the one you are
working on; you can tell because it has a marker next to it in your list of
branches: * master
.
To make a long story short, you never want to work on master. Always work on a branch.
To avoid potential confusion down the road, make your own branch now; this one you can call anything you like (when making contributions you should use a meaningful more name):
git branch my-own-astropy
You are not quite done yet. Git knows about this new branch; run
git branch
and you get:
* master
my-own-astropy
The *
indicates you are still working on master. To work on your branch
instead you need to check out the branch my-own-astropy
. Do that with:
git checkout my-own-astropy
and you should be rewarded with:
Switched to branch 'my-own-astropy'
“Activate” the development version of astropy¶
Right now you have the development version of Astropy, but python will not see it. Though there are more sophisticated ways of managing multiple versions of Astropy, for now this straightforward way will work (if you want to jump ahead to the more sophisticated method look at Python virtual environments).
Note
There are a couple of circumstances in which this quick method of activating your copy of Astropy will NOT work and you need to go straight to using a virtual python environment:
- You use Python 3.
- You want to work on C or Cython code in Astropy.
In the directory where your copy of Astropy is type:
python setup.py develop
Several pages of output will follow the first time you do this; this wouldn’t
be a bad time to get a fresh cup of coffee. At the end of it you should see
something like Finished processing dependencies for astropy==0.3.dev6272
.
To make sure it has been activated change to a different directory outside of the astropy distribution and try this in python:
>>> import astropy
>>> astropy.__version__
'0.3.dev6272'
The actual version number will be different than in this example, but it should have dev in the name.
Warning
Right now every time you run Python, the development version of astropy will be used. That is fine for testing but you should make sure you change back to the stable version unless you are developing astropy. If you want to develop astropy, there is a better way of separating the development version from the version you do science with. That method, using a virtualenv, is discussed at Python virtual environments.
For now remember to change back to your usual version when you are done with this.
Test your development copy¶
Testing is an important part of making sure Astropy produces reliable, reproducible results. Before you try out a new feature or think you have found a bug make sure the tests run properly on your system.
If the test don’t complete successfully, that is itself a bug–please report it.
To run the tests, navigate back to the directory your copy of astropy is in on your computer, then, at the shell prompt, type:
python setup.py test
This is another good time to get some coffee or tea. The number of test is large. When the test are done running you will see a message something like this:
4741 passed, 85 skipped, 11 xfailed
Skips and xfails are fine, but if there are errors or failures please report them.
Try out the development version¶
If you are going through this to ramp up to making more contributions to Astropy you don’t actually have to do anything here.
If you are doing this because you have found a bug and are checking that it still exists in the development version, try running your code.
Or, just for fun, try out one of the new features in the development version.
Either way, once you are done, make sure you do the next step.
“Deactivate” the development version¶
Be sure to turn the development version off before you go back to doing science work with Astropy.
Navigate to the directory where your local copy of the development version is, then run:
python setup.py develop -u
You should really confirm it is deactivated by changing to a different directory outside of the astropy distribution and running this in python:
>>> import astropy
>>> astropy.__version__
'0.2.5'
The actual version number you see will likely be different than this example,
but it should not have 'dev'
in it.


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Life Hack is a tool or technique that makes some aspect of one's life easier or more efficient. It is also a term the technologically (mostly) illiterate throw around to demonstrate that they understand these newfangled computers. Formerly known as "good ideas." So, Myle Life Hack provides you all the life hack method to ease your daily activities.


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addOculusMobileSDKHeadTrackingListener(OculusMobileSDKHeadTrackingListener)
- Method in class com.judax.oculusmobilesdkheadtracking.
OculusMobileSDKHeadTracking
Register a listener that will be notified when head tracking changes occur.
angularAccelerationX
- Variable in class com.judax.oculusmobilesdkheadtracking.
OculusMobileSDKHeadTrackingData
angularAccelerationY
- Variable in class com.judax.oculusmobilesdkheadtracking.
OculusMobileSDKHeadTrackingData
angularAccelerationZ
- Variable in class com.judax.oculusmobilesdkheadtracking.
OculusMobileSDKHeadTrackingData
angularVelocityX
- Variable in class com.judax.oculusmobilesdkheadtracking.
OculusMobileSDKHeadTrackingData
angularVelocityY
- Variable in class com.judax.oculusmobilesdkheadtracking.
OculusMobileSDKHeadTrackingData
angularVelocityZ
- Variable in class com.judax.oculusmobilesdkheadtracking.
OculusMobileSDKHeadTrackingData
C
com.judax.oculusmobilesdkheadtracking
- package com.judax.oculusmobilesdkheadtracking
D
docked
- Variable in class com.judax.oculusmobilesdkheadtracking.
OculusMobileSDKHeadTrackingData
G
getData()
- Method in class com.judax.oculusmobilesdkheadtracking.
OculusMobileSDKHeadTracking
getErrorMessage()
- Method in class com.judax.oculusmobilesdkheadtracking.
OculusMobileSDKHeadTracking
getView()
- Method in class com.judax.oculusmobilesdkheadtracking.
OculusMobileSDKHeadTracking
Returns a view that needs to be added at some point to the application view hierarchy in order to make the head tracking acquisition to work.
H
hasStarted()
- Method in class com.judax.oculusmobilesdkheadtracking.
OculusMobileSDKHeadTracking
headTrackingError(OculusMobileSDKHeadTracking, String)
- Method in interface com.judax.oculusmobilesdkheadtracking.
OculusMobileSDKHeadTrackingListener
headTrackingStarted(OculusMobileSDKHeadTracking, OculusMobileSDKHeadTrackingData)
- Method in interface com.judax.oculusmobilesdkheadtracking.
OculusMobileSDKHeadTrackingListener
I
interpupillaryDistance
- Variable in class com.judax.oculusmobilesdkheadtracking.
OculusMobileSDKHeadTrackingData
L
linearAccelerationX
- Variable in class com.judax.oculusmobilesdkheadtracking.
OculusMobileSDKHeadTrackingData
linearAccelerationY
- Variable in class com.judax.oculusmobilesdkheadtracking.
OculusMobileSDKHeadTrackingData
linearAccelerationZ
- Variable in class com.judax.oculusmobilesdkheadtracking.
OculusMobileSDKHeadTrackingData
linearVelocityX
- Variable in class com.judax.oculusmobilesdkheadtracking.
OculusMobileSDKHeadTrackingData
linearVelocityY
- Variable in class com.judax.oculusmobilesdkheadtracking.
OculusMobileSDKHeadTrackingData
linearVelocityZ
- Variable in class com.judax.oculusmobilesdkheadtracking.
OculusMobileSDKHeadTrackingData
M
mounted
- Variable in class com.judax.oculusmobilesdkheadtracking.
OculusMobileSDKHeadTrackingData
O
OculusMobileSDKHeadTracking
- Class in
com.judax.oculusmobilesdkheadtracking
This class allows to acquire just the head tracking information (orientation) from the Oculus mobile SDK.
OculusMobileSDKHeadTracking()
- Constructor for class com.judax.oculusmobilesdkheadtracking.
OculusMobileSDKHeadTracking
OculusMobileSDKHeadTrackingData
- Class in
com.judax.oculusmobilesdkheadtracking
OculusMobileSDKHeadTrackingData()
- Constructor for class com.judax.oculusmobilesdkheadtracking.
OculusMobileSDKHeadTrackingData
OculusMobileSDKHeadTrackingListener
- Interface in
com.judax.oculusmobilesdkheadtracking
The interface to be implemented by any class that would like to be notified of the Oculus Mobile SDK Head Tracking events.
orientationW
- Variable in class com.judax.oculusmobilesdkheadtracking.
OculusMobileSDKHeadTrackingData
orientationX
- Variable in class com.judax.oculusmobilesdkheadtracking.
OculusMobileSDKHeadTrackingData
orientationY
- Variable in class com.judax.oculusmobilesdkheadtracking.
OculusMobileSDKHeadTrackingData
orientationZ
- Variable in class com.judax.oculusmobilesdkheadtracking.
OculusMobileSDKHeadTrackingData
P
pause()
- Method in class com.judax.oculusmobilesdkheadtracking.
OculusMobileSDKHeadTracking
Call this method on the Activity onPause life cycle event.
R
removeAllOculusMobileSDKHeadTrackingListeners()
- Method in class com.judax.oculusmobilesdkheadtracking.
OculusMobileSDKHeadTracking
Remove all the registered listeners for head tracking event changes.
removeOculusMobileSDKHeadTrackingListener(OculusMobileSDKHeadTrackingListener)
- Method in class com.judax.oculusmobilesdkheadtracking.
OculusMobileSDKHeadTracking
Unregister a listener of head tracking changes.
resume()
- Method in class com.judax.oculusmobilesdkheadtracking.
OculusMobileSDKHeadTracking
Call this method on the Activity onResume life cycle event.
S
start(Activity)
- Method in class com.judax.oculusmobilesdkheadtracking.
OculusMobileSDKHeadTracking
Call this method to initialize the Oculus mobile head tracking.
stop()
- Method in class com.judax.oculusmobilesdkheadtracking.
OculusMobileSDKHeadTracking
Call this method on the Activity onDestroy life cycle event.
T
timeStamp
- Variable in class com.judax.oculusmobilesdkheadtracking.
OculusMobileSDKHeadTrackingData
X
xFOV
- Variable in class com.judax.oculusmobilesdkheadtracking.
OculusMobileSDKHeadTrackingData
Y
yFOV
- Variable in class com.judax.oculusmobilesdkheadtracking.
OculusMobileSDKHeadTrackingData
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public class Dataset<T>
extends java.lang.Object
implements org.apache.spark.sql.execution.Queryable, scala.Serializable
Dataset
is a strongly typed collection of objects that can be transformed in parallel
using functional or relational operations.
A Dataset
differs from an RDD
in the following ways:
- Internally, a Dataset
is represented by a Catalyst logical plan and the data is stored
in the encoded form. This representation allows for additional logical operations and
enables many operations (sorting, shuffling, etc.) to be performed without deserializing to
an object.
- The creation of a Dataset
requires the presence of an explicit Encoder
that can be
used to serialize the object into a binary format. Encoders are also capable of mapping the
schema of a given object to the Spark SQL type system. In contrast, RDDs rely on runtime
reflection based serialization. Operations that change the type of object stored in the
dataset also need an encoder for the new type.
A Dataset
can be thought of as a specialized DataFrame, where the elements map to a specific
JVM object type, instead of to a generic Row
container. A DataFrame can be transformed into
specific Dataset by calling df.as[ElementType]
. Similarly you can transform a strongly-typed
Dataset
to a generic DataFrame by calling ds.toDF()
.
COMPATIBILITY NOTE: Long term we plan to make DataFrame
extend Dataset[Row]
. However,
making this change to the class hierarchy would break the function signatures for the existing
functional operations (map, flatMap, etc). As such, this class should be considered a preview
of the final API. Changes will be made to the interface after Spark 1.6.
| Modifier and Type | Method and Description |
|---|---|
<U> Dataset<U> |
as(Encoder<U> evidence$1)
Returns a new
Dataset where each record has been mapped on to the specified type. |
Dataset<T> |
as(java.lang.String alias)
Applies a logical alias to this
Dataset that can be used to disambiguate columns that have
the same name after two Datasets have been joined. |
org.apache.spark.sql.catalyst.encoders.ExpressionEncoder<T> |
boundTEncoder()
The encoder where the expressions used to construct an object from an input row have been
bound to the ordinals of the given schema.
|
Dataset<T> |
cache()
Persist this
Dataset with the default storage level (MEMORY_AND_DISK ). |
Dataset<T> |
coalesce(int numPartitions)
Returns a new
Dataset that has exactly numPartitions partitions. |
java.lang.Object |
collect()
Returns an array that contains all the elements in this
Dataset . |
java.util.List<T> |
collectAsList()
Returns an array that contains all the elements in this
Dataset . |
long |
count()
Returns the number of elements in the
Dataset . |
Dataset<T> |
distinct()
|
void |
explain()
Prints the physical plan to the console for debugging purposes.
|
void |
explain(boolean extended)
Prints the plans (logical and physical) to the console for debugging purposes.
|
Dataset<T> |
filter(FilterFunction<T> func)
|
Dataset<T> |
filter(scala.Function1<T,java.lang.Object> func)
|
T |
first()
Returns the first element in this
Dataset . |
<U> Dataset<U> |
flatMap(FlatMapFunction<T,U> f,
Encoder<U> encoder)
|
<U> Dataset<U> |
flatMap(scala.Function1<T,scala.collection.TraversableOnce<U>> func,
Encoder<U> evidence$4)
|
void |
foreach(ForeachFunction<T> func)
(Java-specific)
Runs
func on each element of this Dataset . |
void |
foreach(scala.Function1<T,scala.runtime.BoxedUnit> func)
(Scala-specific)
Runs
func on each element of this Dataset . |
void |
foreachPartition(ForeachPartitionFunction<T> func)
(Java-specific)
Runs
func on each partition of this Dataset . |
void |
foreachPartition(scala.Function1<scala.collection.Iterator<T>,scala.runtime.BoxedUnit> func)
(Scala-specific)
Runs
func on each partition of this Dataset . |
GroupedDataset<Row,T> |
groupBy(Column... cols)
Returns a
GroupedDataset where the data is grouped by the given Column expressions. |
<K> GroupedDataset<K,T> |
groupBy(scala.Function1<T,K> func,
Encoder<K> evidence$5)
(Scala-specific)
Returns a
GroupedDataset where the data is grouped by the given key func . |
<K> GroupedDataset<K,T> |
groupBy(MapFunction<T,K> func,
Encoder<K> encoder)
(Java-specific)
Returns a
GroupedDataset where the data is grouped by the given key func . |
GroupedDataset<Row,T> |
groupBy(scala.collection.Seq<Column> cols)
Returns a
GroupedDataset where the data is grouped by the given Column expressions. |
Dataset<T> |
intersect(Dataset<T> other)
|
<U> Dataset<scala.Tuple2<T,U>> |
joinWith(Dataset<U> other,
Column condition)
Using inner equi-join to join this
Dataset returning a Tuple2 for each pair
where condition evaluates to true. |
<U> Dataset<scala.Tuple2<T,U>> |
joinWith(Dataset<U> other,
Column condition,
java.lang.String joinType)
|
<U> Dataset<U> |
map(scala.Function1<T,U> func,
Encoder<U> evidence$2)
(Scala-specific)
Returns a new
Dataset that contains the result of applying func to each element. |
<U> Dataset<U> |
map(MapFunction<T,U> func,
Encoder<U> encoder)
(Java-specific)
Returns a new
Dataset that contains the result of applying func to each element. |
<U> Dataset<U> |
mapPartitions(scala.Function1<scala.collection.Iterator<T>,scala.collection.Iterator<U>> func,
Encoder<U> evidence$3)
(Scala-specific)
Returns a new
Dataset that contains the result of applying func to each partition. |
<U> Dataset<U> |
mapPartitions(MapPartitionsFunction<T,U> f,
Encoder<U> encoder)
(Java-specific)
Returns a new
Dataset that contains the result of applying func to each partition. |
Dataset<T> |
persist()
Persist this
Dataset with the default storage level (MEMORY_AND_DISK ). |
Dataset<T> |
persist(StorageLevel newLevel)
Persist this
Dataset with the given storage level. |
void |
printSchema()
Prints the schema of the underlying
Dataset to the console in a nice tree format. |
org.apache.spark.sql.execution.QueryExecution |
queryExecution() |
RDD<T> |
rdd()
Converts this
Dataset to an RDD . |
T |
reduce(scala.Function2<T,T,T> func)
(Scala-specific)
Reduces the elements of this
Dataset using the specified binary function. |
T |
reduce(ReduceFunction<T> func)
(Java-specific)
Reduces the elements of this Dataset using the specified binary function.
|
Dataset<T> |
repartition(int numPartitions)
Returns a new
Dataset that has exactly numPartitions partitions. |
org.apache.spark.sql.catalyst.encoders.ExpressionEncoder<T> |
resolvedTEncoder()
The encoder for this
Dataset that has been resolved to its output schema. |
Dataset<T> |
sample(boolean withReplacement,
double fraction)
Returns a new
Dataset by sampling a fraction of records, using a random seed. |
Dataset<T> |
sample(boolean withReplacement,
double fraction,
long seed)
Returns a new
Dataset by sampling a fraction of records. |
StructType |
schema()
Returns the schema of the encoded form of the objects in this
Dataset . |
protected DataFrame |
select(Column... cols)
Returns a new
DataFrame by selecting a set of column based expressions. |
protected DataFrame |
select(scala.collection.Seq<Column> cols)
Returns a new
DataFrame by selecting a set of column based expressions. |
<U1> Dataset<U1> |
select(TypedColumn<T,U1> c1,
Encoder<U1> evidence$6)
|
<U1,U2> Dataset<scala.Tuple2<U1,U2>> |
select(TypedColumn<T,U1> c1,
TypedColumn<T,U2> c2)
|
<U1,U2,U3> Dataset<scala.Tuple3<U1,U2,U3>> |
select(TypedColumn<T,U1> c1,
TypedColumn<T,U2> c2,
TypedColumn<T,U3> c3)
|
<U1,U2,U3,U4> |
select(TypedColumn<T,U1> c1,
TypedColumn<T,U2> c2,
TypedColumn<T,U3> c3,
TypedColumn<T,U4> c4)
|
<U1,U2,U3,U4,U5> |
select(TypedColumn<T,U1> c1,
TypedColumn<T,U2> c2,
TypedColumn<T,U3> c3,
TypedColumn<T,U4> c4,
TypedColumn<T,U5> c5)
|
protected Dataset<?> |
selectUntyped(scala.collection.Seq<TypedColumn<?,?>> columns)
Internal helper function for building typed selects that return tuples.
|
void |
show()
Displays the top 20 rows of
Dataset in a tabular form. |
void |
show(boolean truncate)
Displays the top 20 rows of
Dataset in a tabular form. |
void |
show(int numRows)
Displays the content of this
Dataset in a tabular form. |
void |
show(int numRows,
boolean truncate)
Displays the
Dataset in a tabular form. |
SQLContext |
sqlContext() |
Dataset<T> |
subtract(Dataset<T> other)
Returns a new
Dataset where any elements present in other have been removed. |
java.lang.Object |
take(int num)
Returns the first
num elements of this Dataset as an array. |
java.util.List<T> |
takeAsList(int num)
Returns the first
num elements of this Dataset as an array. |
DataFrame |
toDF()
Converts this strongly typed collection of data to generic Dataframe.
|
Dataset<T> |
toDS()
Returns this
Dataset . |
<U> Dataset<U> |
transform(scala.Function1<Dataset<T>,Dataset<U>> t)
Concise syntax for chaining custom transformations.
|
Dataset<T> |
union(Dataset<T> other)
|
Dataset<T> |
unpersist()
Mark the
Dataset as non-persistent, and remove all blocks for it from memory and disk. |
Dataset<T> |
unpersist(boolean blocking)
Mark the
Dataset as non-persistent, and remove all blocks for it from memory and disk. |
org.apache.spark.sql.catalyst.encoders.ExpressionEncoder<T> |
unresolvedTEncoder()
An unresolved version of the internal encoder for the type of this
Dataset . |
public GroupedDataset<Row,T> groupBy(Column... cols)
GroupedDataset
where the data is grouped by the given Column
expressions.cols
- (undocumented)protected DataFrame select(Column... cols)
DataFrame
by selecting a set of column based expressions.
df.select($"colA", $"colB" + 1)
cols
- (undocumented)public SQLContext sqlContext()
sqlContext
in interface org.apache.spark.sql.execution.Queryable
public org.apache.spark.sql.execution.QueryExecution queryExecution()
queryExecution
in interface org.apache.spark.sql.execution.Queryable
public org.apache.spark.sql.catalyst.encoders.ExpressionEncoder<T> unresolvedTEncoder()
Dataset
. This one is
marked implicit so that we can use it when constructing new Dataset
objects that have the
same object type (that will be possibly resolved to a different schema).public org.apache.spark.sql.catalyst.encoders.ExpressionEncoder<T> resolvedTEncoder()
Dataset
that has been resolved to its output schema.public org.apache.spark.sql.catalyst.encoders.ExpressionEncoder<T> boundTEncoder()
public StructType schema()
Dataset
.schema
in interface org.apache.spark.sql.execution.Queryable
public void printSchema()
Dataset
to the console in a nice tree format.printSchema
in interface org.apache.spark.sql.execution.Queryable
public void explain(boolean extended)
explain
in interface org.apache.spark.sql.execution.Queryable
extended
- (undocumented)public void explain()
explain
in interface org.apache.spark.sql.execution.Queryable
public <U> Dataset<U> as(Encoder<U> evidence$1)
Dataset
where each record has been mapped on to the specified type. The
method used to map columns depend on the type of U
:
- When U
is a class, fields for the class will be mapped to columns of the same name
(case sensitivity is determined by spark.sql.caseSensitive
)
- When U
is a tuple, the columns will be be mapped by ordinal (i.e. the first column will
be assigned to _1
).
- When U
is a primitive type (i.e. String, Int, etc). then the first column of the
DataFrame
will be used.
If the schema of the DataFrame
does not match the desired U
type, you can use select
along with alias
or as
to rearrange or rename as required.
evidence$1
- (undocumented)public Dataset<T> as(java.lang.String alias)
Dataset
that can be used to disambiguate columns that have
the same name after two Datasets have been joined.alias
- (undocumented)public DataFrame toDF()
Row
objects that allow fields to be accessed by ordinal or name.public long count()
Dataset
.public void show(int numRows)
Dataset
in a tabular form. Strings more than 20 characters
will be truncated, and all cells will be aligned right. For example:
year month AVG('Adj Close) MAX('Adj Close)
1980 12 0.503218 0.595103
1981 01 0.523289 0.570307
1982 02 0.436504 0.475256
1983 03 0.410516 0.442194
1984 04 0.450090 0.483521
numRows
- Number of rows to show
public void show()
Dataset
in a tabular form. Strings more than 20 characters
will be truncated, and all cells will be aligned right.
public void show(boolean truncate)
Dataset
in a tabular form.
truncate
- Whether truncate long strings. If true, strings more than 20 characters will
be truncated and all cells will be aligned right
public void show(int numRows, boolean truncate)
Dataset
in a tabular form. For example:
year month AVG('Adj Close) MAX('Adj Close)
1980 12 0.503218 0.595103
1981 01 0.523289 0.570307
1982 02 0.436504 0.475256
1983 03 0.410516 0.442194
1984 04 0.450090 0.483521
numRows
- Number of rows to showtruncate
- Whether truncate long strings. If true, strings more than 20 characters will
be truncated and all cells will be aligned right
public Dataset<T> repartition(int numPartitions)
Dataset
that has exactly numPartitions
partitions.numPartitions
- (undocumented)public Dataset<T> coalesce(int numPartitions)
Dataset
that has exactly numPartitions
partitions.
Similar to coalesce defined on an RDD
, this operation results in a narrow dependency, e.g.
if you go from 1000 partitions to 100 partitions, there will not be a shuffle, instead each of
the 100 new partitions will claim 10 of the current partitions.numPartitions
- (undocumented)public <U> Dataset<U> transform(scala.Function1<Dataset<T>,Dataset<U>> t)
def featurize(ds: Dataset[T]) = ...
dataset
.transform(featurize)
.transform(...)
t
- (undocumented)public Dataset<T> filter(scala.Function1<T,java.lang.Object> func)
func
- (undocumented)public Dataset<T> filter(FilterFunction<T> func)
func
- (undocumented)public <U> Dataset<U> map(scala.Function1<T,U> func, Encoder<U> evidence$2)
Dataset
that contains the result of applying func
to each element.func
- (undocumented)evidence$2
- (undocumented)public <U> Dataset<U> map(MapFunction<T,U> func, Encoder<U> encoder)
Dataset
that contains the result of applying func
to each element.func
- (undocumented)encoder
- (undocumented)public <U> Dataset<U> mapPartitions(scala.Function1<scala.collection.Iterator<T>,scala.collection.Iterator<U>> func, Encoder<U> evidence$3)
Dataset
that contains the result of applying func
to each partition.func
- (undocumented)evidence$3
- (undocumented)public <U> Dataset<U> mapPartitions(MapPartitionsFunction<T,U> f, Encoder<U> encoder)
Dataset
that contains the result of applying func
to each partition.f
- (undocumented)encoder
- (undocumented)public <U> Dataset<U> flatMap(scala.Function1<T,scala.collection.TraversableOnce<U>> func, Encoder<U> evidence$4)
Dataset
by first applying a function to all elements of this Dataset
,
and then flattening the results.func
- (undocumented)evidence$4
- (undocumented)public <U> Dataset<U> flatMap(FlatMapFunction<T,U> f, Encoder<U> encoder)
Dataset
by first applying a function to all elements of this Dataset
,
and then flattening the results.f
- (undocumented)encoder
- (undocumented)public void foreach(scala.Function1<T,scala.runtime.BoxedUnit> func)
func
on each element of this Dataset
.func
- (undocumented)public void foreach(ForeachFunction<T> func)
func
on each element of this Dataset
.func
- (undocumented)public void foreachPartition(scala.Function1<scala.collection.Iterator<T>,scala.runtime.BoxedUnit> func)
func
on each partition of this Dataset
.func
- (undocumented)public void foreachPartition(ForeachPartitionFunction<T> func)
func
on each partition of this Dataset
.func
- (undocumented)public T reduce(scala.Function2<T,T,T> func)
Dataset
using the specified binary function. The given func
must be commutative and associative or the result may be non-deterministic.func
- (undocumented)public T reduce(ReduceFunction<T> func)
func
must be commutative and associative or the result may be non-deterministic.func
- (undocumented)public <K> GroupedDataset<K,T> groupBy(scala.Function1<T,K> func, Encoder<K> evidence$5)
GroupedDataset
where the data is grouped by the given key func
.func
- (undocumented)evidence$5
- (undocumented)public GroupedDataset<Row,T> groupBy(scala.collection.Seq<Column> cols)
GroupedDataset
where the data is grouped by the given Column
expressions.cols
- (undocumented)public <K> GroupedDataset<K,T> groupBy(MapFunction<T,K> func, Encoder<K> encoder)
GroupedDataset
where the data is grouped by the given key func
.func
- (undocumented)encoder
- (undocumented)protected DataFrame select(scala.collection.Seq<Column> cols)
DataFrame
by selecting a set of column based expressions.
df.select($"colA", $"colB" + 1)
cols
- (undocumented)public <U1> Dataset<U1> select(TypedColumn<T,U1> c1, Encoder<U1> evidence$6)
Dataset
by computing the given Column
expression for each element.
val ds = Seq(1, 2, 3).toDS()
val newDS = ds.select(expr("value + 1").as[Int])
c1
- (undocumented)evidence$6
- (undocumented)protected Dataset<?> selectUntyped(scala.collection.Seq<TypedColumn<?,?>> columns)
columns
- (undocumented)public <U1,U2> Dataset<scala.Tuple2<U1,U2>> select(TypedColumn<T,U1> c1, TypedColumn<T,U2> c2)
c1
- (undocumented)c2
- (undocumented)public <U1,U2,U3> Dataset<scala.Tuple3<U1,U2,U3>> select(TypedColumn<T,U1> c1, TypedColumn<T,U2> c2, TypedColumn<T,U3> c3)
c1
- (undocumented)c2
- (undocumented)c3
- (undocumented)public <U1,U2,U3,U4> Dataset<scala.Tuple4<U1,U2,U3,U4>> select(TypedColumn<T,U1> c1, TypedColumn<T,U2> c2, TypedColumn<T,U3> c3, TypedColumn<T,U4> c4)
c1
- (undocumented)c2
- (undocumented)c3
- (undocumented)c4
- (undocumented)public <U1,U2,U3,U4,U5> Dataset<scala.Tuple5<U1,U2,U3,U4,U5>> select(TypedColumn<T,U1> c1, TypedColumn<T,U2> c2, TypedColumn<T,U3> c3, TypedColumn<T,U4> c4, TypedColumn<T,U5> c5)
c1
- (undocumented)c2
- (undocumented)c3
- (undocumented)c4
- (undocumented)c5
- (undocumented)public Dataset<T> sample(boolean withReplacement, double fraction, long seed)
Dataset
by sampling a fraction of records.withReplacement
- (undocumented)fraction
- (undocumented)seed
- (undocumented)public Dataset<T> sample(boolean withReplacement, double fraction)
Dataset
by sampling a fraction of records, using a random seed.withReplacement
- (undocumented)fraction
- (undocumented)public Dataset<T> distinct()
Dataset
that contains only the unique elements of this Dataset
.
Note that, equality checking is performed directly on the encoded representation of the data
and thus is not affected by a custom equals
function defined on T
.
public Dataset<T> intersect(Dataset<T> other)
Dataset
that contains only the elements of this Dataset
that are also
present in other
.
Note that, equality checking is performed directly on the encoded representation of the data
and thus is not affected by a custom equals
function defined on T
.
other
- (undocumented)public Dataset<T> union(Dataset<T> other)
Dataset
that contains the elements of both this and the other
Dataset
combined.
Note that, this function is not a typical set union operation, in that it does not eliminate
duplicate items. As such, it is analogous to UNION ALL
in SQL.
other
- (undocumented)public Dataset<T> subtract(Dataset<T> other)
Dataset
where any elements present in other
have been removed.
Note that, equality checking is performed directly on the encoded representation of the data
and thus is not affected by a custom equals
function defined on T
.
other
- (undocumented)public <U> Dataset<scala.Tuple2<T,U>> joinWith(Dataset<U> other, Column condition, java.lang.String joinType)
Dataset
returning a Tuple2
for each pair where condition
evaluates to
true.
This is similar to the relation join
function with one important difference in the
result schema. Since joinWith
preserves objects present on either side of the join, the
result schema is similarly nested into a tuple under the column names _1
and _2
.
This type of join can be useful both for preserving type-safety with the original object types as well as working with relational data where either side of the join has column names in common.
other
- Right side of the join.condition
- Join expression.joinType
- One of: inner
, outer
, left_outer
, right_outer
, leftsemi
.public <U> Dataset<scala.Tuple2<T,U>> joinWith(Dataset<U> other, Column condition)
Dataset
returning a Tuple2
for each pair
where condition
evaluates to true.
other
- Right side of the join.condition
- Join expression.public T first()
Dataset
.public java.lang.Object collect()
Dataset
.
Running collect requires moving all the data into the application's driver process, and
doing so on a very large Dataset
can crash the driver process with OutOfMemoryError.
For Java API, use collectAsList
.
public java.util.List<T> collectAsList()
Dataset
.
Running collect requires moving all the data into the application's driver process, and
doing so on a very large Dataset
can crash the driver process with OutOfMemoryError.
For Java API, use collectAsList
.
public java.lang.Object take(int num)
num
elements of this Dataset
as an array.
Running take requires moving data into the application's driver process, and doing so with
a very large num
can crash the driver process with OutOfMemoryError.
num
- (undocumented)public java.util.List<T> takeAsList(int num)
num
elements of this Dataset
as an array.
Running take requires moving data into the application's driver process, and doing so with
a very large num
can crash the driver process with OutOfMemoryError.
num
- (undocumented)public Dataset<T> persist()
Dataset
with the default storage level (MEMORY_AND_DISK
).public Dataset<T> cache()
Dataset
with the default storage level (MEMORY_AND_DISK
).public Dataset<T> persist(StorageLevel newLevel)
Dataset
with the given storage level.newLevel
- One of: MEMORY_ONLY
, MEMORY_AND_DISK
, MEMORY_ONLY_SER
,
MEMORY_AND_DISK_SER
, DISK_ONLY
, MEMORY_ONLY_2
,
MEMORY_AND_DISK_2
, etc.public Dataset<T> unpersist(boolean blocking)
Dataset
as non-persistent, and remove all blocks for it from memory and disk.blocking
- Whether to block until all blocks are deleted.