<model/>
Pages may contain information that are changeable and as such, must be updated on the user's device. To this end, data are being received from and sent to the server. These data are referred to as models.
Every model must have a unique id which differentiates it from others e.g. users. This is required to keep updating working fluently without interfering with other models that are not changed.
Create a model with html tags:
Or with its custom tag:
Models can be loaded from the server with attribute this-url . See all model attributes here
Model attributes can be rendered by putting the key in double curly brackets, that is, {{KEY}}.
For the object below:
to show the user's first name, we would do {{firstName}}.
To target hobbies, we would do {{hobbies.0}} and {{hobbies.1}} which would render 'eating' and 'sleeping' respectively.
To target socials, we would do {{socials.facebook}} and {{socials.twitter}}.
Alternatively, we could loop over both hobbies
and socials
with
tag attribute this-each
Model attributes can be filtered by appending the desired filters the the attributes name, all separated by pipes (|). e.g. {{firstName|trim|capitalize}}. The value of `firstName` is passed to filters left-to-right, that is to trim first, then to capitalize.
Below are the available filters
| Filter | Parameters | Description |
|---|---|---|
| {{id}} | {{params}} | {{description}} |
Models can be bound to HTML elements, which would render the attributes as described above. To do this, give a link element, that is within the model element, attribute this-bind with its value as the id of that target element: ... The above assumes that the element below exists:
orIf the target contains form elements, they will be filled with the right variables. See more about form elements here.
Models may need to be operated upon CRUD-ly (Create, Read, Update, Delete). To do this, link tags would need be provided and specified what the intent is. Note that all link tags except for Create must be within the model element, that is, the element with
The `this-create` attributes indicates that


{% extends "email/base_wrapper.html" %} {% load account %} {% block content %} Thank you for signing up to Connect. Please confirm your email address so you can get started. {% endblock content %}


curl -i -H "Content-Type: application/json" -X POST -d '{ "body": "new one" }' http://localhost:8080/reucon-commons-example-war/example/form


1 + 2 = {{ 1 + 2 }}
{{phone.snippet}}
The body of the accordion group grows to fit the contents
The food is {{spice}} spicy!


Contact name
City
Country
Is Promoted
Maria Anders
Berlin
Germany
Ana Trujillo
México D.F.
Mexico
Antonio Moreno
México D.F.
Mexico
Thomas Hardy
London
UK


Even more rumors spread. The townsfolk have disliked your family from the very beginning.  The townsfolk take your lack of grief to mean that you are under a spell.  Do you..
Warn your mother and try to flee.
 Better her than you, right?


--- layout: page permalink: "/community-commissioned/longmeadow/" ---
I was commissioned to explore, with staff and pupils from year 5, the value of using photography as a means of recording scientific experiments.
Photography helped their scientific learning, aided their ability to work in small groups, enhanced independent decision - making, and was a particularly successful way to involve learners with special needs.
The children created a permanent gallery of images some of which are shown here.


---
layout: default
title: Mahasiswa Memilih
---
Sheep are the backbone of the economy yet they receive no compensation.Give sheep the vote
New Zealand has a human population of just 4.5M while there are 70M sheep.
Sheep are the backbone of the economy yet they receive no compensation.
 How about some of variety?
Leave no sheep behind. Our mission to give sheep the right to vote and have them represented in parliament. For too long sheep have been exploited for their warm wool and delicious meat. It's time to put our hooves down and baa for what's right. Give sheep the vote!


emus module¶
Module containing the emus object.
-
class
emus.
emus
(psis, cv_trajs=None, neighbors=None, k_B=0.0019872041)[source]¶ Class containing methods and data for the EMUS algorithm. An EMUS object has the following data structures which can be interacted with or modified:
self.psis (3D array): array containing values of the biases in each state. self.cv_trajs (2D Array): array containing the trajectories in cv space. None if not used. self.z (1d array): array containing the normalization constants. Calculated according to the first iteration of EMUS. self.F (2d array): F matrix for the first iteration of EMUS. self.iats (1d array): array containing integrated autocorrelation times of \(\psi_{ii}(x)\) in each window.
Methods
avar_zfe
(state_1, state_2)Calculates the asymptotic variance for the free energy difference between the two states specified. calc_obs
(fdata[, z])Estimates the average of an observable function. calc_pmf
(domain[, cv_trajs, nbins, kT, z])Calculates the potential of mean force for the system. calc_zs
([nMBAR, tol, use_iats, iats])Calculates the normalization constants for the states. -
avar_zfe
(state_1, state_2)[source]¶ Calculates the asymptotic variance for the free energy difference between the two states specified.
Parameters: state_1 : int
Index of the first state.
state_2 : int
Index of the second state.
Returns: errs : 1D array
Array containing each state’s contribution to the asymptotic error. The total asymptotic error is taken by summing the entries.
-
calc_obs
(fdata, z=None)[source]¶ Estimates the average of an observable function.
Parameters: fdata : 2d array-like
Two dimensional data structure where the first dimension corresponds to the state index, and the second to the value of the observable at that time point. Must have the same number of data-points as the collective variable trajectory.
z : 1D array, optional
User-provided values for the normalization constants. If not provided, uses values from the first iteration.
Returns: favg : float
The estimated average of the observable.
-
calc_pmf
(domain, cv_trajs=None, nbins=100, kT=1.0, z=None)[source]¶ Calculates the potential of mean force for the system.
Parameters: domain : tuple
Tuple containing the dimensions of the space over which to construct the pmf, e.g. (-180,180) or ((0,1),(-3.14,3.14))
nbins : int or tuple, optional
Number of bins to use. If int, uses that many bins in each dimension. If tuple, e.g. (100,20), uses 100 bins in the first dimension and 20 in the second.
cvtrajectories : 2D array-like, optional
Two dimensional data structure with the trajectories in cv space. The first dimension is the state where the data was collected, and the second is the value in cv space. If not provided, uses trajectory given in the constructor.
z : 1D array, optional
User-provided values for the normalization constants If not provided, uses values from the first iteration of EMUS.
kT : float, optional
Value of kT to scale the PMF by. If not provided, set to 1.0
Returns: pmf : nd array
Returns the potential of mean force as a d dimensional array, where d is the number of collective variables.
-
calc_zs
(nMBAR=0, tol=1e-08, use_iats=False, iats=None)[source]¶ Calculates the normalization constants for the states.
Parameters: nMBAR : int, optional (default 0)
Maximum number of MBAR iterations to perform.
tol : float, optional (default 1.0E-8)
If the relative residual falls beneath the tolerance, the MBAR iteration is truncated.
use_iats : bool, optional
If true, estimate integrated autocorrelation time in each MBAR iteration. Likely unnecessary unless dynamics are expected to be drastically different in each state. If iats is provided, the iteration will use those rather than estimating them in each step.
iats : 1D array, optional
Array of size L (no. states) with values of the integrated autocorrelation time estimated in each state. These values will be used in each iteration. Overrides use_iats.
Returns: z : 1D array
Values for the Normalization constant in each state.
F : 2D array
Matrix to take the eigenvalue of for MBAR.
iats 1D array
Estimated values of the autocorrelation time. Only returned if use_iats is true.
-


Donut Delights has been serving C-U with the highest quality donuts and coffees since 1971. Nestled in downtown Urbana, Donut Delights has become a haven for “Gotta have it” breakfast treats. Our donuts are baked fresh twice daily and our coffee’s brewed every fifteen minutes. If you have never been to C-U’s hidden gem then you are missing out.
The finest in breakfast treats and coffees since 1971…
Donut Delights was started in 1971 by Melvin Furballski, an immigrant baker from the far east coast of Poland. Known far and wide for his delicate pastries, Melvin fled his homeland in search of a better life for his family. In 1970 he landed in New York City and by chance he found an open seat on a bus heading west. When an unexpected mechanical failure forced a three day delay in Champaign, Illinois, Furballski saw the need for a donut shop, so he decided to stay and make a new life for himself in Urbana, Illinois. With only one hundred dollars to his name, he found an abandoned bread shop and within two days he had one of a kind donuts flying off the racks faster than he could bake them andthings have never slowed down. Now you can find that same great donut recipe plus dozens more still
being made with the same love and care as when Donut Delights first opened.
A bakery fire in the mid-1980s destroyed many of the shop's records...but NOT THE RECIPES. While many people might have viewed the fire as an excuse to quit, Melvin instead used it as an opportunity to reconfigure the interior design, allowing him to add the coffee roastery section as well. So when you visit, be prepared for the inviting mix of aromas from fresh-roasted coffee beans and fresh-baked donuts: It's the closest thing to "Breakfast Heaven" anywhere in the C-U area!


Building a RESTful API and Angular App.
The goal of this book is document everything I learned while building a RESTful API that is consumed by an Angular frontend. This application is by no means a fully functioning app and is intended solely to be a learning tool for understanding how to build an app using the MEAN stack.
The repos used to build this application can be found here:
*Note: I did not build the slugram backend. I built a backend with similar functionality but for the sake of this resource I am going to use and reference the fully built out slugram backend.
Technologies Used:
| Backend | Frontend |
|---|---|
| MonogoDB | Angular |
| Express | SCSS |
| Node | Babel |
| Mongoose | Webpack |
| AWS | Jasmine |
| Chai/Mocha | Karma |


?
Back
Save & Reboot
Software Update
Manual Software Update
Factory Reset
Enter Admin Password to factory reset light.
Then check the "factory reset" box below
and tap "save & reboot" above.
If your light has an issue contact Space Cadets.
Password:
Lock wireless software update:
Deny access to WiFi settings if locked:
Enable ArduinoOTA:
-Factory Reset-
All Settings and Presets will be erased.
Contact Space Cadets to receive password
and guidance on light reset & re-setup.
About
Software Version ##VERSION##
A huge thank you to everyone who helped create WLED!
(c) 2016-2020 Christian Schwinne
Modified For Space Cadets by Isaac N.
Licensed under the MIT license
Server message:
Response error!
Back
Save & Reboot


A new entry into the world of security, AssuRFID brings ease and simplicity to the ever more important task of keeping our workplaces, buildings and technology secure, all while guaranteeing best-in-class security.
AssuRFID combines wireless radio technology with the latest in Electrocardiography, enabling users to make use of the most secure, affordable authentication system.
It uses uncloneable, anti-counterfeited Radio Frequency Identification (RFID) tags embedded into a wristband worn by the user, along with their cardiac rhythm, in order to enable their security with 2-factor authentication.
To ensure easy administration of the system, AssuRFID employs a custom server backed by a database, to which the sensors across the whole campus connect.
When designing AssuRFID, we realised that our technology could be applied to numerous settings. These range from banks, prisons and workplaces, to maternity wards, schools and business-places.
These applications each share a need for easy to use yet secure authentication to open doors, sign into systems, log onto computers and much, much more.
In addition we will have custom models for maternity wards, with tags on the mother and embedded in the baby grow, so the they can only leave with the right child.
Our working prototype, named Assured, comprises a suite of custom applications which implement AssuRFID's vision, including a web server which clients connect to, and a custom Graphical User Interface (GUI) to add new users and log them in.
Our code is fully open source, and can be found online in our GitHub repositories for assured-server and assured-gui.


See: Description
| Class | Description |
|---|---|
| AdafruitRGBExample | |
| ColorSensorDriver | |
| CompassCalibration | |
| FtcOpModeRegister |
Register Op Modes
|
| HTRGBExample | |
| IrSeekerOp |
Follow an IR Beacon
|
| K9AutoTime |
Example autonomous program.
|
| K9IrSeeker |
TeleOp Mode
|
| K9Line |
TeleOp Mode
|
| K9TankDrive |
TeleOp Mode
|
| K9TeleOp |
TeleOp Mode
|
| LinearI2cAddressChange |
A simple example of a linear op mode that shows how to change the I2C address.
|
| LinearIrExample |
A simple example of a linear op mode that will approach an IR beacon
|
| LinearK9TeleOp |
Linear Tele Op Mode
|
| MatrixControllerDemo |
A simple example of all motors and servos oscillating
|
| MatrixK9TeleOp |
TeleOp Mode
|
| MRGyroTest | |
| MRRGBExample | |
| NullOp |
TeleOp Mode
|
| NxtTeleOp |
TeleOp Mode
|
| PushBotAuto |
Provide a basic autonomous operational mode that uses the left and right
drive motors and associated encoders implemented using a state machine for
the Push Bot.
|
| PushBotAutoSensors |
Provide a basic autonomous operational mode that uses the left and right
drive motors and associated encoders, the left arm motor and associated touch
sensor, IR seeker V3 and optical distance sensor implemented using a state
machine for the Push Bot.
|
| PushBotDriveTouch |
Created by Jordan Burklund on 7/30/2015.
|
| PushBotHardware |
Provides a single hardware access point between custom op-modes and the
OpMode class for the Push Bot.
|
| PushBotHardwareSensors |
Provides a single sensor access point between custom op-modes and the
OpMode class for the Push Bot.
|
| PushBotIrEvent |
Provide a basic autonomous operational mode that demonstrates the use of an
IR seeker implemented using a state machine for the Push Bot.
|
| PushBotIrSeek |
Created by Jordan Burklund on 7/30/2015.
|
| PushBotManual |
Provide a basic manual operational mode that uses the left and right
drive motors, left arm motor, servo motors and gamepad input from two
gamepads for the Push Bot.
|
| PushBotManual1 |
Provide a basic manual operational mode that uses the left and right
drive motors, left arm motor, servo motors and gamepad input from only one
gamepad for the Push Bot.
|
| PushBotManualSensors |
Provide a basic manual operational mode that uses the left and right
drive motors, left arm motor, servo motors and gamepad input from two
gamepads for the Push Bot.
|
| PushBotOdsDetectEvent |
Provide a basic autonomous operational mode that demonstrates the use of an
optical distance sensor to detect a line implemented using a state machine
for the Push Bot.
|
| PushBotOdsFollowEvent |
Provide a basic autonomous operational mode that demonstrates the use of an
optical distance sensor to follow a line implemented using a state machine
for the Push Bot.
|
| PushBotSquare | |
| PushBotTelemetry |
Provide telemetry provided by the PushBotHardware class.
|
| PushBotTelemetrySensors |
Provide telemetry provided by the PushBotHardwareSensors class.
|
| PushBotTouchEvent |
Provide a basic autonomous operational mode that demonstrates the use of an
touch sensor to control the arm using a state machine for the Push Bot.
|
| Enum | Description |
|---|---|
| ColorSensorDriver.ColorSensorDevice |
The quickest way to get started is ... TODO: finish docs


created 01/22/00
Experiment with the red and blue concentric circle applet to enhance the stereo effect. You will need some red/blue 3D vision glasses. If you don't have them, try the next method.
Click here to go back to the main menu.
Draw the two sets of concentric circles side by side, separated by about six centimeters. Don't draw identical images; draw one set of circles with increasing offset the closer to your eye they are supposed to be. When displayed, look at the left set with your left eye, the right set with your right eye, and try to merge the two images together.
Click here to go back to the main menu.
Draw 10 complete big circles randomly positioned in the applet. To draw complete circles, test that each randomly generated center is further from the edge than the radius of a circle.
Click here to go back to the main menu.
Draw 10 complete circles randomly positioned in the applet,
with random radii in the range 5 to 25 pixels.
Randomly pick each circle's color
(use a random int and nested if
statements.)
Click here to go back to the main menu.
End of the Exercises


Lizzo Wore the Tiniest Bag I Have Ever Seen in My Life to the American Music Awards
- Lizzo hit up the AMAs red carpet wearing a mini Valentino purse.
- It's so small, you might need glasses to see it.
Forget the AMAs performances. Can we just take a minute to talk about the amazing fashion moments from the ceremony??? Selena Gomez stunned in a shocking lime green Versace dress that would make Kermit the Frog jealous, Billie Eilish hit the red carpet looking like a flossy, bejeweled beekeeper, and Lizzo....Let’s talk about Lizzo.
Lizzo hit the AMAs red carpet in a orange ruffled minidress, custom made by Valentino (I see you, girl!). Of course, she looked AH-mazing, but it wasn’t just the dress that I couldn’t stop looking at...it was Lizzo’s bag, if you can even call it that.
Her’s Lizzo’s full red carpet look:
Aaaaand here’s a much-needed close-up of the bag, since you were totally squinting to see it, don’t even lie:
This is legit Honey, I Shrunk the Kids, but like...a crazy accessories version of that. Lizzo’s bag was also made by Valentino (in partnership with Obvious Plant), and as you can see, she’s clearly intending to win the award for Most Iconic Tiny Bag of 2019. #SorryNotSorry, Jacquemus—you’ve fought valiantly.
 In less than 24 hours, someone has created a whole Twitter account dedicated to the bag, aptly called @lizzostinybag. You can’t make this sh*t up, guys.
Without further ado, here are the best memes about Lizzo’s bag that are sure to leave you in stitches:
You’re welcome.
Related Stories
Selena’s AMAs Dress Is All I Care About, Bye
Billie Eilish Rolled Into the AMAs Wearing a LOOK Starr Bowenbank Editorial Assistant Starr Bowenbank is the editorial assistant who writes about all things pertaining to news, pop culture, and entertainment—you can follow her here.


{{bannerText}}
Update Banner
JSON Stock Quote (and more)
Make an external call from the server, return back the raw JSON, plus more.
{{quoteText | json}}
Sum of Random Integers
Each click of the Compute button will ask the server to inefficiently sum some random integers, allocating a big array to do so.
Enter a Return Array Index of 1048576 or greater to generate an exception.
| Size (# ints in array) | Sum | Int at Index |
|---|---|---|
| {{calc.size}} | {{calc.sum}} | {{calc.intAtIndex}} |


/**
* Imports the config module
* @module config
*/
import config from "../config.js";
/**
* Imports the Checks module.
* @module utilities/checks
*/
import Checks from "../utilities/checks.js";
/**
* Imports the Audio Navigation module.
* @module utilities/audioNavigation
*/
import AudioNavigation from "../utilities/audioNavigation.js";
/**
* Imports the Play/Pause Visual Elements module.
* @module visual/playPauseElements
*/
import PlayPauseElements from "../visual/playPauseElements.js";
/**
* Imports the Meta Data Visual Elements module.
* @module visual/metaDataElements
*/
import MetaDataElements from "../visual/metaDataElements.js";
/**
* Imports AmplitudeJS Callback Utility
* @module utilities/callbacks
*/
import Callbacks from "../utilities/callbacks.js";
/**
* Imports AmplitudeJS Debug Utility
* @module utilities/debug
*/
import Debug from "../utilities/debug.js";
/**
* Import the Visualizations from the FX module.
* @module fx/visualizations
*/
import Visualizations from "../fx/visualizations.js";
/**
* Interacts directly with native functions of the Audio element. Logic
* leading up to these methods are handled by click handlers which call
* helpers and visual synchronizers. These are the core functions of AmplitudeJS.
* Every other function that leads to these prepare the information to be
* acted upon by these functions.
*
* @module core/Core
*/
let Core = (function() {
/**
* Plays the active song. If the current song is live, it reconnects
* the stream before playing.
*
* Public Accessor: Amplitude.play()
*
* @access public
*/
function play() {
Visualizations.stop();
Visualizations.run();
/*
If the audio is live we re-conenct the stream.
*/
Iif (config.active_metadata.live) {
reconnectStream();
}
/*
Mobile remote sources need to be reconnected on play. I think this is
because mobile browsers are optimized not to load all resources
for speed reasons. We only do this if mobile and the paused button
is not clicked. If the pause button was clicked then we don't reconnect
or the user will lose their place in the stream.
*/
Iif (
/Android|webOS|iPhone|iPad|iPod|BlackBerry|IEMobile|Opera Mini/i.test(
navigator.userAgent
) &&
!config.paused
) {
reconnectStream();
}
/*
Play the song and set the playback rate to the playback
speed.
*/
config.audio.play();
config.audio.playbackRate = config.playback_speed;
}
/**
* Pauses the active song. If it's live, it disconnects the stream.
*
* Public Accessor: Amplitude.pause()
*
* @access public
*/
function pause() {
Visualizations.stop();
/*
Pause the active song.
*/
config.audio.pause();
/*
Flag that pause button was clicked.
*/
config.paused = true;
/*
If the song is live, we disconnect the stream so we aren't
saving it to memory.
*/
Iif (config.active_metadata.live) {
disconnectStream();
}
}
/**
* Stops the active song by setting the current song time to 0.
* When the user resumes, it will be from the beginning.
* If it's a live stream it disconnects.
*
* Public Accessor: Amplitude.stop()
*
* @access public
*/
function stop() {
Visualizations.stop();
/*
Set the current time of the song to 0 which will reset the song.
*/
if (config.audio.currentTime != 0) {
config.audio.currentTime = 0;
}
/*
Run pause so the song will stop
*/
config.audio.pause();
/*
If the song is live, disconnect the stream.
*/
Iif (config.active_metadata.live) {
disconnectStream();
}
/*
Run the stop callback
*/
Callbacks.run("stop");
}
/**
* Sets the song volume.
*
* Public Accessor: Amplitude.setVolume( volumeLevel )
*
* @access public
* @param {number} volumeLevel - A number between 1 and 100 as a percentage of
* min to max for a volume level.
*/
function setVolume(volumeLevel) {
/*
If the volume is set to mute somewhere else, we sync the display.
*/
if (volumeLevel == 0) {
config.audio.muted = true;
} else {
config.audio.muted = false;
}
/*
Sets the volume in the config so we can reference it later on.
*/
config.volume = volumeLevel;
/*
Set the volume of the active song.
*/
config.audio.volume = volumeLevel / 100;
}
/**
* Sets the song percentage. If it's a live song, we ignore this because
* we can't skip ahead. This is an issue if you have a playlist with
* a live source.
*
* Public Accessor: Amplitude.setSongLocation( songPercentage )
*
* @access public
* @param {number} songPercentage - A number between 1 and 100 as a percentage of song completion.
*/
function setSongLocation(songPercentage) {
/*
As long as the song is not live, we can set the current time of the
song to the percentage the user passed in.
*/
Eif (!config.active_metadata.live) {
config.audio.currentTime = config.audio.duration * (songPercentage / 100);
}
}
/**
* Skips to a location in a song
*
* Public Accessor: Amplitude.skipToLocation( seconds )
*
* @access public
* @param {number} seconds - An integer containing the seconds to skip to
*/
function skipToLocation(seconds) {
/*
When the active song can be played through, we can check to
see if the seconds will work. We only bind the event handler
once and remove it once it's fired.
*/
config.audio.addEventListener(
"canplaythrough",
function() {
/*
If the active song duration is greater than or equal to the
amount of seconds the user wants to skip to and the seconds
is greater than 0, we skip to the seconds defined.
*/
if (config.audio.duration >= seconds && seconds > 0) {
config.audio.currentTime = seconds;
} else {
Debug.writeMessage(
"Amplitude can't skip to a location greater than the duration of the audio or less than 0"
);
}
},
{ once: true }
);
}
/**
* Disconnects the live stream
*
* Public Accessor: Amplitude.disconnectStream()
*
* @access public
*/
function disconnectStream() {
config.audio.src = "";
config.audio.load();
}
/**
* Reconnects the live stream
*
* Public Accessor: Amplitude.reconnectStream()
*
* @access public\
*/
function reconnectStream() {
config.audio.src = config.active_metadata.url;
config.audio.load();
}
/**
* Sets the playback speed for the song.
*
* @param {number} playbackSpeed The speed we want the song to play back at.
*/
function setPlaybackSpeed(playbackSpeed) {
/*
Set the config playback speed.
*/
config.playback_speed = playbackSpeed;
/*
Set the active song playback rate.
*/
config.audio.playbackRate = config.playback_speed;
}
/*
Return publically facing functions
*/
return {
play: play,
pause: pause,
stop: stop,
setVolume: setVolume,
setSongLocation: setSongLocation,
skipToLocation: skipToLocation,
disconnectStream: disconnectStream,
reconnectStream: reconnectStream,
setPlaybackSpeed: setPlaybackSpeed
};
})();
export default Core;
|


Ohio report finds many pregnancy-related deaths preventable
by EndPlayCOLUMBUS, Ohio (AP) - The Ohio Department of Health says nearly 200 women died from pregnancy-related causes over a recent eight-year period and many of their deaths were preventable.
A report the department issued Friday identified 186 pregnancy-related deaths between 2008 and 2016, a ratio of 14.7 per 100,000 live births.
The leading underlying causes were heart conditions, infections, severe bleeding and pre-eclampsia and eclampsia.
Data showed 57% of the deaths from 2012 to 2016 could have been prevented. The state says it's received $12 million in federal grants that it plans to direct to support its maternal mortality prevention efforts.
Ohio's pregnancy-related mortality ratio is significantly lower than the U.S. ratio.


There are a number of great resources available around Houdini that have helped me and likely can help you as you explore Houdini. Smashing Magazine has a great Houdini article (unfortunately a little outdated now) that'll give you a good high-level overview. I've also given a talk, Magic Tricks with Houdini, the latest version is embedded, if videos are more your speed
If you're looking for more demos, @iamvdo has some truly fantastic stuff over on his website css-houdini.rocks. The Google Chrome Labs team also has a great set of demos with source you can play with. I've personally learned most by tweaking the samples here.
Because everyone loves reading the raw specs, and there are lots of them for Houdini, you can find them below:
Finally, there are people. Tab Atkins is the one responsible for first getting me interested in Houdini and they have their name plastered all over the specs. Surma has been an invaluable resource answering all of my Houdini questions. Surma fingers are all over the Google Chrome Labs demos. Finally, there's Ian Kilpatrick whose actually implementing this stuff in Blink! Ian built the Masonry Layout API demo!
And of course, I'm always happy to chat! @snugug on Twitter, the Internet, and in general.


Some people have asked me to post an update about my family's life...
My family only includes me and Katie for now. We also have a dog, Luke Skywalker, but he's not part of the family; he's a pet.
Katie started OU Nursing School in Oklahoma City this semester. In May of 2010, she plans to have a B.S. in Nursing and be licensed as an R.N.
Nursing school is hard—it's actually hard on both of us. Katie had the summer off and didn't work or take any classes. It was wonderful for me; she had lots of time to spend with me when I came home from work. Nursing school is quite different. Katie spends 60 hours a week at school or doing homework. (Thankfully, she is able to always keep our weekly date night free from school-work.)
I think we've had three phases of adjustment to school. For the first week, I struggled greatly with not getting so much free-time from my wife. After realizing my selfishness in some areas, I've done much better. The second phase was when nursing school still felt new. The newness helped ease the stress of my wife's intense schooling. Now, we are in the last phase. This phase is when we finally realize that nursing school is difficult for both of us - period. We're at the point that (almost) every day is hard and we now realize that we must rely on Christ's strength in order to persevere through this phase of life.
We're quite thankful that Katie will have over a month off for Christmas (which will include our first anniversary). That will be a needed time for us to recover from this first semester.
Perhaps, at another time, I will tell you why we believe God has called us to put Katie through nursing school.
Please pray that our marriage would be strengthened by this trial and that we would continue to press more deeply into Christ.
-PJ


| tab_source_note {gt} | R Documentation |
Add a source note to the footer part of the gt table. A source note is
useful for citing the data included in the table. Several can be added to the
footer, simply use multiple calls of tab_source_note()
and they will be
inserted in the order provided. We can use Markdown formatting for the note,
or, if the table is intended for HTML output, we can include HTML formatting.
tab_source_note(data, source_note)
data |
A table object that is created using the |
source_note |
Text to be used in the source note. We can optionally use
the |
An object of class gt_tbl
.
2-7
Other Create or Modify Parts:
tab_footnote()
,
tab_header()
,
tab_options()
,
tab_row_group()
,
tab_spanner_delim()
,
tab_spanner()
,
tab_stubhead()
,
tab_style()
# Use `gtcars` to create a gt table; # add a source note to the table # footer that cites the data source tab_1 <- gtcars %>% dplyr::select(mfr, model, msrp) %>% dplyr::slice(1:5) %>% gt() %>% tab_source_note( source_note = "From edmunds.com" )


{% extends "layout.html" %} {% block page_title %} Money Claims Prototype {% endblock %} {% block content %} BETA This is a new service – your feedback will help us to improve it. Respond to a money claim Defence options Defence options What do you want to do? What do you want to do? Defend now Prepare your defence now. Defend now and also make a counterclaim Prepare can your defence now and create a counterclaim against the claimant. You'll be asked to pay a court fee as part of your counterclaim. Cancel {% endblock %}
BETA This is a new service – your feedback will help us to improve it.
What do you want to do?
Cancel


SAFRS is an acronym for SqlAlchemy Flask-Restful Swagger. The purpose of this framework is to help python developers create a self-documenting JSON API for sqlalchemy database objects and relationships. These objects can be serialized to JSON and can be created, retrieved, updated and deleted through the JSON API. Optionally, custom resource object methods can be exposed and invoked using JSON. Class and method descriptions and examples can be provided in yaml syntax in the code comments. The description is parsed and shown in the swagger web interface. The result is an easy-to-use swagger/OpenAPI and JSON:API compliant API implementation
Use Logic Bank to govern SQLAlchemy update transaction logic - multi-table derivations, constraints, and actions such as sending mail or messages. Logic consists of both:
Rules - 40X more concise using a spreadsheet-like paradigm, and
Python - control and extensibility, using standard tools and techniques
Logic Bank is based on SQLAlchemy - it handles before_flush
events
to enforce your logic.
Source
User Email Validation:
if "@" not in value: raise ValidationError("Email Validation Error {}".format(value))
Rules:
Rule.constraint(validate=StoreModel,as_condition=lambda row: 'X' not in row.name, error_msg="Store Names({row.name}) should not contain X") Rule.count(StoreModel.item_count, as_count_of=ItemModel) Rule.parent_check(validate=ItemModel, error_msg="no parent", enable=True)


Chapter 9 Basic Statistics
You can skip this chapter if …
-
You can identify some common descriptive statistics
-
You can report descriptive statistics
-
You can describe the statistical formula for the mean and standard deviation
9.1 Descriptive stats
Descriptive statistics, unsurprisingly, describe the data. But why do we need to describe data?
Imagine that we had a field of cows.
We know that the first heifer has a weight of 211.3kg
And that the second heifer has a weight of 200.4kg
We can keep going, recording weights for every cow until we know all of them.
They say that knowledge is power, and so now we know the weights of all these cows, we should be able to make better choices. Unfortunately, we often can’t make choices on an individual basis, so we need to make a choice that suits the most cows. How can we condense this information down?
Statistics is really just condensing the real world down into simpler mathematical descriptions.
9.1.1 Mathematical notation
I’ve changed my mind several times about whether I should teach mathematical notation when talking about statistics. I’ve decided to include it in this chapter, but if you find yourself glazing over, don’t worry too much. Sometimes I don’t bother teaching this.
I’ve made the decision to talk about it because fundamentally, understanding what is happening when you calculate a mean (or a standard deviation) can help you understand some of the more complicated parts of statistics. You will likely never be quizzed on this, so I wouldn’t try to memorise them, but they can be a useful thing to know.
<IP_ADDRESS> General notation
Generally, in mathematical notation:
Upper case letters denote a variable name (\(X\), \(Y\), \(Z\) …)
Lower case letters denote a specific value of the variable (\(x\), \(y\), \(z\) …)
Sample data is indicated with lower case letters (\(v\){\(x_1\), \(x_2\) … \(x_n\)})
So in this example we might say Let heifer weight be \(X\).
9.1.2 The mean
Let’s create a dataset of heifer weight:
coos <- tibble(heifer_id = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12),
weight = c(211.3, 200.4, 220.1, 200.8, 222.0, 209.3,
195.8, 220.4, 225.2, 218.7, 193.7, 209.7))
We always want to visualise our data first - but just knowing weight isn’t that interesting:
coos %>%
ggplot(aes(x = heifer_id, y = weight, colour = as.factor(heifer_id))) +
geom_point() +
scale_y_continuous(limits = c(0, 250)) +
scale_x_continuous(limits = c(0,12), breaks = seq(0,12,1)) +
theme_classic() +
theme(legend.position = "none") +
labs(y = "Weight (kg)",
x = "Heifer ID")
There’s not much that we can say about this chart. Most heifers seem to weigh around 207kg. But that’s an estimate that comes from me just eyeballing the data. We could draw a line at around 207kg, a line of best fit, which is a rough guess that tries to minimise the amount of distance between the line and each point.
coos %>%
ggplot(aes(x = heifer_id, y = weight, colour = as.factor(heifer_id))) +
geom_point() +
scale_y_continuous(limits = c(0, 250)) +
scale_x_continuous(limits = c(0,12), breaks = seq(0,12,1)) +
theme_classic() +
theme(legend.position = "none") +
labs(y = "Weight (kg)",
x = "Heifer ID") +
geom_segment(x = 0, xend = 12, y = 207, yend = 207)
We could use maths to draw that line in a better place . . .
If we add up all the values of weight and divide by the number of cows . . .
## [1] 210.6167
We get the exact same value as if we asked R to calculate the mean directly (using the handy summarise
function):
## # A tibble: 1 x 1
## `mean(weight)`
## <dbl>
## 1 211.
<IP_ADDRESS> Notation of the mean
We all know how to calculate the mean, but what does that look like in mathematical notation?
The equation for the mean is:
\[\overline{x} = \frac{\sum x^i}{n} \]
Let’s break this equation down:
-
\(\overline{x}\) is the mean of the sample
-
\(\sum\), the greek letter sigma means ‘add all this together’
-
\(x^i\) means ‘every data point in sample’
-
\(n\) means ‘number of observations in sample’.
9.1.3 The mean as a model
So now we know that \(\overline{x}\) = 210.6kg. We have created an imaginary model cow. This model cow has a weight of 210.6kg, and we know that she is not exactly real, but we can choose to make use of her, because she has a little bit of information from all of the samples.
9.2 Median
There are other imagniary cows we could build to represent our dataset.
coos %>%
ggplot(aes(y = weight)) +
geom_boxplot() +
theme_classic() +
theme(legend.position = "none") +
labs(y = "Weight (kg)")
The thick middle line in this boxplot represents the median cow.
Now the formula for the median is a little more complicated than I want to get into here, but the median essentially puts all of the cows in order:
## # A tibble: 12 x 2
## heifer_id weight
## <dbl> <dbl>
## 1 11 194.
## 2 7 196.
## 3 2 200.
## 4 4 201.
## 5 6 209.
## 6 12 210.
## 7 1 211.
## 8 10 219.
## 9 3 220.
## 10 8 220.
## 11 5 222
## 12 9 225.
And then we pick the middle value. We have 12 cows, so our two middle values are 209.7 and 211.3. So to find the median, we add those two values together and divide by 2.
## # A tibble: 1 x 1
## median_weight
## <dbl>
## 1 210.
9.3 Mode
The final simple model we will talk about is the mode, or the most frequent value in a dataset. The mode isn’t that useful, and so there isn’t actually an inbuilt function to visualise or calculate it.
mode <- function(v) {
uniqv <- unique(v)
uniqv[which.max(tabulate(match(v, uniqv)))]
}
coos %>%
summarise(mode_weight= (mode(weight)))
## # A tibble: 1 x 1
## mode_weight
## <dbl>
## 1 211.
The mode is a bit . . . useless . . . but it is still a model cow, so it also gets its own picture in this example.
9.4 Which model?
The mean, mode and median cow all try to give a good approximation of most cows in our field. We know that they can’t describe every cow in detail (because we don’t want to have to learn every single cow’s weight off by heart), but they all have to lose important information.
With each of these models - we hope that our calculated weight is close to most of the population. We know that there is always natural variation in the world, and some cows might be very heavy, and some cows might be very thin, but our hope is that our models are fair representations of most cows in our dataset. But each model takes a different approach to this.
The mean is also described as the central tendency, and it takes into account every single individual in the sample. However, if you have a very skinny or very heavy cow, they will (by virtue of how the mean is calcualted) have a big impact on the final value.
The median takes the ‘middle’ value of the dataset, and can be very useful when you have extreme outliers in a dataset, because it essentially ignores those values. The median is sometimes referred to as the most resilient statistic because so long as at least 50% of your values are normal, the median will be close to at least 50% of your individuals.
The mode, as the most common value in the dataset, but this is rarely very useful in biological data. What if we had two very small cows who weighed 100kg, and every other cow of normal weight had a unique value? The mode would make its imaginary cow 100kg, even though every other cow in the dataset was over 100kg heavier. However, you can use the mode for nominal data, which can be useful, e.g. what is the most common name of cow?
9.5 Measures of variation
In the previous section we talked about outliers, and how they can affect these three models of our cows.
We’ve already looked at ways to visualise distributions, but lets remind ourselves:
The histogram shows us how many cows are in each weight category (or ‘bin’). We can overlay the mean, median and mode on this, to see which one gets closest to the ‘middle’.
coos %>%
ggplot(aes(x = weight)) +
geom_histogram(binwidth = 10) +
theme_classic() +
geom_segment(x = 210.6, xend = 210.6, y = 0, yend = 4, colour = "pink2") +
geom_segment(x = 210.5, xend = 210.5, y = 0, yend = 3.5, colour = "yellow1") +
geom_segment(x = 211.3, xend = 211.3, y = 0, yend = 4, colour = "lightblue1")
Now these three lines (the median (yellow), mean (pink) and mode (blue)) are all very close to one another. So close in fact that the mean and median are nearly on top of one another. This suggests to us that our field of heifers is fairly normal. Most observations are around the mean, and so the mean and the median are fairly similar. We have very few unusual observations (very few underweight or overweight cows).
Again, this is just us eyeballing the data. It can be useful to put numbers on how variable a dataset is.
9.5.1 Variance
Variance (\(\sigma^2\)) describes how far away each observation in our sample is from the sample mean.
If we drew a line representing the mean (\(\overline{x}\)) on our scatteplot of each individual value, we could measure how far away each point was from the line. If we added all these numbers together they would sum to zero (if you want to see a proof of this jump here).
coos %>%
ggplot(aes(x = heifer_id, y = weight, colour = as.factor(heifer_id))) +
geom_point() +
scale_y_continuous(limits = c(0, 250)) +
scale_x_continuous(limits = c(0,12), breaks = seq(0,12,1)) +
theme_classic() +
theme(legend.position = "none") +
labs(y = "Weight (kg)",
x = "Heifer ID") +
geom_segment(x = 0, xend = 12, y = 210.6, yend = 210.6, colour = "pink2")
So we need to transform the distances by squaring them. Now, if we were dealing with hundreds of observations we would of course have a much bigger number, so we need to standardise them by the number of observations (just like we do with the mean calculation).
We can calculate the variance of the cows’ weight very easily:
## # A tibble: 1 x 1
## variance
## <dbl>
## 1 118.
<IP_ADDRESS> Formula for variance
Variance is calculated like this:
\[\sigma^2 = \frac{\sum(x-\overline{x})^2}{n-1} \]
Breaking this equation down …
-
\(\sigma^2\) is the symbol for variance
-
\(\sum(x-\overline{x})^2\) means ’take each value of \(x\) and subtract the mean (\(\overline{x\)}). Square this value (\(^2\)) and add them all together (\(\sum\))
-
Divide this value by the total number in the sample, minus 1 (\(n-1\)). We say \(n-1\) because we are estimating the variance of a sample population1
9.5.2 Standard Deviation
The standard deviation (\(\sigma\)) is very similar to variance (\(\sigma^2\)). Can you guess what the difference is?
The standard deviation is the square root of the variance. When we calculate the variance, we simply sum all the squares together, but we really want to express this number in the same unit as the original measurements (not the square of the measurements). So if we take the square root of the variance, we calculated the standard deviation. This makes the standard deviation a really useful add-on to the mean, because its expressed in the same units.
We can of course calculate this:
## # A tibble: 1 x 1
## standard_deviation
## <dbl>
## 1 10.9
<IP_ADDRESS> Formula for standard deviation.
\[\sigma = \sqrt \frac{\sum(x-\overline{x})^2}{n-1} \]
Breaking this equation down …
-
\(\sigma\) is the symbol for standard deviation
-
\(\sum(x-\overline{x})^2\) means ’take each value of \(x\) and subtract the mean (\(\overline{x\)}). Square this value (\(^2\)) and add them all together (\(\sum\))
-
Divide this value by the total number in the sample, minus 1 (\(n-1\))
-
Take the square root (\(\sqrt\)) of this value.
9.5.3 Standard Error
The third and final measure of variance I’ll discuss in this chapter is the standard error (\(\sigma_\overline{x}\)).
The standard error is an attempt to describe how far away the sample mean (\(\overline{x}\)) is from the true population mean (\(\mu\))?
In our cows example, we’ve measured 12 cows, but we know this is not all the cows in the world. If, in theory, we could measure every single cow in the world, we could calculate the variance of the population (we wouldn’t need to say \((n-1)\) in our variance calculation because we would know exactly how many cows we had).
\[\sigma_{population} = \sqrt \frac{\sum(x-\overline{x})^2}{n_{population}} \]
In this case \(n_{population}\) is every single cow in the world. So if we know how many cows are in our sample, we could calculate the variance of our sample (\(\sigma^2_{sample}\)) in a slightly different way:
:
\[\sigma^2_{sample} = \frac{\sigma^2_{population}}{n_{sample}}\]
Now we hardly ever calculate our sample variance this way because we are rarely ever in a position where we know every single value.
Remember that above we saw that the standard deviation (\(\sigma\)) was the square root of the variance (\(\sigma^2\)). The standard error is the standard deviation divided by the square root of the sample size. This is a bit complicated - and harks back to old algebra lessons about moving variables from one side of an equation to another, and you can read more about it on wiki.
All you need to know is this:
The standard error of the mean is the standard deviation of its sampling distribution.
The standard error of the mean is an estimate of how good the mean is at describing the whole population.
It will always be smaller than the standard deviation.
<IP_ADDRESS> Formula for the standard error of the mean
The standard error of the mean is calculated like this:
\[\sigma_{\overline{x}} = \frac{\sigma}{\sqrt{n}} \]
Breaking this equation down …
-
\(\sigma_{\overline{x}}\) is the symbol for the standard error of the mean
-
\(\sigma\) is the standard deviation of the sample
-
Divide this value by the square root of the total number in the sample (\(\sqrt{n}\))
And like always, we can calculate this, although there’s no inbuilt function to do this, we know the equation is pretty simple:
## # A tibble: 1 x 1
## standard_error_mean
## <dbl>
## 1 3.14
9.6 Calculating descriptive stats
So with our cows, we can calculate lots of different values that each say something a little bit different about our dataset. On their own, these numbers are not always useful, but its important to recognise the relationship between a lot of these calculations.
Lets calculate all of these things in one table:
coos %>%
summarise(median_weight = median(weight),
mode_weight = mode(weight),
mean_weight = mean(weight),
variance_weight = var(weight),
sd_weight = sd(weight),
sem_weight = sd(weight)/sqrt(length(weight)))
## # A tibble: 1 x 6
## median_weight mode_weight mean_weight variance_weight sd_weight sem_weight
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 210. 211. 211. 118. 10.9 3.14
9.7 Reporting on heifer weight
If we wanted to report on heifer weight, we would want to give our best description of the data, which is usually the mean and standard deviation of a normally distributed dataset. And it’s always a good idea to point your reader towards a visualisation of the data.
To report any statistic, you first describe the result in plain English, and then, in brackets, add the proof (which is usually the statistical result and/or a figure.)
The type of model you choose is entirely up to you. There’s no ‘perfect’ answer, just the one that best represents your data.
Heifers in this field weighed on average 210.6kg +/- 10.88kg (median = 210.5kg, Figure 9.15)
coos %>%
ggplot(aes(y = weight)) +
geom_boxplot() +
theme_classic() +
theme(legend.position = "none") +
labs(y = "Weight (kg)")
Footnotes might contain something useful - or something silly. I guess you’ll need to click it to see! But remember, you can’t trust me implicitly - or anyone really↩


| Manage Suites |
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User Guide
Introduction How to use this site?

  In this site, you can analysis your data by several machine learning algorithms and view your data by data visualization tools.
  We ONLY accept CSV files which you can easily generate it by Excel or other software. Because of computation and server restriction, the maximum size of .csv file is 1 MB. The format of CSV file shall follow the requirement of different tools. Unacceptable files will be blocked to upload.
Tools tutorial and Sample Data : :
1. K-Means Clustering >> CLICK HERE TO DOWNLOAD SAMPLE DATA!
2. Linear Regression >> CLICK HERE TO DOWNLOAD SAMPLE DATASET FOR TRAINING ! >> CLICK HERE TO DOWNLOAD SAMPLE DATASET FOR PREDICTION!
3. Logistic Regression >> CLICK HERE TO DOWNLOAD SAMPLE DATASET FOR TRAINING ! >> CLICK HERE TO DOWNLOAD SAMPLE DATASET FOR PREDICTION!
4. Decision Tree >> CLICK HERE TO DOWNLOAD SAMPLE DATASET FOR TRAINING ! >> CLICK HERE TO DOWNLOAD SAMPLE DATASET FOR PREDICTION!
5. Pie Chart >> CLICK HERE TO DOWNLOAD SAMPLE DATASET
6. Line Chart >> CLICK HERE TO DOWNLOAD SAMPLE DATASET


Yoshida PORTER to Open Doors to New Store PORTER EXCHANGE
A military camp concept.
by Charlie ZhangAfter just releasing its TANKER-inspired BE@RBRICK, Yoshida PORTER has announced a new store opening in the heart of Shibuya, Tokyo. Dubbed PORTER EXCHANGE, the store will be located at Shibuya PARCO, the same establishment where Hajime Sorayama and Daniel Arsham will lead the upcoming “2G” exhibition.
PORTER EXCHANGE, or “PX,” riffs on military inspirations, designing the space to reflect the same commitment to quality that you find in mil-spec. Filled with olive and warm yellow tones, the store’s interior is half an army camp and half a cozy wooden cabin. Most of the space is occupied with a large quilted nylon tarp with neon signage that reads “HEART AND SOUL INTO EVERY STITCH.” Inside is a wall that’s packed with a full range of PORTER’s signature Tanker silhouette, along with some exclusive PX items that are being offered for the store’s opening. A low-hanging bench is placed in the middle, while wooden counters are placed at the sides — the outermost areas of the space are enclosed by green metal fences.
In line with PORTER EXCHANGE’s opening, a bevy of exclusives have been made. Spanning a set of bags, sweaters and other accessories, one noteworthy piece is a collectible basketball with SPALDING. The ball comes in black with bright orange highlights, stamped with both “SPALDING” and “PORTER” branding over two panels. Another highlight is a pair of gloves made in collaboration with Winning Boxing. The silhouette is available in either black or olive colorways complemented by orange lining. As a finishing note, each glove boasts a large patch logo at the bottom wrist area. Other pieces like small pouches and bum bags round up the concise assemblage.
Scroll through the gallery above for a look at the inside of PORTER EXCHANGE. The store will open at Shibuya PARCO on November 22 where a full range of exclusive will be available.
Elsewhere, UNITED ARROWS & SONS have delivered a bespoke FW19 lookbook.
PORTER EXCHANGE Shibuya PARCO
15-1 Udagawacho, Shibuya-ku
Tokyo 151-8377, Shibuya PARCO 1F
Tel: 03-6416-5271
10 a.m. – 9 p.m. JST


Ocean [ah49], ocean, in Crystal Sea
Last updated Turn #65
Ocean [ah49], ocean, in Crystal Sea
------------------------------------------------------------------------
26: Fast Sail [ht2u], ship, 3 hulls, 1 rowing port, 9 sails, arrived from
26: Ocean [ai49], owner:
26: Stewart [k0u], Pen, with 12 sailors
27: Fast Sail [ht2u], ship, 3 hulls, 1 rowing port, 9 sails, departed for
27: Ocean [ag49].
Routes leaving Ocean:
North, to Ocean [ag49], 6 days
East, to Ocean [ah50], 6 days
South, to Ocean [ai49], The Inner Sea, 6 days
West, to Ocean [ah48], 6 days


Marks: Point encodes the salary and line encodes the salary difference
Channels: The horizontal positioning encodes the salary amount, size
encodes the magnitude of difference in salary, and color encodes the gender associated with
the salary
Who is the audience?
This is widely relevant data as most people work in these industries
and are probably interested in learning the income disparity between
men and women in their industry or even just learning about what the
average pay looks like in a particular industry. This data would also
be of particular interest to working women, women in general, and people
who are passionate or curious about women’s rights or socioeconomic issues.
What message and questions does this visualization convey and answer?
This visualization addresses one primary question: what does the pay gap look
like in different roles in the US? It examines the pay gap across specific
industries and roles by looking at sex-segregated data of average income, revealing
that while certain roles have more comparable levels of salary between the two genders,
men are paid more than women on average in the US in the same role across different jobs and industries.
It also highlights which roles and industries are more women-friendly, and
which ones are not as progressive.
What data is encoded in the visualization?
How much men and
women get paid on average, respectively, in a particular industry, and
what the difference is between their salaries.
What tasks do readers perform on the visualization?
Readers can compare the difference in income between women and men working
in the same industry.
How are the five principles applied to this visualization?
Truthful: This visualization is straightforward and not misleading.
The labels for position and salary are clear; the salaries start at
0 and go up to 120 which captures a comprehensive picture of the range
of salaries and the horizontal axis that represents salary goes up in
consistent increments of 10. The scale seems to be accurate because
the horizontal axis is labeled and the salary is clearly denoted to be
in the thousands. The graph has a simple but clear title and credits its sources.
Functional: It makes effective use of visual encodings to convey its messages.
The color not only adds a visually pleasing element but is also effective and
efficient at conveying what represents women’s vs men’s salaries and giving
a visual representation of the difference between them. It is simple and easy
to interpret so that anyone could understand the visualization at a glance.
Beautiful: Although, it's not particularly vivid or memorable, the
chart is very simple and aesthetically pleasing because it is
minimalist with some pops of color. There is no chart junk cluttering the space.
Insightful: It effectively answers the questions that we have and reveals which
industries have bigger pay gaps.
Enlightening: This highlights an important and relevant socioeconomic issue.
Seeing as how the number of working women is rising and a significant percentage
of households are dual-income with women contributing financially now more than
ever, this visualization is very informative. It highlights which industries are
more progressive and women-friendly, which can help shape how people view certain
industries or even shape their career choices. It also might bring about further
questions regarding the social implications of women facing discrimination in the
workplace.
Why is this visualization good?
The visualization is good because it meets all of the principles of good design.
It is not only aesthetically pleasing because of
its minimalist style but it is also effective at communicating the message. It is
insightful because it answers the question it aims to address and meets the. It
conveys a very powerful message about something that is important and has significant
societal impact. It credits reliable sources and has clear labeling and scaling,
maintaining graphical integrity and truthful portrayal of the data.
Marks: The boxes are used as areas of the exact same size mark.
Channels: The color opacity and position of each percentage are used as channels.
Who is the audience?
The audience is people interested in Jeopardy! and the frequency of Daily Doubles in certain locations. This visualization
could also be helpful for individuals that are playing on Jeopardy!, in order to create a strategy of hitting Daily Doubles
at certain points of the game.
What message and questions does this visualization convey and answer?
The visualization conveys the frequency of Daily Doubles in certain positions of the Jeopardy! board over 31 seasons.
It answers the question of which clue positions most frequently feature the hidden Daily Double component of the game.
What data is encoded in the visualization?
The visualization counts the number of Daily Doubles that appear in each
position of the game board over the course of 31 seasons, producing a percentage
of the total number of Daily Doubles per board location from those seasons.
How does the visualization encode the data?
The data is encoded with percentages and the opacity of the purple in each clue position,
providing two ways to read the data from the visualization.
What tasks do readers perform on the visualization?
Readers can compare percentages of Daily Doubles in each position on the board by looking at either
percentages or the opacity of the section. Readers can also easily identify extremes by looking at the
opacity of the purple in each area of the board.
How are the five principles applied to this visualization?
Truthful: The visualization is truthful because it includes a gif that shows how
the percentages were calculated, as it shows how the Daily Doubles are tallied over the 31 seasons.
The number is then divided by the total number of Daily Doubles in the past 31 seasons, shown at the
bottom of the visualization to get the percentage of Daily Doubles in each clue position on the Jeopardy! board.
The highest percentages have the darkest colors, and vice versa, so the graph accurately portrays opacity and percentage information.
Functional: The visual is functional because it clearly answers the question of “where do
Daily Doubles most frequently occur on the Jeopardy! board?”. The percentages allow readers to easily identify
the most/least frequent positions and percentages in each position of the board. It uses the position of each
percentage to relate exactly to the actual Jeopardy! board used in the game.
Beautiful: The visual is beautiful because it uses a purple color to easily allow for comparison
between sections, with each section clearly defined by bold black lines to separate the color fields. The visual
also takes the same shape as the Jeopardy! board, which is aesthetically useful for the purpose of the visual.
Insightful: The visual is insightful because it allows the reader to make conclusions about
what rows/columns have the most/least frequent chances of featuring a Daily Double on the Jeopardy! board.
Enlightening: The visual is enlightening because the reader is presented with conclusions
from the visual that they would not have come up with otherwise. Without this visual, it would be very difficult
to determine which clue positions in Jeopardy! have the highest frequency of Daily Doubles.
Why is this visualization good?
Overall, this is a good visualization. The visualization clearly depicts the frequency of Daily Doubles
in certain clue positions over 31 seasons of Jeopardy!. The shape of the visual clearly allows readers
familiar with the game to view each position of the board and view the percentage of Daily Doubles per position.
The opacity of purple adds to the ease of reading the visualization, where readers can compare clue sections and easily
identify high/low percentages of Daily Doubles in each position. Readers can also draw conclusions from this visualization,
such as that the fourth row of the Jeopardy board has the highest frequency of Daily Doubles. It is a useful graphic for
people interested in Jeopardy! gameplay and the frequency of “random” Daily Doubles on the board.
Marks: The slices encode time spent doing activity.
Channels: The position encodes the time of the day when an activity is done.
The color encodes what specific activity is being done during the time frame.
Black vs. white is used to encode whether the time is in am or pm.
Who is the audience?
One potential audience for this graphic is a child’s parents who would like to know how their child spent the day.
What message and questions does this visualization convey and answer?
This visualization displays what the child was doing at any time during the day and effectively conveys how long the child spent doing a certain activity.
What data is encoded in the visualization?
The data encoded in the visualization is the length of time a child spent doing each different activity.
The data also encodes what time the child was doing the activity at.
How does the visualization encode the data?
The visualization encodes the data by using slices of the pie chart to display one hour during the day.
This works well because all slices are the same size so it is easy to compare how long the child spent doing each activity.
The visualization also uses colors to encode the categorical variable of what specific activity the child was doing at a certain time.
In this case, color hues are appropriate because there is no need for a gradient since it is not an ordinal variable.
Finally, the visualization encodes am and pm times by using a black vs white color hue.
What tasks do readers perform on the visualization?
Readers are able to compare the time spent doing each activity by counting the number of slices.
They are also able to easily see that the activity that takes the most time is sleeping because of the color hues.
How are the five principles applied to this visualization?
Truthful: This visualization is truthful because of the accuracy of having all slices be the same so it is not misleading by favoring any certain activity.
Functional: The visualization is functional because of how it is easy to understand and does not confuse the viewer.
Beautiful: It is beautiful because of the bright colors used in the pie chart which make it more appealing to the eye.
The font of the legend makes it appear as if a child handwrote it on top of the lines so it has a childlike appearance to it.
Insightful: The viewer gains insight to how much time the child spends on a certain activity and also knows exactly what time during the day the child did that activity.
Enlightening: The visualization enlightens us as to when and how much time is spent on a certain activity as opposed to showing a bar graph which would only show the length of time spent on an activity.
Why is this visualization good?
This visualization is good because it is simple and conveys the daily activities of the child well.
It is easy to understand and does not require much interpretation on the viewers part apart from counting the number of slices the child spent doing an activity.
The labels for the time of day make the visualization even more useful as it gives the viewer a full understanding of the child’s schedule during the day.
Marks: Area encodes the amount of a compound present in honey
Channels: The size of each slice encodes the quantity of a compound
Who is the audience?
People who consume honey.
What message and questions does the visualization convey and
answer?
The primary question that this visualization addresses is: What
compounds is honey composed of and in what quantities?
What data is encoded in the visualization?
The compounds
that make up honey and their respective quantities are encoded in
this visualization
What tasks do readers perform on the visualization?
Readers
are able to compare the different quantities of compounds that make
up honey.
Why is this visualization bad?
First and foremost, the
visualization is bad because it is not truthful. The angles and
portions of the pie chart are not proportional to the actual
quantity that each slice is supposed to represent. For example, even
though fructose and sucrose are supposed to make up 38.5% and 37.1%
of honey, respectively, the slice that represents fructose makes up
almost twice the amount of area that sucrose does. It does not
credit the source that it got its data from so we cannot be sure
that this information is credible or not, and it is also not very
enlightening as it does not really answer any important questions or
have any implications of real significance.
Analysis: This is a re-designed pie chart that is more accurate. I think that this is the best visualization I produced because like the original visualization, it's visually pleasing and easy to understand but addresses the main issue of graphical integrity. Here, we see that glucose (38.5%) and fructose (37.1%) are much more closer in terms of area and angle on the pie chart, and the other values are also more accurately represented
Analysis: This is a bar chart that represents the quantity values in the bars. Though it is more truthful than the original visualization, this visualization is not as intuitive as a pie chart. For example, a reader would intuitively know that a pie chart is comprehensive and incorporates all possibilities whereas a bar chart does not necessarily encompass all possibilities.
Analysis: This is a tree map, which is visually similar to the pie chart. However, redesign #1 is easier to read and easier to compare the relative sizes of each slice because the slices are closer together, as opposed to the tree map where it's not as easy to compare the area of fructose and glucose because they are farther apart.
Marks: The pie chart areas are the marks used.
Channels: The pie chart uses size/area and color to convey the sizes and separate the percentages.
Who is the audience?
The audience is people watching the WCVB channel.
What message and questions does this visualization convey and
answer?
The visualization addresses worries caused by the Covid-19 pandemic of some people.
What data is encoded in the visualization?
The data depicts percentages of either one group of people or multiple that express different concerns.
How does the visualization encode the data?
The visualization places the three percentages into a pie chart, in an effort to compare the sizes of different fears from Covid-19.
What tasks readers perform on the visualization (e.g., identify extremes, compare values, seeing trends)? Readers are comparing the sizes of different sections of the pie chart, therefore comparing the amount of people that fear certain outcomes of the Covid-19 pandemic.
How are the five principles applied to this visualization?
Truthful: It is not truthful, as the percentages of the circle add up to more than 100% and the angles of the different sections are very inaccurate for the percentages they represent.
Functional: It is functional, despite the color selections being very similar to one another. For the most part, it is easy to read as a visual.
Beautiful: The graph is beautiful, with clean outlines of each section and bright colors. The colors also go well together.
Insightful: The graph is not insightful, as it is very confusing as to where the data for these percentages was obtained and the inaccurate section sizes do not allow the audience to draw meaningful conclusions.
Enlightening: The graph is somewhat enlightening, giving the audience an idea of a percentage of people that fear certain outcomes of the pandemic.
Why is this visualization bad?
This visualization is ultimately bad because it completely misuses the concept of a pie chart.
A pie chart should always have the sum of its section percentages equal 100%, while this chart adds up to 178%.
Additionally, since the percentages add up to more than 100%, it is unclear whether some of these sections overlap
or if the data is based on the same group of people at all. The colors of the graph are very similar, which do not
allow for a clear distinction between each section and fear. Furthermore, the incorrect sizes of each section proportional
to 100% does not allow for the audience to draw meaningful conclusions from the comparison of these sections.
What design choices and visual encodings did you use? I used a bar chart that displays the percentages of the group of people interviewed about their different COVID-19 fears.
How does your redesign compare with the original? It is much more clear what percentage of people fear each potential outcome of COVID-19 in their lives, with the ability to compare between the sizes of percentages more easily.
Why and where do you think your redesign improves upon the original? It proportionally displays the percentages of people in each group, allowing for readers to more accurately compare the two. It is lacking in definition of data and detailing whether the percentages overlap within the same group of people interviewed, but this information was not provided.
What trade-offs and compromises did you make? Remember there is rarely a one-size-fits-all design. The bar chart might be slightly less beautiful and appealing than the pie chart visually, but I think it is much more functional and insightful than the pie chart option. The bar chart functions well as one succinct chart that displays the necessary info in a proportional manner.
Analysis: This is a series of three pie charts that uses the same pie chart style as the original graph, but uses pie charts correctly to depict the data. Since the three percentages overlap and don't add to 100%, each one can only be accurately depicted in one pie chart each.
Analysis: This is a bar chart using icons instead of bars to denote the percentages of each worry in the group surveyed. It converts the percentages into the number of people out of 10 that fear each item in the y-axis.
Marks: The line mark encodes the change from the previous month in nonfarm payrolls in the United States.
Channels: The position channel encodes the year.
Who is the audience?
The audience is the general public who are looking to understand information about the May 2020 jobs report and what it indicates for the US economy.
What message and questions does this visualization convey and
answer?
This visualization conveys that there was a sharp decrease in change in nonfarm payrolls in April 2020 but also a sharp increase in May 2020.
It also conveys that from 2015 to 2020, there were mostly small increases in the change in nonfarm payrolls.
What data is encoded in the visualization?
The data encoded is the change in nonfarm payrolls from the previous month in the United States.
How does the visualization encode the data?
The visualization encodes the data by using a line graph to show how the data varies over time.
In addition, data labels are used on the two most recent data points, April 2020 and May 2020 to help identify those as outliers.
What tasks readers perform on the visualization (e.g., identify extremes, compare values, seeing trends)? Readers can easily identify the extreme values they see in April 2020 and May 2020, which are obvious because of the scale used to display the y-axis. It is difficult to compare values for any other time period, as the rest of the data seems to be almost a straight line, again because of the scale used for the y-axis.
How are the five principles applied to this visualization?
Truthful: This visualization can be misleading because it displays the change in jobs, so when we see positive 2.5M jobs in May, that is only the increase from the 20.7M lost in April.
Functional: The visualization is not perfectly functional because of the scale of the y-axis and how it is hard to decipher the values of every data point besides April 2020 and May 2020.
Beautiful: It has some beauty because of the blue used for the line, but the chart is not very beautiful other than that.
Insightful: The insight that the viewer is led to believe is that jobs are doing even better in May 2020 than they were before April 2020.
However, this is not true since it is only the increase from the previous month, so jobs are still 18.2M below what they were in March 2020 (-20.7 + 2.5 = -18.2).
Enlightening: The visualization enlightens us about the increase in jobs in May 2020, and at least lets us know that the downward trend of April 2020 did not continue into May 2020.
Why is this visualization bad?
This visualization is not the best because of how it is misleading viewers into thinking jobs are above their March 2020 levels, when really jobs are still far below their historical norms.
In addition, the scale of the y-axis makes it hard to understand the other data points and what changes are occurring during 2015 - 2020.
Finally, the chart also fails to go back to another period of recession, so the time period displayed by the chart is misleading as it was a historically strong period for jobs growth.
The chart would be more informative if they compared the data points in April and May 2020 to another recession so that viewers could have better context.
Analysis: The shaded area allows the viewer to understand the economic context of the time period to help provide a potential explanation for the sharp decrease in jobs.
What design choices and visual encodings did you use? I included a shaded area on the line chart to indicate when the economy was in a recession. I also changed the units being used on the y-axis so that instead of the change in jobs, it shows the actual number of jobs so that the viewer can more easily understand how COVID has impacted jobs in the US and that job levels are not yet back to their pre-pandemic levels.
How does your redesign compare with the original? My redesign uses the same time period as the original but different units on the y-axis. In addition, I think the shading helps the viewer understand the economic context that is driving the sharp decrease in jobs.
Why and where do you think your redesign improves upon the original? My redesign improves upon the original by making it easier to understand the current level of payrolls as of May 2020 and does not mislead viewers into thinking job levels have already rebounded fully.
What trade-offs and compromises did you make? Remember there is rarely a one-size-fits-all design. One trade-off I had to make is that it is now harder to understand the change from month to month in payrolls. I think this is worth it though because we are more concerned about the bigger picture of US job levels than trying to find out what the change was over the short term period of the past two months.
Analysis: The x-axis has been extended further back in time to show the 2008 recession as well. This allows the viewer to compare the job loss during the last recession to the job loss being experienced in 2020. The ability to compare data to a similar historical time period gives the viewer enough context to draw conclusions about how serious the job loss experienced in 2020 was.


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We have seen how to define a unified arithmetic system that encompasses ordinary numbers, complex numbers, rational numbers, and any other type of number we might decide to invent, but we have ignored an important issue. The operations we have defined so far treat the different data types as being completely independent. Thus, there are separate packages for adding, say, two ordinary numbers, or two complex numbers. What we have not yet considered is the fact that it is meaningful to define operations that cross the type boundaries, such as the addition of a complex number to an ordinary number. We have gone to great pains to introduce barriers between parts of our programs so that they can be developed and understood separately. We would like to introduce the cross-type operations in some carefully controlled way, so that we can support them without seriously violating our module boundaries.
One way to handle cross-type operations is to design a different function for each possible combination of types for which the operation is valid. For example, we could extend the complex-number package so that it provides a function for adding complex numbers to ordinary numbers and installs this in the table using the tag list("complex","javascript_number"):[1]
// to be included in the complex package function add_complex_to_javascript_num(z, x) { return make_from_real_imag(real_part(z) + x, imag_part(z)); } put("add", list("complex", "javascript_number"), (z, x) => tag(add_complex_to_javascript_num(z, x)));
This technique works, but it is cumbersome. With such a system, the cost of introducing a new type is not just the construction of the package of functions for that type but also the construction and installation of the functions that implement the cross-type operations. This can easily be much more code than is needed to define the operations on the type itself. The method also undermines our ability to combine separate packages additively, or least to limit the extent to which the implementors of the individual packages need to take account of other packages. For instance, in the example above, it seems reasonable that handling mixed operations on complex numbers and ordinary numbers should be the responsibility of the complex-number package. Combining rational numbers and complex numbers, however, might be done by the complex package, by the rational package, or by some third package that uses operations extracted from these two packages. Formulating coherent policies on the division of responsibility among packages can be an overwhelming task in designing systems with many packages and many cross-type operations.
In the general situation of completely unrelated operations acting on completely unrelated types, implementing explicit cross-type operations, cumbersome though it may be, is the best that one can hope for. Fortunately, we can usually do better by taking advantage of additional structure that may be latent in our type system. Often the different data types are not completely independent, and there may be ways by which objects of one type may be viewed as being of another type. This process is called coercion. For example, if we are asked to arithmetically combine an ordinary number with a complex number, we can view the ordinary number as a complex number whose imaginary part is zero. This transforms the problem to that of combining two complex numbers, which can be handled in the ordinary way by the complex-arithmetic package.
In general, we can implement this idea by designing coercion functions that transform an object of one type into an equivalent object of another type. Here is a typical coercion function, which transforms a given ordinary number to a complex number with that real part and zero imaginary part:
function javascript_number_to_complex(n) { return make_complex_from_real_imag(contents(n), 0); }
put_coercion("javascipt_number", "complex", javascript_number_to_complex);
Once the coercion table has been set up, we can handle coercion in a uniform manner by modifying the apply_generic function of section \ref{sec:data-directed}. When asked to apply an operation, we first check whether the operation is defined for the arguments' types, just as before. If so, we dispatch to the function found in the operation-and-type table. Otherwise, we try coercion. For simplicity, we consider only the case where there are two arguments.[2] We check the coercion table to see if objects of the first type can be coerced to the second type. If so, we coerce the first argument and try the operation again. If objects of the first type cannot in general be coerced to the second type, we try the coercion the other way around to see if there is a way to coerce the second argument to the type of the first argument. Finally, if there is no known way to coerce either type to the other type, we give up. Here is the function:
function apply_generic(op, args) { const type_tags = map(type_tag, args); const fun = get(op, type_tags); if (fun !== false) { return fun(map(contents, args)); } else { if (length(args) === 2) { const type1 = head(type_tags); const type2 = head(tail(type_tags)); const a1 = head(args); const a2 = head(tail(args)); const t1_to_t2 = get_coercion(type1, type2); const t2_to_t1 = get_coercion(type2, type1); if (t1_to_t2 !== false) { return apply_generic(op,list(t1_to_t2(a1), a2)); } else if (t2_to_t1 !== false) { return apply_generic(op, list(a1, t2_to_t1(a2))); } else { return Error("No method for these types", list(op, type_tags)); } } else { return Error("No method for these types", list(op, type_tags)); } } }
This coercion scheme has many advantages over the method of defining explicit cross-type operations, as outlined above. Although we still need to write coercion functions to relate the types (possibly $n^2$ functions for a system with $n$ types), we need to write only one function for each pair of types rather than a different function for each collection of types and each generic operation.[3] What we are counting on here is the fact that the appropriate transformation between types depends only on the types themselves, not on the operation to be applied.
On the other hand, there may be applications for which our coercion scheme is not general enough. Even when neither of the objects to be combined can be converted to the type of the other it may still be possible to perform the operation by converting both objects to a third type. In order to deal with such complexity and still preserve modularity in our programs, it is usually necessary to build systems that take advantage of still further structure in the relations among types, as we discuss next.
The coercion scheme presented above relied on the existence of natural
relations between pairs of types. Often there is more global
structure in how the different types relate to each other. For
instance, suppose we are building a generic arithmetic system to
handle integers, rational numbers, real numbers, and complex numbers.
In such a system, it is quite natural to regard an integer as a
special kind of rational number, which is in turn a special kind of
real number, which is in turn a special kind of complex number. What
we actually have is a so-called hierarchy of types, in which,
for example, integers are a
subtype of rational numbers (i.e.,
any operation that can be applied to a rational number can
automatically be applied to an integer). Conversely, we say that
rational numbers form a
supertype of integers. The particular
hierarchy we have here is of a very simple kind, in which each type
has at most one supertype and at most one subtype. Such a structure,
called a tower, is illustrated in Figure \ref{fig:tower}.
\begin{figure}[H]
\centering
\maxsizebox{\linewidth}{0.8\paperheight}{\includegraphics[scale=0.8]{{img\string_original/ch2-Z-G-66}.svg}}
\caption{A tower of types.}
\label{fig:tower}
\end{figure}
If we have a tower structure, then we can greatly simplify the problem of adding a new type to the hierarchy, for we need only specify how the new type is embedded in the next supertype above it and how it is the supertype of the type below it. For example, if we want to add an integer to a complex number, we need not explicitly define a special coercion function integer_to_complex. Instead, we define how an integer can be transformed into a rational number, how a rational number is transformed into a real number, and how a real number is transformed into a complex number. We then allow the system to transform the integer into a complex number through these steps and then add the two complex numbers.
We can redesign our apply_generic
function
in the following
way: For each type, we need to supply a raise
function, which
raises
objects of that type one level in the tower. Then when the
system is required to operate on objects of different types it can
successively raise the lower types until all the objects are at
the same level in the tower. (Exercises \ref{ex:raise}
and \ref{ex:apply-with-raise}
concern the details of implementing such a strategy.)
Another advantage of a tower is that we can easily implement the
notion that every type inherits
all operations defined on a
supertype. For instance, if we do not supply a special
function
for
finding the real part of an integer, we should nevertheless expect
that real_part will be defined for integers by virtue of the
fact that integers are a subtype of complex numbers. In a tower, we
can arrange for this to happen in a uniform way by modifying apply_generic. If the required operation is not directly defined for
the type of the object given, we raise the object to its supertype and
try again. We thus crawl up the tower, transforming our argument as we
go, until we either find a level at which the desired operation can be
performed or hit the top (in which case we give up).
Yet another advantage of a tower over a more general hierarchy is that
it gives us a simple way to lower
a data object to the simplest
representation. For example, if we add $2+3i$ to $4-3i$, it would be
nice to obtain the answer as the integer 6 rather than as the complex
number $6+0i$. Exercise \ref{ex:simplify} discusses a way to implement
such a lowering operation. (The trick is that we need a general way
to distinguish those objects that can be lowered, such as $6+0i$, from
those that cannot, such as $6+2i$.)
\begin{figure}[H]
\centering
\maxsizebox{\linewidth}{0.8\paperheight}{\includegraphics[scale=0.8]{{img\string_original/ch2-Z-G-67}.svg}}
\caption{Relations among types of geometric figures.}
\label{fig:relations-among-figures}
\end{figure}
If the data types in our system can be naturally arranged in a tower,
this greatly simplifies the problems of dealing with generic operations
on different types, as we have seen. Unfortunately, this is usually
not the case. Figure \ref{fig:relations-among-figures} illustrates a
more complex arrangement of mixed types, this one showing relations
among different types of geometric figures. We see that, in general,
a type may have more than one subtype. Triangles and quadrilaterals,
for instance, are both subtypes of polygons. In addition, a type may
have more than one supertype. For example, an isosceles right
triangle may be regarded either as an isosceles triangle or as a right
triangle. This multiple-supertypes issue is particularly thorny,
since it means that there is no unique way to raise
a type in the
hierarchy. Finding the correct
supertype in which to apply an
operation to an object may involve considerable searching through the
entire type network on the part of a
function
such as apply_generic. Since there generally are multiple subtypes for a
type, there is a similar problem in coercing a value down
the type
hierarchy. Dealing with large numbers of interrelated types while
still preserving modularity in the design of large systems is very
difficult, and is an area of much current research.[4]
coercearguments of each type to their own type. For example, in addition to the javascript_number_to_complex coercion shown above, he would do:
function javascript_number_to_javascript_number(n) { return n; } function complex_number_to_complex_number(n) { return n; } put_coercion("javascript_number", "javascript_number", javascript_number_to_javascript_number); put_coercion("complex_number", "complex_number", complex_number_to_complex_number);
function exp(x, y) { return apply_generic("exp", list(x, y)); }
// following added to JavaScript-number package put("exp", list("javascript_number", "javascript_number"), (x, y) => tag(math_exp(x, y))); // primitive math_exp
compatiblewith the rest of the system and will not lead to problems in adding new levels to the tower. \end{Exercise} \stepcounter{ExerciseDisplayNumber} \begin{Exercise} This section mentioned a method for
simplifyinga data object by lowering it in the tower of types as far as possible. Design a function drop that accomplishes this for the tower described in exercise \ref{ex:raise}. The key is to decide, in some general way, whether an object can be lowered. For example, the complex number $1.5+0i$ can be lowered as far as "real", the complex number $1+0i$ can be lowered as far as "integer", and the complex number $2+3i$ cannot be lowered at all. Here is a plan for determining whether an object can be lowered: Begin by defining a generic operation project that
pushesan object down in the tower. For example, projecting a complex number would involve throwing away the imaginary part. Then a number can be dropped if, when we project it and raise the result back to the type we started with, we end up with something equal to what we started with. Show how to implement this idea in detail, by writing a drop function that drops an object as far as possible. You will need to design the various projection operations[5] and install project as a generic operation in the system. You will also need to make use of a generic equality predicate, such as described in exercise \ref{ex:equ?}. Finally, use drop to rewrite apply_generic from exercise \ref{ex:apply-with-raise} so that it
simplifiesits answers. \end{Exercise} \stepcounter{ExerciseDisplayNumber} \begin{Exercise} Suppose we want to handle complex numbers whose real parts, imaginary parts, magnitudes, and angles can be either ordinary numbers, rational numbers, or other numbers we might wish to add to the system. Describe and implement the changes to the system needed to accommodate this. You will have to define operations such as sine and cosine that are generic over ordinary numbers and rational numbers. \end{Exercise}
graphof relations among types and automatically generate those coercion functions that can be inferred from the ones that are supplied explicitly.
ontology) seems intractably difficult. The main difference between the confusion that existed ten years ago and the confusion that exists now is that now a variety of inadequate ontological theories have been embodied in a plethora of correspondingly inadequate programming languages. For example, much of the complexity of object-oriented programming languages—and the subtle and confusing differences among contemporary object-oriented languages—centers on the treatment of generic operations on interrelated types. Our own discussion of computational objects in chapter 3 avoids these issues entirely. Readers familiar with object-oriented programming will notice that we have much to say in chapter 3 about local state, but we do not even mention
classesor
inheritance.In fact, we suspect that these problems cannot be adequately addressed in terms of computer-language design alone, without also drawing on work in knowledge representation and automated reasoning.