In Bitcoin 0.9 OP_RETURN became an accepted part of Bitcoin transactions, allowing 40 bytes of data to be used for... whatever. OP_RETURN is still being implemented and the effects are yet to be seen. This site isn't any old Bitcoin transaction ticker, it has an eye to the metadata and a pulse on Bitcoin's future. Just like Bitcoin, our code is open. You can take a look on GitHub.


Pitas
Description
This pita recipe is fast enough to make during the week, as it does not use yeast! Baking powder is the key ingredient to make light, fluffy pitas.
These pitas go amazingly with grilled chicken/vegetables and hummus!
Tips
Let the dough rest for 15 minutes before shaping into pitas. I've found that waiting less time causes the pitas to not be very fluffy.
Make sure to thoroughly mix the baking powder into the dough so that it is evenly incorporated.
This recipe makes 6-8 small pitas
If you are going to be making pitas, it's worth buying a SilPain as it REALLY makes a difference.
List of Ingredients
- 300 grams of All-Purpose Flour
- 200 mL of Cold Water
- 2 Tablespoons of Canola Oil or Extra-Virgin Olive Oil
- 1 Teaspoon of Salt
- 2 Teaspoons of Baking Powder
Recipe Steps
- Preheat the over to 400 degrees (Fahrenheit).
- Combine half of the flour, water, oil, salt, and baking powder and mix thoroughly.
- Add in the remaining flour and mix until all the dough pieces form together to make a dough ball.
- Let the dough rest for 15 minutes.

- Roll out the pitas using a rolling pin into circle shapes.
- Place each pita on a baking sheet (preferably with a Silpain); you'll need to do this in two patches.
- Bake the pitas for 9 minutes (just before getting browned on top).
- Let rest for 5 minutes and enjoy!


{% include "header.html" %}
About Me
This is what I do.
Hey, I'm Bart.
I'm a registered Chartered Accountant and Tech Enthusiast.
From a young age I've always been intrigued about computer science and technology. After school I obtained both Bachelors and Honours degrees in Accounting, after which I gained invaluable experience as an auditor for the next 4 and a half years of my life.
I've always been interested in automating workflows for finanace related obstacles. When I'm not spending time with family or friends, I navigate towards doing research and playing around with code on the computer. See some of the projects I've completed in my GitHub repo.
Feel free to get in touch if you would like to colloborate with me in the future.


Welcome to Mono County, the heart of the Eastern Sierra. Mono County is located along US395 in between Bishop and Lake Tahoe. It has famous Yosemite National Park on the westside and the state of Nevada on the east. It is a paradise for hikers and flyfishers.
As is the tradition, the fishing season in June Lake has started on the last Sunday of April. Parts of some of the lakes were still frozen, but that did not prevent anybody from having fun and catching big fish
Tioga Pass opened relatively early this year. On May 10 the gates at the east entrance of Yosemite National Park were already opened.
With Tioga Pass open again, crowds are about to discover the Whoa Nellie deli at the Mobil Station again, at the junction of US395 and the Tioga Road.My favorite dishes there are the steak sandwich and the lobster tacquitos.
The annual friends of Bodie festival takes place on August 8. Tis members only event starts at 6.30pm and includes BBQ dinner and a special tour
Like every year, the Mammoth ski area transformed into a paradise for mountain bikers. People can go up the mountain with the gondola en bike back down using one of several popular trails, such as the popular Kamikaze trail.
Once again, there is an opportunity to row around near the South Tufa area of Mono Lake. Check with the Mono Lake Committee for details


Copyright (C) 1990 The Regents of the University of California. All rights reserved.
This code is derived from software contributed to Berkeley by Vern Paxson.
The United States Government has rights in this work pursuant to contract no. DE-AC03-76SF00098 between the United States Department of Energy and the University of California.
Redistribution and use in source and binary forms with or without modification are permitted provided that: (1) source distributions retain this entire copyright notice and comment, and (2) distributions including binaries display the following acknowledgement: "This product includes software developed by the University of California, Berkeley and its contributors" in the documentation or other materials provided with the distribution and in all advertising materials mentioning features or use of this software. Neither the name of the University nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.


My name is Lucas Winkler and I am striving to become an efficient web developer. I am currently a student at Conestoga College for the Computer Programmer/Analyst Three Year Diploma Program. I do not have any professional experience in the web development field at the moment.
As far as personal projects I don't not have anything worth talking about. This website is my first real project and it is built in HTML5, CSS3, jQuery and any other small libraries. Eventually it will be re-built in React and Bootstrap. My plan is to create multiple projects over the summer that are worth displaying on this website.


Adults in low-income households are certainly more likely to be uninsured than people in higher income households, but not all uninsured adults are in families with low incomes. Texas has the highest number of uninsured adults, but does not have the highest number of families in poverty. Therefore, there must be other factors that contribute to whether an individual is insured, or not. Examples such as the price of healcare in the US or general insurance prices.
In the U.S., people living below the poverty level have higher rates of cigarette smoking than the general population. However, poverty does not appear to be the only factor that contributes to the likelihood of cigarette smoking. Education can play a massive role in the popularity of smoking. States like Utah and Massachusetts fall around the same median in terms of poverty rates, but have some of the drasticly different rates of smoking relative to their populations.
The U.S. is one of the wealthiest countries in the world and accordingly has high obesity rates; one-third of the population has obesity plus another third is overweight. It has been suggested that individuals who live in impoverished regions have poor access to fresh food. Poverty-dense areas are oftentimes called “food deserts,” implying diminished access to fresh food. Again, there are states, such as New Hampshire, that have very low poverty rates but still have relatively higher rates of obesity.
Compared with other age groups, young adults are the most likely to go without coverage. This can be expected since young adults tend to be much healthier than other age groups, and can typically risk being uninsured. Massachusetts appears to have the lowest number of uninsured individuals, while Texas has the highest rate, but a much lower median age. An interesting case is Florida, where the median age is much higher than other states at 42, but has much higher rates of poverty.
It does appear that states with a large proportion of older age groups tend to have the highest rates of smoking. Perhaps states where the median age is higher have individuals that have smoked for many years, and have chosen not to quit. As opposed to states like Utah where the median age is very low, and much of the population doesn't smoke at all.
There does appear to be slight correlation between a states median age and level of obesity. It seems that higher age groups are more succeptible to being obese, but that is likely not always the case. Older age groups are typically less active than younger groups, and would therefore do much to explain why an older person is more likely to be obese. Clearly there are other factors, like poverty and income that may affect obesity levels.
Many uninsured people cite the high cost of insurance as the main reason they lack coverage. Most uninsured people are in low-income families and have at least one worker in the family. Reflecting the more limited availability of public coverage in some states. This is quite clearly the case in Texas where the level of uninsured individuals is the highest in the country.
A household's income contributes directly to the level of poverty. Therefore, just as states with high rates of poverty tend to have a greater population of individuals that smoke, states with lower median household income will tend to also have higher rates of smoking. States with relatively higher median incomes have some of the lowest rates of smoking.
Overall obesity prevalence does tend to decrease with increased levels of income. Just as poverty affects the availability of healthy food options, as does income. Lower income residents liekly do not have the same access to low cost healthy food choices as does higher income groups. So we can see that groups earning an income over 70K a year see little to no obesity levels.


Chapter 1 Python
Python is a versatile language that can be used for data analysis, data cleaning, web scraping and data visualization (and much more). It is easy to read (in comparison to some other languages) and a great language for beginners to learn and master. It also has tons of open source code libraries and lots of support online (chances are if you have an issue, someone else will have already had that issue) via Stack Overflow.
1.1 Getting started
1.1.1 Via: Anaconda (recommended)
Anaconda conveniently installs Python, the Jupyter Notebook, and other commonly used packages for scientific computing and data science
- Download Anaconda which automatically downloads Python for you (Select the Python 3.6 or most up-to-date version. Python 2 has slightly different syntax).
- I usually use Anaconda to access jupyter-notebooks so rarely have to even open Anaconda once it’s on my machine.
- Follow with correct installation
- To open jupyter notebook - I usually hit
cmd+space
on my Mac and type in ‘jupyter-notebook’
1.1.2 Via: pip
- If you don’t want to download anaconda you can follow the alternative route here.


alles
Darkness cannot drive out darkness; only light can do that.
Hate
cannot drive out hate; only love can do that.
[x]
[x]
Darkness cannot drive out darkness; only light can do that.
Hate
cannot drive out hate; only love can do that.


25 migrants found in refrigerated container bound for Felixstowe
Twenty-five migrants have been found in a refrigerated container on a ferry that was heading to Felixstowe from the Netherlands.
The Britannia Seaways ship returned to the port of Vlaardingen, near Rotterdam, after the stowaways were discovered.
Gert Jakobsen, vice president of communications with the ship's operator DFDS, said the migrants were found after they made a hole in the trailer.
The roll on, roll off ferry was not far off the Dutch coast en route to the Suffolk town when the discovery was made at around 5pm on Tuesday.
Mr Jakobsen said police were waiting at the port and the migrants were taken to hospital by ambulance to check for hypothermia.
"We don't know about the checks. Of course, they were not in a very good condition" he said.
"Unfortunately this is not the first time we have had such a situation. Of course, it's rare they hide in a refrigerated trailer. "But we do find migrants even though we try to prevent it."– Gert Jakobsen, DFDS
It is not known the ages or sex of any of the migrants or where they are from.
Dutch police said 23 people were transferred to a police station after medical check-ups, while two were receiving extra care in hospital.
Dogs are searching for other migrants were still on the ship.
It comes weeks after the bodies of 39 Vietnamese migrants were found in a refrigerated lorry container in Essex after arriving on a ferry from Zeebrugge, Belgium.
And yesterday, eight people were found in the back of a lorry on the A14 in Cambridgeshire and a man was arrested on suspicion of assisting unlawful immigration.


Represents a Solr response. Parses the raw response into a set of stdClass objects and associative arrays for easy access.
Currently requires json_decode which is bundled with PHP >= 5.2.0, Alternatively can be installed with PECL. Zend Framework also includes a purely PHP solution.
Located in /Response.php (line 32)
Constructor. Takes the raw HTTP response body and the exploded HTTP headers
Get character encoding of this response. Should usually be utf-8, but just in case
Get the HTTP status message of the response
Get the raw response as it was given to this object
Magic get to expose the parsed data and to lazily load it
Documentation generated on Tue, 02 Oct 2007 12:55:38 -0400 by phpDocumentor 1.4.0


Online Tools
Image Optimizer
FavIcon Generator
Email Riddler
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Gradient Image
Button Maker
Online Tools
Image Optimizer
FavIcon Generator
Email Riddler
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Gradient Image
Button Maker


ECE is programmed as a place where curiosity is celebrated and intellectual collisions occur naturally. Traffic flow and sightlines are carefully designed to foster collaboration, and interdisciplinary learning and cooperation. Displacement and demand-controlled ventilation, heat-recovery chillers and chilled-beam technology help bring energy use to 46% below ASHRAE 90.1-2007 standards, an exceptional achievement considering the energy-intensive nature of a lab facility with an instructional clean room. The design reflects a LEED Platinum certification.
This project started with a small design team, but eventually grew to over 10 people by the time the design finished. With new people joining at different times, we were able to leverage their new ideas and turn those into concepts we could evaluate against the current design. Some of the images you see below are renderings I created to test out ideas related to shading and light.
Our prototyping techniques usually involved several different applications, such as digital modeling, physical modeling and full scale mock-ups. We would evaluate the physical prototypes on site with the users so that they could hold the artifact that would eventually become their building. The most important element of this project was the relationship between the users and the architectural team. It was truly a team effort and we relied on their design feedback just as much as they relied on us to produce a building they could call home.
The building you see below was a collaborative team effort of more than 100 people (users, architects, engineers, contractors). The result is an award winning, sustainable building that the client loves. All the hard work, long hours (80+ hours a week for several weeks) and stressful decisions were worth it when I first saw the completed building. It’s the project I’m proudest of because it’s the one I should have failed hardest at. Although I was overwhelmed, I was never afraid. I leaned heavily on my teammates and the office for their support in detailing, advice on big decisions, feedback on design direction, and help managing a growing team.
It was the clients goal to make this project highly efficient. ECE is one of the most sustainable buildings in the country with a LEED Platinum certification, an EPA EnergyStar rating of 99 out of 100 and a goal to be net-zero energy.


/**
* @module Marathon
*/
import { ROOT_RUNNER } from './types';
import { marathonObject } from './utils';
import { combineRunners } from './runners/combined-runner';
export * from './runners/base-runner';
export * from './runners/scoped-runner';
export * from './runners/combined-runner';
export const RootRunnerState = {
UNREGISTERED: 'UNREGISTERED',
REGISTERED: 'REGISTERED'
};
/**
* @typedef {function(*): MarathonObject} RunnerFunction
*
* Create a root application runner.<br/>
* Only use this for the root application. If you want to group runners together, see #combineRunners.
*
* @kind function
* @memberof module:Marathon
* @instance
* @name combineRoot
*
* @see {@link createRunner}
* @see {@link createScopedRunner}
* @see {@link combineRunners}
*
* @param {...RunnerFunction} runners - Runners created with calls to #createRunner, #createScopedRunner, or #combineRunners.
*
* @example
* import { combineRoot } from '@mashvp/marathon';
*
* import someRunner from './some-runner';
* import someCombinedRunner from './some-combined-runner';
* import someScopedRunner from './some-scoped-runner';
*
* const rootRunner = combineRoot(someRunner, someCombinedRunner, someScopedRunner);
*
* document.addEventListener('DOMContentLoaded', () => {
* rootRunner.register({ someGlobalValue: 42 });
* });
*
* @returns {MarathonObject}
*/
export const combineRoot = (...runners) => {
const combinedRunner = combineRunners(...runners);
let state = RootRunnerState.UNREGISTERED;
let instance = null;
const registerCurrentInstance = () => {
try {
instance.register();
} catch (err) {
console.error(
`Marathon: Runner '${instance.name}' crashed while registering.\n\t${err}`
);
}
state = RootRunnerState.REGISTERED;
};
const register = (globals = {}) => {
if (state === RootRunnerState.REGISTERED) {
throw new Error('Marathon: RootRunner: Already registered.');
}
if (instance) {
return registerCurrentInstance();
}
instance = combinedRunner(globals);
registerCurrentInstance();
};
/**
* Unregisters the whole application runner tree.
*
* @param {boolean} [keepTree=false] - Keeps the runner tree intact. The next call to #register will reuse the previous tree. Note that globals will not be updated.
*/
const unregister = (keepTree = false) => {
instance.unregister();
if (!keepTree) {
instance = null;
}
state = RootRunnerState.UNREGISTERED;
};
return marathonObject(ROOT_RUNNER, {
name: 'RootRunner',
register,
unregister
});
};


Ravencoin Price Chart | Market Capitalization | RVN to USD Calculator | RVN ROI Calculator | News | Description | Team | Related Events | Community | Similar coins to Ravencoin RVN
dan.07-Aug-19 08:39 PM
there goes btc
Tek07-Aug-19 08:40 PM
its only a year or two old and it already listed more places than KMD
imagine that
70 markets for raven
dan.07-Aug-19 08:40 PM
i dont know if i should be happy or sad, been accumulating KMD again since 7500
Caglio07-Aug-19 08:40 PM
@Tek do you think they have marketing team ?
Dan, if we go up, you will be happy
Tek07-Aug-19 08:40 PM
kmd only has 30 markets
Caglio07-Aug-19 08:41 PM
Honestly.... that might take a while
Tek07-Aug-19 08:41 PM
@Caglio not sure
Caglio07-Aug-19 08:41 PM
I already told myself I burnt this 10k
Which is shame, if I didnt invest in here, I could be almost out of a debt
cascrypto07-Aug-19 08:41 PM
nice dan. with time always a winner, btfd
Caglio07-Aug-19 08:41 PM
This way, I am not
dan.07-Aug-19 08:41 PM
yeah but i had 0 reason to buy except on feeling. i am straight up buying a falling knife as we speak
i will buy more at 6500
Caglio07-Aug-19 08:42 PM
Alright, I wish you good luck kind sir
I really hope your investment gets x 10
dan.07-Aug-19 08:42 PM
we will see what happens
highly unlikely kmd, and any alt coin with active devs will slide in obscurity
james isnt going anywhere
Caglio07-Aug-19 08:43 PM
Alright
I must be a bearer of a bad news
I am a bitch, a madman, many things
But... I am trying to be and stay real
james is a good thing in komodo
James not going anywhere
Imagine something happens to james
dan.07-Aug-19 08:44 PM
youre not wrong
Caglio07-Aug-19 08:44 PM
Which I hope wont, but imagine something happens to him
scytale07-Aug-19 08:44 PM
anyone saw the inverse head and shoulders formation?(edited)
RealmX is a next-generation product developed by Block Hop. It is the world’s first free to play sandbox game powered by blockchain…
 I dont believe it will with this efforts ( from the marketing side ), thats what pisses me off the most
I have my money in it, and I will be the happiest guy if I am wrong, about it all
I am not selling, not before KMD hits 5$ anyway
scytale07-Aug-19 08:50 PM
i remember someone said once that forks get like 1% of the value of the chain they forked


int
RefCheckCompositionalRefinementOneAtom(
ClientData clientData,
Tcl_Interp * interp,
int argc,
char ** argv
)
- optional
- Side Effects trnasition relations for new modules will be built if -r option is used
- See Also
optional
- Defined in
ref.c
int
RefCheckNohiddenRefinement(
ClientData clientData,
Tcl_Interp * interp,
int argc,
char ** argv
)
- optional
- Side Effects required
- See Also
optional
- Defined in
ref.c
int
RefCheckSimulation(
ClientData clientData,
Tcl_Interp * interp,
int argc,
char ** argv
)
- optional
- See Also
optional
- Defined in
refSym1.c
boolean
RefCheckTransitionInvariant(
Mdl_Module_t * module,
Sym_Info_t * symInfo,
mdd_t * transInv,
mdd_t * initSet,
array_t * eventIdArray,
array_t * histFreeIdArray,
array_t * histDependIdArray,
array_t * transQuanIdArr,
boolean verbose,
boolean checkAtEnd
)
- optional
- Side Effects required
- See Also
optional
- Defined in
ref.c
array_t *
RefConjunctsAddExtra(
Sym_Info_t * symInfo,
mdd_t * extra
)
- Add one mdd to the conjuncts array and return a new
conjuncts array. The user is responsible for freeing
the new array and all the mdds in the new array
- Side Effects none
- See Also
RefUnprimedIdsMerge
- Defined in
refSym1.c
void
RefDumpIds(
array_t * a
)
- optional
- Side Effects required
- See Also
optional
- Defined in
ref.c
mdd_t *
RefGetFlatSpecTransRelation(
Mdl_Module_t * specModule,
Sym_Info_t * specSymInfo,
Mdl_Module_t * implModule,
Sym_Info_t * implSymInfo,
array_t * specIdArray,
array_t * implIdArray
)
- optional
- Side Effects specIdArray and implId array will be filled with matching mddIds
from specification and implementation
- See Also
optional
- Defined in
ref.c
void
RefMddArrayFree(
array_t * mddArray
)
- Free the MDD array along with all MDDs contained in it
- Side Effects none
- Defined in
refUtil.c
mdd_t *
RefMddBuildNoChange(
Sym_Info_t * symInfo
)
- Build one conjunct for each variable x,
saying that (x' = x), and take conjunction
of all these
- Side Effects none
- See Also
RefMddBuildObseq
- Defined in
refSym1.c
mdd_t *
RefMddBuildObseq(
Mdl_Module_t * impl,
Sym_Info_t * implSymInfo,
Mdl_Module_t * spec,
Sym_Info_t * specSymInfo
)
- Preconditions:
1. Each interface variable of the specification needs to be
an interface variable of the implementation.
2. Each external variable of the specification needs to be
an interface or external variable of the implementation.
If these conditions are met, this routine returns an MDD that
forces equality for corresponding UNPRIMED variables of
specification and implementation.
- See Also
RefMddBuildNoChange
- Defined in
refSym1.c
array_t *
RefPrimedIdsMerge(
Sym_Info_t * symInfo1,
Sym_Info_t * symInfo2
)
- merge primed ids of two modules and return the
result in a new array. It is the caller's responsibility
to free this array
- Side Effects none
- See Also
RefUnprimedIdsMerge
- Defined in
refSym1.c
array_t *
RefUnprimedIdsMerge(
Sym_Info_t * symInfo1,
Sym_Info_t * symInfo2
)
- merge unprimed ids of two modules and return the
result in a new array. It is the caller's responsibility
to free this array
- Side Effects none
- See Also
RefPrimedIdsMerge
- Defined in
refSym1.c
boolean
Ref_CheckAtomRefinement(
Mdl_Manager_t * mdlManager,
Mdl_Module_t * spec,
Atm_Atom_t * specAtom,
Mdl_Module_t * impl,
lsList varNameList,
lsList forceVarNameList,
Tcl_Interp * interp,
boolean checkRef,
boolean verbose,
boolean keepIntermediate,
int impBoundary,
boolean checkAtEnd,
char * orderFileName
)
- Given implementation, specification and and atom atom1 in implementation,
construct new modules newimplementation and newspecification
such that
1. new implementation contains atom1 only
2. new specification contains heuristically chosen atoms
from specification and implementation that control variables
controlled by atom1, but do not include atom1 itself
- Side Effects required
- See Also
optional
- Defined in
ref.c
boolean
Ref_CheckImplCompatibility(
Mdl_Module_t * spec,
Mdl_Module_t * impl
)
- optional
- Side Effects none
- See Also
optional
- Defined in
refUtil.c
boolean
Ref_CheckNohiddenRefinement(
Mdl_Module_t * implModule,
Sym_Info_t * implSymInfo,
Mdl_Module_t * specModule,
Sym_Info_t * specSymInfo,
boolean verbose,
boolean checkAtEnd
)
- Currently all variables of specModule need to be present in implModule
The algorithm for doing this is as follows:
-- First check if the initial states of specModule are contained in implModule
If not FAIL
-- do reachability of implModule
During each stage of reachability
1. restrict trans relation of implModule to states in the frontier (ie build (frontier & implTrans))
2. If restricted implementation trans does NOT imply specification trans, then FAIL and print error trace
-- If reachability completes, PASS
- Side Effects required
- See Also
optional
- Defined in
ref.c
boolean
Ref_CheckSimulationUsingPre(
Mdl_Module_t * implModule,
Sym_Info_t * implSymInfo,
Mdl_Module_t * specModule,
Sym_Info_t * specSymInfo,
boolean verbose
)
- Check if specModule is a simulates of implModule
- Side Effects required
- See Also
optional
- Defined in
refSym1.c
int
Ref_End(
Tcl_Interp * interp,
Main_Manager_t * manager
)
- optional
- Side Effects required
- See Also
optional
- Defined in
ref.c
int
Ref_Init(
Tcl_Interp * interp,
Main_Manager_t * manager
)
- optional
- Side Effects required
- See Also
optional
- Defined in
ref.c
boolean
Ref_IsMemberOfAtomList(
lsList atomList,
Atm_Atom_t * atom
)
- optional
- Side Effects none
- See Also
optional
- Defined in
refUtil.c
boolean
Ref_IsMemberOfNameList(
lsList nameList,
char * name
)
- optional
- Side Effects none
- See Also
optional
- Defined in
refUtil.c
Var_Variable_t *
Ref_LookupVarList(
lsList varList,
char * name
)
- optional
- Side Effects none
- See Also
optional
- Defined in
refUtil.c
int
Ref_Reinit(
Tcl_Interp * interp,
Main_Manager_t * manager
)
- optional
- Side Effects required
- See Also
optional
- Defined in
ref.c
Atm_Atom_t *
Ref_VariableReadControlAtom(
Var_Variable_t * var
)
- Given a variable, get its controlling atom.
This differs from Var_VariableReadControllingAtom
in that it also works for array and bit-vector variables.
In the case of arrays and bit-vectors (or arrays of bit vectors)
the atom controlling the first element is returned.
- See Also
Var_VariableReadControlAtom
- Defined in
ref.c


{% load static %}
Est.2014
Release Members in 2019: 여러분께 Release 회원들을 소개합니다.
Joined : {{ member.joined_date|date:"y/m/d" }}
Birthday : {{ member.birthday|date:"y/m/d" }}
{{ member.email }}
{{ member.github }}


Services
Services are made available to scripts through system.import
as
explained in Language.
There are 2 built-in services at the moment (We hope to expand this list. Your contribution can help)
- HTTP service: perform plain HTTP requests
- Test service: Functions for starting scripts, ending service functions and asserting conditions.
To plug in your own service, you don’t need to configure anything.
Just host a service bridge that listens to the HTTP POST requests described
below.
In the script, import the service by specifying the url like this
var yourService = system.import('yourhost:yourport/yourpath');
...
yourService.yourServiceFunction('your function', args);
Each time yourServiceFunction
is executed, a POST request like this will
be performed:
POST http://yourhost:yourport/yourpath/{functionName}
Content-Type: application/json
{ scriptExecutionId: "se98237",
executionId: "e9",
args: ['your function, [whateverTheContentOfArgsWas]]
}
Your service bridge can respond to this POST request in 1 of 2 ways:
1) I have received the notification to start the serviceFunction, I’ll call you back when it’s done.
Then the response body can be empty and should return status 200
.
Later, when the service bridge has finished the work for the function, the service bridge should call back to the server with a POST request like this:
POST http://RockScriptServer:port/command
Content-Type: application/json
{ endFunction: {
scriptExecutionId: "se98237",
executionId: "e9",
result: {
examplePropertyOne: "hello",
examplePropertyTwo: "world",
}
}
The result is the return value. It’s optional and can be any valid json value.
If the Content-Type
is set to application/json
, then the result is parsed as
JSON and the JSON value will be made available as a JSON object.
For any other Content-Type
, a string is returned as the return value of the
service function.
2) The service function is already done, please continue the script.
Then the response should look like this:
200 OK
Content-Type: application/json
{ ended: true,
result: "any JSON value"
}
The service function completes immediately and the result is passed as the return value and the script continues.
This approach 2) should not be done for service functions that take a long time because the engine will perform the initial request blocking. To implement non-blocking server execution of your service function, use approach 1) above.


 I feel though the foundations core content I have learned to be less fearful of not knowing everything and instead be more confident in my abilities. Entering foundations, I thought I had poor written communication skills as I had always struggled with it in the past. By the end of foundations my written communication skills improved but more importantly my mindset had changed from a fixed mindset to a growth mindset.
Programing requires perseverance and collaboration with others. Understanding your values helps you keep motivated and work on a problem in an efficient manner in terms of your mental state of mind, you don’t want to just keep banging your head on a problem you need to know where your head is at and take a break or a different approach. Empathy and self-awareness is a key aspect of a successful team, you need to know your current state of mind and also understanding how others are feeling in order to communicate and collaborate in a manner most suited for all members in the team.
I was surprised by how difficult I found some of it compared to the techy stuff which made logical sense. Core content was not black and white and left me feeling indecisive especially when talking about myself and my values.
In the earlier sprints it was hard for me to reflect on my values and who I was as a person, I had not done that before. Understanding yourself is not an easy feat and its hard at times to acknowledge your own strengths and weaknesses.
The driving forces behind the core content is the tech industry itself, the industry has developed into a more team focused approach rather than relying on one outstanding individual. I think dev also realises the overwhelming nature of learning to program, it requires a learner to be pushed continually week after week to build the confidence and knowledge required to stay in the learning zone without being stressed out.
At times it felt like I was wasting my time, however by the end of the process once time had pasted I started to understand that the core learnings were improving my programming as when I was encountering issues I was finding myself reflecting on myself and the situation rather than just bashing out code meaninglessly until the problem was solved.
I hope to get employment in the tech industry after completion of this course, once employed I hope to hone my skillset both tech and non-tech further until I can contribute in some way to the everyday lives of New Zealanders.
My biggest strength going into bootcamp will be my determination and ability to keep an open mind.
My biggest weakness will be communicating verbally to a group of my peers and communicating tech issues with others.
Speaking in front of a group of people about programming or about myself will be very challenging.
I would like to see my confidence and skills as a leader develop during Bootcamp.
I want to get the most out of this experience and I have made a commitment to myself to reflect each day to make sure I am on track and to seek as much feedback from facilitators and peers as possible.


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Chapter 4 Introduction to Data Wrangling
In this chapter we present some very basic data handling and processing functions (data wrangling) that will be necessary for doing basic analyses, comparisons, and graphics. Most of the commands presented in this section stress the functions and R packages in the tidyverse - a set or family of packages that have similar syntax and behaviors.
4.1 Tidy Data
What is tidy data? Tidy data is a standard way of mapping the meaning of a dataset to its structure. A dataset is messy or tidy depending on how rows, columns and tables are matched up with observations, variables and types. In tidy data:
Each variable forms a column.
Each observation forms a row.
Each type of observational unit forms a table.
4.2 Subset Observations using filter
The objective here is to examine a subset of observations, or rows of the dataset.
Suppose we wish to examine a subset of the gapminder data for only one country, Jon’s favorite country, Australia!! The following code starts by taking the gapminder dataset and then “pipes” it into the filtering (selecting rows) action so that only dataset rows from Australia are selected. The pipe function is %>% and is similar to a plumbing pipe that goes one direction: from left to right. After the “Australia” rows are selected, the result is “piped” into the head function for display. The head function says show the top 12 rows. When no rows are specified in the head function, the default is 6 rows. Note that the filter function resides in the dplyr package within the tidyverse family.
If the tidyverse or dplyr packages have been loaded, you don’t need to supply the dplyr::
prefix to the filter
command.
%>% dplyr::filter(country=="Australia") %>% head(n=12) gapminder
## # A tibble: 12 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Australia Oceania 1952 69.1 8691212 10040.
## 2 Australia Oceania 1957 70.3 9712569 10950.
## 3 Australia Oceania 1962 70.9 10794968 12217.
## 4 Australia Oceania 1967 71.1 11872264 14526.
## 5 Australia Oceania 1972 71.9 13177000 16789.
## 6 Australia Oceania 1977 73.5 14074100 18334.
## 7 Australia Oceania 1982 74.7 15184200 19477.
## 8 Australia Oceania 1987 76.3 16257249 21889.
## 9 Australia Oceania 1992 77.6 17481977 23425.
## 10 Australia Oceania 1997 78.8 18565243 26998.
## 11 Australia Oceania 2002 80.4 19546792 30688.
## 12 Australia Oceania 2007 81.2 20434176 34435.
# will produce the same output as
#gapminder %>% filter(country=="Australia") %>% head(n=12)
4.3 Subset using multiple conditions
Let’s select observations by continent and year. The head
function will then show some of the rows selected. Here the gapminder dataframe is piped to the filter
function to select rows to be further piped to the head()
function for display. The logical condtion inside filter
restricts continent to “Oceania” AND (AND condition is “&”) year to be 1997. Both of these conditions must be TRUE for the row to enter the dataframe to displayed by the head()
function.
%>%
gapminder ::filter(continent=="Oceania" & year==1997) %>%
dplyrhead()
## # A tibble: 2 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Australia Oceania 1997 78.8 18565243 26998.
## 2 New Zealand Oceania 1997 77.6 3676187 21050.
Notice that two filter
statements produce the same result.
%>%
gapminder ::filter(continent=="Oceania") %>%
dplyr::filter(year==1997) %>%
dplyrhead()
## # A tibble: 2 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Australia Oceania 1997 78.8 18565243 26998.
## 2 New Zealand Oceania 1997 77.6 3676187 21050.
The next example uses an “or” condition to specify the desired rows in the first filter
expression - the next filter
permits only observations from 1997.
%>%
gapminder ::filter(continent=="Oceania" | continent =="Americas") %>%
dplyr::filter(year==1997) %>%
dplyrhead()
## # A tibble: 6 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Argentina Americas 1997 73.3 36203463 10967.
## 2 Australia Oceania 1997 78.8 18565243 26998.
## 3 Bolivia Americas 1997 62.0 7693188 3326.
## 4 Brazil Americas 1997 69.4 168546719 7958.
## 5 Canada Americas 1997 78.6 30305843 28955.
## 6 Chile Americas 1997 75.8 14599929 10118.
The next example selects observations/rows from a list of countries and also restricts year to 1997.
%>%
gapminder filter(country %in% c("Australia", "New Zealand","Argentina") & year==1997) %>%
head()
## # A tibble: 3 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Argentina Americas 1997 73.3 36203463 10967.
## 2 Australia Oceania 1997 78.8 18565243 26998.
## 3 New Zealand Oceania 1997 77.6 3676187 21050.
The next example selects observations by omitting one continent (Oceania is excluded) and then specifies a year. The code that causes “omit” is the “!=” syntax. In the code year==1997
, the double equal sign ==
means make a logical check if year is 1997. Only rows where both aspects of the filter conditions pass through to be displayed by head
. Again, the logical operator “AND” is expressed by the &
expression.
%>%
gapminder filter(continent!="Oceania" & year==1997) %>%
head()
## # A tibble: 6 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1997 41.8 22227415 635.
## 2 Albania Europe 1997 73.0 3428038 3193.
## 3 Algeria Africa 1997 69.2 29072015 4797.
## 4 Angola Africa 1997 41.0 9875024 2277.
## 5 Argentina Americas 1997 73.3 36203463 10967.
## 6 Austria Europe 1997 77.5 8069876 29096.
Please note that in all the above examples, the filter
function accepts/rejects rows or observations in a dataframe according to the logical conditions specified inside the filter function.
4.4 Saving as a new dataframe
Here we save the the modified dataset as a new dataframe called gap97 by using an assignment arrow “<-” to save the resulting pipeline to gap97.
<- gapminder %>%
gap97 filter(continent!="Oceania" & year==1997)
#
::glimpse(gap97) dplyr
## Rows: 140
## Columns: 6
## $ country <fct> "Afghanistan", "Albania", "Algeria", "Angola", "Argentina", …
## $ continent <fct> Asia, Europe, Africa, Africa, Americas, Europe, Asia, Asia, …
## $ year <int> 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, …
## $ lifeExp <dbl> 41.763, 72.950, 69.152, 40.963, 73.275, 77.510, 73.925, 59.4…
## $ pop <int> 22227415, 3428038, 29072015, 9875024, 36203463, 8069876, 598…
## $ gdpPercap <dbl> 635.3414, 3193.0546, 4797.2951, 2277.1409, 10967.2820, 29095…
4.5 Subset using top_n
Let’s make a dataset based on the ten countries in 1997 with highest gdp.
%>%
gapminder filter(year==1997) %>%
top_n(n = 10, wt = gdpPercap) %>%
head(n=10)
## # A tibble: 10 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Austria Europe 1997 77.5 8069876 29096.
## 2 Canada Americas 1997 78.6 30305843 28955.
## 3 Denmark Europe 1997 76.1 5283663 29804.
## 4 Japan Asia 1997 80.7 125956499 28817.
## 5 Kuwait Asia 1997 76.2 1765345 40301.
## 6 Netherlands Europe 1997 78.0 15604464 30246.
## 7 Norway Europe 1997 78.3 4405672 41283.
## 8 Singapore Asia 1997 77.2 3802309 33519.
## 9 Switzerland Europe 1997 79.4 7193761 32135.
## 10 United States Americas 1997 76.8 272911760 35767.
4.6 Subset using select
The filter
function relates to the rows of the dataframe. Sometimes we might want to include only a few of the variables (columns) in a dataset. We frequently want to create a data subset with only a few variables when the original dataset has many variables. The select
function is used to select and rename variables.
# the next command selects three variables and renames two of them:
%>%
gapminder ::select(country, Year=year,LifeExp=lifeExp) %>%
dplyrhead()
## # A tibble: 6 × 3
## country Year LifeExp
## <fct> <int> <dbl>
## 1 Afghanistan 1952 28.8
## 2 Afghanistan 1957 30.3
## 3 Afghanistan 1962 32.0
## 4 Afghanistan 1967 34.0
## 5 Afghanistan 1972 36.1
## 6 Afghanistan 1977 38.4
# to change the order of display, puts year first in the list of variables
%>%
gapminder select(year,everything()) %>%
head()
## # A tibble: 6 × 6
## year country continent lifeExp pop gdpPercap
## <int> <fct> <fct> <dbl> <int> <dbl>
## 1 1952 Afghanistan Asia 28.8 8425333 779.
## 2 1957 Afghanistan Asia 30.3 9240934 821.
## 3 1962 Afghanistan Asia 32.0 10267083 853.
## 4 1967 Afghanistan Asia 34.0 11537966 836.
## 5 1972 Afghanistan Asia 36.1 13079460 740.
## 6 1977 Afghanistan Asia 38.4 14880372 786.
The profiling_num
command from the funModeling package produces a lot of output, some we might not want. We will
show how to modify the output of this command here. The command produces a dataframe which has many columns we might not wish to display or consider further.
We begin by removing some columns of summary statistics that we wish to ignore. Selecting a list of column names with a “minus” - sign in front of the list will remove these items from the dataframe and keep the rest in place. The command below pipes the modified dataframe to the kable
command in the knitr package for a more pleasing tabular display.
# Let's observe the contents of profiling_num:
::profiling_num(gapminder) %>%
funModeling::glimpse() dplyr
## Rows: 4
## Columns: 16
## $ variable <chr> "year", "lifeExp", "pop", "gdpPercap"
## $ mean <dbl> 1.979500e+03, 5.947444e+01, 2.960121e+07, 7.215327e+03
## $ std_dev <dbl> 1.726533e+01, 1.291711e+01, 1.061579e+08, 9.857455e+03
## $ variation_coef <dbl> 0.008722066, 0.217187544, 3.586268548, 1.366182632
## $ p_01 <dbl> 1952.0000, 33.4926,<PHONE_NUMBER>, 369.2201
## $ p_05 <dbl> 1952.0000, 38.4924,<PHONE_NUMBER>, 547.9964
## $ p_25 <dbl> 1965.750, 48.198, 2793664.000, 1202.060
## $ p_50 <dbl> 1979.5000, 60.7125, 7023595.5000, 3531.8470
## $ p_75 <dbl> 1.993250e+03, 7.084550e+01, 1.958522e+07, 9.325462e+03
## $ p_95 <dbl> 2007.000, 77.437, 89822054.500, 26608.333
## $ p_99 <dbl> 2.007000e+03, 8.023892e+01, 6.319900e+08, 3.678357e+04
## $ skewness <dbl> 0.0000000, -0.2524798, 8.3328742, 3.8468819
## $ kurtosis <dbl> 1.783217, 1.873099, 80.716151, 30.431702
## $ iqr <dbl> 2.750000e+01, 2.264750e+01, 1.679156e+07, 8.123402e+03
## $ range_98 <chr> "[1952, 2007]", "[33.4926, 80.23892]", "[154117.92, 631…
## $ range_80 <chr> "[1957, 2002]", "[41.5108, 75.097]", "[946367.1, 548013…
# now remove unwanted columns from summary display
::profiling_num(gapminder) %>%
funModelingselect(-c("variation_coef","skewness","kurtosis","range_98","range_80","p_01","p_99")) %>%
::kable(.,digits=2) knitr
| variable | mean | std_dev | p_05 | p_25 | p_50 | p_75 | p_95 | iqr |
|---|---|---|---|---|---|---|---|---|
| year | 1979.50 | 17.27 | 1952.00 | 1965.75 | 1979.50 | 1993.25 | 2007.00 | 27.50 |
| lifeExp | 59.47 | 12.92 | 38.49 | 48.20 | 60.71 | 70.85 | 77.44 | 22.65 |
| pop | 29601212.32 | 106157896.74 | 475458.90 | 2793664.00 | 7023595.50 | 19585221.75 | 89822054.50 | 16791557.75 |
| gdpPercap | 7215.33 | 9857.45 | 548.00 | 1202.06 | 3531.85 | 9325.46 | 26608.33 | 8123.40 |
In the next command we take a different approach - we explicitly select the statistics (columns) we want to keep and display. The most commonly used summaries are chosen.
::profiling_num(gapminder) %>%
funModelingselect(c("variable","mean","std_dev","p_25","p_50","p_75")) %>%
::kable(.,digits=2) knitr
| variable | mean | std_dev | p_25 | p_50 | p_75 |
|---|---|---|---|---|---|
| year | 1979.50 | 17.27 | 1965.75 | 1979.50 | 1993.25 |
| lifeExp | 59.47 | 12.92 | 48.20 | 60.71 | 70.85 |
| pop | 29601212.32 | 106157896.74 | 2793664.00 | 7023595.50 | 19585221.75 |
| gdpPercap | 7215.33 | 9857.45 | 1202.06 | 3531.85 | 9325.46 |
4.7 Order using arrange
Sometimes we might want to know the countries with the largest or smallest values of some variables. In the following examples we sort/order by the values of life expectancy. In the code below, when we use the command filter(year==1997)
, the double equal sign means make a logical check if year is 1997, and only allow dataframe rows where this is true to pass through to the next stage of the analysis pipeline. The desc
function means the values will be arranged in descending values (large to small) - default is ascending (small to large).
# This command will show the countries with highest life expectancy because
# the data are arranged in descending order of life expectancy (larger to smaller)
%>%
gapminder ::filter(year==1997) %>%
dplyr::select(country, continent, lifeExp) %>%
dplyr::arrange(desc(lifeExp)) %>%
dplyrhead()
## # A tibble: 6 × 3
## country continent lifeExp
## <fct> <fct> <dbl>
## 1 Japan Asia 80.7
## 2 Hong Kong, China Asia 80
## 3 Sweden Europe 79.4
## 4 Switzerland Europe 79.4
## 5 Iceland Europe 79.0
## 6 Australia Oceania 78.8
# This command uses the default ascending (increasing) order with
# respect to life expectancy (order smaller to larger)
%>%
gapminder filter(year==1997) %>%
select(country, continent, lifeExp) %>%
arrange(lifeExp) %>%
head()
## # A tibble: 6 × 3
## country continent lifeExp
## <fct> <fct> <dbl>
## 1 Rwanda Africa 36.1
## 2 Sierra Leone Africa 39.9
## 3 Zambia Africa 40.2
## 4 Angola Africa 41.0
## 5 Afghanistan Asia 41.8
## 6 Liberia Africa 42.2
The top_n
function from the dplyr package will select the n rows with the largest values of a variable. This is similar to the code above that orders the rows - then use head
function to select the number of desired rows.
This first example uses the default alphabetical ordering of country name.
%>%
gapminder filter(year==1997) %>%
select(country, continent, lifeExp) %>%
::top_n(n=6,wt=lifeExp) %>%
dplyr::kable(.,digits=2) knitr
| country | continent | lifeExp |
|---|---|---|
| Australia | Oceania | 78.83 |
| Hong Kong, China | Asia | 80.00 |
| Iceland | Europe | 78.95 |
| Japan | Asia | 80.69 |
| Sweden | Europe | 79.39 |
| Switzerland | Europe | 79.37 |
The results can then be ordered by the life expectancy:
%>%
gapminder filter(year==1997) %>%
select(country, continent, lifeExp) %>%
::top_n(n=6,wt=lifeExp) %>%
dplyr::arrange(desc(lifeExp)) %>%
dplyr::kable(.,digits=2) knitr
| country | continent | lifeExp |
|---|---|---|
| Japan | Asia | 80.69 |
| Hong Kong, China | Asia | 80.00 |
| Sweden | Europe | 79.39 |
| Switzerland | Europe | 79.37 |
| Iceland | Europe | 78.95 |
| Australia | Oceania | 78.83 |
The countries with the largest life expectancy can then be ordered by another variable like population. Here we find the 6 countries in 1997 with the highest life expectancy - then display them in order of population size.
%>%
gapminder filter(year==1997) %>%
select(country, continent, lifeExp, pop) %>%
::top_n(n=6,wt=lifeExp) %>%
dplyr::arrange(desc(pop)) %>%
dplyr::kable(.,digits=2) knitr
| country | continent | lifeExp | pop |
|---|---|---|---|
| Japan | Asia | 80.69 | 125956499 |
| Australia | Oceania | 78.83 | 18565243 |
| Sweden | Europe | 79.39 | 8897619 |
| Switzerland | Europe | 79.37 | 7193761 |
| Hong Kong, China | Asia | 80.00 | 6495918 |
| Iceland | Europe | 78.95 | 271192 |
4.8 Grouped Filter
Another useful verb in the tidyverse is group_by
. Suppose we wanted to view the two countries with the highest life expectancy in 1997, in each continent.
%>%
gapminder filter(year==1997) %>%
select(country, continent, lifeExp, pop) %>%
::group_by(continent) %>%
dplyr::top_n(n=2,wt=lifeExp) %>%
dplyr::arrange(continent) %>%
dplyr::kable() knitr
| country | continent | lifeExp | pop |
|---|---|---|---|
| Reunion | Africa | 74.772 | 684810 |
| Tunisia | Africa | 71.973 | 9231669 |
| Canada | Americas | 78.610 | 30305843 |
| Costa Rica | Americas | 77.260 | 3518107 |
| Hong Kong, China | Asia | 80.000 | 6495918 |
| Japan | Asia | 80.690 | 125956499 |
| Sweden | Europe | 79.390 | 8897619 |
| Switzerland | Europe | 79.370 | 7193761 |
| Australia | Oceania | 78.830 | 18565243 |
| New Zealand | Oceania | 77.550 | 3676187 |
4.9 New Variables Using mutate
In many problems we may wish to create a new variable based on an existing variable. Here we illustrate by making a new variable - the natural logarithm of population - based on the original variable pop
using the mutate
function.
%>%
gapminder ::mutate(logpopulation = log(pop)) %>%
dplyr::glimpse() dplyr
## Rows: 1,704
## Columns: 7
## $ country <fct> "Afghanistan", "Afghanistan", "Afghanistan", "Afghanista…
## $ continent <fct> Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, As…
## $ year <int> 1952, 1957, 1962, 1967, 1972, 1977, 1982, 1987, 1992, 19…
## $ lifeExp <dbl> 28.801, 30.332, 31.997, 34.020, 36.088, 38.438, 39.854, …
## $ pop <int> 8425333, 9240934, 10267083, 11537966, 13079460, 14880372…
## $ gdpPercap <dbl> 779.4453, 820.8530, 853.1007, 836.1971, 739.9811, 786.11…
## $ logpopulation <dbl> 15.94675, 16.03915, 16.14445, 16.26115, 16.38655, 16.515…
If I want to change the name of the new variable from logpopulation
to something shorter like logPop
, we could re-run the mutate
command, or use a rename
function.
In addition we create a new version of the gapminder dataset that contains the new variable - called gapVers1. This dataframe is now available to be used in the ongoing analysis.
<- gapminder %>%
gapVers1 ::mutate(logpopulation = log(pop)) %>%
dplyr::rename(logPop=logpopulation)
dplyr#
::glimpse(gapVers1) dplyr
## Rows: 1,704
## Columns: 7
## $ country <fct> "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", …
## $ continent <fct> Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, …
## $ year <int> 1952, 1957, 1962, 1967, 1972, 1977, 1982, 1987, 1992, 1997, …
## $ lifeExp <dbl> 28.801, 30.332, 31.997, 34.020, 36.088, 38.438, 39.854, 40.8…
## $ pop <int> 8425333, 9240934, 10267083, 11537966, 13079460, 14880372, 12…
## $ gdpPercap <dbl> 779.4453, 820.8530, 853.1007, 836.1971, 739.9811, 786.1134, …
## $ logPop <dbl> 15.94675, 16.03915, 16.14445, 16.26115, 16.38655, 16.51555, …
The next code uses a mutate
command with logical conditions to make a new, two-level categorical variable region as a character variable. Then we use mutate
again to convert region
(character) to a factor variable named regionf
. In statistical models, factor variables are preferred, but in data handling stages, character versions are probably easier to manipulate.
The if_else
function from dplyr has the form `if_else(logical condition,value if TRUE, value if FALSE).
The next example uses the “T-pipe” function %T>%
to break the piping so that the result of the second mutate
flows to both glimpse
and to head
- in this construction, it is understood the output of glimpse
does not pipe to head
, but rather the original data flow from the second mutate
.
%>%
gapminder ::mutate(region = if_else(country=="Oceania","Oceania","NotOceania")) %>%
dplyr::mutate(regionf = as_factor(region)) %T>%
dplyr::glimpse() %>%
dplyrhead()
## Rows: 1,704
## Columns: 8
## $ country <fct> "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", …
## $ continent <fct> Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, …
## $ year <int> 1952, 1957, 1962, 1967, 1972, 1977, 1982, 1987, 1992, 1997, …
## $ lifeExp <dbl> 28.801, 30.332, 31.997, 34.020, 36.088, 38.438, 39.854, 40.8…
## $ pop <int> 8425333, 9240934, 10267083, 11537966, 13079460, 14880372, 12…
## $ gdpPercap <dbl> 779.4453, 820.8530, 853.1007, 836.1971, 739.9811, 786.1134, …
## $ region <chr> "NotOceania", "NotOceania", "NotOceania", "NotOceania", "Not…
## $ regionf <fct> NotOceania, NotOceania, NotOceania, NotOceania, NotOceania, …
## # A tibble: 6 × 8
## country continent year lifeExp pop gdpPercap region regionf
## <fct> <fct> <int> <dbl> <int> <dbl> <chr> <fct>
## 1 Afghanistan Asia 1952 28.8 8425333 779. NotOceania NotOceania
## 2 Afghanistan Asia 1957 30.3 9240934 821. NotOceania NotOceania
## 3 Afghanistan Asia 1962 32.0 10267083 853. NotOceania NotOceania
## 4 Afghanistan Asia 1967 34.0 11537966 836. NotOceania NotOceania
## 5 Afghanistan Asia 1972 36.1 13079460 740. NotOceania NotOceania
## 6 Afghanistan Asia 1977 38.4 14880372 786. NotOceania NotOceania
4.10 Simple Counting Using tally() and count()
We frequently wish to know how many observations/rows satisfy a set of conditions. We will filter the observations for the given conditions, then count them using the tally()
or count()
functions from dplyr.
Essentially, count()
is a short-hand for group_by() + tally()
.
For example, what if we want to know how many observations are from continent ‘Americas’ in 1997.
These examples have no grouping, no group_by
is being used.
%>%
gapminder ::filter(year==1997) %>%
dplyr::filter(continent=="Americas") %>%
dplyr::tally() dplyr
## # A tibble: 1 × 1
## n
## <int>
## 1 25
%>%
gapminder ::filter(year==1997) %>%
dplyr::filter(continent=="Americas") %>%
dplyr::count() dplyr
## # A tibble: 1 × 1
## n
## <int>
## 1 25
Now we group by continent.
%>%
gapminder ::filter(year==1997) %>%
dplyr::group_by(continent) %>%
dplyr::filter(continent=="Americas") %>%
dplyr::tally() dplyr
## # A tibble: 1 × 2
## continent n
## <fct> <int>
## 1 Americas 25
#
%>%
gapminder ::filter(year==1997) %>%
dplyr::group_by(continent) %>%
dplyr::tally() dplyr
## # A tibble: 5 × 2
## continent n
## <fct> <int>
## 1 Africa 52
## 2 Americas 25
## 3 Asia 33
## 4 Europe 30
## 5 Oceania 2
%>%
gapminder ::filter(year==1997) %>%
dplyr::group_by(continent) %>%
dplyr::filter(continent=="Americas") %>%
dplyr::count() dplyr
## # A tibble: 1 × 2
## # Groups: continent [1]
## continent n
## <fct> <int>
## 1 Americas 25
#
%>%
gapminder ::filter(year==1997) %>%
dplyr::count(continent) dplyr
## # A tibble: 5 × 2
## continent n
## <fct> <int>
## 1 Africa 52
## 2 Americas 25
## 3 Asia 33
## 4 Europe 30
## 5 Oceania 2
4.11 Missing Values
If a variable is not complete and contains empty places, these are denoted in R as NA
. We will often wish to create a dataframe without any missing values, or discover how many rows contain variables with missing values.
First let’s create a small dataset with missing values:
<- c(1,2,NA,4)
x <- c(11,12,13,NA)
y <- c(7,8,9,10)
z <- data.frame(x,y,z)
tempdf tempdf
## x y z
## 1 1 11 7
## 2 2 12 8
## 3 NA 13 9
## 4 4 NA 10
# count missing values for variable x
%>%
tempdf ::summarise(count = sum(is.na(x))) dplyr
## count
## 1 1
# count rows with missing values for variable y
%>%
tempdf ::tally(is.na(y)) dplyr
## n
## 1 1
# subset of rows with complete data for specified columns
%>%
tempdf ::select(y,z) %>%
dplyr::drop_na() %>%
tidyrhead()
## y z
## 1 11 7
## 2 12 8
## 3 13 9
# drop rows with missing values in all variables
%>%
tempdf ::drop_na() %>%
tidyrhead()
## x y z
## 1 1 11 7
## 2 2 12 8
Use base is.na
function
%>%
tempdf filter(!is.na(x), # allow obs with nonmissing x
!is.na(y), # allow obs with nonmissing y
!is.na(z)) # allow obs with nonmissing z
## x y z
## 1 1 11 7
## 2 2 12 8
Next, some code that will execute a filter that will permit only rows with entirely complete data in x to pass through to the dataset,
%>%
tempdf filter(x %>% is.na() %>% magrittr::not()) %>%
head()
## x y z
## 1 1 11 7
## 2 2 12 8
## 3 4 NA 10


@* * Copyright 2023 HM Revenue & Customs * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @import config.ApplicationConfig @import views.html.helpers._ @import uk.gov.hmrc.govukfrontend.views.html.components._ @this(gmpMain: gmp_main, govukButton : GovukButton) @()(implicit request: Request[_], messages: Messages, ac:ApplicationConfig) @gmpMain(title = Messages("gmp.thank_you.header") + " - " + Messages("service.title") + " - " + Messages("gov.uk"),supportLinkEnabled = false, dashboardLinkEnabled = false) {
@Messages("gmp.or.go.to") @Messages("gmp.gov.uk.homepage").


Test ID
When running in Single Test mode, you have to provide the ID of the test you want to run.
You can find this information in the interstitial page of the test.
Automatch URL
When running in Automatch mode, introduce here the URL of the resource that you are willing
to test.
Tag
When running in tag mode, introduce the tag of the tests you want to run.
Project
When running in project mode, no value is required.


A:
B:
Number of surgeons:
Number of doctors:
Number of nurses:
BUSY
VACANT
Neighbour: Requesting two doctors


public class TableInstance
extends java.lang.Object
ScoreTable
Each store table will store the information of CourseAssistant
CourseInstance
Section
and rows and columns in the ScoreTable
By using this table, we can quick access result in ScoreTable
for
example: ScoreTable
will give a final result of indexes. We can
obtaining the pairing section and course assistance by using indexes and
tableInstance.| Constructor and Description |
|---|
TableInstance(int row,
int col,
CourseAssistant ca,
Section sec,
CourseInstance course,
double score)
Constructs a new
TableInstance objects with given row number, column
number, courseAssistant, course, score. |
| Modifier and Type | Method and Description |
|---|---|
CourseAssistant |
getCa()
A getter to get the CourseAssistant.
|
int |
getCol()
A getter to get the column.
|
CourseInstance |
getCourse()
A getter to get the course.
|
int |
getRow()
A getter to get the row.
|
double |
getScore()
A getter to get the score.
|
Section |
getSec()
A getter to get the Section.
|
public TableInstance(int row, int col, CourseAssistant ca, Section sec, CourseInstance course, double score)
TableInstance
objects with given row number, column
number, courseAssistant, course, score.row
- a integer which representing the row number in
ScoreTable
.col
- a integer which representing the row number in
ScoreTable
.CourseAssistant
- a courseAsssistant for pairing
CourseAssistant
.Section
- a Section for pairing Section
.CourseInstance
- the course instance of the section
CourseInstance
.double
- a double which is the score/cost/weight to
assessing the section pairing with the
courseAssisstant .public CourseInstance getCourse()
public int getCol()
public int getRow()
public CourseAssistant getCa()
public Section getSec()
public double getScore()


Exercise 4
In this exercise, we will respond to element events and remove a shopping checklist item from the list of items on the page. We will use "shoplist.html" as a starting point and proceed with the following steps:
- When the checkbox is clicked, update the
complete
state of the checklist item. - When the delete button is clicked, remove the item from the checklist and update the page.
- What improvements would you suggest?
- Initial focus
- Reset entry form
- Check for duplicate items


{% extends "layout.html" %} {% block page_title %} Money Claims Prototype {% endblock %} {% block content %} {% include "includes/phase_banner_beta.html" %} Back Why didn't you respond until now? Continue {% include 'prototypes/prototype-apr-2019/includes/contact.html' %} {% endblock %}
Continue


See Modal With Bootsrap
Push Notification
Lorem ipsum, dolor sit amet consectetur adipisicing elit. Repellat, deserunt!
Subscribe
Not now


Hyatt Building Supply was established in 1947 and proudly serves Damascus and the surrounding community.
Monday - Friday : 7am - 6pm
Saturday : 8am - 4pm
Sunday : 10am - 2pm
We offer a wide variety of tools, hardware, paint, plumbing, electrical, garden and pet supplies. Ask our associates about our specials and seasonal stock.
We have a full service lumber yard with delivery. Ask our lumber sales department about all your special order needs.
Order online and have it shipped to our store for pickup.


Eight American swimmers are practicing for the Olympics! Each has a different first name (including Emily), last name (including Hall-Odom), and home city (including Charlotte), and each practices a different number of whole hours per day between 1 and 8.
- Ms. Ironwood practices fewer hours per day than Ms. Stafford.
- Ms. Holloway, Ms. Marshall, and Ms. Williams all practice 6 or fewer hours per day.
- Hazel practices an even number of hours per day.
- Ms. De La Cruz practices 7 hours per day IF AND ONLY IF Ms. Williams is the swimmer from San Diego.
- Abigail Castillo is not from Boulder.
- Gladys and Hazel are from Escondido and San Diego, in some order.
- The person from Allentown practices more than Ms. De La Cruz, who practices more than Hazel.
- Denise (who is not Ms. De La Cruz) and Faith both practice 6 or more hours per day.
- The swimmer from Austin pratices 6 hours per day.
- EITHER the swimmer from Seattle practices at least 7 hours per day OR Ms. Marshall is the swimmer from Phoenix.
- Beth (who is not from Allentown), Ms. Williams, the woman from Boulder, and the woman who practices four hours per day all have the same trainer.
- The people who practice the least are Abigail, Charlize, and Ms. Stafford, in some order.
- Ms. Ironwood is from Seattle or Austin.


Motivated young individual with an interest in handling large data sets to achieve business goals. Two years of experience in managing a university database using Microsoft SQL Server, learning various programming languages such as C++ and Python, and creating interfaces designed to serve the user's goals for a particular software.
As my first job, this job helped me learn how to balance school and work life. As a courtesy clerk, I interacted with customers and assisted them in any way they needed. My main task was to bag groceries but I also did pretty much anything in the store that needed to be done in the store.
For the past two years, I've worked with the various IT staff to manage the university's database system. This had me learning Microsoft SQL Server to write queries, generate views, and create stored procedures to help university employees achieve their goals with our data. This typically involved speaking with various faculty/staff members either in person or via email to help them create reports with the data we supply.
As the recipient of a selective and competitive internship process, I was tasked with creating something using one of Jenzabar's various products that people would use in their everyday work-duties. I worked closely with the campus police throughout the summer to get rid of their expensive paper ticket system and move to a quick and efficient online form using Jenzabar Form-Flow. The officer just has to type in the license plate to automatically fill in the rest of the information about the ricket recipient, choose the violation and location of the incident in a dropdown, and then the ticket is automatically inserted into the database and an email is sent to the ticket recipient. I was also lucky enough to receive the internship for the summer of 2019 and I was invited out to San Diego for the annual Jenzabar conference to do a presentation on how I achieved my goals with the campus police and how to help other university's achieve similar goals.
GPA: 3.35
GPA: 3.65
GPA: 4.13
When I'm not focused on my classes or work, I love to travel and to live an active lifestyle. All throughout the year, I'm saving up my money and planning my next trip. I really enjoy the entire process, from finding the best flight deals, finding the perfect accomodations, and creating itineraries for the places I'm visiting. This past winter break, I visited Italy and my plan is to do atleast one international trip per year to just really see more of the world and experience everything the world has to offer. My ideal trip is having a mix of historical and cultural sites but also having amazing nature to get away from the hustle and bustle and just hike or kayak for a day or two.
When I'm back at home, I like to go to the gym almost every single day. It personally makes me feel better throughout the day and it gives me a sense of making progress with something. I also play video games quite frequently, I own an Xbox One X and a Nintendo Switch and I try to play the best games both of those platforms have to offer. At the end of the day, I do like to keep up with the trending shows and movies; my personal favorite shows at the moment are Game of Thrones, The Office, Parks and Recreation, King of the Hill, and Silicon Valley. If I have any time left, I'll usually scroll through the r/technology or r/futurology subreddits to keep up with all the latest stuff happening in the field.


Flo, Almasi, Destiny, Alex, Isis, and Zeba
Women are our society. The mothers of our children and the wives of their husbands the sisters to their sisters and role models to their family Different backgrounds and cultures. Empowering each other and encouraging each other to reach for progress is key. So lets celebrate what makes us diferent and concentrate on what makes us the same . WOMANHOOD
The Carribean spirit flowing through her veins. Independence and pride plating her character. Joy and happiness fill her festive heart as she travels on the paths in her life. The love and support that fills her is immesurable. A heart of gold and and her bronzed complexion complete her. One cannot compare the Hatian woman.
The African woman. Proud and Strong . Her deep skin glistening in the sun .The roots and culture of her foremothers reflecting in all her actions. Her children raised the African way taught how to behave, live and love everyday. Enduarance and patience the key factors in her life. Strength her guide. Look thats an African woman. The life of the motherland.
Grace and beauty .Her morals and attiude positive. Wonder how she does it? Her Sari trailing behind her as she sways from side to side. Her eyes they tell a story worth hearing. Her humility is worth noticing but so is her pride. A wonder of those around her there is none that can match to an Indian woman for she is one of a kind.


Product Catalog
Optoelectronic Semiconductors
Related Product Groups
LED (Light Emitting Diodes)SMD packages in different performance classes and brightness levels |
LEDs for General LightingWhite and colored LEDs designed for general lighting applications. |
Infrared Emitter, Detectors and Sensors |
Laser Diodes |
Intelligent Displays |
Discontinued Products |
Additional Documentation and Information
| Document Type | Title | Date | File Type | File Size |
|---|---|---|---|---|
| Product Family Overview | Short-Form Catalog Chapter 1: LED for Automotive, Consumer and Industry Applications | 2012-10-01 | 1180 KB | |
| Product Family Overview | Short-Form Catalog Chapter 1a: LED Type Designation overview | 2012-10-01 | 79 KB | |
| Product Family Overview | Short-Form Catalog Chapter 2: LED for General Lighting (SSL) | 2012-10-01 | 343 KB | |
| Product Family Overview | Short-Form Catalog Chapter 3: Intelligent Displays | 2012-10-01 | 265 KB | |
| Product Family Overview | Short-Form Catalog Chapter 4: Infrared Components | 2012-10-01 | 1124 KB | |
| Product Family Overview | Short-Form Catalog Chapter 5: Laser Diodes | 2012-10-01 | 154 KB | |
| Product Family Overview | Short-Form Catalog Chapter 6: Tape and Reel | 2012-10-01 | 694 KB | |
| Product Family Overview | Short-Form Catalog: Complete Download | 2012-10-01 | 3697 KB |


Coach Park reveals reason Quang Hai is chosen as Vietnam U22 captain
Coach Park Hang-seo has named Hanoi FC midfielder Nguyen Quang Hai Vietnam U22’s captain at the upcoming SEA Games 30 in the Philippines, the Vietnam Football Federation has announced.
by Thể thao 247, https://www.facebook.com/thethao247.vnHai’s deputies are forward Tien Linh and defender Thanh Thinh.
Park’s decision surprised many as Vietnam U22 has 2 other experienced players: Trong Hoang and Hung Dung.
“I chose Quang Hai because the SEA Games is for under 22 players.
“Hai was the Vietnamese team’s captain in the AFC U23 Championship 2018. Trong Hoang and Hung Dung were over 22 then, but Quang Hai got more votes from his teammates,” Park said.
He added that Trong Hoang’s and Hung Dung’s experience will be very helpful for the U22 team.
Vietnam U22 will play their first SEA Games 30 match next Monday.
>>> U22 Thailand announces squad list for SEA Games 30
>>>OFFICIAL: Vietnam women football team’s roster announced for SEA Games


The space colonization algorithm starts at a root node and builds tree nodes into a cloud of "attraction points".
1. Each node finds the surrounding attraction points within the radius of influence 'di' and calculates the normalized vector from the node to attraction point.
2. Each node then grows a new node along the averaged vector from all of the normalized vectors.
3. Once all of the new nodes are created, a check is performed to remove any attraction point that is within kill distance 'dk'.
4. This process is repeated until no attraction point is within the distance 'di' of any node, or no attraction points remain.
Runions, A., Lane, B., and Prusinkiewicz, P. (2007). Modeling trees with a space colonization algorithm. In
Proceedings of the Third Eurographics conference on
Natural Phenomena, NPH’07, pages 63–70, Aire-laVille, Switzerland, Switzerland. Eurographics Association.


Create streams lazily when they are read from or written to.
lazystream: 1.0.0
Sometimes you feel the itch to open all the files at once. You want to pass a bunch of streams around, so the consumer does not need to worry where the data comes from.
From a software design point-of-view this sounds entirely reasonable. Then there is that neat little function fs.createReadStream()
that opens a file and gives you a nice fs.ReadStream
to pass around, so you use what the mighty creator deities of node bestowed upon you.
Error: EMFILE, too many open files
─ node
This package provides two classes based on the node's Streams3 API (courtesy of readable-stream
to ensure a stable version).
A wrapper for readable streams. Extends stream.PassThrough
.
fn
{Function}options
{Object}PassThrough
stream, accessible by fn
.Creates a new readable stream. Once the stream is accessed (for example when you call its read()
method, or attach a data
-event listener) the fn
function is called with the outer lazystream.Readable
instance bound to this
.
If you pass an options
object to the constuctor, you can access it in your fn
function.
new lazystream.Readable(function (options) {
return fs.createReadStream('/dev/urandom');
});
A wrapper for writable streams. Extends stream.PassThrough
.
fn
{Function}options
{Object}PassThrough
stream, accessible by fn
.Creates a new writable stream. Just like the one above but for writable streams.
new lazystream.Writable(function () {
return fs.createWriteStream('/dev/null');
});
$ npm install lazystream --save
<EMAIL_ADDRESS>node_modules/lazystream
└──<EMAIL_ADDRESS>readable-stream
2.x.Fork it, branch it, send me a pull request. We'll work out the rest together.
Chris Talkington and his node-archiver for providing a use-case.
Copyright (c) 2013 J. Pommerening, contributors.
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
 IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.


Other people will see this on your back.
Must be a number, sadly.
We use Janus, an open-source WebRTC server, to set up browser-to-browser media connections!


|
ActiveTcl User Guide
|
||
Tk_SetClassProcs - register widget specific procedures
#include <tk.h>
Tk_SetClassProcs(tkwin, procs, instanceData)
Tk_SetClassProcs is called to register a set of procedures that are used as callbacks in different places.
The structure pointed to by procs contains the following:
typedef struct Tk_ClassProcs { unsigned int size; Tk_ClassWorldChangedProc *worldChangedProc; Tk_ClassCreateProc *createProc; Tk_ClassModalProc *modalProc; } Tk_ClassProcs;
The size field is used to simplify future expansion of the structure. It should always be set to (literally) sizeof(Tk_ClassProcs).
worldChangedProc is invoked when the system has altered in some way that requires some reaction from the widget. For example, when a font alias (see the font manual entry) is reconfigured, widgets configured to use that font alias must update their display accordingly. worldChangedProc should have arguments and results that match the type Tk_ClassWorldChangedProc:
typedef void Tk_ClassWorldChangedProc( ClientData instanceData);
The instanceData parameter passed to the worldChangedProc will be identical to the instanceData paramter passed to Tk_SetClassProcs.
createProc is used to create platform-dependant windows. It is invoked by Tk_MakeWindowExist. createProc should have arguments and results that match the type Tk_ClassCreateProc:
typedef Window Tk_ClassCreateProc( Tk_Window tkwin, Window parent, ClientData instanceData);
The tkwin and instanceData parameters will be identical to the tkwin and instanceData parameters passed to Tk_SetClassProcs. The parent parameter will be the parent of the window to be created. The createProc should return the created window.
modalProc is invoked after all bindings on a widget have been triggered in order to handle a modal loop. modalProc should have arguments and results that match the type Tk_ClassModalProc:
typedef void Tk_ClassModalProc( Tk_Window tkwin, XEvent *eventPtr);
The tkwin parameter to modalProc will be identical to the tkwin parameter passed to Tk_SetClassProcs. The eventPtr parameter will be a pointer to an XEvent structure describing the event being processed.
Copyright © 2000 Ajuba Solutions. Copyright © 1995-1997 Roger E. Critchlow Jr.


{% extends 'base.html' %} {% block content %}
1. Make sure you have adblock installed and enabled.
2. Select a different source from the icon in the top right of the player.
3. If problems continue, use the lecacy version.


What You See Is What You Wiki [v2]
A user-editable website created by Paul Frazee. Uses the Dat Web API to read and write the site.
Instructions
- Press Fork to create your own copy.
- Press Edit to make changes.
- Press Publish to make the changes permanent.
History
v2. July 22, 2017. Editor improvements.
- Customized Summernote's controls to provide more capabilities.
- Pictures are now added to the wiki as files instead of data URLs.
v1. July 16, 2017. It works!
Got the editor code added (using Summernote) and created a basic self-modifying site.


Full Pipeline
- start_date: start date of RV calculations
- end_date: end date of RV calculations
- cadence: how often you want to calculate [seconds]
- csv_name: file name to save calculations
Example usage
start_date = datetime.datetime(2021, 8, 26, 8, 00, 0)
end_date = datetime.datetime(2021, 9, 3, 0, 00, 0)
cadence = 24*3600
csv_name = 'csv_name'
component_calc(start_date, end_date, cadence, csv_name)
Function
def component_calc(start_date, end_date, cadence, csv_name=None):
"""
function to calculate rv components using pipeline functions
Parameters
----------
start_date: start date of RV calculations
end_date: end date of RV calculations
cadence: how often to calculate RV components
csv_name: name of file to store calculations in
Returns
-------
"""
# create file names
if csv_name is None:
csv_name = str(start_date)
csv_name = os.path.join(CsvDir.CALC, csv_name + '.csv')
bad_dates_csv = os.path.join(CsvDir.CALC, csv_name + '_bad_dates.csv')
# print out csv title
print("Beginning calculation of values for csv file: " + csv_name)
# List of header strings
row_contents = ['date_obs', 'date_jd', 'v_quiet', 'v_disc', 'v_phot', 'v_conv', 'f_bright', 'f_spot', 'f', 'Bobs',
'vphot_bright', 'vphot_spot', 'f_small', 'f_large', 'f_network', 'f_plage', 'f_nonconv',
'quiet_flux', 'ar_flux', 'conv_flux', 'pol_flux', 'pol_conv_flux', 'vconv_quiet', 'vconv_large',
'vconv_small']
# Append a list as new line to an old csv file
utils.append_list_as_row(csv_name, row_contents)
# get hmi data products
time_range = datetime.timedelta(seconds=22)
physobs_list = [a.Physobs.los_velocity, a.Physobs.los_magnetic_field, a.Physobs.intensity]
# get dates list
xy = (end_date - start_date).seconds + (end_date - start_date).days * 24 * 3600
dates_list = [start_date + datetime.timedelta(seconds=cadence * x) for x in range(0, int(xy / cadence))]
for i, date in enumerate(dates_list):
# convert the date to a string -- required for use in csv file
date_str, date_obj, date_jd = utils.get_dates(date)
# pull image within specified time range
result = Fido.search(a.Time(str(date_obj - time_range), str(date_obj + time_range)),
a.Instrument.hmi, physobs_list[0] | physobs_list[1] | physobs_list[2])
# add file to list
file_download = Fido.fetch(result)
# remove unusable file types
good_files = []
for file in file_download:
name, extension = os.path.splitext(file)
if extension == '.fits':
good_files.append(file)
if len(good_files) != 3:
# append these values to the csv file
save_vals = [date_str, 'not three good files']
utils.append_list_as_row(bad_dates_csv, save_vals)
# print that the files are missing
print('\nNot three good files: ' + date_str + ' index: ' + str(i))
pass
else:
# convert to map sequence
map_seq = sunpy.map.Map(sorted(good_files))
# check for missing data types
missing_map = False
# split into data types
for j, map_obj in enumerate(map_seq):
if map_obj.meta['content'] == 'DOPPLERGRAM':
vmap = map_obj
elif map_obj.meta['content'] == 'MAGNETOGRAM':
mmap = map_obj
elif map_obj.meta['content'] == 'CONTINUUM INTENSITY':
imap = map_obj
else:
missing_map = True
if missing_map:
print("Missing a data product for " + date_str)
# add the data
# append these values to the csv file
save_vals = [date_str, 'missing data product']
utils.append_list_as_row(bad_dates_csv, save_vals)
pass
else:
# coordinate transformation for maps
x, y, pd, r, d, mu = ctfuncs.coordinates(vmap)
wij, nij, rij = ctfuncs.vel_coords(x, y, pd, r, vmap)
# remove bad mu values
vmap, mmap, imap = ctfuncs.fix_mu(mu, [vmap, mmap, imap])
# calculate relative positions
deltaw, deltan, deltar, dij = sfuncs.rel_positions(wij, nij, rij, vmap)
# calculate spacecraft velocity
vsc = sfuncs.spacecraft_vel(deltaw, deltan, deltar, dij, vmap)
# optimized solar rotation parameters
a1 = 14.713
a2 = -2.396
a3 = -1.787
a_parameters = [a1, a2, a3]
# calculation of solar rotation velocity
vrot = sfuncs.solar_rot_vel(wij, nij, rij, deltaw, deltan, deltar, dij, vmap, a_parameters)
# calculate corrected velocity
corrected_vel = vmap.data - np.real(vsc) - np.real(vrot)
# corrected velocity maps
map_vel_cor = sfuncs.corrected_map(corrected_vel, vmap, map_type='Corrected-Dopplergram',
frame=frames.HeliographicCarrington)
# limb brightening
Lij = lbfuncs.limb_polynomial(imap)
# calculate corrected data
Iflat = imap.data / Lij
# corrected intensity maps
map_int_cor = sfuncs.corrected_map(Iflat, imap, map_type='Corrected-Intensitygram',
frame=frames.HeliographicCarrington)
# magnetic noise level
B_noise = 8
# calculate unsigned field strength
Bobs, Br = sfuncs.mag_field(mu, mmap, B_noise)
# corrected observed magnetic data map
map_mag_obs = sfuncs.corrected_map(Bobs, mmap, map_type='Corrected-Magnetogram',
frame=frames.HeliographicCarrington)
### calculate magnetic threshold
# magnetic threshold value (G) from Yeo et. al. 2013
Br_cutoff = 24
# mu cutoff value
mu_cutoff = 0.3
# calculate magnetic threshold
active, quiet = sfuncs.mag_thresh(mu, mmap, Br_cutoff=Br_cutoff, mu_cutoff=mu_cutoff)
# calculate intensity threshold
fac_inds, spot_inds = sfuncs.int_thresh(map_int_cor, active, quiet)
# create threshold array
thresh_arr = sfuncs.thresh_map(fac_inds, spot_inds)
# full threshold maps
map_full_thresh = sfuncs.corrected_map(thresh_arr, mmap, map_type='Threshold',
frame=frames.HeliographicCarrington)
### velocity contribution due to convective motion of quiet-Sun
v_quiet = v_quiet(map_vel_cor, imap, quiet)
### velocity contribution due to rotational Doppler imbalance of active regions (faculae/sunspots)
# calculate photospheric velocity
v_phot, vphot_bright, vphot_spot = v_phot(quiet, active, Lij, vrot, imap, mu, fac_inds,
spot_inds)
### velocity contribution due to suppression of convective blueshift by active regions
# calculate disc-averaged velocity
v_disc = v_disc(map_vel_cor, imap)
# calculate convective velocity
v_conv = v_disc - v_quiet
### filling factor
# calculate filling factor
f_bright, f_spot, f = sfuncs.filling_factor(mu, mmap, active, fac_inds, spot_inds)
### unsigned magnetic flux
# unsigned observed flux
unsigned_obs_flux = sfuncs.unsigned_flux(map_mag_obs, imap)
### calculate the area filling factor
pixA_hem = ctfuncs.pix_area_hem(wij, nij, rij, vmap)
area = sfuncs.area_calc(active, pixA_hem)
f_small, f_large, f_network, f_plage, f_nonconv =sfuncs.area_filling_factor(active, area, mu, mmap,
fac_inds)
### get the unsigned flux
quiet_flux, ar_flux, conv_flux, pol_flux, pol_conv_flux = sfuncs.area_unsigned_flux(map_mag_obs, imap,
area,
active)
### get area weighted convective velocities
vconv_quiet, vconv_large, vconv_small = sfuncs.area_vconv(map_vel_cor, imap, active, area)
# make array of what we want to save
save_vals = [v_quiet, v_disc, v_phot, v_conv, f_bright, f_spot, f, unsigned_obs_flux, vphot_bright,
vphot_spot, f_small, f_large, f_network, f_plage, f_nonconv, quiet_flux, ar_flux,
conv_flux, pol_flux, pol_conv_flux, vconv_quiet, vconv_large, vconv_small]
# round stuff
rounded = np.around(save_vals, 3)
round_vals = [date_str, date_jd]
for val in rounded:
round_vals.append(val)
# append these values to the csv file
utils.append_list_as_row(csv_name, round_vals)
# print that the date is completed
print('\nCalculations and save to file complete for ' + date_str + ' index: ' + str(i))
print('Calculation complete for dates:', start_date, 'to', end_date)


Medium - Callable Blocks of Code
A function is a block of code that will only run once called upon. You can pass data such as strings, integers, floats, booliens and more through a function.
As a result of this, functions are able to also return data.
So how do we create a function?
Functions are created by using def
followed by the name of the function. As demonstrated below:
def greeting(): print(f"Hello, welcome to the functions tutorial")
Calling a function is as easy as adding two round brackets to the end of a word.
def greeting(): print(f"Hello, welcome to the functions tutorial") greeting() >>> "Hello, welcome to the functions tutorial"
As mentioned above, you are able to pass infomation through functions, these are known as arguments.
Arguments are specified in between the round brackets when defining the function. You are allowed to pass as many arguments as you want, all you need to do is make sure they are seperated by commas. Here's an example:
def greeting(name, age): print(f"Hello, {name}. Welcome to the functions tutorial") print(f"From the data I've gathered, you are {age} years old") greeting('James', 17) >>> "Hello, James. Welcome to the functions tutorial" >>> "From the data I've gathered, you are 17 years old"
Here's another example of passing data through a function:
def multiply_by_pi(number): pi = 3.14159265359 # by using the return method, the function then sets the variable to that value return number * pi result = multiply_by_pi(15) print(result) >>> 47.1238898038
There you go, you should now understand the basics of functions in Python. Have a go at creating one of your own functions using repl.it. It's a super convenient online Python interpreter.


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EDtv (1999)
A Film Review by Mark O'Hara
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Ron Howard's 'EDtv' is full of ideas that double back upon their own trails, like tricky raccoons on the run.
Where else but in Hollywood would you find a work that indicts television, made by people whose roots are in television? Ron Howard starred in 'The Andy Griffith Show' and 'Happy Days.' Rob Reiner, who plays Mr. Whitaker the network honcho, was the meathead Michael Stivic in 'All in the Family.' Woody Harrelson, the title character's brother Ray, attained major airtime in 'Cheers.' Even Clint Howard, a fixture in all of his brother's movies, played a couple of seasons in 'Gentle Ben.'
Their experience certainly serves them well in 'EDtv,' as we see realistic treatments of what goes on behind the camera. It seems that each character in the film has an agenda, a collection of motives that are always self-serving and selfish. In the end the film is a dead-on satire that entertains but, paradoxically, contradicts itself with a Hollywood ending.
A producer at a floundering cable television network, Cynthia (Ellen DeGeneres) conceives a risky scheme intended to boost ratings. She runs a contest in which countless average citizens audition to have their daily lives put on the air. What is Cynthia's hope? That she'll save her own career as well as the careers of her staff. It's a last-ditch attempt, and it succeeds. Though at first the life of Ed (Matthew McConnaughey) appears boring (how long can viewers be captivated by tours of Ed's apartment and by the flavor of his down-home stories?), the program soon becomes as exciting as the juiciest soap opera.
When does the ho-hum documentary become drama? When the camera crew follows Ed to his brother Ray's apartment, 'EDtv' watchers discover Ray has spent the night with a woman who is not his girlfriend. Said girlfriend Shari (Jenna Elfman, TV's Dharma) phones immediately to confront Ray. The nastiness escalates when Ed takes the crew to the digs of the offended Shari, his purpose to plead Ray's case. Passionate whims take over, however, and we witness an unwitting kiss as Ed and Shari fall for each other.
As the title character, Matthew McConnaughey turns in his best performance in memory. Ron Howard directs well the sequences in which we see Ed's Texas charm. Ed is a 31 year-old under-achiever, a video store clerk who is unremarkable but nevertheless likable. In previous roles McConnaughey seemed two-dimensional at best, but here we care about his character when the television show begins to make off with his dignity. Sure, Ed has fun for awhile; McConnaughey elicits our sympathy for Ed when things start to go really wrong.
Woody Harrelson has no trouble being the most flamboyant presence in the film. His Ray is passionate, rascally, vindictive. We can imagine this guy writing a book called 'My Brother Pissed On Me,' though he has never even read a book. It's unfortunate that Harrelson's screen time dwindles as the film progresses.
Jenna Elfman is fine as Shari; she plays well the part of being embarrassed by the ever-present cameras. Shari is a driver for UPS, and of course becomes the object of national scrutiny as her budding, somewhat forbidden romance with Ed appears in every living room. Her wounded act becomes stale after awhile, though. As Ed and Ray's stepfather Al, Martin Landau delivers another memorable supporting performance. Too bad this movie appears too early in the year to be remembered for the Oscars. In a small role, Clint Howard is convincing as the man faced with following Ed's footsteps, coordinating the broadcast from the television truck. This outing brings some characterization, as Clint Howard's technician has hair plugs: "I've stopped payment on the plugs. You gave me doll hair!" he rages into a phone at one point. Perhaps this Howard deserves a bigger part next movie - his face is certainly sharp and intriguing in middle age.
An unusual motif is noticeable in the way characters spend their spare time. As they watch television - which is always on, in homes and bars - these people seem constantly to be exercising. They peddle curious stationary bikes, lift weights, all in pursuit of the slimness and fitness that the filmmakers suggest are included in the goals of average Americans. Even more curious is Ed's status as blue-collar hero: in fact he's a chameleon with his accent, switching from a neutral middle-American diction to an East Texas twang at will. The result for the viewer carries a disappointing, Roseanne-like effect. This guy's posing as a good old boy but embraces the same materialistic and romantic ends as the rest of us.
Another of the film's agendas is to meet the standards of political correctness. In portraying the audience of Whitaker's network, the writers show the gamut of races and preferences: a homosexual couple, an African-American couple, even a group of factory workers. Although the device of highlighting these viewers' responses is right out of 'The Truman Show,' it serves as a fascinating sort of subplot. Many of the responses themselves are hilarious, but their ultimate purpose is a self-reflexive parody. Let's face it - we American viewers are the targets of Howard's double-edged satire. Yes, the movie attacks the executive schlumps, but it also assaults the mind-sets that demand the type of programming of which EDtv is a sleazy example. What you have to love about the film is that it pokes so much fun not only at the average Joe - represented by Ed Pekurney and his family - but at the average jerk who tunes in.
There's no question Ron Howard knows the formula for a slick and successful Hollywood movie. In 'EDtv' he and his screenwriters, veterans Lowell Ganz and Babaloo Mandel, depict life's hard knocks and nuances; but the last quarter of the film neatly fits back into the mold of happy and romantic closure. The film is generally pleasing, especially in its treatment of Americans' obsession with media in all its crafty forms. Ideas flow freely here, and fans can only hope that at least one of the films in his future will buttress Howard's headiness with a risky and less mainstream ending.
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