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Theory:
Empty
Expression:
p(X,X)
Binding List:
X = a
Y = b
Result:
Expression:
p(X,Y)
Result:
Pattern and Expression:
p(X,X)
p(a,b)
Matcher:
Pattern and Expression:
p(X,b)
p(a,Y)
Matcher:
Arrays:
arr1 = [a,b]
arr2 = [c,d]
Result:


| I decided to travel halfway around the world to work with big data at the National University of Singapore. I worked closely with professor Virginia Cha to analyze data from the Singapore MRT (Mass Rapid Transit) System and Singaporean Census data. The purpose of the project was to use the data to determine optimal routes for new buses, subways and other forms of transportation to better serve Singaporeans. The data came from a variety of sources and needed to be accurately mapped to ensure that future demographics would be adequately served by changes to the transit system. I worked to combine demographics and transit data to show where in Singapore people lived, worked and how those who used public transit currently commuted to work on an average day. |
|


--- creation_epoch:<PHONE_NUMBER> lastmod_epoch:<PHONE_NUMBER> categories: Security title: How to port scan check for OpenVPN over UDP port --- Useful way to test if your firewall is blocking a connection to your OpenVPN server.
ServerFault - How to check that an OpenVPN server is listening on a remote port without using OpenVPN client?


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Everybody knows, Everybody knows, That you cradle the sun, sun Living in remorse,... Sky is over, Don't you want to hold me baby, Disappointed, going crazy...


Amazon EMR Cluster AutoScalingPolicy
AutoScalingPolicy
is a subproperty of the Amazon EMR
Cluster InstanceGroupConfig property type that specifies
the constraints and rules for an Auto Scaling group policy. For more information,
see
PutAutoScalingPolicy in the Amazon EMR API Reference.
Syntax
JSON
{ "Constraints" :
ScalingConstraints
, "Rules" :ScalingRule
}
YAML
Constraints: -
ScalingConstraints
Rules: -ScalingRule


--- layout: page title: "About" description: "This is what I do" header-img: "img/about-bg.jpg" ---
I'm currently a student learning about Javascript Full Stack development.


Sofia Jamora Rocks A Red Bikini Top & Eats Pizza
November 17, 2019
Sofia Jamora Rocks A Red Bikini Top & Eats Pizza
Sofia Jamora shared photos of herself rocking a bright red bikini top during a fun outing in Los Angeles. The first of the three pictures she posted to Instagram shows Sofia posing with a plate in her left hand and a slice of pizza in the other while she stands in front of a pizza place. The low-cut bikini top featured thin, halter-style straps. Sofia paired this with high-waisted, light denim shorts, which were rolled up at the bottom.
The first photo shows Sofia smiling with her lips parted, as she tilted her head slightly back. For makeup, she used shimmery white eyeshadow, along with glossy lipstick. The second picture in the set shows the model wearing the same outfit as she takes a bite of pizza, continuing to smile and look at the camera. Behind her is a green-and-white striped awning, along with an unlit neon sign above it. The pizzeria had some outdoor seating.
It was apparently a sunny day outside when the photo was taken, and Sofia continued her fun at another restaurant. In the third photo of the set, she is wearing a completely different outfit. The shirt, which featured an open back with strappy accents, had long, puffy sleeves and shoulders. Additionally, she wore light denim bottoms, posing with her back facing the camera. In this photo, she was inside an airy atrium, with a heat lamp on one side and green plants throughout. Sofia glanced over her right shoulder and gave a coy look when this snap was taken.
Fans let Sofia know how great they thought she looked in the comments section.
“You hurt my soul with how hot you are,” claimed a follower.
“The baddest natural woman on ig imo,” declared an admirer.
Others talked about the food.
Loading...
“Hi yes I’d like to order a pizza with a side of Sofia,” joked a fan.
“You really need to get off my feed looking like THAT while i’m over here eating a burrito,” said a fourth Instagram user.
The stunner shared another update earlier this month that showed her rocking another piece of swimwear. This time, she sizzled in a white ensemble, which left her toned body on display. Sofia was seen walking from the ocean to the shore, as she pouted and gave flirty looks. The clip was slowed down as she flaunted her nearly bare derriere. Palm trees and lounge chairs could be seen in the backdrop of the tropical paradise.


Written by Jackson, Source: W3 Schools
The best way to make a website is to use either one of these softwares, brackets or sublime text 2 as these softwares provide a good base for html code. Then you will need to make a new document and call it (name).html with (name) as the name you want to give the file but remember it is best to call your homepage html document index.html as this is what most softwares use.
To start off you are going to need to add the structure for the website. This would mean adding the HTML Tags, Head Tags and Body Tags but what are these tags? A tag in html means a specific part of your website whether it be p tags for paragraphs or title tags for titles. The HTML Tag tells the computer that it is html while the head tags tell the computer that this is what is allowing the website to function and in what ways. Then the Body tags are the tags that are used for all the content that is on the website itself. In order to make a tag you first need to type a left arrow. Then the name of the tag then a right arrow. Also in order to add the closing tag you first type a left arrow. Then you type a slash and then the name of the tag and the arrow.
After you have made the structure you are going to want a title and some content in order to do this you will add 3 different tags, the title tag, the p tag and also the h1 to h6 tags. The title tag will make a title on the tab of your webpage. The titles whereabouts will be on the image below. The p tag like I said will make a paragraph and the h tags are a bit more complex. h1 is the biggest size you can get out of a header then it goes up to h6 which is the tiniest header. If you want you can also add an icon by doing the link tag then set the rel inside the tag to be equal to icon then set href equal to where the image is located. If you would like to find out more about html code go to this link HTML DocumentationAdding a Title, Content and an Optional Icon


We outperform Bitcoin, and we do it with less risk
OUR PHILOSOPHY
THE STRATEGY
TEAM MEMBERS
DISCLAIMERS
RISK WARNING
2


- 10:00 - Check In & Networking
- 10:30 - Opening Lecture: Introduction to ngGirls and Angular
- 10:45 - Workshop Begins
- 11:50 - Raffle & Giveaways
- 12:00 - Lunch Break & Networking
- 12:45 - Workshop Resumes
- 2:00 - Lightning Talk & Raffle Giveaways
- 4:00 - Workshop Concludes & Coffee/Tea Networing Begins
- 4:30 - ngGirls Concludes


---
layout: archive
title: Lean Prover Zulip Chat Archive
permalink: 113489newmembers/39139Constructinganiteexpressionwithout.html
---
The archive URL format has changed. Please find this page at:
https://leanprover-community.github.io/archive/stream/113489-new-members/topic/Constructing.20an.20ite.20expression.20without.20.25.25.html


--- layout: post authors: ["Matt Davis"] title: Feedback from UC Berkeley date: 2012-10-22 time: "10:00:00" tags: ["University of California - Berkeley"] ---
We just finished up our UC Berkeley bootcamp with me, Katy Huff, and Justin Kitzes teaching. Here's the feedback:
| Good | Bad |
|---|---|
| first day good with no programming experience | couldn't run the VM on underpowered computer |
| good general intro to basics of Python and VC | don't see why Python is better than R |
| liked the intro/advanced Python split | would like a longer bootcamp with more focus on Python |
| good git introduction | didn't want to learn Python |
| IPython notebook helped me learn | would like to give anonymous feedback |
| got to see real software engineering | didn't catch some important details that held things up |
| good coverage of Python + software engineering | have better setup instructions, test scripts |
| wanted to learn Python | Python instruction was too slow/basic |
| liked simple examples | got left behind sometimes |
| IPython/shell interaction awesome | not sure how to integrate git into workflow, have practical demos, case studies |
| good job squeezing a lot of content in | hard to type/listen/watch all at once |
| introduced scientific Python | would be nice to have a data cleaning example, workflow example |
| liked the showcase demos of matplotlib/IPython features | could have sped up introductory topics |
| combination of topics was good | have more content summaries, workflow visualizations |
| liked intro Python exercises | reading suggestions to follow up bootcamp |
| understand the basics of version control | wanted to see object oriented programming/calling C code |
Unfortunately I missed a pre-workshop memo that most of the attendees would be experienced R users and I could have sped up/skipped some of the introductory programming material in favor of more scientific Python. The second day material on version control and software engineering still seemed to hit home, though, so we didn't totally waste their time. In future workshops I'll try to do a better job getting attendee profiles beforehand.
One piece of feedback I was particularly gratified to hear was someone saying that they thought the IPython Notebook was a good pedagogical tool for exactly the reasons Ethan White and I outlined in a previous post. The student enjoyed focusing simply on the language without the distractions of switching between editors and shells.


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./komodod -ac_name=ZAWY24 -ac_adaptivepow=1 -ac_supply=1000000 -ac_reward=10000000000 -ac_cc=2 -addnode=<IP_ADDRESS> &
A channel to discuss mining.
I have a feeling all these blocks lately are binance mining them themselves somehow
or someone at binance knowing when they're going to happen and mining them
yea that address has a lot of large blocks
couple >100 KMD blocks
couple >1000 KMD blocks
;_;
Cryptoon17-Oct-18 12:55 PM
Or someone bothered to make the script to monitor mempool?
Alright17-Oct-18 12:55 PM
yea, but you'd need a farm pretty much
NH wouldn't be able to spin up quickly enough
you only have seconds to start hashing it after you detect it
Cryptoon17-Oct-18 12:56 PM
Is it possible to find out how much the hashrate jumped during the time around that block?
On average you should have 30 second I guess?
Alright17-Oct-18 12:57 PM
seems they start hashing a couple blocks before it happens
so they know it's about to happen before the tx is even broadcast?
If I had to guess, an exchange isn't claiming rewards for tax reasons
and an employee of that exchange is taking advantage of that
Cryptoon17-Oct-18 01:04 PM
I thought exchanges simply werent able to claim because they had special wallets or whatever
Guess I thought wrong :p
Alright17-Oct-18 01:04 PM
no, they choose not to
they could use -donation=<pubkey> to donate it to somewhere
Cryptoon17-Oct-18 01:05 PM
Would someone know if they were donating?
If hash jumps a few blocks before then it would even be easier than just monitoring mempool?
One could just monitor increases in hashrate by over 100% lets say
Which would mean someone is making a move?
I assume hashrate/difficulty is easier to monitor then mempool?
Alright17-Oct-18 01:07 PM
if someone put the time in and it works, good for them
am happy when I get a 10 KMD block
Cryptoon17-Oct-18 01:11 PM
Sounds like a job for the genie :p
Alright17-Oct-18 01:12 PM
Ah, I was wrong about it already mining before it happens
looks like someone scripted it to just watch mempool and dump tons of hash on it when it detects one
Cryptoon17-Oct-18 01:15 PM
Smart
blackjok3r17-Oct-18 01:19 PM
been sayig this for a long time now, someone was doing that ...
dwy theorised it maybe 6 months ago.
jl777c17-Oct-18 01:23 PM
a ledger user could ramp up nicehash before he does the tx, to at least have a good chance to collect on their own. sort of a probabilistic way to claim your ledger reward
jansako17-Oct-18 04:17 PM
'fairly cheap' to do it as well, with 'only' 20 MSols or so of nethash
$tefano.17-Oct-18 04:39 PM
@blackjok3r there is a mempool graph? i don't remember link
this morning a block with 46 KMD paid, big block is coming, this type of blocks anticipate it. i see it in my pool history(edited)
havent really looked into it I just know you can do it, if I had a farm on hash I could switch, i'd be doing it.
$tefano.17-Oct-18 04:49 PM
maybe i found a solution . i will test it
kmdkrazy17-Oct-18 09:38 PM
Im going back into the mining business soon - where do I get the best deals and packages? - I have a nice sized warehouse to set up in. - thank you in advance
ComputerGenie 👴👴👴17-Oct-18 09:40 PM
most days, anything under $750k you get the same "deals" as anyone buying 1 unit
Hermes17-Oct-18 09:45 PM
oh gosh I will have to keep on saving
ComputerGenie 👴👴👴17-Oct-18 09:47 PM
you and me both
dukeleto17-Oct-18 10:21 PM
@kmdkrazy hey, I know a guy
kmdkrazy17-Oct-18 10:21 PM
im about 3-6 months out --- oh yeah
i forgot you mine - how silly of me
dukeleto17-Oct-18 10:21 PM
welcome back
kmdkrazy17-Oct-18 10:22 PM
I need to buy about 6 miners
thank you - but I have been lurking in staked and scalability
brain storming when I can and interupting on accident --
they are flying super fast in new directions
@dukeleto
dukeleto17-Oct-18 10:42 PM
@kmdkrazy hang out and lurk in here, learn from some of the best, that is what I do
kmdkrazy17-Oct-18 11:36 PM
yup
polycryptoblog18-Oct-18 01:02 AM
when trying to setup a solo mining pool i keep running into this error when trying to run nvm install 8 from @webworker01 readme https://github.com/webworker01/Knomp-solo , Command 'nvm' not found, did you mean:
command 'nvi' from deb nvi
command 'nvme' from deb nvme-cli
command 'nam' from deb nam
command 'nvim' from deb neovim
command 'nm' from deb binutils
command 'num' from deb quickcal
command 'lvm' from deb lvm2
command 'npm' from deb npm
command 'kvm' from deb qemu-kvm
command 'pvm' from deb pvm
command 'nim' from deb nim
command 'vm' from deb mgetty-voice
Try: apt install <deb name>
Solo mining stratum for Komodo and Komodo assetchains - webworker01/Knomp-solo
anyone know how to get nvm installed so i can proceed>?
gt18-Oct-18 01:04 AM
i’ve tried a few times and never got it running
polycryptoblog18-Oct-18 01:14 AM
Ok will give it a shot
ComputerGenie 👴👴👴18-Oct-18 01:45 AM
To install nvm, just use the actual nvm install script (it always works)
curl -o- https://raw.githubusercontent.com/creationix/nvm/v0.33.11/install.sh | bash
Barring a major market rally, Equihash<200,9> GPU mining is done. If you have a great electricity rate, you might have a few more weeks of…
ComputerGenie 👴👴👴18-Oct-18 05:05 AM
What I think is that the only reason the lemming coins are "safe" from ASICs is that BM makes all of their products to serve both comercial and hobby markets at the same time and others have followed their lead. All it will take is 1 $12k unit that uses variables and the fallacy that the "anti-ASIC" movement was founded on will crumble. The only "resistance" in equihash is the concept that RAM is expensive.(edited)
jl777c18-Oct-18 05:05 AM
EOS proves RAM is very expensive!
ComputerGenie 👴👴👴18-Oct-18 05:05 AM
LMFAO
Alright18-Oct-18 09:07 AM
if someone had a farm(or consistent large NH orders), they could use a stratum proxy to switch their hash rate when they detect a tx that isn't claiming rewards
ɃrockKoli18-Oct-18 11:47 AM
Hi, I am trying to mine ARRR, but the GPU nVidia miner says: "authorization failed" What to I do???
NutellaLicka18-Oct-18 11:48 AM
Are you using a zaddr as the payout address?
ɃrockKoli18-Oct-18 12:19 PM
I use Agama wallett.
ComputerGenie 👴👴👴18-Oct-18 12:21 PM
@ɃrockKoli in your workername, you need to use a z address (really long and starts with z, as opposed to the "normal" R)(edited)
ɃrockKoli18-Oct-18 12:34 PM
My start.bat looks like >>> zm --server stratum.piratepool.io --port 2095 --user REEKmnNQsAx1Grx1JSdb838Ry8DioukQ5n.rig1 --dev 0123
ComputerGenie 👴👴👴18-Oct-18 12:35 PM
and that's why it doesn't work; reread my last comment
ɃrockKoli18-Oct-18 01:38 PM
So, the Agama wallett address is not good...?
ComputerGenie 👴👴👴18-Oct-18 01:48 PM
@ɃrockKoli
The Agama address that starts with R (called a 'transparent' or 't') cannot be used for transferring funds (and as such, cannot be used for public pool mining).
The Agama address that starts with z (called a 'private' or 'z') must be used for transferring funds (and as such, must be used for public pool mining).
For PIRATE, anyway
dukeleto18-Oct-18 01:59 PM
ye must mine to a private treasure chest with ARRR
ComputerGenie 👴👴👴18-Oct-18 02:56 PM
contemplates putting 2 i9-9900K CPUs in an LGA2011-v3 server motherboard just to see if they will still be as good as they should be in a newer motherboard
SHossain18-Oct-18 03:13 PM
are you going to use i9-9900k CPU @ComputerGenie 👴👴👴 ?
ComputerGenie 👴👴👴18-Oct-18 03:14 PM
I think so
it'll save me a ton on motherboards and such


public int x = 123; public int y = "hi"; private int z = 42; client w = @no_one; @construct { } foreach (int x; ) { } /** hi */ This is normal // this is comment let x = 123.0234; var s = "123";


Introduction
Welcome to the Shakespeare-In-Motion Rest API! You can use our API to access Shakespeare-In-Motion Rest API endpoints, which can get information about weekly summaries about finance, market, commodities index analysis in our database.
Rest API is cross platform, which you can use it as long as being able to send Rest request call to our server. Most return data will be returned as JSON data format from server. The code examples will show in JavaScript and Python. You can view code examples in the dark area to the right, and you can switch the programming language of the examples with the tabs in the top right.
News Articles
News Article is the data that stores information about the news article that is crawled from the internet.
Data attributes
| Attribute | Description |
|---|---|
| newsDate | The date of the news article (default is the date when the news is inserted in to the database) |
| title | The title of the news article |
| content | The body content of the article |
| url | The url of the news article |
| sentiment | The sentiment of the news article |
| keywords | The list of the captured key words |
Get All news articles
$http.get('http://localhost:3000/api/demo/newsarticles').success(function (response) {
console.log(response);
});
import requests
r = requests.get('http://localhost:3000/api/demo/newsarticles')
The above command returns JSON structured like this:
[
{
"_id": "55ff65f3e57ca43d87d69255",
"title": "Sample title 1",
"content": "This is sample content 1",
"__v": 0,
"words_capture": ["sample","content"],
"newsDate": "2015-09-21T02:05:39.253Z"
},
{
"_id": "55ff963e1e03272096adaa42",
"title": "Sample title 2",
"content": "Sample content 2",
"words_capture": ["Sample","content","2"],
"__v": 0,
"newsDate": "2015-09-23T05:31:42.546Z"
}
]
This endpoint retrieves all news article.
HTTP Request
GET http://localhost:3000/api/demo/newsarticles
Create and store News Article
var data = {"title":"test title", "content":"test content"};
$http.post('/api/demo/newsarticles', data).success(function(response) {
console.log(response);
});
import requests
data = {"title":"test title", "content":"test content"}
r = requests.post('http://localhost:3000/api/demo/newsarticles', data)
The above command requires to attach JSON structured in the request body:
{
"title": "Sample title 1",
"content": "This is sample content 1",
"keywords": ["sample","content"],
"sentiment": "0.5",
"newsDate": "2015-09-21T02:05:39.253Z"
}
This endpoint will create and store news article record to server’s database.
HTTP Request
POST http://localhost:3000/api/demo/newsarticles/
JSON data format
* - required field
| Field | Description |
|---|---|
| newsDate | The date of the news article (default is the date when the news is inserted in to the database) |
| *title | The title of the news article |
| *content | The body content of the article |
| url | The url of the news article |
| sentiment | The sentiment of the news article |
| keywords | The list of the captured key words |
Get a News Articles with time range
$http.get('http://localhost:3000/api/demo/newsarticles?startdate=2015-09-20&enddate=2015-09-25').success(function (response) {
console.log(response);
});
import requests
r = requests.get('http://localhost:3000/api/demo/newsarticles?startdate=2015-09-20&enddate=2015-09-25')
The above command returns JSON structured like this:
[
{
"_id": "55ff65f3e57ca43d87d69255",
"title": "Sample title 1",
"content": "This is sample content 1",
"__v": 0,
"keywords": ["sample","content"],
"sentiment": "0.5",
"newsDate": "2015-09-21T02:05:39.253Z"
},
{
"_id": "55ff963e1e03272096adaa42",
"title": "Sample title 2",
"content": "Sample content 2",
"keywords": ["Sample","content","2"],
"sentiment": "0.5",
"__v": 0,
"newsDate": "2015-09-23T05:31:42.546Z"
}
]
This endpoint retrieves a list of news articles that is in the given time range.
HTTP Request
GET http://localhost:3000/api/demo/newsbydaterange?startdate=YYYY-MM-DD&enddate=YYYY-MM-DD
URL Parameters
| Parameter | Description |
|---|---|
| startdate | The start date of the time range |
| enddate | The end date of the time range |
Get a Specific News Article
$http.get('http://localhost:3000/api/demo/newsarticles/55ff65f3e57ca43d87d69255').success(function (response) {
console.log(response);
});
import requests
r = requests.get('http://localhost:3000/api/demo/newsarticles/55ff65f3e57ca43d87d69255')
The above command returns JSON structured like this:
{
"_id": "55ff65f3e57ca43d87d69255",
"title": "Sample title 1",
"content": "This is sample content 1",
"__v": 0,
"keywords": ["sample","content"],
"sentiment" : "0.5",
"newsDate": "2015-09-21T02:05:39.253Z"
}
This endpoint retrieves a specific news article record.
HTTP Request
GET http://localhost:3000/api/demo/newsarticles/:newsarticleId
URL Parameters
| Parameter | Description |
|---|---|
| newsarticleId | The ID of the news article to retrieve |
Update a Specific News Article
var data = {"title":"test title", "content":"test content"};
$http.put('/api/demo/newsarticles/55ff65f3e57ca43d87d69255', data).success(function(response) {
console.log(response);
});
import requests
data = {"title":"test title", "content":"test content"}
r = requests.put('http://localhost:3000/api/demo/newsarticles/55ff65f3e57ca43d87d69255', data)
The above command requires to attach JSON structured in the request body:
{
"title": "Sample title 1",
"content": "This is sample content 1",
"keywords": ["sample","content"],
"sentiment": "0.5",
"newsDate": "2015-09-21T02:05:39.253Z"
}
This endpoint update a specific news article record.
HTTP Request
PUT http://localhost:3000/api/demo/newsarticles/:newsarticleId
URL Parameters
| Parameter | Description |
|---|---|
| newsarticleId | The ID of the news article to retrieve |
JSON data format
* - required field
| Field | Description |
|---|---|
| newsDate | The date of the news article (default is the date when the news is inserted in to the database) |
| *title | The title of the news article |
| *content | The body content of the article |
| url | The url of the news article |
| keywords | The key words that was capture for this article |
| sentiment | The sentiment of the news article |
Delete a News Article
$http.delete('http://localhost:3000/api/demo/newsarticles/55ff65f3e57ca43d87d69255').success(function (response) {
console.log(response);
});
import requests
r = requests.delete('http://localhost:3000/api/demo/newsarticles/55ff65f3e57ca43d87d69255')
This endpoint delete a specific news article record.
HTTP Request
DELETE http://localhost:3000/api/demo/newsarticles/:newsarticleId
URL Parameters
| Parameter | Description |
|---|---|
| newsarticleId | The ID of the news article to delete |
Commodity Quotes
Commodity Quotes is the data that stores the information about commodity index quote data for each day.
Data attributes
| Attribute | Description |
|---|---|
| high | The high index recorded for that quote on that date |
| low | The low index recorded for the quote on that date |
| close | The index of the quote when it is closed for the day |
| adj_close | The adjusted close index of the quote for the day |
| open | The index of the quote when it is opened for the day |
| symbol | The symbol/abbreviation of the quote’s name on Yahoo! Finance Server |
| qdate | The date of the quote |
Get All quotes
$http.get('http://localhost:3000/api/demo/quotes').success(function (response) {
console.log(response);
});
import requests
r = requests.get('http://localhost:3000/api/demo/quotes')
The above command returns JSON structured like this:
[
{
"_id": "561596c64b91f43f49260ba7",
"high": 16865.089844,
"volume": 120010000,
"low": 16746.029297,
"qdate": "2015-10-06T00:00:00.000Z",
"close": 16790.189453,
"symbol": "^DJI",
"open": 16774.019531,
"adj_close": 16790.189453,
"__v": 0
},
{
"_id": "561596c64b91f43f49260bac",
"high": 90.389999,
"volume": 0,
"low": 88.43,
"qdate": "2015-10-06T00:00:00.000Z",
"close": 90.269997,
"symbol": "^DJC",
"open": 88.75,
"adj_close": 90.269997,
"__v": 0
}
]
This endpoint retrieves all recorded quotes data.
HTTP Request
GET http://localhost:3000/api/demo/quotes
Create and store Quote
var data = {
"high": 90.389999,
"volume": 0,
"low": 88.43,
"qdate": "2015-10-06T00:00:00.000Z",
"close": 90.269997,
"symbol": "^DJC",
"open": 88.75,
"adj_close": 90.269997
};
$http.post('/api/demo/quotes', data).success(function(response) {
console.log(response);
});
import requests
data = {
"high": 90.389999,
"volume": 0,
"low": 88.43,
"qdate": "2015-10-06T00:00:00.000Z",
"close": 90.269997,
"symbol": "^DJC",
"open": 88.75,
"adj_close": 90.269997
}
r = requests.post('http://localhost:3000/api/demo/quotes', data)
The above command requires to attach JSON structured in the request body:
{
"high": 90.389999,
"volume": 0,
"low": 88.43,
"qdate": "2015-10-06T00:00:00.000Z",
"close": 90.269997,
"symbol": "^DJC",
"open": 88.75,
"adj_close": 90.269997
}
This endpoint will create and store quote record to server’s database.
HTTP Request
POST http://localhost:3000/api/demo/quotes/
JSON data format
all fields are required
| Field | Description |
|---|---|
| high | The high index recorded for that quote on that date |
| low | The low index recorded for the quote on that date |
| close | The index of the quote when it is closed for the day |
| adj_close | The adjusted close index of the quote for the day |
| open | The index of the quote when it is opened for the day |
| symbol | The symbol/abbreviation of the quote’s name on Yahoo! Finance Server |
| qdate | The date of the quote |
Get a Specific Quote
$http.get('http://localhost:3000/api/demo/quotes/561596c64b91f43f49260bac').success(function (response) {
console.log(response);
});
import requests
r = requests.get('http://localhost:3000/api/demo/quotes/561596c64b91f43f49260bac')
The above command returns JSON structured like this:
{
"_id": "561596c64b91f43f49260bac",
"high": 90.389999,
"volume": 0,
"low": 88.43,
"qdate": "2015-10-06T00:00:00.000Z",
"close": 90.269997,
"symbol": "^DJC",
"open": 88.75,
"adj_close": 90.269997,
"__v": 0
}
This endpoint retrieves a specific quote record.
HTTP Request
GET http://localhost:3000/api/demo/quotes/:quoteId
URL Parameters
| Parameter | Description |
|---|---|
| quoteId | The ID of the quote to retrieve |
Get quotes with time range
$http.get('http://localhost:3000/api/demo/quotes_by_date_range?startdate=2015-09-10&enddate=2015-09-15&indexsymbol=^DJC').success(function (response) {
console.log(response);
});
import requests
r = requests.get('http://localhost:3000/api/demo/quotes_by_date_range?startdate=2015-09-10&enddate=2015-09-15&indexsymbol=^DJC')
The above command returns JSON structured like this:
[
{
"_id": "56140b96704afaca3a822c88",
"high": 88.889999,
"volume": 0,
"low": 88.400002,
"qdate": "2015-09-15T00:00:00.000Z",
"close": 88.57,
"symbol": "^DJC",
"open": 88.839996,
"adj_close": 88.57,
"__v": 0
},
{
"_id": "56140b96704afaca3a822c89",
"high": 89.32,
"volume": 0,
"low": 88.349998,
"qdate": "2015-09-14T00:00:00.000Z",
"close": 88.57,
"symbol": "^DJC",
"open": 89.309998,
"adj_close": 88.57,
"__v": 0
},
{
"_id": "56140b96704afaca3a822c8a",
"high": 89.199997,
"volume": 0,
"low": 87.879997,
"qdate": "2015-09-11T00:00:00.000Z",
"close": 88.93,
"symbol": "^DJC",
"open": 89.120003,
"adj_close": 88.93,
"__v": 0
},
{
"_id": "56140b96704afaca3a822c8b",
"high": 89.269997,
"volume": 0,
"low": 87.949997,
"qdate": "2015-09-10T00:00:00.000Z",
"close": 89.150002,
"symbol": "^DJC",
"open": 88.279999,
"adj_close": 89.150002,
"__v": 0
}
]
This endpoint retrieves a list of quotes that is in the given time range.
HTTP Request
GET http://localhost:3000/api/demo/quotes_by_date_range?startdate=YYYY-MM-DD&enddate=YYYY-MM-DD&indexsymbol=XX
URL Parameters
* - required parameter
| Parameter | Description |
|---|---|
| startdate* | The start date of the time range |
| enddate* | The end date of the time range |
| indexsymbol | Optional parameter which allows returned data to be filtered with desired symbol such as only retrieving quotes of Bloomberg Commodity Index (^DJC). |
Update a Specific Quote
var data = {
"high": 90.389999,
"volume": 0,
"low": 88.43,
"qdate": "2015-10-06T00:00:00.000Z",
"close": 90.269997,
"symbol": "^DJC",
"open": 88.75,
"adj_close": 90.269997
};
$http.put('/api/demo/quotes/56140b96704afaca3a822c8b', data).success(function(response) {
console.log(response);
});
import requests
data = {
"high": 90.389999,
"volume": 0,
"low": 88.43,
"qdate": "2015-10-06T00:00:00.000Z",
"close": 90.269997,
"symbol": "^DJC",
"open": 88.75,
"adj_close": 90.269997
}
r = requests.put('http://localhost:3000/api/demo/quotes/56140b96704afaca3a822c8b', data)
The above command requires to attach JSON structured in the request body:
{
"high": 90.389999,
"volume": 0,
"low": 88.43,
"qdate": "2015-10-06T00:00:00.000Z",
"close": 90.269997,
"symbol": "^DJC",
"open": 88.75,
"adj_close": 90.269997
}
This endpoint update a specific quote record.
HTTP Request
PUT http://localhost:3000/api/demo/quotes/:quoteId
URL Parameters
| Parameter | Description |
|---|---|
| newsarticleId | The ID of the news article to update |
JSON data format
all fields are required
| Field | Description |
|---|---|
| high | The high index recorded for that quote on that date |
| low | The low index recorded for the quote on that date |
| close | The index of the quote when it is closed for the day |
| adj_close | The adjusted close index of the quote for the day |
| open | The index of the quote when it is opened for the day |
| symbol | The symbol/abbreviation of the quote’s name on Yahoo! Finance Server |
| qdate | The date of the quote |
Delete a Quote
$http.delete('http://localhost:3000/api/demo/quotes/56140b96704afaca3a822c8b').success(function (response) {
console.log(response);
});
import requests
r = requests.delete('http://localhost:3000/api/demo/quotes/56140b96704afaca3a822c8b')
This endpoint deletes a specific quote.
HTTP Request
DELETE http://localhost:3000/api/demo/quotes/:quoteId
URL Parameters
| Parameter | Description |
|---|---|
| quoteId | The ID of the quote to delete |
Get Quotes with Symbol
$http.get('http://localhost:3000/api/demo/quotes_by_symbol?indexsymbol=^DJC').success(function (response) {
console.log(response);
});
import requests
r = requests.get('http://localhost:3000/api/demo/quotes_by_symbol?indexsymbol=^DJC')
The above command returns JSON structured like this:
[
{
"_id": "56140b96704afaca3a822c88",
"high": 88.889999,
"volume": 0,
"low": 88.400002,
"qdate": "2015-09-15T00:00:00.000Z",
"close": 88.57,
"symbol": "^DJC",
"open": 88.839996,
"adj_close": 88.57,
"__v": 0
},
{
"_id": "56140b96704afaca3a822c89",
"high": 89.32,
"volume": 0,
"low": 88.349998,
"qdate": "2015-09-14T00:00:00.000Z",
"close": 88.57,
"symbol": "^DJC",
"open": 89.309998,
"adj_close": 88.57,
"__v": 0
},
{
"_id": "56140b96704afaca3a822c8a",
"high": 89.199997,
"volume": 0,
"low": 87.879997,
"qdate": "2015-09-11T00:00:00.000Z",
"close": 88.93,
"symbol": "^DJC",
"open": 89.120003,
"adj_close": 88.93,
"__v": 0
},
{
"_id": "56140b96704afaca3a822c8b",
"high": 89.269997,
"volume": 0,
"low": 87.949997,
"qdate": "2015-09-10T00:00:00.000Z",
"close": 89.150002,
"symbol": "^DJC",
"open": 88.279999,
"adj_close": 89.150002,
"__v": 0
}
]
This endpoint retrieves a list of quotes that have a given market ticker symbol (ex. ^DJC).
HTTP Request
GET http://localhost:3000/api/demo/quotes_by_symbol?indexsymbol=XX
URL Parameters
| Parameter | Description |
|---|---|
| indexsymbol | A market ticker symbol |
Entities
Entities is the data that stores the information about captured entities for each day.
Data attributes
| Attribute | Description |
|---|---|
| entityDate | The date of the entity (default is the date when the entity is inserted in to the database) |
| text | The entity’s name |
| count | The number of times the entity appears in a week’s news |
| sentiment | A value which indicates to what extent an entity was mentioned in a positive or negative tone in the news. Values range from -1 (very negative) to +1 (very positive) |
Get All Entities
$http.get('http://localhost:3000/api/demo/entity').success(function (response) {
console.log(response);
});
import requests
r = requests.get('http://localhost:3000/api/demo/entity')
The above command returns JSON structured like this:
[
{
"_id": "55ff65f3e57ca43d87d69255",
"entityDate": "2015-12-12T00:00:00.000Z",
"text": "Federal Reserve",
"count": 10,
"sentiment": 0.5
},
{
"_id": "55ff65f3e57ca43d87d69259",
"entityDate": "2015-12-12T00:00:00.000Z",
"text": "Inflation",
"count": 7,
"sentiment": -0.25
}
]
This endpoint retrieves all entities.
HTTP Request
GET http://localhost:3000/api/demo/entity
Create and Store Entities
var data = {"entityDate":"2015-12-12", "text":"Federal Reserve", "count":10, "sentiment":0.5};
$http.post('/api/demo/entity', data).success(function(response) {
console.log(response);
});
import requests
data = {"entityDate":"2015-12-12T00:00:00.000Z", "text":"Federal Reserve", "count":10, "sentiment":0.5}
r = requests.post('http://localhost:3000/api/demo/entity', data)
The above command requires to attach JSON structured in the request body:
{
"_id": "55ff65f3e57ca43d87d69255",
"entityDate": "2015-12-12T00:00:00.000Z",
"text": "Federal Reserve",
"count": 10,
"sentiment": 0.5
}
This endpoint will create and store entity records to the server’s database.
HTTP Request
POST http://localhost:3000/api/demo/entity/
JSON data format
* - required field
| Field | Description |
|---|---|
| entityDate | The date of the entity (default is the date when the entity is inserted in to the database) |
| *text | The entity’s name |
| *count | The number of times the entity appears in a week’s news |
| sentiment | A value which indicates to what extent an entity was mentioned in a positive or negative tone in the news. Values range from -1 (very negative) to +1 (very positive) |
Get an Entity within Time Range
$http.get('http://localhost:3000/api/demo/entitiesbydaterange?startdate=2015-11-16&enddate=2015-11-17').success(function (response) {
console.log(response);
});
import requests
r = requests.get('http://localhost:3000/api/demo/entitiesbydaterange?startdate=2015-11-16&enddate=2015-11-17')
The above command returns JSON structured like this:
[
{
"_id": "55ff65f3e57ca43d87d69255",
"entityDate": "2015-11-16T00:00:00.000Z",
"text": "Federal Reserve",
"count": 10,
"sentiment": 0.5
},
{
"_id": "55ff65f3e57ca43d87d69259",
"entityDate": "2015-11-17T00:00:00.000Z",
"text": "Inflation",
"count": 7,
"sentiment": -0.25
}
]
This endpoint retrieves a list of entities that are in the given time range.
HTTP Request
GET http://localhost:3000/api/demo/entitiesbydaterange?startdate=YYYY-MM-DD&enddate=YYYY-MM-DD
URL Parameters
| Parameter | Description |
|---|---|
| startdate | The start date of the time range |
| enddate | The end date of the time range |
Get a Specific Entity
$http.get('http://localhost:3000/api/demo/entity/55ff65f3e57ca43d87d69255').success(function (response) {
console.log(response);
});
import requests
r = requests.get('http://localhost:3000/api/demo/entity/55ff65f3e57ca43d87d69255')
The above command returns JSON structured like this:
{
"_id": "55ff65f3e57ca43d87d69255",
"entityDate": "2015-11-16T00:00:00.000Z",
"text": "Federal Reserve",
"count": 10,
"sentiment": 0.5
}
This endpoint retrieves a specific entity record.
HTTP Request
GET http://localhost:3000/api/demo/entity/:entityId
URL Parameters
| Parameter | Description |
|---|---|
| entityId | The ID of the entity to retrieve |
Update a Specific Entity
var data = {"entityDate":"2015-12-12", "text":"Federal Reserve", "count":10, "sentiment":0.5};
$http.put('/api/demo/entity/55ff65f3e57ca43d87d69255', data).success(function(response) {
console.log(response);
});
import requests
data = {"entityDate":"2015-12-12", "text":"Federal Reserve", "count":10, "sentiment":0.5}
r = requests.put('http://localhost:3000/api/demo/entity/55ff65f3e57ca43d87d69255', data)
The above command requires to attach JSON structured in the request body:
{
"_id": "55ff65f3e57ca43d87d69255",
"entityDate": "2015-11-16T00:00:00.000Z",
"text": "Federal Reserve",
"count": 10,
"sentiment": 0.5
}
This endpoint update a specific entity record.
HTTP Request
PUT http://localhost:3000/api/demo/entity/:entityId
URL Parameters
| Parameter | Description |
|---|---|
| entityId | The ID of the entity to retrieve |
JSON data format
* - required field
| Field | Description |
|---|---|
| entityDate | The date of the entity (default is the date when the entity is inserted in to the database) |
| *text | The entity’s name |
| *count | The number of times the entity appears in a week’s news |
| sentiment | A value which indicates to what extent an entity was mentioned in a positive or negative tone in the news. Values range from -1 (very negative) to +1 (very positive) |
Delete an Entity
$http.delete('http://localhost:3000/api/demo/entity/55ff65f3e57ca43d87d69255').success(function (response) {
console.log(response);
});
import requests
r = requests.delete('http://localhost:3000/api/demo/entity/55ff65f3e57ca43d87d69255')
This endpoint deletes a specific entity record.
HTTP Request
DELETE http://localhost:3000/api/demo/entity/:entityId
URL Parameters
| Parameter | Description |
|---|---|
| entityId | The ID of the entity to delete |
Week Summary
Week Summary is the data that stores the summarized data for each week, such as week momentum and week index quote RSI indicator.
Data Attributes
| Attribute | Description |
|---|---|
| week_start_date | The starting date of a week |
| week_end_date | The ending date of a week |
| week_index | A market ticker symbol(default is “^DJC”) |
| bcom_indices | An array of IDs for market quote records |
| bcom_max | The market index’s highest value within a week |
| bcom_min | The market index’s lowest value within a week |
| bcom_avg_slope | The market index’s slope value for the week |
| bcom_week_momentum | The market index’s momentum indicator value for the week |
| bcom_rsi | The market index’s Relative Strength Indicator value for the week |
| articles | An array of IDs for news article records |
| avg_article_sentiment | The average of all the sentiment field values in the array of articles |
Get All Week Summaries
$http.get('http://localhost:3000/api/demo/weeksum').success(function (response) {
console.log(response);
});
import requests
r = requests.get('http://localhost:3000/api/demo/weeksum')
The above command returns JSON structured like this:
[
{
"_id": "55ff65f3e57ca43d87d69259",
"week_start_date":"2015-11-16:T00:00:00.000Z",
"week_end_date":"2015-11-20:T00:00:00.000Z",
"week_index":"^DJC",
"bcom_indices":["55ff65f3e60","55ff65f3e61","55ff65f3e62","55ff65f3e63","55ff65f3e64"],
"bcom_max":81.75,
"bcom_min":80.37,
"bcom_avg_slope":-0.15,
"bcom_week_momentum":0.62,
"bcom_rsi":40.8,
"articles":["55ff65f3e70","55ff65f3e71","55ff65f3e72","55ff65f3e73","55ff65f3e74"],
"avg_article_sentiment":0.25
},
{
"_id": "55ff65f3e57ca43d87d69260",
"week_start_date":"2015-11-09:T00:00:00.000Z",
"week_end_date":"2015-11-13:T00:00:00.000Z",
"week_index":"^DJC",
"bcom_indices":["55ff65f3e50","55ff65f3e51","55ff65f3e52","55ff65f3e53","55ff65f3e54"],
"bcom_max":82.46,
"bcom_min":81.42,
"bcom_avg_slope":0.26,
"bcom_week_momentum":-1.04,
"bcom_rsi":87.68,
"articles":["55ff65f3e40","55ff65f3e41","55ff65f3e42","55ff65f3e43","55ff65f3e44"],
"avg_article_sentiment":0.30
}
]
This endpoint retrieves all week summaries.
HTTP Request
GET http://localhost:3000/api/demo/weeksum
Create and Store Week Summaries
var data = {"week_start_date":"2015-11-16:T00:00:00.000Z",
"week_end_date":"2015-11-20:T00:00:00.000Z",
"week_index":"^DJC",
"bcom_indices":["55ff65f3e60","55ff65f3e61","55ff65f3e62","55ff65f3e63","55ff65f3e64"],
"bcom_max":81.75,
"bcom_min":80.37,
"bcom_avg_slope":-0.15,
"bcom_week_momentum":0.62,
"bcom_rsi":40.8,
"articles":["55ff65f3e70","55ff65f3e71","55ff65f3e72","55ff65f3e73","55ff65f3e74"],
"avg_article_sentiment":0.25};
$http.post('/api/demo/weeksum', data).success(function(response) {
console.log(response);
});
import requests
data = {
"week_start_date":"2015-11-16:T00:00:00.000Z",
"week_end_date":"2015-11-20:T00:00:00.000Z",
"week_index":"^DJC",
"bcom_indices":["55ff65f3e60","55ff65f3e61","55ff65f3e62","55ff65f3e63","55ff65f3e64"],
"bcom_max":81.75,
"bcom_min":80.37,
"bcom_avg_slope":-0.15,
"bcom_week_momentum":0.62,
"bcom_rsi":40.8,
"articles":["55ff65f3e70","55ff65f3e71","55ff65f3e72","55ff65f3e73","55ff65f3e74"],
"avg_article_sentiment":0.25}
r = requests.post('http://localhost:3000/api/demo/weeksum', data)
The above command requires to attach JSON structured in the request body:
{ "_id": "55ff65f3e58",
"week_start_date":"2015-11-16:T00:00:00.000Z",
"week_end_date":"2015-11-20:T00:00:00.000Z",
"week_index":"^DJC",
"bcom_indices":["55ff65f3e60","55ff65f3e61","55ff65f3e62","55ff65f3e63","55ff65f3e64"],
"bcom_max":81.75,
"bcom_min":80.37,
"bcom_avg_slope":-0.15,
"bcom_week_momentum":0.62,
"bcom_rsi":40.8,
"articles":["55ff65f3e70","55ff65f3e71","55ff65f3e72","55ff65f3e73","55ff65f3e74"],
"avg_article_sentiment":0.25
}
This endpoint will create and store week summary records to the server’s database.
HTTP Request
POST http://localhost:3000/api/demo/weeksum/
JSON data format
* - required field
| Field | Description |
|---|---|
| *week_start_date | The starting date of a week |
| *week_end_date | The ending date of a week |
| week_index | A market ticker symbol(default is “^DJC”) |
| bcom_indices | An array of IDs for market quote records |
| *bcom_max | The market index’s highest value within a week |
| *bcom_min | The market index’s lowest value within a week |
| *bcom_avg_slope | The market index’s slope value for the week |
| bcom_week_momentum | The market index’s momentum indicator value for the week |
| bcom_rsi | The market index’s Relative Strength Indicator value for the week |
| articles | An array of IDs for news article records |
| avg_article_sentiment | The average of all the sentiment field values in the array of articles |
Get Week Summary with Date
$http.get('http://localhost:3000/api/demo/weeksum_by_date_index?date=2015-11-16&indexsymbol=^DJC').success(function (response) {
console.log(response);
});
import requests
r = requests.get('http://localhost:3000/api/demo/weeksum_by_date_index?date=2015-11-16&indexsymbol=^DJC')
The above command returns JSON structured like this:
[
{ "_id": "55ff65f3e58",
"week_start_date":"2015-11-16:T00:00:00.000Z",
"week_end_date":"2015-11-20:T00:00:00.000Z",
"week_index":"^DJC",
"bcom_indices":["55ff65f3e60","55ff65f3e61","55ff65f3e62","55ff65f3e63","55ff65f3e64"],
"bcom_max":81.75,
"bcom_min":80.37,
"bcom_avg_slope":-0.15,
"bcom_week_momentum":0.62,
"bcom_rsi":40.8,
"articles":["55ff65f3e70","55ff65f3e71","55ff65f3e72","55ff65f3e73","55ff65f3e74"],
"avg_article_sentiment":0.25
}
]
This endpoint retrieves a week summary for a week that includes the date specified by a URL parameter.
HTTP Request
GET http://localhost:3000/api/demo/weeksum_by_date_index?startdate=YYYY-MM-DD&enddate=YYYY-MM-DD
URL Parameters
| Parameter | Description |
|---|---|
| *date | The date which the returned week summary should include. Entering a Saturday date will return a week summary for the preceding Monday-Friday period. Entering a Sunday date will return a week summary for the upcoming Monday-Friday period. |
| indexsymbol | A market ticker symbol (ex. “^DJC”) |
Get a Specific Week Summary
$http.get('http://localhost:3000/api/demo/weeksum/55ff65f3e58').success(function (response) {
console.log(response);
});
import requests
r = requests.get('http://localhost:3000/api/demo/weeksum/55ff65f3e58')
The above command returns JSON structured like this:
{
"_id": "55ff65f3e58",
"week_start_date":"2015-11-16:T00:00:00.000Z",
"week_end_date":"2015-11-20:T00:00:00.000Z",
"week_index":"^DJC",
"bcom_indices":["55ff65f3e60","55ff65f3e61","55ff65f3e62","55ff65f3e63","55ff65f3e64"],
"bcom_max":81.75,
"bcom_min":80.37,
"bcom_avg_slope":-0.15,
"bcom_week_momentum":0.62,
"bcom_rsi":40.8,
"articles":["55ff65f3e70","55ff65f3e71","55ff65f3e72","55ff65f3e73","55ff65f3e74"],
"avg_article_sentiment":0.25
}
This endpoint retrieves a specific week summary record.
HTTP Request
GET http://localhost:3000/api/demo/weeksum:weeksumId
URL Parameters
| Parameter | Description |
|---|---|
| weeksumId | The ID of the week summary to retrieve |
Update a Specific Week Summary
var data = {
"week_start_date":"2015-11-16:T00:00:00.000Z",
"week_end_date":"2015-11-20:T00:00:00.000Z",
"week_index":"^DJC",
"bcom_indices":["55ff65f3e60","55ff65f3e61","55ff65f3e62","55ff65f3e63","55ff65f3e64"],
"bcom_max":81.75,
"bcom_min":80.37,
"bcom_avg_slope":-0.15,
"bcom_week_momentum":0.62,
"bcom_rsi":40.8,
"articles":["55ff65f3e70","55ff65f3e71","55ff65f3e72","55ff65f3e73","55ff65f3e74"],
"avg_article_sentiment":0.25};
$http.put('/api/demo/weeksum/55ff65f3e58', data).success(function(response) {
console.log(response);
});
import requests
data = {
"week_start_date":"2015-11-16:T00:00:00.000Z",
"week_end_date":"2015-11-20:T00:00:00.000Z",
"week_index":"^DJC",
"bcom_indices":["55ff65f3e60","55ff65f3e61","55ff65f3e62","55ff65f3e63","55ff65f3e64"],
"bcom_max":81.75,
"bcom_min":80.37,
"bcom_avg_slope":-0.15,
"bcom_week_momentum":0.62,
"bcom_rsi":40.8,
"articles":["55ff65f3e70","55ff65f3e71","55ff65f3e72","55ff65f3e73","55ff65f3e74"],
"avg_article_sentiment":0.25}
r = requests.put('http://localhost:3000/api/demo/weeksum/55ff65f3e58', data)
The above command requires to attach JSON structured in the request body:
{
"_id": "55ff65f3e58",
"week_start_date":"2015-11-16:T00:00:00.000Z",
"week_end_date":"2015-11-20:T00:00:00.000Z",
"week_index":"^DJC",
"bcom_indices":["55ff65f3e60","55ff65f3e61","55ff65f3e62","55ff65f3e63","55ff65f3e64"],
"bcom_max":81.75,
"bcom_min":80.37,
"bcom_avg_slope":-0.15,
"bcom_week_momentum":0.62,
"bcom_rsi":40.8,
"articles":["55ff65f3e70","55ff65f3e71","55ff65f3e72","55ff65f3e73","55ff65f3e74"],
"avg_article_sentiment":0.25
}
This endpoint updates a specific week summary record.
HTTP Request
PUT http://localhost:3000/api/demo/weeksum/:weeksumId
URL Parameters
| Parameter | Description |
|---|---|
| weeksumId | The ID of the week summary to retrieve |
JSON data format
* - required field
| Field | Description |
|---|---|
| *week_start_date | The starting date of a week |
| *week_end_date | The ending date of a week |
| week_index | A market ticker symbol(default is “^DJC”) |
| bcom_indices | An array of IDs for market quote records |
| *bcom_max | The market index’s highest value within a week |
| *bcom_min | The market index’s lowest value within a week |
| *bcom_avg_slope | The market index’s slope value for the week |
| bcom_week_momentum | The market index’s momentum indicator value for the week |
| bcom_rsi | The market index’s Relative Strength Indicator value for the week |
| articles | An array of IDs for news article records |
| avg_article_sentiment | The average of all the sentiment field values in the array of articles |
Delete a Week Summary
$http.delete('http://localhost:3000/api/demo/weeksum/55ff65f3e58').success(function (response) {
console.log(response);
});
import requests
r = requests.delete('http://localhost:3000/api/demo/weeksum/55ff65f3e58')
This endpoint deletes a specific week summary record.
HTTP Request
DELETE http://localhost:3000/api/demo/weeksum/:weeksumId
URL Parameters
| Parameter | Description |
|---|---|
| weeksumId | The ID of the week summary to delete |
Get News from Alchemy
Get News from Alchemy
$http.get('http://localhost:3000/api/demo/get_news_from_alchemy?startdate=2015-11-16&enddate=2015-11-20').success(function (response) {
console.log(response);
});
import requests
r = requests.get('http://localhost:3000/api/demo/get_news_from_alchemy?startdate=2015-11-16&enddate=2015-11-20')
This endpoint runs the ArticleCrawler.py and ArticleDatabaseClean.py Python scripts to access the Alchemy API to obtain news.
HTTP Request
GET http://localhost:3000/api/demo/get_news_from_alchemy?startdate=YYYY-MM-DD&enddate=YYYY-MM-DD
Get Week Summary
Crawl and Generate Week Summary
$http.get('http://localhost:3000/api/demo/crawl_and_generate_week_summary?startdate=2015-11-16&enddate=2015-11-20').success(function (response) {
console.log(response);
});
import requests
r = requests.get('http://localhost:3000/api/demo/crawl_and_generate_week_summary?startdate=2015-11-16&enddate=2015-11-20')
This endpoint runs the IndexCrawler.py Python script to obtain market quotes from Yahoo Finance Server. After the market quotes have been retrieved, a week summary is generated and stored in the database.
HTTP Request
GET http://localhost:3000/api/demo/crawl_and_generate_week_summary?startdate=YYYY-MM-DD&enddate=YYYY-MM-DD


Taking inspiration from Google's Summer of Code, WSoC is an initiative made by the Open Source Community to guide student developers into developing Open Source Projects.
Allowing them to gain experience while working as a team to develop projects, along with collaboratively working towards an end goal to develop a great project.
Options you are provided with
Front End : Tests your front end skills, based on understanding, and working with HTML and CSS.
Back End : Tests your capability to work with the Back End of a project, and majorly focused on Python, and the Django Framework.
Before you begin
You can attempt both the frontend as well as the backend tasks in case you wish to.
Try to commit every time you feel like you've added a significant feature, this allows you to track your own progress and allows us to see the changes made to the project better.
Do not be worried about the entire process being overwhelming, try to break down the problems into different portions to simplify the problem statement given to you.
Stay well aware of the fact that none of the tasks require you to try to memorize all the commands from BootStrap or the Django framework, and instead going through the documentation is expected of you.


These global functions and variables are available in all scripts.
| Sleep | Suspends the script for a specified number of milliseconds |
| WaitFor | Suspends the script until the specified object is "ready" |
| Random | Returns a pseudo-random number from a specified range |
| SetScriptTimeSlice | The script will be automatically suspended after a specified time interval |
| MakeRGBA | Packs the Red, Green, Blue and Alpha color values into one number |
| GetRValue | Extracts a Red portion of a color |
| GetGValue | Extracts a Green portion of a color |
| GetBValue | Extracts a Blue portion of a color |
| GetAValue | Extracts an Alpha portion of a color |
| ToString | Converts a specified value to a string |
| ToInt | Converts a specified value to an integer number |
| ToBool | Converts a specified value to a boolean (logical) value |
| ToFloat | Converts a specified value to a floating point number |
| Game (read only) | The one and only game object reference |
Suspends the script for a specified number of milliseconds
The Sleep function suspends the script and returns control back to the engine. If you have a script that runs for a very long time, you should periodically call the Sleep function, otherwise the game will seem to be "stuck" until the script finishes.
Suspends the script until the specified object is "ready"
The object is "ready", when it doesn't perform any action (like talking, walking etc.).
Returns a pseudo-random number from a specified range
The script will be automatically suspended after a specified time interval
If you have a script that runs for a very long time, you should return control to the engine from time to time. You can do it either manually by calling the Sleep function, or you can set the time-slice interval. In that case, the engine will suspend the script automatically, perform other tasks, and return to the script again.
Packs the Red, Green, Blue and Alpha color values into one number
All the color components range from 0 to 255. The alpha component specifies the transparency of the resulting color. It ranges from 0 (completely transparent) to 255 (opaque).
Extracts a Red portion of a color
Extracts a Green portion of a color
Extracts a Blue portion of a color
Extracts an Alpha portion of a color
Converts a specified value to a string
Converts a specified value to an integer number
Converts a specified value to a boolean (logical) value
Converts a specified value to a floating point number


|
octargs-doxygen
0.4.0
|
Default implementation of parser literals dictionary (char specialization) More...
#include <dictionary.hpp>
Public Types | |
| using | base_type = dictionary< char > |
| Public Types inherited from oct::args::dictionary< char > | |
| enum | usage_literal |
| using | char_type = char |
| using | string_type = std::basic_string< char_type > |
| using | string_vector_type = std::vector< string_type > |
Static Public Member Functions | |
| static const std::map< usage_literal, string_type > & | get_usage_literals_map () |
Default implementation of parser literals dictionary (char specialization)


| Total Rides | Total Miles | Current Members | Current Volunteers |
|---|---|---|---|
| {{affiliateData.totalRideCount}} | {{affiliateData.totalMiles}} | {{affiliateData.totalActiveMembers}} | {{affiliateData.totalActiveVolunteers}} |
{{serverMessage}}
| Comments | Author | |
|---|---|---|
| {{comment.message}} | {{comment.author}} {{comment.email}} |


I had a dream, which was not all a dream.
The bright sun was extinguish'd, and the stars
Did wander darkling in the eternal space,
Rayless, and pathless, and the icy earth
Swung blind and blackening in the moonless air;
Morn came and went—and came, and brought no day,
And men forgot their passions in the dread
Of this their desolation; and all hearts
Were chill'd into a selfish prayer for light:
And they did live by watchfires—and the thrones,
The palaces of crowned kings—the huts,
The habitations of all things which dwell,
Were burnt for beacons; cities were consum'd,
And men were gather'd round their blazing homes
To look once more into each other's face;
Happy were those who dwelt within the eye
Of the volcanos, and their mountain-torch:
A fearful hope was all the world contain'd;
Forests were set on fire—but hour by hour
They fell and faded—and the crackling trunks
Extinguish'd with a crash—and all was black.
The brows of men by the despairing light
Wore an unearthly aspect, as by fits
contine


--- title: 'Peaks First Climbed in the Himalayas: 1909-2017' author: Ani Ruhil date: '2018-01-27' slug: peaks-first-climbed-in-the-himalayas-1909-2017 categories: - Himalayas - R tags: ["Himalayas", "Mountaineering"] editor_options: chunk_output_type: console ---
I’ve been itching to get back into the himal database and Elizabeth Hawley’s passing jolted me back into action. I wanted to start by looking at the peaks themselves, hoping this would give me a better understanding of the fields before I delve into the expedition data. We have data on 457 peaks, but only 377 are open and only 309 of these have been climbed. Information about the date of the summiting is missing for 3 peaks (we have the year and the month but not the day) so that drops the dataset down to 306 peaks.
There is a lot one could play with but I decided to restrict myself to what may be the natural questions of interest to most – when were peaks first climbed, by whom? What region of the Himalayas has seen the most peaks climbed. What countries have the summiters come from and has this changed over time? What about the seasons when most summits were made? The peak heights themselves, were the smaller peaks necessarily climbed first?
load("./data/cpeaks2.RData")
cpeaks2$Summit.MonthDay = gsub(" ", "/", cpeaks2$PSMTDATE)
library(tidyr)
cpeaks2 = unite(cpeaks2, Summit.Year, c(PYEAR, Summit.MonthDay),
sep = "/", remove = FALSE)
cpeaks2$Summit.Date = as.Date(cpeaks2$Summit.Year, format = "%Y/%b/%d")
How many summits have been made per year, starting with the first record of Alexander Kellas’ ascent of Langpo on September 14, 1909?
library(dplyr)
tab.01 = cpeaks2 %>% select(PKNAME, PYEAR) %>% group_by(PYEAR) %>%
summarise(Climbs = n()) %>% arrange(PYEAR)
library(ggplot2)
ggplot(tab.01, aes(x = as.numeric(PYEAR), y = Climbs)) + geom_bar(stat = "identity") +
labs(x = "Year", y = "Count", title = "Number of Peaks First Summited",
subtitle = "(by Year)", caption = "Source: The Himalayan Database (2017)") +
scale_x_continuous(breaks = seq(1905, 2020, by = 5)) + theme_minimal() +
coord_flip() + geom_rect(aes(xmin = 1914, xmax = 1918, ymin = -Inf,
ymax = Inf), alpha = 0.01, fill = "beige") + geom_rect(aes(xmin = 1939,
xmax = 1945, ymin = -Inf, ymax = Inf), alpha = 0.01, fill = "beige") +
geom_text(aes(x = 1916, y = 10, label = "World War I"), color = "black") +
geom_text(aes(x = 1942, y = 10, label = "World War II"),
color = "black")
What countries have most of the first summiters come from?
df.02 = cpeaks2 %>% select(PKNAME, PYEAR, Summit.Date, country1:country5) %>%
group_by(PKNAME, PYEAR, Summit.Date) %>% gather(key = "CountryNum",
value = "Nationality", 4:8) %>% filter(!is.na(Nationality))
tab.02 = df.02 %>% group_by(Nationality) %>% summarise(Nations = n()) %>%
arrange(-Nations)
ggplot(tab.02) + geom_bar(aes(x = reorder(Nationality, Nations),
y = Nations), stat = "identity") + labs(x = "Nationality",
y = "Count", title = "Number of Summiters", subtitle = "(by Country)",
caption = "Source: The Himalayan Database (2017)") + coord_flip() +
theme_minimal()
Nepalese climbers (127 peaks) lead the way, no pun intended, and for obvious reasons since almost nobody climbs a significant peak without Sherpas since Alexander Kellas recognized their remarkable abilities during his Garhwal/Sikkim treks in 1907-1921. Japanese come a close second (100 peaks), followed by climbers from the United Kingdom (52 peaks), the French (25), the Americans (24), West Germans (19), the Austrians (16), New Zealand and Switzerland (14 peaks each), and then India (10 peaks). The rest of the countries come in with single digits.
If we focus only on the countries with 10 or more peaks summited by their climbers, breaking the preceding distribution down by the year of the ascent reveals some interesting patterns.
tab.03 = df.02 %>% group_by(PYEAR, Nationality) %>% summarise(Number = n())
ggplot(subset(tab.03, Nationality %in% c("India", "New Zealand",
"Switzerland", "UK", "Nepal", "USA", "Austria", "W Germany",
"France", "Japan")), aes(x = as.numeric(PYEAR), y = Number)) +
geom_line(aes(group = Nationality, color = Nationality),
stat = "identity") + facet_wrap(~Nationality, ncol = 2,
scales = "fixed") + theme_minimal() + scale_y_continuous(breaks = c(0:130)) +
theme(legend.position = "none") + labs(x = "Year", y = "Number of peaks",
title = "Number of peaks first summited", subtitle = "(by Year and Country)",
caption = "Source: The Himalayan Database")
For this subset of countries, most climbs occurred after 1950, with UK expeditions operating in the Himalayas, to be expected given their colonial presence in the sub-continent until 1947. Note the number of Japanese summits starting in 1952 but zero activity in the preceding years.
Which climber has made the most first ascents? The top 16, are shown below. These are all climbers who had four or more first ascents; expanding the filter any wider would result in too large a list. For example, 19 climbers have 3 ascents each and 62 have 2 ascents each!
df.03 = cpeaks2 %>% select(PKNAME, PYEAR, summiter1:summiter5) %>%
group_by(PKNAME, PYEAR) %>% gather(key = "SummitNum", value = "Summiter",
3:7) %>% filter(Summiter != "11 members" & Summiter != "15 international scout students and 6 Sherpas" &
Summiter != "2 unknown members" & Summiter != "2 unknown Sherpas") %>%
group_by(Summiter) %>% summarise(Summits = n()) %>% arrange(-Summits)
tab.04 = df.03 %>% filter(Summits >= 4) %>% arrange(-Summits,
Summiter)
library(kableExtra)
knitr::kable(tab.04, booktabs = TRUE, "html") %>% kable_styling(full_width = FALSE)
| Summiter | Summits |
|---|---|
| Paulo Grobel | 7 |
| Tamotsu Ohnishi | 7 |
| Ang Temba Sherpa | 6 |
| Mingma Tsering Sherpa | 5 |
| Pasang Phutar Sherpa (I) | 5 |
| Takehiko Yanagihara | 5 |
| Chuldim Nuru Sherpa | 4 |
| David Gottlieb | 4 |
| Dennis Davis | 4 |
| Dorje Sherpa | 4 |
| Juergen Wellenkamp | 4 |
| Koji Mizutani | 4 |
| Pasang Sherpa | 4 |
| Peter Boultbee | 4 |
| Tashi Sherpa | 4 |
| Wilfred Noyce | 4 |
I am also curious about climbs by region and year, just to see what patterns this exercise reveals.
df.04 = cpeaks2 %>% select(PKNAME, PYEAR, HIMAL, REGION) %>%
group_by(REGION) %>% filter(REGION != "Unclassified")
tab.05 = df.04 %>% group_by(PYEAR, REGION) %>% summarise(Number = n())
ggplot(tab.05, aes(x = as.numeric(PYEAR), y = Number)) + geom_line(aes(group = REGION),
stat = "identity") + facet_wrap(~REGION, ncol = 2, scales = "fixed") +
theme_minimal() + scale_y_continuous(breaks = c(0:130)) +
theme(legend.position = "none") + labs(x = "Year", y = "Number of peaks climbed in a given year",
title = "Number of peaks first summited", subtitle = "(by Year and Region)",
caption = "Source: The Himalayan Database")
So the Kanchenjunga-Janak region saw most of the early climbs but since 2000, most first summits have occurred in the Khumbu-Rolwaling-Makalu region. That suggests a westward sweep since Kangchenjunga-Janak is the eastern-most Himal. I’ll have to look into this because I know there are several excellent works on the history of mountaineering in the Himalayas.
Time to close this post with a quick peak at seasons. Conventionally, Spring (March - May) and Autumn (September - November) are the major climbing seasons, followed by a few who attempt summits in Winter (December - February), with minimal to zero activity in Summer (June - August) when the monsoon sweeps in.
df.05 = cpeaks2 %>% select(PKNAME, PYEAR, PSEASON, HEIGHTM, REGION) %>%
filter(PSEASON != "Unknown")
tab.06 = df.05 %>% group_by(PYEAR, PSEASON) %>% summarise(Number = n())
ggplot(tab.06, aes(x = as.numeric(PYEAR), y = Number, group = PSEASON)) +
geom_line(stat = "identity") + facet_wrap(~PSEASON, ncol = 2,
scales = "fixed") + theme_minimal() + scale_y_continuous(breaks = c(0:130)) +
theme(legend.position = "none") + labs(x = "Year", y = "Number of peaks climbed",
title = "Peaks first summited", subtitle = "(by Year and Season)",
caption = "Source: The Himalayan Database")
Now that is interesting and at odds with what was expected. Digging into these climbs I see a total of 15 first ascents, 10 between 2000 and 2009 and the remaining between 1935 and 1999. The majority (seven) are in Kanjiroba-Far West, and all 15 are for peaks with heights of 7,362 meters or less.
I’ll end by looking at the distribution of peak heights by region, just because I love ggridges
.
library(ggridges)
ggplot(cpeaks2, aes(x = HEIGHTM, y = REGION)) + geom_density_ridges(alpha = 0.2,
aes(fill = REGION)) + theme_minimal() + theme(legend.position = "none") +
labs(x = "Peak Height (in meters)", y = "Region", title = "Peak Heights by Himalayan Region",
subtitle = "(in meters)", caption = "Source: The Himalayan Database (2017)")
This has been an interesting exercise but it isn’t foolproof. I don’t have the dates when a peak was opened to climbers, and some of the notes in the database suggest that some claims of summiting may not necessarily be true. To make matters worse, in a handful of cases the same climber changes nationalities across peaks.1 Finally, when the summit teams included more than one individual from the same country but the team included members from two or more countries, it is difficult to sort out nationalities with accuracy. This leads to an undercount for some nations. Some, if not all, of these issues will hopefully be resolved by employing the expedition database.
For example, Pasang Dawa Lama is listed as Indian for the two 1953 ascents of Ghyuthumba Main and Dudh Kundali – both with Herbert Tichy – but as Nepalese for the famous Cho Oyu ascent of 1954 (again with Herbert Tichy). Similarly, Tenzing Norgay is listed as Indian but the Nepalese government claims him as their own. I suspect this Nepalese/Indian flip-flop occurs mostly with Sherpas and hence for breakouts by nationality the Indian and Nepalese tallies should be recognized as undercounts/overcounts.↩


RT @FakePewResearch: Enjoyment of cranberry sauce: Watching it slide out of the can: 98%; eating it: 2% #TurkeyStats
11/22/2011, 9:05:02 AM
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link--- layout: tweet ---
RT @FakePewResearch: Enjoyment of cranberry sauce: Watching it slide out of the can: 98%; eating it: 2% #TurkeyStats
11/22/2011, 9:05:02 AM
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link


Just printed off "Works on my machine" badge. Now to hang it up... http://www.codinghorror.com/blog/archives/000818.html
10/20/2008, 11:09:02 AM
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link--- layout: tweet ---
Just printed off "Works on my machine" badge. Now to hang it up... http://www.codinghorror.com/blog/archives/000818.html
10/20/2008, 11:09:02 AM
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{% extends "eventapp/base.html" %} {% load i18n %} {% block title %}{% trans "Entry identification code recovery" %} {{ entry.id }}{% endblock %} {% block content %}
{% blocktrans with email|escape as email %}The entry identification code has been sent to {{ email }}.{% endblocktrans %}
{% endblock %}


True story behind Princess Anne and Andrew Parker Bowles' affair in The Crown
Princess Anne and Andrew Parker Bowles romance features in season three of The Crown, but is it actually true?
by Poppy Danby, https://www.mirror.co.uk/authors/poppy-danby/It's a controversial storyline that has both shocked and intrigued fans of The Crown, but how true is the upcoming plot, about the illicit affair between Princess Anne and Andrew Parker Bowles?
Andrew was a British army officer when he first met his wife, Camilla in 1965 at a debutante party.
The pair hit it off immediately and dated on and off for eight years.
However, royal sources claim that not everything was as smooth as it looked on the surface.
It's believed that among other flings, Andrew sparked up an affair with Princess Anne in 1970 - when she was 20 and he was 31.
Speaking on the podcast Royals, royal commentator Angela Mollard said: “It was well-known that Andrew Parker Bowles had multiple lovers.”
She added: “This is really funny. Princess Anne, who we always think is the grumpy one with the bun - she had an affair with Andrew Parker Bowles.”
And there's more evidence of the royal romance, in Tina Brown's biography, The Diana Chronicles.
The journalist wrote: “Andrew adored women. And, like his father, he was a royal groupie.
“One of his earlier diversions was a torrid affair with Princess Anne, who, in her stern way, has always enjoyed a roll in the hay.”
However, the affair sparked something of a royal love triangle – as Camilla wanted to get revenge.
And royal biographer, Penny Junor, has previously suggested that Camilla might never have started a relationship with Prince Charles, if her boyfriend wasn't romancing his sibling.
She said: “[Camilla] was passionately in love with [Andrew] but he was a cad, he was bonking other people, some of her friends.
“There was a lovely story when she was walking through London in the evening and spotted his car parked outside the flat of a very good friend of hers. So she let the tyres down and wrote a message in lipstick on his windscreen.
“So when she was introduced to Charles and he thought she was pretty special...he thought she was a bit of alright and she thought, 'Andrew is at the moment off with Princess Anne, you know her brother, teach Andrew a lesson.' So she had a fling with Charles.”
Camilla and Charles first met at a polo match in Windsor Great Park in 1970 and she allegedly told him:
“My great-grandmother was the mistress of your great-great-grandather. I feel we have something in common.”
But things got complicated and Junor recalled: “It was clear that Princess Anne was in love with Andrew Parker Bowles, Camilla was in love with him too, and Charles was in love with Camilla.”
Things came to a head in 1973 when Andrew finally decided to ditch his womanising ways and marry Camilla.
Princess Anne also married Mark Phillips later that year, and Charles and Diana married eight years later in 1981.
However, the royals' love lives have remained intertwined. As Charles continued to hold a torch for Camilla, with the pair finally settling down and getting wed in 2005.


{% extends 'template.html' %} {% block content %}
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This paper introduces a new socio-inspired algorithm referred to as expectation algorithm (ExA), which is mainly inspired from the society individuals. The ExA modeled the variables of the problems as individuals of a society. The variables select their values by expecting the values of the other variables minimizing the objective function. The performance of the algorithm is validated by solving 50 unconstrained test problems with dimensions up to 30. The solutions were compared with several recent algorithms such as Covariance Matrix Adaptation Evolution Strategy, Artificial Bee Colony, Comprehensive Learning Particle Swarm Optimization, Self-adaptive Differential Evolution Algorithm, Backtracking Search Optimization Algorithm, Ideology Algorithm and Multi-Cohort Intelligence algorithm. The Wilcoxon signed rank test was carried out for the statistical analysis and verification of the performance. The results from this study highlighted that the ExA outperformed most of the other algorithms in terms of function evaluations and computational time. The prominent features of the ExA algorithm along with the limitations are discussed as well.


Theoreticians typically strive for simplicity, looking for the smallest set of primitives on which they can build a general methodology. In computer science theory, for example, numerous languages have been devised that reflect this ideal by providing in a few primitives an expressive power at least equivalent to Turing machines. Practitioners view such efforts the way they view Turing machines: they make an interesting abstraction, but they don't tell us much about how to build systems. To a practitioner, the value of a language lies in its utility, and both excessive simplicity and excessive complexity can interfere with its utility.
A reasonable way to balance the pressures of breadth and simplicity is to support heterogeneity. This implicitly recognizes the usefulness of small, specialized languages and tools. For example, the programming language embodied in a typical spreadsheet program is extremely useful. But it is certainly not general, in that there are many applications for which it is inappropriate. Such tools are therefore most useful if they can be combined with other specialized tools. Each tool is developed independently, making the development effort manageably small. But by embedding the tool in an environment with appropriate interfaces between tools, the utility of the tool is greatly magnified.
Looser integration of diverse tools and capabilities has numerous advantages. Existing, familiar tools do not need to be discarded in favor of a new, all-encompassing tool. This is particularly valuable when expertise with complex tools has built up over time. Moreover, capabilities can come in "bite sized" modules, making them easier to learn, and rendering them more acceptable to a conservative clientele. For commercial software, tools from different vendors can be mixed, drawing from the strengths of each. This has the side effect that competition among vendors is enhanced because fewer customers are "locked in". This results in better tools at lower prices. Finally, accepted environments supporting heterogeneity means that innovative tools with specialized capabilities have a market. They do not need to be all-encompassing to be salable. So innovation is encouraged. This concept has been proven with the notion of "plug-ins" available for a number of commercial software platforms.
There are also significant disadvantages. User interfaces are likely to be different with different tools, making them more difficult to learn. And integration may not be complete.
The Tycho project is trying to maximize the advantages and minimize (though probably not eliminate) the disadvantages. Our group has a history of embracing heterogeneity in this manner. The Ptolemy software system, which has been under development at U.C. Berkeley since 1990, addresses the problem of mixing "models of computation", or semantic models, in system-level design of electronic systems. The Tycho system addresses the problem of mixing syntactic models. For example, while Ptolemy can support mixtures of discrete-event and dataflow modeling, Tycho can support mixtures of textual and graphical syntaxes.
In both Ptolemy and Tycho, the goals are accomplished through a strict and carefully designed object-oriented software architecture. A key principal is that of "information hiding". Objects should be able to interact with one another without knowing about either their internal semantic or syntactic models.
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Next: Release Notes -
Tycho Home Page


Dijit I18N Demo (locale= dir=)
Use the tree to select a language or a language/country combo; the page will reload in the specified locale. Note that tree is also rerendered using the specified language for top level tree items. Arabic and Hebrew display right-to-left so be sure to try those.
This won't work perfectly unless you've done a full build of dojo.cldr, see the CLDR readme.
Country data courtesy of Geonames.


Before Change
self.data_loader = data_loader
if size is not None and (type(size) is not int or size < 1):
raise ValueError("The total size of the dataset must be an integer greater than zero.")
self.size = size
if type(batch_size) is not int or batch_size < 1:
raise ValueError("The batch size must be an integer greater than zero.")
self.batch_size = batch_size
def get_batch(self):
Provide the next batch for training in the form of a tuple `(x, y)`. The generator should loop over the data
After Change
Wrapper class on top of the MXNet/Gluon native data loader :class:`mxnet.gluon.data.DataLoader`.
def __init__(self, data_loader, size, batch_size):
Create a data generator wrapper on top of an MXNet :class:`DataLoader`.
:param data_loader:
:type data_loader: `mxnet.gluon.data.DataLoader`
:param size: Total size of the dataset.
:type size: int
:param batch_size: Size of the minibatches.
:type batch_size: int
super(MXDataGenerator, self).__init__(size=size, batch_size=batch_size)
from mxnet.gluon.data import DataLoader
if not isinstance(data_loader, DataLoader):


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Techspot is a software company which gives you quality products
We have been an established software company in the mainstream IT industry since 2001
Established in 2001, Techspot is a NYSE listed global consulting and IT services company with more thThe Company offers various services, such as consulting services, business services, technology services and outsourcing services. The Company's consulting services include enterprise applications, digital transformation, insights and analytics, and change and learning.
Business services include Data Analytics, Digital and Oracle. Its technology services include application management; cloud, infrastructure and security; engineering services; enterprise mobility; Internet of things (IoT) and testing. Its outsourcing services include application outsourcing, business process outsourcing, customer service, finance and accounting, human resources, and sourcing and procurement.subsidiaries include Infosys BPO Limited, an 209,000 employees. From a capital of US$250, we have grown to become a US$11.12 billion (LTM Q1 FY19 revenues) company with a market capitalization of approximately US$ 42.4 billion.


--- layout: default title: Contact description: The Chi Hack Night is organized by Derek Eder, Christopher Whitaker and (formerly) Juan-Pablo Velez. ---
The Chi Hack Night is organized by Derek Eder, Christopher Whitaker and (formerly) Juan-Pablo Velez.
Derek is a web developer with a passion for open government, open source and open data. He is Founder and Parter at DataMade, and Co-Founder of Open City.
Christopher runs the civic technology consulting firm CivicWhitaker, consults for the Smart Chicago Collaborative and is the Midwest Brigade Coordinator for Code for Ameria.
Juan-Pablo is a data analyst and civic technologist. He is a leader and collaborator at Open City and Co-Founder of the Data Science for Social Good Summer Fellowship.
Have a talk you'd like to give at the Chi Hack Night? See our speaker guidelines.


The LearningStudio Eventing Library
LearningStudio's Eventing capabilities reduce the need to “poll” for status changes by pushing them directly to your application. This library handles the details of subscribing to messages, managing subscriptions, and verifying authenticity of received messages. This user guide describes the various functionality and use cases, but you can also skip straight to the auto-generated documentation if you prefer.
Pre-Requisites
API Documentation
You should be sure to read the API documentation and background information for LearningStudio Eventing while using this library. Here are some key places to start:
Library Usage
Setup
To begin, import the classes and configure the client that you'll be using.
import com.pearson.pdn.learningstudio.eventing.*;
Configuration config = new Configuration();
config.setPrincipalId("{principalId}"); // application id
config.setPrincipalSecret("{principalSecret}"); // application secret
config.setEventingServer("{eventingServer}"); // url of eventing server
// client to use in following examples
EventingClient eventing = new EventingClient(config);
Create a subscription
Next, create a subscription to start receiving messages at a specified endpoint.
Subscription sub = new Subscription();
sub.setCallbackUrl("{callbackUrl}"); // url to receive message
// each of these filters are optional, but one of them must exist
sub.setMessageType("{messageType}"); // message type (most useful)
sub.setSystem("{system}"); // publishing system id (rarely used)
sub.setSubSystem("{subSystem}"); // publishing subsystem id (rarely used)
sub.setClient("{client}"); // campus id (useful for vendors)
sub.setClientString("{clientString}"); // campus string (useful for vendors)
// Include additional filters (optional)
Map tags = new HashMap();
tags.put("{key}","{value}"); // Applicable {key} values: UserId, CourseId
sub.setTags(tags);
try {
eventing.subscribe(sub);
// these properties were populated
sub.getId(); // unique identifier for subscription
sub.getCreateDate(); // creation date of subscription
sub.getPrincipalId(); // principal that created subscription
}
catch(EventingException e) {
// subscribe failed
}
Delete a subscription
Your subscriptions can be deleted with the subscription identifier.
try {
eventing.unsubscribe("{subscriptionId}");
}
catch(EventingException e) {
// unsubscribe failed
}
Lookup all subscriptions
All of your existing subscriptions can be retrieved.
try {
List subs = eventing.getSubscriptions();
// might be empty
}
catch(EventingException e) {
// subscriptions lookup failed
}
Lookup a specific subscription
A specific subscription can be retrieved with the subscription identifier.
try {
Subscription sub = eventing.getSubscription("{subscriptionId}");
// subscription found
}
catch(EventingException e) {
// subscription lookup failed
}
Verifiying authenticity of delivered messages
Messages delivered to a listening endpoint can be trusted after verifying authenticity.
Delivery delivery = new Delivery();
// populate the following with received parameters of same name
delivery.setAttemptId("{attemptId}"); // unique delivery id (optional)
delivery.setAuthorization("{authorization}"); // auth signature
delivery.setMessageId("{messageId}"); // message id
delivery.setMessageType("{messageType}"); // event type
delivery.setPayloadContentType("{payloadContentType}"); // application/json
delivery.setPayload("{payload}"); // json payload
try {
eventing.verifyAuthenticity(delivery);
// authorized
}
catch(EventingException e) {
// not authorized
}


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