TABLE OF CONTENT
Well, probably you are not, but let me grumble a little bit anyway.
Mockingbird knows how to mock.
I’ve been working with various code bases throughout of my career, and there is one pattern which I see rather often. As you may have already guessed it’s the unit-tests and mocking I’m going to talk about here. To give it a nice catchy start, I’d claim here, that mocks should be used when you have to, but not when you can.

All the examples are going to be made up, but I hope you’d get the point.
So, let’s start with the typical Greeter
example. This is a “Hello, world” of Unit Testing. In one way or another, this sample gets repeated in all the articles, posts and books dedicated to unit-tests and mocking frameworks.
IGreeter{
string Greet(string name, string title);
}
public class Greeter: IGreeter{
public string Greet(string name, string title){
return "Hello," + title + " " + name;
}
}
public class Client{
private IGreeter _greeter;
public Client(IGreeter greeter){
_greeter = greeter;
}
public string FormatPageHeader(string name, string title){
return "<h>" + _greeter(name, title) + "</h>";
}
}
public class ClientTests {
pubic void Test(){
var mock = new Mock<IGreeter>();
mock.Setup(greeter => greeter.Greet("John", "Mr.")).Returns("Hello, Mr. John");
var result = new Client(mock.Object).FormatPageHeader("John", "Mr.");
Assert.AreEquals(result, "<h>Hello, Mr. John</h>"); //All good here, what does it test, tho?
}
}
So far so good. Tests are green. The Greeter interface isn’t perfect, though. Two strings
as a parameter? So easy to mess up, isn’t it? Most probably you have a method like that in your project.
Ok then, imagine that you decided to make this method less error prone. You don’t have much time for a proper refactoring because you have a feature to work on. Let’s just swap the parameters. It’s much more natural for a human to put the title before the name. Depends on the culture, I know.
(“John”, “Mr.”) is more awkward compared to (“Mr.”, “John”) and (“Dr.”, “Smith”).
So, we’ll end up with the greeter like that:
//Version 2
public class Greeter2 : IGreeter{
public string Greet(string title, string name){
return "Hello, " + title + " " + name;
}
}
IGreeter{
string Greet(string title, string name);
}
add.
, commit
, push
. Our beloved build server will pick the changes up, will run the tests and will fail of course. We forgot to update the test for the IGreeter
implementation. Once they fixed we’re good, aren’t we?
Not really, remember the Client
class? Now it’s incorrect, we still pass the title and the name in the old order, even though our test claims that everything is fine.
Here is the paradox, we introduced mocking so that we can test our class in isolation, but we had to put some code to make that mock return something. It’s still the logic, isn’t it? Now IGreeter
has at least two implementations. Most probably in your codebase, you have tens of implementations for the mocked class. Just because it’s a very much reused dependency, and you have to mock it all over again.
We can improve that test, tune it, but the only way we can make it fail is to repeat the Greeter
logic in our mock set up. But wait a minute, if we have the same implementation, why don’t we reuse the existing code?
The more complex your mocked object is the more complex your mock setup becomes.  Just like the ClientTest
above. The only positive outcome is the extra 20c which your company pays to Amazon for that CPU time you waisted on the build server.
It’s not rare to have more than one dependency. I can imagine that our Client
could use some IHmlRenderer
which would consume the IGreeter
result. You would mock that one too, right?
Sweet as. You now have a test which verifies how two mocks are integrated with each other. How does that prove your code correctness, thought? I don’t know.
I’m going to step back now, and talk about it from another point of view. What does the typical system look like?  If you look at type dependency diagram you would see a group of clusters, where each cluster is a somewhat tightly coupled set of classes. This is how we manage complexity, we break down one large class into small pieces, but those pieces would never be used in isolation. Each of them would be responsible for a little piece of work. You would write a separate test suite for them, mock out all the dependencies (they all belong to the same cluster).
How is that different to the attempt to test a private method?
That little convenience wrapper for the standard library class is nothing more than an implementation detail.
But integration tests are slow… #
That would be an expected note. If we come back to our ClientTest
. What performs better, the test with mock, or the test with the concrete Greeter implementation? I guess the answer is obvious. We shouldn’t forget that that test doesn’t prove anything. It is slower, it allocates more, and it’s wrong. I’d say it’s harmful.
So you’re going to send out those emails every time you test? #
Ok, that is a good question. Remember I said that we should mock when we have to, not when we can? That’s exactly the right situation for the test isolation.
 I saw systems like that, it’s a nightmare to maintain. Manually setting up the dependency of the dependency is really not the way to go. It’s hard, and everyone would avoid writing a new test. But that’s an easy task to solve.
How do we build a dependency tree in the runtime? I hope you’re using the DI framework which wires up interfaces and implementations together, it knows how to create a new dependency, it knows when and how to rebuild a dependency tree. If you’re building a web service you should reset the state between requests, so keep as many dependencies request scoped as it’s possible.
The same pattern is applicable for your test suite. Everything is request scoped and stateless already, so treat each test as a ‘request’ and let the DI container to build the system under test for you.
Of course, you would have to set up the DI container differently.
Module (or cluster) level test would build the dependency tree for that module but would mock out the rest of the world. Like no HTTP calls, no DB, no emails, you’ve got the point.
At this point, you would realize that you don’t need that test which verifies that your utility class can split the string, but you would be sure that your MortgageCalculator
does the job. After all, that is your business rule, and that is the feature you’re building. Unless you are the low-level framework developer of course. Most of us are not, though.
Once you’ve got all the clusters well tested, all the interfaces established you may try to break it down a little bit. Or you may want to leave it as is, it’s up to you.
Imagine you want to extract some logic into the separate class. Now your system has a new dependency, but that logic has already been tested. You don’t need to set up a new mock, you don’t need to repeat the logic of the extracted class in your test set up, you don’t need to update the test to instantiate the tested class. It’s just there, and your safety net still works. If you extract and modify the logic, you would break the test. Do you see the beauty? Extracting the class and introducing a new dependency is as easy as moving the logic into a private method.
Summary #
The approach above would give you the following benefits:
- Fewer meaningful, useless and harmful tests
- No need to maintain duplicated implementation (mock set ups)
- Feature driven tests, tests that verify that the cluster does the job
- An easy to refactor code base
- You can improve module level access (no need to make the class public just for the testing)
From my experience tests, based on that cluster approach, are much more reliable. When they break, they actually mean that the system is dysfunctional, when they pass you can be sure that you did not break the logic.
We still have to run integration tests to be sure that the third party integrations are working, that the system’s runtime configuration is valid.
I am more than open to any criticism, feel free to tear that post apart.


The refresh anchor shows an in progress status for refreshing items with an AJAX request. Click to show its in progress status and open an example of a confirm dialog.
Dialogs are keyboard accessible. They also support autohide and modal properties. You can also override the dialog class and supply your own implementation of the dialog.


The Open Application Standard Platform for Java (OASP4j) provides a standardized architecture blueprint, an open best-of-breed technology stack as well as industry proven best practices and code conventions for a cloud ready SPRING based server.
The OASP4J offers you a holistic documentation for building your business application with Java and Spring.
The OASP4J fills the gaps between JEE standards and open-source frameworks to provide a production-ready solution. As we are utilizing existing standards and open-source solutions, only the missing links but little code will be found here.
The OASP4J provides a sample application that shows the usage of the technology stack, its modules and the best-practices in action. See how everything works together including integration with the client of the OASP4JS.


Worked on Zomato’s UI Kit and Payments UI kit written in React-Redux and TypeScript. Took care of it’s development using Docker and gulp tasks for transpiling the app.
Developed Zomato’s Alexa skill app running on Node server. It became the most requested skill on Alexa within the first week of it’s launch.
Worked on project PWA, Structured the api output in an optimised data structure to reduce the data flow. Increasing 20% of user traffic.
Part of AMP project, which aimed to reduce page load time for slower connections using Google’s AMP project. This reduced page load time from 3s to 1s.

Used Qt and Cantorlibs to build scripting capability inside LabPlot. CantorLibs was used to execute input by user and get the output.
Wrote parser for Python2, Python3, maxima and R in C++, which parse them to Columns of LabPlot.
Parser supported basic data structure like dictionary, arrays, lists etc which were translated into columns for LabPlot allowing dynamic plotting of graphs.
KNetWalk is a KDE Game that was written in Qt4 and KDELibs4. I ported the game to Qt5 and KDE Frameworks 5 to have the compatibility to run on latest os shipping Qt5. This change attracted a lot of users who thanked me for the much needed port for KNetWalk.
DTU startup, selected as their premier web developer for developing and managing their online e-commerce. Worked on designing database keeping in mind future goals and scalability. Built single dynamic dashboard with access layer to change the products and the information on the main site.


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java.lang.Object | +--marf.Classification.Classification
Class Classification
Abstract Classification Module
$Header: /cvsroot/marf/marf/src/marf/Classification/Classification.java,v <IP_ADDRESS> 2003/02/16 18:08:53 mokhov Exp $
| Field Summary | |
private int |
iDumpMode
Indicates in which format dump training data. |
protected FeatureExtraction |
oFeatureExtraction
Reference to the FeatureExtraction object |
protected Result |
oResult
Classification Result |
protected TrainingSet |
oTrainingSet
TrainingSet Container |
| Constructor Summary | |
protected |
Classification(FeatureExtraction poFeatureExtraction)
Generic Classification Constructor |
| Method Summary | |
abstract boolean |
classify()
Generic classification routine. |
void |
dump()
Generic implementation of dump() for TrainingSet |
Result |
getResult()
Retrieves the classification result |
private java.lang.String |
getTrainingSetFilename()
Constructs global cluster finlename for the TrainingSet. |
private void |
loadTrainingSet()
Loads TrainingSet from a file. |
void |
restore()
Generic implementation of restore() for TrainingSet |
private void |
saveTrainingSet()
Saves TrainingSet to a file. |
boolean |
train()
Generic training routine for building/updating mean vectors in the training set. |
| Methods inherited from class java.lang.Object |
|
| Field Detail |
protected FeatureExtraction oFeatureExtraction
protected TrainingSet oTrainingSet
protected Result oResult
private int iDumpMode
Can either be TrainingSet.DUMP_GZIP_BINARY
or TrainingSet.DUMP_CSV_TEXT
, with the former being the default.
| Constructor Detail |
protected Classification(FeatureExtraction poFeatureExtraction)
poFeatureExtraction
- FeatureExtraction module reference| Method Detail |
public abstract boolean classify() throws ClassificationException
true
if classification was successful; false
otherwiseClassificationException
- public boolean train() throws ClassificationException
super.train();
true
if training successful (i.e. mean vector was updated), false
otherwiseClassificationException
- public void dump() throws java.io.IOException
dump
in interface StorageManager
java.io.IOException
- public void restore() throws java.io.IOException
restore
in interface StorageManager
java.io.IOException
- private final void saveTrainingSet() throws java.io.IOException
java.io.IOException
- private final void loadTrainingSet() throws java.io.IOException
restore()
java.io.IOException
- public final Result getResult()
private final java.lang.String getTrainingSetFilename()
Filename constructed using fully-qualified class of either TrainingSet or a classifier name with global clustering info such as preprocessing and feature extraction methods, so that ony that cluster can be reloaded after.
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Text formatting information.
More...
#include <formatting.hpp>
Text formatting information.
◆ operator=()
◆ operator==()
| bool rtf::Formatting::operator== |
( |
const Formatting & |
obj | ) |
const |
◆ m_backgroundColor
| Color rtf::Formatting::m_backgroundColor |
◆ m_font
| Font rtf::Formatting::m_font |
Font object (name, family)
◆ m_fontColor
| Color rtf::Formatting::m_fontColor |
◆ m_fontSize
| int rtf::Formatting::m_fontSize = 0 |
◆ m_horizontalAlign
| std::string rtf::Formatting::m_horizontalAlign |
Paragraph horizontal alignment
◆ m_isBold
| bool rtf::Formatting::m_isBold = false |
If text is bold
| Value | Description |
| False | Normal text |
| True | Characters are bold |
◆ m_isItalic
| bool rtf::Formatting::m_isItalic = false |
If text is italic
| Value | Description |
| False | Normal text |
| True | Characters are italic |
◆ m_isOutlined
| bool rtf::Formatting::m_isOutlined = false |
If text is outlined (Macintosh only)
| Value | Description |
| False | Normal text |
| True | Characters are outlined |
◆ m_isStruckOut
| bool rtf::Formatting::m_isStruckOut = false |
If text is struck out
| Value | Description |
| False | Normal text |
| True | Characters are struck out |
◆ m_isSub
| bool rtf::Formatting::m_isSub = false |
If text is subscript
| Value | Description |
| False | Normal text |
| True | Characters are subscript |
◆ m_isSup
| bool rtf::Formatting::m_isSup = false |
If text is superscript
| Value | Description |
| False | Normal text |
| True | Characters superscript |
◆ m_isUnderlined
| bool rtf::Formatting::m_isUnderlined = false |
If text is underlined
| Value | Description |
| False | Normal text |
| True | Characters are underlined |
◆ m_listLevel
| int rtf::Formatting::m_listLevel = 0 |
◆ m_parInTable
| bool rtf::Formatting::m_parInTable = false |
◆ m_verticalAlign
| std::string rtf::Formatting::m_verticalAlign |
Paragraph vertical alignment
The documentation for this class was generated from the following files:


Weaknesses
Thunderjawn It is a large Combat Class machine, one of the two largest machines of this class. Its size, power, and array of heavily damaging weapons make it one of the most feared of all machines.


Provides Some of the most important evaluation measures for evaluating a model. Just by giving the real and predicted class, measures such as accuracy, sensitivity, specificity, ppv, npv, fmeasure, mcc and ... will be returned.
| Version: | 1.1.0 |
| Published: | 2016-07-27 |
| Author: | Babak Khorsand [aut, cre], Javad Zahiri [ths], Abdorreza Savadi [ths] |
| Maintainer: | Babak Khorsand <khorsand at yahoo.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL] |
| NeedsCompilation: | no |
| CRAN checks: | EvaluationMeasures results |
| Reference manual: | EvaluationMeasures.pdf |
| Package source: | EvaluationMeasures_1.1.0.tar.gz |
| Windows binaries: | r-devel: EvaluationMeasures_1.1.0.zip, r-release: EvaluationMeasures_1.1.0.zip, r-oldrel: EvaluationMeasures_1.1.0.zip |
| OS X binaries: | r-release: EvaluationMeasures_1.1.0.tgz, r-oldrel: EvaluationMeasures_1.1.0.tgz |
Please use the canonical form https://CRAN.R-project.org/package=EvaluationMeasures to link to this page.


| Id | Type of Meeting | Meeting Title | Category | Place | Time | Duration |
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| 1 | Urgent | School Dean Nomination | Educational | PWR | Tuesday, 10:00 AM | 1 Hour |
| 2 | Urgent | School Dean Nomination | Educational | PWR | Tuesday, 10:00 AM | 1 Hour |
| 3 | Urgent | School Dean Nomination | Educational | PWR | Tuesday, 10:00 AM | 1 Hour |
| 4 | Urgent | School Dean Nomination | Educational | PWR | Tuesday, 10:00 AM | 1 Hour |


default implementation of the error callback More...
#include <PxDefaultErrorCallback.h>
Public Member Functions | |
| PxDefaultErrorCallback () | |
| ~PxDefaultErrorCallback () | |
| virtual void | reportError (PxErrorCode::Enum code, const char *message, const char *file, int line) |
| Reports an error code. More... | |
| Public Member Functions inherited from PxErrorCallback | |
| virtual | ~PxErrorCallback () |
default implementation of the error callback
This class is provided in order to enable the SDK to be started with the minimum of user code. Typically an application will use its own error callback, and log the error to file or otherwise make it visible. Warnings and error messages from the SDK are usually indicative that changes are required in order for PhysX to function correctly, and should not be ignored.
| PxDefaultErrorCallback::PxDefaultErrorCallback | ( | ) |
| PxDefaultErrorCallback::~PxDefaultErrorCallback | ( | ) |
|
virtual |
Reports an error code.
| code | Error code, see PxErrorCode |
| message | Message to display. |
| file | File error occured in. |
| line | Line number error occured on. |
Implements PxErrorCallback.


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class DFG extends AnyRef
Class describing DFGs in Pillars.
- Alphabetic
- By Inheritance
- DFG
- AnyRef
- Any
- Hide All
- Show All
- Public
- All
Instance Constructors
-
new
DFG(name: String)
create a DFG
create a DFG
- name
the name of this DFG
Value Members
-
final
def
!=(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
final
def
##(): Int
- Definition Classes
- AnyRef → Any
-
final
def
==(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
var
II: Int
The the targeted II.
-
def
addOpNode(node: OpNode): Unit
Add an opNode into DFG.
Add an opNode into DFG.
- node
the opNode being added
-
def
addValNode(node: ValNode): Unit
Add a valNode into DFG.
Add a valNode into DFG.
- node
the valNode being added
-
def
applyOp(name: String): OpNode
Get an opNode from DFG using its name.
Get an opNode from DFG using its name.
- name
the name of opNode
-
def
applyVal(name: String): ValNode
Get a valNode from DFG using its name.
Get a valNode from DFG using its name.
- name
the name of valNode
-
final
def
asInstanceOf[T0]: T0
- Definition Classes
- Any
-
def
checkPrimaryInput(): Unit
Check whether a node has primary input.
-
def
clone(): AnyRef
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws( ... ) @native() @HotSpotIntrinsicCandidate()
-
final
def
eq(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
-
def
equals(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
var
func2regMap: Map[Integer, List[Integer]]
A map between the ID of the module corresponding a funcNode and the IDs of its fan-out registers.
-
var
funcDirect2funcMap: Map[Integer, List[List[Integer]]]
A map between the ID of the module corresponding a funcNode and the IDs of its fan-out modules corresponding funcNodes.
-
final
def
getClass(): Class[_]
- Definition Classes
- AnyRef → Any
- Annotations
- @native() @HotSpotIntrinsicCandidate()
-
def
getOpSize(): Int
Get the num of opNodes.
-
def
getValSize(): Int
Get the num of valNodes.
-
def
hashCode(): Int
- Definition Classes
- AnyRef → Any
- Annotations
- @native() @HotSpotIntrinsicCandidate()
-
final
def
isInstanceOf[T0]: Boolean
- Definition Classes
- Any
-
def
loadTXT(filename: String, targetedII: Int = 1): Unit
Load a DFG from a TXT file, not used in real process.
Load a DFG from a TXT file, not used in real process.
- filename
the file name of TXT file
- targetedII
the targeted II
- var name: String
-
final
def
ne(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
-
final
def
notify(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native() @HotSpotIntrinsicCandidate()
-
final
def
notifyAll(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native() @HotSpotIntrinsicCandidate()
-
var
opNodes: ArrayBuffer[OpNode]
Operators and primary I/O in a DFG.
-
var
opNodesMap: Map[String, Int]
A map between opNodes and a identification number.
-
def
printDFG(): Unit
Debug Function to print DFG into screen.
-
var
reg2funcMap: Map[Integer, List[List[Integer]]]
A map between the ID of a register and the IDs of its fan-out modules corresponding funcNodes.
-
var
regConnect: Map[Integer, List[Integer]]
A map between the ID of a register and the IDs of its fan-out registers.
-
var
regNum: Int
The number of registers.
-
final
def
synchronized[T0](arg0: ⇒ T0): T0
- Definition Classes
- AnyRef
-
var
synthesizable: Boolean
A parameter indicating whether this DFG is synthesizable.
-
def
toString(): String
- Definition Classes
- AnyRef → Any
-
def
updateSchedule(filename: String, latencyMap: Map[String, Integer] = null, skewMap: Map[String, Integer] = null, outFilename: String = null): Unit
Update a unscheduled result TXT file with latency and skew.
Update a unscheduled result TXT file with latency and skew.
- filename
the file name of result TXT file
- latencyMap
a map between the name of a opNode and its latency
- skewMap
a map between the name of a opNode and its skew
- outFilename
the file name of output file
-
var
valNodes: ArrayBuffer[ValNode]
Output values of operators in a DFG.
-
var
valNodesMap: Map[String, Int]
A map between valNodes and a identification number.
-
final
def
wait(arg0: Long, arg1: Int): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
final
def
wait(arg0: Long): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... ) @native()
-
final
def
wait(): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
Deprecated Value Members
-
def
finalize(): Unit
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws( classOf[java.lang.Throwable] ) @Deprecated @deprecated
- Deprecated
(Since version ) see corresponding Javadoc for more information.


--- title: Dplyr Functionality with Data.Table author: Amy Linehan authors: ["alinehan"] date: '2020-04-10' slug: dplyr-functionality-with-data-table categories: - R tags: - R - dplyr - data.table subtitle: '' summary: 'Using data.table to perform common dplyr data cleaning and analyses' lastmod: '2020-04-10T13:38:07-07:00' featured: no image: caption: '' focal_point: '' preview_only: no projects: [] draft: True ---
For the first year of my career as a consultant, my manager wrote his scripts in data.table and I somehow avoided ever having to really understand what the heck he was doing. It took a full year, but eventually I started understanding and using data.table in my scripts. Now, don’t get me wrong I’m definitely still a tidyverse girl and prefer ctrl+alt+m to brackets any day. But, I can’t deny that there are situations where using data table is faster, easier and cleaner.
The goal of this post is to consolidate all the common dplyr code I rely on regularly and the corresponding translation in data.table. We’ll do another post that dives more into data.table and the pros/cons/tricks to it, but this is more of just a translator between popular dplyr functionalities and data.table. For this, I’ll just be using the standard mtcars dataset.
cars <- mtcars %>% tibble::rownames_to_column(var="car_type")
#dplyr
data_dplyr <- as.data.frame(cars) %>%
mutate(eco_flg = mpg > 20 )
# data.table
data_dt <- as.data.table(cars)
data_dt[,eco_flg:=F]
data_dt <- data_dt[mpg>20,eco_flg:=T]
# dplyr
data_dplyr %>% group_by(eco_flg) %>% count()
## # A tibble: 2 x 2
## # Groups: eco_flg [2]
## eco_flg n
## <lgl> <int>
## 1 FALSE 18
## 2 TRUE 14
# data.table
data_dt[,.N,by=eco_flg]
## eco_flg N
## 1: TRUE 14
## 2: FALSE 18
# dplyr
data_dplyr %>% group_by(eco_flg, gear) %>% summarise(avg_hp = mean(hp))
## # A tibble: 6 x 3
## # Groups: eco_flg [2]
## eco_flg gear avg_hp
## <lgl> <dbl> <dbl>
## 1 FALSE 3 187.
## 2 FALSE 4 123
## 3 FALSE 5 258
## 4 TRUE 3 104.
## 5 TRUE 4 82.8
## 6 TRUE 5 102
# data.table
data_dt[,list(avg_hp= mean(hp)),by=list(eco_flg,gear)]
## eco_flg gear avg_hp
## 1: TRUE 4 82.8000
## 2: TRUE 3 103.5000
## 3: FALSE 3 187.3077
## 4: FALSE 4 123.0000
## 5: TRUE 5 102.0000
## 6: FALSE 5 258.0000
In this example, let’s build some sample data to join onto mtcars. Let’s say we want to pull data for our friends’ cars:
friends <- c("Nellie", "Drake", "JP", "Lily")
cars <- c("Ferrari Dino", "Honda Civic", "Fiat X1-9", "Smart Car")
friends_cars <- data.frame(friends,cars) %>%
mutate_at(vars(friends, cars),
funs(as.character))
## Warning: funs() is soft deprecated as of dplyr 0.8.0
## Please use a list of either functions or lambdas:
##
## # Simple named list:
## list(mean = mean, median = median)
##
## # Auto named with `tibble::lst()`:
## tibble::lst(mean, median)
##
## # Using lambdas
## list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
## This warning is displayed once per session.
head(friends_cars)
## friends cars
## 1 Nellie Ferrari Dino
## 2 Drake Honda Civic
## 3 JP Fiat X1-9
## 4 Lily Smart Car
First, let’s join mtcars onto friends using dplyr:
friends_cars %>% left_join(data_dplyr, by = c("cars"= "car_type"))
## friends cars mpg cyl disp hp drat wt qsec vs am gear carb
## 1 Nellie Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## 2 Drake Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## 3 JP Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## 4 Lily Smart Car NA NA NA NA NA NA NA NA NA NA NA
## eco_flg
## 1 FALSE
## 2 TRUE
## 3 TRUE
## 4 NA
Now, let’s try the same join with data.table:
friends_cars <- as.data.table(friends_cars)
friends_cars[]
## friends cars
## 1: Nellie Ferrari Dino
## 2: Drake Honda Civic
## 3: JP Fiat X1-9
## 4: Lily Smart Car


Returns as of .
percent of precincts reporting
Turnout: %
| Candidate | Votes | Percent | |
|---|---|---|---|
| Republican Rick Scott | % | ||
| Democrat Charlie Crist | % | ||
| Libertarian Adrian Wyllie | % |
Map key
Counties are shaded by how many votes a candidate is ahead.
The darker the color, the bigger the lead. This highlights counties that have the biggest effect on the race.
Hover over a county to see its results.
Scott leading
Crist leading
Wyllie leading


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move this script to profiler


Lectures
Slides from lecture are posted below.
| # | Date | Link |
|---|---|---|
| 1 | Aug 28, 2018 | Lecture 1 PPT |
| 2 | Aug 30, 2018 | |
| 3 | Sept 04, 2018 | Lecture 3 (Project 1) |
| 4 | Sept 06, 2018 | |
| 5 | Sept 11, 2018 | Correlation and Convolution |
| 6 | Sept 18, 2018 | Edge Detection |
| 7 | Sept 20, 2018 | SIFT Features |
| 6 | Sept 25, 2018 | Linear Review 1, 2 |
| 7 | Sept 27, 2018 | Projective Geometry |
| 6 | Oct 03, 2018 | - |
| 11 | Oct 05, 2018 | Project 2: Pano. Stitching |
| 6 | Oct 09, 2018 | Camera Calibration |
| 11 | Oct 11, 2018 |


Vapaée DEX is a decentralized exchange that allows you specify the price and amount of the token you want to buy or sell and create an order for the ledger.
The main currency is DEX and can be obtained by Staking TLOS in the Telos Wrapper
Vapaée DEX also offers instant token-swap convertion pools of token pairs, using DEX token as main currency.
The Swap is sync with its corresponding market's order book and it is smart enough to automatically take an order when the price is better than the swapping price.
Vapaée DEX offers several ways for TLOS farming. You can use your TLOS as collateral to take a loan in DEX for the same amount, one DEX for each TLOS you deposit. But when you come back to cancel your loan, you will get back more than the initial amount of TLOS because we will put the in the REX system for you.
The DEX token is the main currency of the DEX.
The old version of Vapaée DEX will be shut down on Q1 2022 and with that, all user orders will be canceled and the funds, along with the remaining deposits will be returned to the owners using an auto-withdrawal process.
join the telegram group to stay updated with the latest news about this auto-withdrawal.
The vapaeetokens contract will drop all DEX functionality and keep only standard token functionality. That means all tokens will keep working as normal.
Later in the road map new great features will be added to the vapaeetokens contract and all tokens will be able to use them.
Kwallet App is the best wallet for Telos blockchain (and any other EOS.IO compatible network). It has its own stable coin EuroT and is completely integrated with Vapaée DEX.
This product is a Koinonos and Vapaée collaboration work.
Check the website https://koinonos.io/
Kwallet App integrates the instant token-swap convertion feature of Vapaée DEX for every token on Telos blockchain.


| Home | Trees | Indices | Help |
|---|
|
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The central point of the application. It handles the main run loop, Scene transitions, and propagating redraw events to the Scene's children. It is also the owner of the GestureDispatch, ActionManager, Scheduler, and the GTKInterface.
Normally only one Director should exist per application.
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bool
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GTKLayout (or None )
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| Scene methods. | |||
Scene
(or None )
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| Run methods. | |||
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float
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showingFPS Whether or not to display the framerate. |
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size The size of the main application window. |
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gestureDispatch The application's GestureDispatch. |
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actionManager The application's ActionManager. |
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scheduler The application's Scheduler. |
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backgroundColor The application's background color. |
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gtkLayout The application's main gtk.Layout. |
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| Scene methods. | |||
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runningScene The currently-running scene. |
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| Run methods. | |||
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framerate The application's framerate. |
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Initialization method. If windowSize is not defined, the default is
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Whether or not the Director is displaying frames per second. Default
is
|
Sets whether or not the Director displays frames per second.
|
Returns the size of the main application window.
|
Returns the GestureDispatch for the application, which sends out
notifications of GestureEvent
|
Reutrns the ActionManager for the application, which manages the Action
|
Returns the Scheduler for the application, which manages the Timer
|
Sets the color for the background of the application.
|
Sets the main window for the application. If window is
|
Returns the main gtk.Layout to which cocosCairo draws. Use this method
to perform PyGTK actions such as attaching
|
Returns the Scene which is currently running or
|
Starts the application with a Scene. This starts the main run loop.
|
Replaces the currently running Scene with a new Scene.
|
Pushes a new Scene onto the stack of Scenes.
|
Ends all animations, unregisters all GestureDispatch listeners, and clears out the stack of
Scene |
Private method which handles setting the next Scene. This should not normally be called manually. |
Returns the animation interval. Default is
|
Sets the animation interval, that is, the frames per second for the application. Note that this must be set before runWithScene is called, otherwise this method will have no effect.
|
Private method which calculates how much time has elapsed since the last loop iteration. This method should generally not be called manually. |
Takes a screenshot of the application. Note that PyGTK widgets will not be rendered to the image.
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Begins saving a sequence of image stills to be rendered to video.
|
Stops recording and, if FFmpeg is available, will automatically render the video. If there is already a movie file with the same name as the one given in startRecording, that movie file will be deleted. |
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showingFPSWhether or not to display the framerate.
|
sizeThe size of the main application window.
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gestureDispatchThe application's GestureDispatch.
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actionManagerThe application's ActionManager.
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schedulerThe application's Scheduler.
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backgroundColorThe application's background color.
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gtkLayoutThe application's main gtk.Layout.
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runningSceneThe currently-running scene.
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framerateThe application's framerate.
|
| Home | Trees | Indices | Help |
|---|
| Generated by Epydoc 3.0.1 on Fri Apr 9 14:54:58 2010 | http://epydoc.sourceforge.net |


My Life
Welcome to the website where you get to learn all about Brayden Lemke! On this webpage you will learn about what I like to do for fun!
Snowboarding
- I love to snowboard! When the first snowfall hits, I am up in the mountains on my snowboard enjoying the cool winter air as I zip past the trees. Nothings better.
Computer Programming
- I have been coding since I was a wee little lad. Not really, I started to learn how to code about 2 years ago and have learned A LOT! Right now I have learned how to code in, HTML, CSS, JavaScript, JQuery, Java, C, and Git.
Basketball
- Basketball is so much fun! I have been playing basketball since I was 4. Whenever I have free time in the summer I love to go to a basketball court and just shoot around.
Photography
- I really like to take pictures! It is fun to be able to walk around and look at things differently. Once I get started taking pictures I can't stop, it's too much fun.


Next: read-char, Previous: write-byte, Up: Streams Dictionary
peek-char
&optional peek-type input-stream eof-error-p
eof-value recursive-p ⇒ char
peek-type—a character or t or nil.
input-stream—input stream designator. The default is standard input.
eof-error-p—a generalized boolean. The default is true.
eof-value—an object. The default is nil.
recursive-p—a generalized boolean. The default is false.
char—a character or the eof-value.
peek-char obtains the next character in input-stream without actually reading it, thus leaving the character to be read at a later time. It can also be used to skip over and discard intervening characters in the input-stream until a particular character is found.
If peek-type is not supplied or nil, peek-char returns the next character to be read from input-stream, without actually removing it from input-stream. The next time input is done from input-stream, the character will still be there. If peek-type is t, then peek-char skips over whitespace_2 characters, but not comments, and then performs the peeking operation on the next character. The last character examined, the one that starts an object, is not removed from input-stream. If peek-type is a character, then peek-char skips over input characters until a character that is char= to that character is found; that character is left in input-stream.
If an end of file_2 occurs and eof-error-p is false, eof-value is returned.
If recursive-p is true, this call is expected to be embedded in a higher-level call to read or a similar function used by the Lisp reader.
When input-stream is an echo stream, characters that are only peeked at are not echoed. In the case that peek-type is not nil, the characters that are passed by peek-char are treated as if by read-char, and so are echoed unless they have been marked otherwise by unread-char.
(with-input-from-string (input-stream " 1 2 3 4 5") (format t "~S ~S ~S" (peek-char t input-stream) (peek-char #\4 input-stream) (peek-char nil input-stream))) |> #\1 #\4 #\4 ⇒ NIL
*readtable*, *standard-input*, *terminal-io*.
If eof-error-p is true and an end of file_2 occurs an error of type end-of-file is signaled.
If peek-type is a character, an end of file_2 occurs, and eof-error-p is true, an error of type end-of-file is signaled.
If recursive-p is true and an end of file_2 occurs, an error of type end-of-file is signaled.
Next: read-char, Previous: write-byte, Up: Streams Dictionary


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Copyright © mobileCreature 2015 - . All Rights Reserved. License (MIT).


1. login to CollectiveAccess
You need to authenticate yourself in order to use CollectiveAccess REST api.
Give your user credentials and click "Get token".
Current server:
2. Choose primary type
Here you can choose what kind of items you are creating.
Choose primary type (objects, entities etc.).
3. Set defaults
Here you can set default type and default locale for your import.
4.  Here you can map terms in your data to CA types.
Choose field from you data that contains types.
Default type is used for all non-mapped documents.
NOTE: If you do not set default type, then documents that are not mapped aren't saved to CA.


FCC Basejump - File Metadata Microservice
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Upload


TECHNOLOGY SERVICES
AND CONSULTING
-<PHONE_NUMBER>
- Terrebonne, Quebec
Website Design
Infocinc is on the cutting edge of web technologies: responsive design, NodeJs http servers, Facebook and Google+ integration, rich snippets and more. We also offer to track the behavior of your users using Google Analytics and custom-made scripts. Responsive design is Google's recommended configuration for smartphone-optimized websites.
Website Optimization
Using responsive design techniques, Infocinc improves your website's image and the layout of its content on mobile devices and tablet computers. According to a survey by Google, more than 50% of users said that a bad mobile experience made them less likely to engage with a company. More than 67% of mobile users are more likely to buy products or services on a mobile-friendly site.
Web Scraping
A scraper is a small computer program that extracts information from websites. The recovered data can then be stored on a database for later analysis. Infocinc offers its expertise with data integration solutions such as Kapow Katalyst. This software is used by many corporations in the financial sector and the automobile industry.
Data Analysis
It is important to interpret business data correctly. Infocinc offers its expertise with the KDB+ columnar database and its proprietary language Q to build you a suite of analytics. KDB+ and Q are widely used by the financial sector and other industries to process in realtime billions of records.
Custom Dashboards
Data visualization is essential to establish trends and identify explanatory models (e.g. mixture of Gaussians, decision trees, Bayesian nets). Infocinc has expertise in web technologies and modeling techniques (e.g. logistic regression, neural nets, clustering). We offer to build you a web application for viewing your data from any of your devices.
Excel Automation
An alternative to visualizing your data on the web, use the power and maturity of Excel for building intelligent reports based on the data you carefully collected. Infocinc offers automation of your reports using VBA macros and Excel's configurable external data connections.
Problem Solving
Do you need to clarify certain technical issues about your next project ? We offer help to resolve your technical problems: software bugs, system crashes, viruses, choice of hardware etc. Our experience in problem solving allows us to approach less familiar areas of knowledge with confidence. Do not hesitate to contact us for help with your current technical difficulties. Infocinc can build custom software solutions if the situation arises.
- Nicolas Dutil
- Ph.D. Computer Science
I am a Montreal-born computer scientist with a research background in Bioinformatics and information theory. I received my M.Sc. and Ph.D degrees in computer science from McGill University in 2003 and 2011. I started my professional career as a kernel engineer at Nexsan Technologies. In 2012, I moved to the financial services giant Morgan Stanley as a technology associate. I left Morgan Stanley to start my own business: Infocinc. My objective is to use my formal academic training and previous work experiences to create and provide technology services of high quality.
- Melanie Bertrand
- Public Relations
Melanie has a growing role as a co-founder of Infocinc. Active on social networks, she communicates the services offered by our company. She is also an avid websurfer, and regularly performs a series of smoke testing while projects are ongoing. Her empathy for others is an exceptional quality that guides us to improve the presentation of our services. Melanie is valued for her softness and calm. She's an indispensable resource for the proper operation of our business.


UP v2
So, this is the all-new UP version. It is more based on my own blog, which is a some kind of branch of this theme.
The biggest changes here are:
- Assets are now managed by bower;
- Build are now made by grunt;
Procfile.dev
to use with foreman to watch changes everywhere (assets, pages, posts);- Removed the hardcoded Jekyll from Gemfile and replaced it with the github pages Gem;
- Bumped all assets versions;
- Some semantics fixed in HTML;
Usage
To get started, with Ruby, Bundler, Node.js and NPM
previously in your PATH
, run the init script:
./script/init.sh
This will install all needed dependencies and build
the the assets for you (just run grunt
).
You can also start it up in watch mode with
foreman start -f Procfile.dev
Update
To update, the easiest way is to backup your _posts folder and override everything else. If you don’t want to do that, you will have to hack around the changes and do it by hand.
If you have any issues or suggestions, ping me at the up project.
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{% extends "vueAdmin/base_site.html" %} {% load i18n static %} {# site stylesheet section #} {% block extrastyle %} {{ block.super }} {% endblock %} {# site stylesheet section #} {% block app %}
{% trans "We've emailed you instructions for setting your password, if an account exists with the email you entered. You should receive them shortly." %}
{% trans "If you don't receive an email, please make sure you've entered the address you registered with, and check your spam folder." %}


--- layout: default title: Arrow - Page not found permalink: 404.html description: Page not found ---


Child overriding the parent content section.
An introduced section after overriding content section. It has a property: ${value}.


By conditioning on $X_n$, we have seen that for any state $j$,
$$ P(X_{n+1} = j) = P_{n+1}(j) = \sum_{i \in S} P_n(i)P(i, j) $$In principle, this allows us to calculate the distributions $P_n$ sequentially, starting with the inital distribution and transition matrix. That's what the distribution
method does.
The input for distribution
is a numerical transition matrix. Symbolic calculations, on the other hand, can quickly get intractable, and indeed, so can numerical calculations when the state space and run time of the chain are both large.
To see why, let's try to get a sense of what is involved. Suppose $n \ge 1$ and let $i$ and $j$ be any two states. The one-step transition probability $P_1(i, j)$ is just $P(i, j)$ which can be read off the transition matrix. Next, by conditioning on the first move,
$$ P_2(i, j) = \sum_{k \in S} P_1(i, k)P(k, j) = \sum_{k \in S} P(i, k)P(k, j) $$If you have taken some linear algebra you will recognize this as an element in the product of the transition matrix with itself. Indeed, if you denote the 2-step transition matrix by $\mathbb{P}_2$, then
$$ \mathbb{P}_2 = \mathbb{P}^2 $$By induction, the $n$-step transition matrix is $$ \mathbb{P}_n = \mathbb{P}^n $$
The $n$-step transition matrix is the $n$th power of the one-step transition matrix.
 Yet it is important to understand how the chain behaves in the long run. Fortunately, a marvelous theorem comes to the rescue.
Every irreducible and aperiodic Markov Chain with a finite state space exhibits astonishing regularity after it has run for a while.
For such a chain, for all states $i$ and $j$,
$$ P_n(i, j) \to \pi(j) ~~~ \text{as } n \to \infty $$That is, the $n$-step transition probability from $i$ to $j$ converges to a limit that does not depend on $i$. Moreover,
$\pi(j) > 0$ for all states $j$, and
$\sum_{j \in S} \pi(j) = 1$
That is, as $n \to \infty$, the limit $\pi$ of the distributions $P_n$ is a probability distribution in which all terms are positive. It is called the stationary or invariant or steady state distribution of the chain.
The proof of this theorem is beyond the scope of this course. In fact the result is true in greater generality, for some classes of Markov Chains on infinitely many states. In Prob140 we will assume that it is true for irreducible aperiodic finite state chains, and then see what it implies.
First, let's recall that we have seen this behavior in examples. Here is the transition diagram for a lazy reflecting random walk on five states.
Here is the same information in a transition matrix.
def ref_walk_probs(i, j):
if i-j == 0:
return 0.5
elif 2 <= i <= 4:
if abs(i-j) == 1:
return 0.25
else:
return 0
elif i == 1:
if j == 2:
return 0.5
else:
return 0
elif i == 5:
if j == 4:
return 0.5
else:
return 0
trans_tbl = Table().states(s).transition_function(ref_walk_probs)
refl_walk = trans_tbl.toMarkovChain()
refl_walk
Let's start the chain out with an arbitrary initial distribution.
s = np.arange(1, 6)
init_probs = [0.1, 0.2, 0.3, 0.25, 0.15]
initial = Table().states(s).probability(init_probs)
initial
Here is $P_3$, the distribution of $X_3$. This is what we are calling the "distribution at time 3".
refl_walk.distribution(initial, 3)
Here is the distribution at time 100:
refl_walk.distribution(initial, 100)
And here it is at time 200:
refl_walk.distribution(initial, 200)
As the theorem predicts, the distribution at time $n$ has a limit as $n$ gets large. The limit is a probability distribution whose elements are all positive.
Let's start the chain out with a different initial distribution; one that puts all its mass at state 3. The limit distribution is still the same.
initial2 = Table().states(s).probability([0, 0, 1, 0, 0])
refl_walk.distribution(initial2, 100)
As the theorem says, $\lim_n P_n(i, j)$ is $\pi(j)$, which depends on the end state $j$ but not on the start state $i$. In the long run, a Markov Chain forgets where it started.
The distribution
method is a way to identify the stationary distribuion $\pi$ if you have a numerical transition matrix and a computer equipped with the prob140
Python library. But there is a more general way of finding $\pi$.
Let $n \ge 0$ and let $i$ and $j$ be two states. Then
$$ P_{n+1}(i, j) = \sum_{k \in S} P_n(i, k)P(k, j) $$Therefore
\begin{align*} \lim_{n \to \infty} P_{n+1}(i, j) &= \lim_{n \to \infty} \sum_{k \in S} P_n(i, k)P(k, j) \\ \\ &= \sum_{k \in S} \big{(} \lim_{n \to \infty} P_n(i, k) \big{)} P(k, j) \end{align*}We can exchange the limit and the sum because $S$ is finite. Now apply the theorem on convergence to stationarity:
$$ \pi(j) = \sum_{k \in S} \pi(k)P(k, j) $$These are called the balance equations.
In matrix notation, if you think of $\pi$ as a row vector, these equations become
$$ \pi = \pi \mathbb{P} ~~~~~ \text{or, equivalently,} ~~~~~ \pi\mathbb{P} = \pi $$Let's see how this works out for our lazy reflecting random walk. Here is its transition matrix:
refl_walk
According to the balance equations,
$$ \pi(1) = \sum_{k=1}^s \pi(k)P(k, 1) $$That is, we're multiplying $\pi$ by the 1
column of $\mathbb{P}$ and adding. So
Follow the same process to get all five balance equations:
\begin{align*} \pi(1) &= 0.5\pi(1) + 0.25\pi(2) \\ \pi(2) &= 0.5\pi(1) + 0.5\pi(2) + 0.25\pi(3) \\ \pi(3) &= 0.25\pi(2) + 0.5\pi(3) + 0.25\pi(4) \\ \pi(4) &= 0.25\pi(3) + 0.5\pi(4) + 0.5\pi(5) \\ \pi(5) &= 0.25\pi(4) + 0.5\pi(5) \end{align*}Some observations make the system easy to solve.
So the distribution $\pi$) is $$ \big{(} \pi(1), 2\pi(1), 2\pi(1), 2\pi(1), \pi(1) \big{)} $$
As $\pi$ is a probability distribution, it sums to 1. As its total is $8\pi(1)$, we have
$$ \pi = \big{(} \frac{1}{8}, \frac{2}{8}, \frac{2}{8}, \frac{2}{8}, \frac{1}{8} \big{)} $$This agrees with what we got by using distribution
for $n=100$. In fact we could just have used the method steady_state
to get $\pi$:
refl_walk.steady_state()
The steady state isn't an element of the state space $S$ – it's the condition of the chain after it has been run for a long time. Let's examine this further.
To see what is being "balanced" in these equations, imagine a large number of independent replications of this chain. For example, imagine a large number of particles that are moving among the states 1 through 5 according to the transition probabilities of the lazy reflectin walk, and suppose all the particles are moving at instants 1, 2, 3, $\ldots$ independently of each other.
Then at any instant and for any state $j$, there is some proportion of particles that is leaving $j$, and another proportion that is entering $j$. The balance equations say that those two proportions are equal.
Let's check this by looking at the equations again: for any state $j$,
$$ \pi(j) = \sum_{k \in S} \pi(k)P(k, j) $$For every $k \in S$ (including $k=j$), think of $\pi(k)$ as the proportion of particles leaving state $k$ after the chain has been run a long time. Then the left hand side is the proportion leaving $j$. The generic term in the sum on the right is the proportion that left $k$ at the previous instant and are moving to $j$. Those are the particles entering $j$. When the two sides are equal, the chain is balanced.
The theorem on convergence to stationarity says that the chain approaches balance as $n$ gets large. If it actually achieves balance, that is, if $P_n = \pi$ for some $n$, then it stays balanced. The reason:
$$ P_{n+1}(j) = \sum_{i \in S} P_n(i)P(i, j) = \sum_{i \in S} \pi(i)P(i, j) = \pi(j) $$by the balance equations.
In particular, if you start the chain with its stationary distribution $\pi$, then the distribution of $X_n$ is $\pi$ for every $n$.
It's not very hard to show that if a probability distribution solves the balance equations, then it has to be $\pi$, the limit of the marginal distributions of $X_n$. We won't do the proof; it essentially repeats the steps we took to derive the balance equations. You should just be aware that an irreducible, aperiodic, finite state Markov Chain has exactly one stationary distribution.
This is particularly helpful if you happen to guess a solution to the balance equations. If the solution that you have guessed is a probability distribution, you have found the stationary distribution of the chain.
Let $j$ be a state, and let $I_m(j)$ be the indicator of the event $\{X_m = j\}$. The proportion of time the chain spends at $j$, from time 1 through time $n$, is
$$ \frac{1}{n} \sum_{m=1}^n I_m(j) $$Therefore, the expected proportion of time the chain spends at $j$, given that it started at $i$, is
$$ \frac{1}{n} \sum_{m=1}^n E(I_m(j) \mid X_0 = i) = \frac{1}{n} \sum_{m=1}^n P(X_m = j \mid X_0 = i) = \frac{1}{n} \sum_{m=1}^n P_m(i, j) $$Now recall a property of convergent sequences of real numbers: If $x_n \to x$ as $n \to \infty$, then the sequence of averages also converges to $x$: $$ \frac{1}{n} \sum_{m=1}^n x_m \to x ~~~ \text{as } n \to \infty $$
Take $x_n = P_n(i, j)$. Then by the theorem on convergence to stationarity,
$$ P_n(i, j) \to \pi(j) ~~~ \text{as } n \to \infty $$and hence the averages also converge: $$ \frac{1}{n} \sum_{m=1}^n P_m(i, j) \to \pi(j) ~~~ \text{as } n \to \infty $$
Thus the long run expected proportion of time the chain spends in state $j$ is $\pi(j)$, where $\pi$ is the stationary distribution of the chain.
For example, the lazy reflecting random walk of this section is expected to spend about 12.5% of its time at state 1, 25% of its time at each of states 2, 3, and 4, and the remaining 12.5% of its time at state 5.


package shared
Type Members
-
trait
HasAggregationDepth extends Params
Trait for shared param aggregationDepth (default: 2).
Trait for shared param aggregationDepth (default: 2). This trait may be changed or removed between minor versions.
-
trait
HasBlockSize extends Params
Trait for shared param blockSize.
Trait for shared param blockSize. This trait may be changed or removed between minor versions.
-
trait
HasCheckpointInterval extends Params
Trait for shared param checkpointInterval.
Trait for shared param checkpointInterval. This trait may be changed or removed between minor versions.
-
trait
HasCollectSubModels extends Params
Trait for shared param collectSubModels (default: false).
Trait for shared param collectSubModels (default: false). This trait may be changed or removed between minor versions.
-
trait
HasDistanceMeasure extends Params
Trait for shared param distanceMeasure (default: "euclidean").
Trait for shared param distanceMeasure (default: "euclidean"). This trait may be changed or removed between minor versions.
-
trait
HasElasticNetParam extends Params
Trait for shared param elasticNetParam.
Trait for shared param elasticNetParam. This trait may be changed or removed between minor versions.
-
trait
HasFeaturesCol extends Params
Trait for shared param featuresCol (default: "features").
Trait for shared param featuresCol (default: "features"). This trait may be changed or removed between minor versions.
-
trait
HasFitIntercept extends Params
Trait for shared param fitIntercept (default: true).
Trait for shared param fitIntercept (default: true). This trait may be changed or removed between minor versions.
-
trait
HasHandleInvalid extends Params
Trait for shared param handleInvalid.
Trait for shared param handleInvalid. This trait may be changed or removed between minor versions.
-
trait
HasInputCol extends Params
Trait for shared param inputCol.
Trait for shared param inputCol. This trait may be changed or removed between minor versions.
-
trait
HasInputCols extends Params
Trait for shared param inputCols.
Trait for shared param inputCols. This trait may be changed or removed between minor versions.
-
trait
HasLabelCol extends Params
Trait for shared param labelCol (default: "label").
Trait for shared param labelCol (default: "label"). This trait may be changed or removed between minor versions.
-
trait
HasLoss extends Params
Trait for shared param loss.
Trait for shared param loss. This trait may be changed or removed between minor versions.
-
trait
HasMaxIter extends Params
Trait for shared param maxIter.
Trait for shared param maxIter. This trait may be changed or removed between minor versions.
-
trait
HasNumFeatures extends Params
Trait for shared param numFeatures (default: 262144).
Trait for shared param numFeatures (default: 262144). This trait may be changed or removed between minor versions.
-
trait
HasOutputCol extends Params
Trait for shared param outputCol (default: uid + "output"). This trait may be changed or removed between minor versions.
-
trait
HasOutputCols extends Params
Trait for shared param outputCols.
Trait for shared param outputCols. This trait may be changed or removed between minor versions.
-
trait
HasPredictionCol extends Params
Trait for shared param predictionCol (default: "prediction").
Trait for shared param predictionCol (default: "prediction"). This trait may be changed or removed between minor versions.
-
trait
HasProbabilityCol extends Params
Trait for shared param probabilityCol (default: "probability").
Trait for shared param probabilityCol (default: "probability"). This trait may be changed or removed between minor versions.
-
trait
HasRawPredictionCol extends Params
Trait for shared param rawPredictionCol (default: "rawPrediction").
Trait for shared param rawPredictionCol (default: "rawPrediction"). This trait may be changed or removed between minor versions.
-
trait
HasRegParam extends Params
Trait for shared param regParam.
Trait for shared param regParam. This trait may be changed or removed between minor versions.
-
trait
HasRelativeError extends Params
Trait for shared param relativeError (default: 0.001).
Trait for shared param relativeError (default: 0.001). This trait may be changed or removed between minor versions.
-
trait
HasSeed extends Params
Trait for shared param seed (default: this.getClass.getName.hashCode.toLong).
Trait for shared param seed (default: this.getClass.getName.hashCode.toLong). This trait may be changed or removed between minor versions.
-
trait
HasSolver extends Params
Trait for shared param solver.
Trait for shared param solver. This trait may be changed or removed between minor versions.
-
trait
HasStandardization extends Params
Trait for shared param standardization (default: true).
Trait for shared param standardization (default: true). This trait may be changed or removed between minor versions.
-
trait
HasStepSize extends Params
Trait for shared param stepSize.
Trait for shared param stepSize. This trait may be changed or removed between minor versions.
-
trait
HasThreshold extends Params
Trait for shared param threshold.
Trait for shared param threshold. This trait may be changed or removed between minor versions.
-
trait
HasThresholds extends Params
Trait for shared param thresholds.
Trait for shared param thresholds. This trait may be changed or removed between minor versions.
-
trait
HasTol extends Params
Trait for shared param tol.
Trait for shared param tol. This trait may be changed or removed between minor versions.
-
trait
HasValidationIndicatorCol extends Params
Trait for shared param validationIndicatorCol.
Trait for shared param validationIndicatorCol. This trait may be changed or removed between minor versions.
-
trait
HasVarianceCol extends Params
Trait for shared param varianceCol.
Trait for shared param varianceCol. This trait may be changed or removed between minor versions.
-
trait
HasWeightCol extends Params
Trait for shared param weightCol.
Trait for shared param weightCol. This trait may be changed or removed between minor versions.


|
2DGameSDK
1.0
2D Game Engine in C++ based on SFML
|
Defines a projectile that is rendered by a sequence of textures. More...
#include <SequencedProjectile.h>
Public Member Functions | |
| SequencedProjectile (GameBase *game, int type, std::vector< sf::Texture * > sequence, Shape< KinematicShapePhysics > *shapes, sf::Transform start, sf::Vector2f velocity, int maxLifetime=100, int seqFrames=1, int seqStart=0) | |
| Constructs a new Sequenced Projectile. More... | |
| Public Member Functions inherited from game::Projectile | |
| Projectile (GameBase *game, int type, RenderStrategy *renderer, Shape< KinematicShapePhysics > *shapes, sf::Transform start, sf::Vector2f velocity, int maxLifetime=100) | |
| Constructs a new Projectile. More... | |
| virtual | ~Projectile () |
| Destroys the Projectile. More... | |
| virtual int | GetType () |
| Retreives the user defined Type. More... | |
| virtual int | GetZIndex () |
| Retreives the Z Index. More... | |
| virtual void | SetZIndex (int zIndex) |
| Sets the Z Index. More... | |
| virtual void | OnTick () |
| Updates the Projectile. More... | |
| virtual void | OnRender (sf::RenderTarget *target, sf::RenderStates states=sf::RenderStates::Default) |
| Renders the projectile. More... | |
| virtual int | OnCollision (CollisionEventType type, cpArbiter *arb) |
| Collision callback method. More... | |
| Public Member Functions inherited from game::GameObject | |
| GameObject (ObjectType objType, GameBase *game) | |
| Creates a new GameObject. More... | |
| virtual | ~GameObject () |
| Destroys the GameObject. More... | |
| int | GetId () |
| Retreives the Id. More... | |
| ObjectType | GetObjectType () |
| Retreives the Object Type. More... | |
| virtual void | OnTickEnded () |
| Post update event. More... | |
Additional Inherited Members | |
| Static Public Member Functions inherited from game::VisualObject | |
| static bool | SortByZOrder (VisualObject *a, VisualObject *b) |
| Compares visual objects for sort. More... | |
| Protected Member Functions inherited from game::Projectile | |
| void | onDestroy () |
| Protected Member Functions inherited from game::GameObject | |
| GameBase * | getGame () |
| Retreives the Game. More... | |
| Protected Attributes inherited from game::Projectile | |
| int | mType |
| RenderStrategy * | mRenderer = nullptr |
| Shape< KinematicShapePhysics > * | mShape |
| int | mZIndex = constants::DEFAULT_ZINDEX_PROJECTILE |
| cpBody * | mBody |
| int | mLifetime = 1 |
| int | mDestructionDelay = 1 |
| bool | mDestroying = false |
| bool | mDestroyed = false |
Defines a projectile that is rendered by a sequence of textures.
| game::SequencedProjectile::SequencedProjectile | ( | GameBase * | game, |
| int | type, | ||
| std::vector< sf::Texture * > | sequence, | ||
| Shape< KinematicShapePhysics > * | shapes, | ||
| sf::Transform | start, | ||
| sf::Vector2f | velocity, | ||
| int | maxLifetime = 100 , |
||
| int | seqFrames = 1 , |
||
| int | seqStart = 0 |
||
| ) |
Constructs a new Sequenced Projectile.
| game | the game |
| type | the user defined projectile type |
| sequence | the texture sequence |
| shapes | the physical shapes |
| start | the spawn point in world coordinates |
| velocity | the velocity vector |
| maxLifetime | the maximum lifetime (-1 for infinite) |
| seqFrames | the frames per rendered texture |
| seqStart | the sequence start index |


Tip: You can change the Editor layout by clicking on the Settings button:
Settings
For your pair programming assignment this week you should create a website for the Zoo. You site should include the following:


Tizen Advanced UI (TAU) framework provides helper script to support some creating usable components for the Web applications.
| Namespace of Helper | Description |
|---|---|
| SnapListMarqueeStyle | Provides creating marquee-able and expandable list style with SnapListview. |
You can use TAU helper as follows:
<script> var helperInstance = tau.helper.NameSpace </script>


118 min
Course
Annotations have rapidly become the preferred approach to integrating powerful third-party libraries into your own Java...
118 min
Course
In Django Basics, you learned how to set up the skeleton of a video tutuorial library. But for a real-world application you'd...
3 hours
Course
WoocCommerce is a leading Ecommerce plugin for WordPress and allows you to easily turn any WordPress site into a site...
3 hours
Course
JavaFX is the de facto way to make Graphical User Interfaces (or GUIs) in Java. It is robust and customizable, and getting familia...


LEARN SUBTRACTION
HOME
CONTACT
Project By -
Shivam Parmar
SID - 14103036
Email -<EMAIL_ADDRESS>Github -
Learn Subtraction
Submitted To -
Dr. Shailendra Singh


Hi there! Welcome to my portfolio. :) As you can probably tell, it's a work in progress. I'm excited to be diving into the world of front-end web development, and I'm hoping to populate this page with lots of neat, interesting projects as I become an accomplished web developer rock start! Be sure to come back and check out my progress! :D
Here are some of my cool projects I'm working on. More to come!
Check out and follow my development networks on the internets!
website created by fcc29936747


{% extends "base.html" %} {% block extra_scripts %} {% endblock %} {% block content %} Page is better on a big screen. Does not work well on mobile.
| megapack projects in operation | {{ extra.summary.megapack.project_cnt }} |
| Total 🔋 battery capacity in GWh | {{ "%.1f" | format(extra.summary.megapack.gwh) }} |
| Number of megapacks installed | {{ extra.summary.megapack.mp_count }} |
california operation
or ⚡ usa
for all large project in the USA.
| Name | State | Country | Status | Go live | Type | MWh | MW |
|---|---|---|---|---|---|---|---|
| {{ p["name"] }} {{p.smileys}} | {{ p["state"] }} | {{ p["country"] }} | {{ p["status"] }} | {{ p["start operation"] }} | {{ p["type"] }} | {{ p["capacity mwh"] }} | {{ p["power mw"] }} |
| Total |
satellite view
.
For some projects you can see the megapacks. And for others, using the Google Maps satellite shows more.
E.g. for the moss landing
project in California, the Google Maps data already shows the megapacks installed whereas the mapbox data still shows an old substation.
Planning
Construction
Operation
{{ "%.1f" | format(y.total_gwh) }} GWh
in total, {{ y.perc_stat_year }}% ESS vs {{ y.perc_cars }}% cars
{{ "%.1f" | format(extra.cars.expl.total_gwh) }} GWh
into perspective. With all battery cells Tesla sold to date, you could power:
ca {{ "%.1f" | format(extra.cars.expl.hours_germany) }} hours
ca {{ "%.1f" | format(extra.cars.expl.minutes_world) }} minutes
.


For Profit
Created for kids in kindergarten to simplify their life using technology.
2 young ladies teamed up and designed a website and is now creating projects for children. Taisiia is an adventurous and young lady. Simone is humorous and is very innovative. We hope you like our products.
In this project we learned to work collaboratively, incrementally
and how to
In this project we worked on creating a cascading fountain. This fountain us designed for schools and kindergardens for children to play with and look at.


ESP IP or host:
Help
Restart ESP
Heap
Files to send:
If file exists:
Backup
Overwrite
Abort
Upload
Progress:
Refresh File List
Save Changes
Cancel
Edit Selected File
Execute Selected LUA File
Delete Selected File
Delete the selected file?
Click the Delete button again to confirm
Rename Selected File
New file name
(Enter new name and click the Rename button again.)
Cancel


GMaps.js - A Javascript library that simplifies your life
GMaps.js allows you to use the potential of Google Maps in a simple way. No more extensive documentation or large amount of code.
Visit the examples in hpneo.github.com/gmaps
Go to the API Documentation hpneo.github.io/gmaps/documentation.html
Build
If you would like to build gmaps from source run the following at the terminal:
git clone https://github.com/HPNeo/gmaps.git
cd gmaps
npm install
grunt
Changelog
0.4.15
- Add overlay to mouseTarget when click event is set
- addControl/createControl now accepts HTML elements or HTML strings
- Add containsLatLng to google.maps.Circle
0.4.14
- Fix bug in drawPolygon
- Hide context menu before the zoom is changed
0.4.13
- Allow unitSystem setting in travelRoute
- Add functionality to remove controls
- Delegates non custom events to google.map
- Convert featureType and elementType toLowerCase in static maps
0.4.12
- Adds ability to listen for clicks on overlays
0.4.11
- Add RadarSearch to the places layer
- Update default control styles to match new Google Maps release.
0.4.10
- Fix and optimize removeMarkers
- Fix bug in addMarker (issue #270)
0.4.9
- Add UMD support (AMD, CommonJS, browser globals)
- Add retina support
- FitZoom only use visible markers
0.4.8
0.4.7
- Add callback for failure in getRoutes
- Update marker clusterer after remove marker
- Add support for string arrays to arrayToLatLng
0.4.6
- Allow initialising GMaps without new
- Added styled map support for static maps
- Fixed name display for styled maps
- Allow no zoom for static map request
0.4.5
- Fix IE8 bug using array_map
- Add Grunt and Bower support
0.4.4
- Fix buildContextMenu reference in addMarker
0.4.3
- Fix removePolylines and removePolygons
0.4.2
0.4.1
0.4.0
- Split gmaps.js in modules
0.3.5
- Enable new Google Maps style
0.3.4
- Add support for context menu in multiple maps
0.3.3
- Fix destination as address in getRoutes
0.3.2
- Support for removing Fusion Tables and GeoRSS/KML layers with removeLayer
0.3.1
- Improve event binding at adding markers, polylines or polygons
0.3
- Add native events to google.maps objects and custom events to GMaps maps
- Check for Google Maps library and defined element when initialize
- Allow route origins to be a string or array
0.2.31
- Fix context menu position bug
0.2.30
- New feature: StreetView Panoramas
0.2.29
- New methods: removePolyline and removePolygon
- Tests for Styled MapTypes
0.2.28
- Test suite
- Fix double event firing bug
0.2.27
- Allow create context menus for markers
0.2.26
- Fix bug in getElevations
- Rename fitBounds to fitLatLngBounds
0.2.25
- Support for GeoJSON in drawPolygon
- Use ‘complete’ instead of ‘always’ in GMaps.geolocate
0.2.24
- New feature: Overlay Map Types
0.2.23
- Add full support to google.maps.PolylineOptions
- New method: removeMarker
0.2.22
0.2.21
- Support to add google.maps.Marker objects in addMarker and addMarkers methods.
0.2.20
- Add support for other HTML block elements instead “div” (like “section”).
0.2.19
- Use MarkerClusterer to group markers
0.2.18
- Check if GMaps is defined before load extensions
0.2.17
- Fix bug with disableDefaultUI option in constructor
0.2.16
- Fix another bug in createMarker
0.2.15
0.2.14
- Adding IDs, classes and innerHTML to createControl. (Note: Use ‘content’ instead ‘text’ in createControl)
0.2.13
- Add support for Places library in addLayer
0.2.12
- Fix map events without MouseEvent object
- Fix bug in drawCircle and drawRectangle
- Fix bug in zoomIn and zoomOut
- New methods: removePolygon and removePolygons
0.2.11
- Add support to Panoramio in addLayer
0.2.10
0.2.9
- Extend the drawSteppedRoute and travelRoute functions
0.2.8
0.2.7
- New method: removeRoutes
- Access all native methods of google.maps.Map class
0.2.6
- Support for multiple overlays
0.2.5
- Add support to all marker events
- Add suport for animations at show and remove overlays
<IP_ADDRESS>
- Create GMaps class only when Google Maps API is loaded
0.2.4
- New feature: Elevation service
0.2.3
0.2.2
- Minor improvements to support Backbone.js
- Fix controls position
0.2.1
- More default values in GMaps constructor.
0.2
- Remove jQuery dependency.
<IP_ADDRESS>
- New method “removePolylines” and alias “cleanRoute”
<IP_ADDRESS>
- New methods: fitZoom and fitBounds
<IP_ADDRESS>
<IP_ADDRESS>
- New options in GMaps constructor: width and height
<IP_ADDRESS>
- New methods: loadFromFusionTables and loadFromKML
0.1.12
- New feature: KML and GeoRSS
- Fix bug in getFromFusionTables
0.1.11
- New feature: Fusion Tables
0.1.10
- New feature: Custom controls
0.1.9
<IP_ADDRESS>
- Better GMaps.Route methods
<IP_ADDRESS>
- Fix typo in Polyline events
- Add InfoWindow events
<IP_ADDRESS>
<IP_ADDRESS>
- Add drag and dragstart events to Marker
<IP_ADDRESS>
- Add avoidHighways, avoidTolls, optimizeWaypoints, unitSystem and waypoints options in getRoutes
- New method: createMarker
<IP_ADDRESS>
- geolocation and geocode methods are static now (using them with GMaps.geolocation and GMaps.geocode)
<IP_ADDRESS>
- Fix typo in geocode method
- Allow all MapOptions in constructor (see ‘MapOptions’ section in Google Maps API Reference)
<IP_ADDRESS>
- Add pane option (‘floatPane’, ‘floatShadow’, ‘mapPane’, ‘overlayImage’, ‘overlayLayer’, ‘overlayMouseTarget’, ‘overlayShadow’) in drawOverlay
- New methods: removeOverlay and removeOverlays
<IP_ADDRESS>
- Change pane (‘floatPane’ to ‘overlayLayer’) in drawOverlay
<IP_ADDRESS>
0.1.8
- New feature: Overlays
- New method: drawCircle
<IP_ADDRESS>
- Bug fix: zoomIn/zoomOut can change zoom by argument
- New method: setZoom
0.1.7
0.1.6
- New feature: Geofence (with markers)
- New method: drawPolygon
- Bug fix: Change reserved word in Context menu
0.1.5
- New feature: Geocoding
- New method: drawSteppedRoute (similar to travelRoute)
0.1.4
- New events in addMarker
- Add step_number property in travelRoute method
0.1.3
- New feature: Context menu (for map and marker only)
- New method: travelRoute
- Change setCenter to panTo in GMaps setCenter method
- Save entire route data in routes array (instead saving only route path)
- Context menu and Route example (using travelRoute)
0.1.2
- drawPolyline can accept both an array of LatLng objets or an array of coordinates
- New methods: getRoutes and drawRoute
- Route example
0.1.1
- Rename drawRoute method to drawPolyline (more accurate)
- Marker example
0.1 - Initial release
- Map events
- Geolocation
- Add Markers
- Marker infoWindows
- Draw routes and circles
- Initial examples
License
MIT License. Copyright 2014 Gustavo Leon. http://github.com/hpneo
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.


Use this tool to make a playlist for Spotify from your Last.fm recommendations
Click this link to add the songs to the Spotify.
Alternatively, you can also copy the code below to your clipboard and pase into a blank playlist in Spotify.


Do we have the correct information?
Before you continue, update your information with Business BCeID or log in with a different account.
Please confirm the Business Type of {{currentUser.businessname}}.
What type of Corporation is {{currentUser.businessname}}?
Private corporationWhat type of Organization is {{currentUser.businessname}}?
General PartnershipWhat type of Organization is {{currentUser.businessname}}?
SocietyIf the applicant is applying as an Indigenous nation under the nation's name or the applicant type is not listed, please contact the Liquor and Cannabis Regulation Branch for assistance
1-800-555-5555
cannabisregs@gov.bc.ca


--- layout: post title: Nexenta iSCSI with COMSTAR/ZFS integration date: 2009-03-03 07:45:50.000000000 +05:30 categories: - Nexenta - Opensolaris tags: - comstar - iscsi - Nexenta - Opensolaris - scsi status: publish type: post published: true meta: _edit_last: '1' author: login: admin email<EMAIL_ADDRESS>display_name: admin first_name: '' last_name: '' ---
[I've made a few small updates correcting typos and inserted additional information to turn off any existing shareiscsi=on zfs volumes]
Opensolaris has had the capability to share an iscsi target for sometime. This was done in the userland via the iscsi target daemon.
The COMSTAR project on opensolaris was started to build a fast scsi target on the platform. From the project page:
COMSTAR is a software framework that enables you to turn any OpenSolaris host into a SCSI target that can be accessed over the network by initiator hosts. COMSTAR breaks down the huge task of handling a SCSI target subsystem into independent functional modules. These modules are then glued together by the SCSI Target Mode Framework (STMF).
COMSTAR provides
With intergration of COMSTAR to ZFS, Nexenta Core Platform provides for very easy setup as an iSCSI target. These latest changes to ZFS are available both in the latest beta2 release of NCP, as well as the commercial NexentaStor v1.1.5.
How to setup a NCP2 box as an iSCSI target
First, shutdown older iSCSI target service, and start the new one
svcadm disable -s iscsitgt
svccfg delete -f iscsitgt
Also if you have any zfs volumes with shareiscsi=on, set them to shareiscsi=off. This is required to setup the new zfs integrated scsi correctly.
Enable iscsi/target and it's dependency
svcadm enable -s stmf
svcadm enable -s iscsi/target
Install the switch by creating the file:
touch /etc/shareiscsi.conf
Now create a dataset (volume) which is to be shared via iSCSI
zfs create -V 500m tank/zvol1
Using -V creates a volume type dataset, and it reserves the given amount of space on the pool for it's data. In our case we reserved 500 Mb of space for our volume zol1. To create an SCSI target using this, simply run.
zfs set shareiscsi=on tank/zvol1
And we're now set :). To confirm that the target has been configured, check that output of
itadm list-target -v
Performance gain
This implementation of an iSCSI target provides 2-3 times the performance of the older userland implementation in opensolaris. The numbers stack up as follows:
Parameters: 4 workers x 8 outstanding IOs
COMSTAR iSCSI target
50000 IOPS, 512B, 97% CPU, 24MB/sec <- Reads
40000 IOPS, 512B, 92% CPU, 17MB/sec <-WritesOLD UserLand iSCSI target
17000 IOPS, 512B, 98% CPU, 8MB/sec <- Reads
15000 IOPS, 512B, 98% CPU, 7.5MB/sec <-Writes
The tests were conducted with IOMeter, using a 100M zvol with zil disabled. Also these were done over a 1G network between the target and the initiator.
More information on COMSTAR can be found on it's project page, and the changes can be found in the nexenta-on package in NCP2.


MMU Bulletin
MMU Bulletin is MMU Bulletin Board with Material Design.
Login with your MMU Google Account (@student.mmu.edu.my)
Login
Need help? Contact Me
MMU Bulletin is MMU Bulletin Board with Material Design.
Login with your MMU Google Account (@student.mmu.edu.my)
Need help? Contact Me


--- layout: example permalink: /examples/monaco/ exampleId: monaco stylesheets: - /libs/monaco-editor/min/vs/editor/editor.main.css -<EMAIL_ADDRESS>- /examples/monaco/monaco.css overview: |- This example demonstrates building a realtime source code editor with the Monaco Editor. This example uses the Monaco Collaborative Extensions to display remote cursors and selections, as well as to simplify the management of local and remote edits. ---


- dag (airflow.models.BaseOperator attribute), [1]
- DAG (class in airflow.models)
- DagBag (class in airflow.models)
- dagbag_report() (airflow.models.DagBag method)
- DagModel (class in airflow.models)
- DagPickle (class in airflow.models)
- DagRun (class in airflow.models)
- DagStat (class in airflow.models)
- DatabricksHook (class in airflow.contrib.hooks.databricks_hook)
- DatabricksSubmitRunOperator (class in airflow.contrib.operators.databricks_operator), [1]
- DatadogHook (class in airflow.contrib.hooks.datadog_hook)
- DatadogSensor (class in airflow.contrib.sensors.datadog_sensor)
- DataFlowHook (class in airflow.contrib.hooks.gcp_dataflow_hook), [1]
- DataFlowJavaOperator (class in airflow.contrib.operators.dataflow_operator), [1]
- DataFlowPythonOperator (class in airflow.contrib.operators.dataflow_operator), [1]
- DataflowTemplateOperator (class in airflow.contrib.operators.dataflow_operator), [1]
- DataprocClusterCreateOperator (class in airflow.contrib.operators.dataproc_operator), [1]
- DataprocClusterDeleteOperator (class in airflow.contrib.operators.dataproc_operator), [1]
- DataprocClusterScaleOperator (class in airflow.contrib.operators.dataproc_operator), [1]
- DataProcHadoopOperator (class in airflow.contrib.operators.dataproc_operator), [1]
- DataProcHiveOperator (class in airflow.contrib.operators.dataproc_operator), [1]
- DataProcHook (class in airflow.contrib.hooks.gcp_dataproc_hook)
- DataProcPigOperator (class in airflow.contrib.operators.dataproc_operator), [1]
- DataProcPySparkOperator (class in airflow.contrib.operators.dataproc_operator), [1]
- DataProcSparkOperator (class in airflow.contrib.operators.dataproc_operator), [1]
- DataProcSparkSqlOperator (class in airflow.contrib.operators.dataproc_operator), [1]
- DataprocWorkflowTemplateBaseOperator (class in airflow.contrib.operators.dataproc_operator)
- DataprocWorkflowTemplateInstantiateInlineOperator (class in airflow.contrib.operators.dataproc_operator), [1]
- DataprocWorkflowTemplateInstantiateOperator (class in airflow.contrib.operators.dataproc_operator), [1]
- DatastoreExportOperator (class in airflow.contrib.operators.datastore_export_operator), [1]
- DatastoreHook (class in airflow.contrib.hooks.datastore_hook), [1]
- DatastoreImportOperator (class in airflow.contrib.operators.datastore_import_operator), [1]
- DayOfWeekSensor (class in airflow.contrib.sensors.weekday_sensor)
- db() (airflow.contrib.hooks.cloudant_hook.CloudantHook method)
- DbApiHook (class in airflow.hooks.dbapi_hook)
- deactivate_stale_dags() (airflow.models.DAG static method)
- deactivate_unknown_dags() (airflow.models.DAG static method)
- delete() (airflow.contrib.hooks.gcs_hook.GoogleCloudStorageHook method), [1]
- delete_cluster() (airflow.contrib.hooks.redshift_hook.RedshiftHook method), [1]
- delete_collection() (airflow.contrib.hooks.azure_cosmos_hook.AzureCosmosDBHook method), [1]
- delete_connection() (airflow.contrib.hooks.gcp_sql_hook.CloudSqlDatabaseHook method)
- delete_database() (airflow.contrib.hooks.azure_cosmos_hook.AzureCosmosDBHook method), [1]
|
- delete_directory() (airflow.contrib.hooks.ftp_hook.FTPHook method)
- delete_document() (airflow.contrib.hooks.azure_cosmos_hook.AzureCosmosDBHook method), [1]
- delete_file() (airflow.contrib.hooks.ftp_hook.FTPHook method)
- delete_function() (airflow.contrib.hooks.gcp_function_hook.GcfHook method)
- delete_instance() (airflow.contrib.hooks.gcp_sql_hook.CloudSqlHook method)
- delete_objects() (airflow.hooks.S3_hook.S3Hook method), [1]
- delete_operation() (airflow.contrib.hooks.datastore_hook.DatastoreHook method), [1]
- delete_subscription() (airflow.contrib.hooks.gcp_pubsub_hook.PubSubHook method)
- delete_topic() (airflow.contrib.hooks.gcp_pubsub_hook.PubSubHook method)
- delete_version() (airflow.contrib.hooks.gcp_mlengine_hook.MLEngineHook method), [1]
- deps (airflow.models.BaseOperator attribute), [1]
- describe_cluster_snapshots() (airflow.contrib.hooks.redshift_hook.RedshiftHook method), [1]
- describe_directory() (airflow.contrib.hooks.ftp_hook.FTPHook method)
- describe_endpoint() (airflow.contrib.hooks.sagemaker_hook.SageMakerHook method), [1], [2]
- describe_endpoint_config() (airflow.contrib.hooks.sagemaker_hook.SageMakerHook method), [1], [2]
- describe_model() (airflow.contrib.hooks.sagemaker_hook.SageMakerHook method), [1], [2]
- describe_object() (airflow.contrib.hooks.salesforce_hook.SalesforceHook method)
- describe_training_job() (airflow.contrib.hooks.sagemaker_hook.SageMakerHook method), [1], [2]
- describe_training_job_with_log() (airflow.contrib.hooks.sagemaker_hook.SageMakerHook method), [1], [2]
- describe_transform_job() (airflow.contrib.hooks.sagemaker_hook.SageMakerHook method), [1], [2]
- describe_tuning_job() (airflow.contrib.hooks.sagemaker_hook.SageMakerHook method), [1], [2]
- DiscordWebhookHook (class in airflow.contrib.hooks.discord_webhook_hook)
- DiscordWebhookOperator (class in airflow.contrib.operators.discord_webhook_operator)
- does_collection_exist() (airflow.contrib.hooks.azure_cosmos_hook.AzureCosmosDBHook method), [1]
- does_database_exist() (airflow.contrib.hooks.azure_cosmos_hook.AzureCosmosDBHook method), [1]
- download() (airflow.contrib.hooks.gcs_hook.GoogleCloudStorageHook method), [1]
- download_file() (airflow.contrib.hooks.azure_data_lake_hook.AzureDataLakeHook method), [1]
- download_mail_attachments() (airflow.contrib.hooks.imap_hook.ImapHook method)
- downstream_list (airflow.models.BaseOperator attribute), [1]
- DruidCheckOperator (class in airflow.operators.druid_check_operator)
- DruidDbApiHook (class in airflow.hooks.druid_hook)
- DruidHook (class in airflow.hooks.druid_hook)
- DruidOperator (class in airflow.contrib.operators.druid_operator)
- ds_add() (in module airflow.macros)
- ds_format() (in module airflow.macros)
- DummyOperator (class in airflow.operators.dummy_operator)
|


This sample uses a config file to setup individual namespaces that should be shown, with optional style properties.
Module 1 & Module 2 log output is shown, as well as output from a direct call to dlog with a supplied namespace ('InlineModule').


{% extends 'base.html' %} {% block banner_text %} {% load static %}
Some quick example text to build on the card title and make up the bulk of the card's content.


Title
Project Name
Issue Type
Component
Search String
Stack Trace
Occurrences
Labels
Populate From Splunk
Create JIRA


{% extends "menu/megamenu_extra_base.html" %} {% load cms_tags menu_tags i18n %} {% block megamenu_extra_num_cols %}threecol{% endblock %} {% block megamenu_extra %}
{% blocktrans %} Browse and filter Floodlight stories by topic, location, language, project and organization. {% endblocktrans %}
{% blocktrans %} Organizations are groups of people creating stories together. They can be non-profits, school groups, neighborhoods, or any storytellers who share a common goal. {% endblocktrans %}
{% blocktrans %} Projects are collections of stories about a common theme, place, issue, event or movement. They can be open to contributions from any Floodlight user, or constrained to a group of authors. {% endblocktrans %}


Scores
0
Highest
0
How to play
How the game works
You can move the board in for directions: up, down, left, right
Tiles with the same number merge into one when they touch.
Your goal: Add them up to reach 2048!
How to control
You can press the "Show keys" button to show the arrow control on screen (for phone user)
Use your arrow keys (or the arrows on the screen) to move the tiles.
Use New game or ("r" key) to play a new game


package tagobjects
- Alphabetic
- Public
- All
Value Members
-
object
CPU extends Tag
Tag object that indicates a test is CPU-intensive (i.e., consumes a lot of CPU time when it runs).
Tag object that indicates a test is CPU-intensive (i.e., consumes a lot of CPU time when it runs).
The corresponding tag annotation for this tag object is
org.scalatest.tags.CPU
. This tag object can be used to tag test functions (in style traits other thanSpec
, in which tests are methods not functions) as being CPU-intensive. See the "tagging tests" section in the documentation for your chosen styles to see the syntax. Here's an example forFlatSpec
:package org.scalatest.examples.tagobjects.cpu
import org.scalatest._ import tagobjects.CPU
class SetSpec extends FlatSpec {
"An empty Set" should "have size 0" taggedAs(CPU) in { assert(Set.empty.size === 0) } } -
object
ChromeBrowser extends Tag
Tag object that indicates a Selenium test uses the Chrome browser.
-
object
Disk extends Tag
Tag object that indicates a test is disk-intensive (i.e., consumes a lot of disk-IO bandwidth when it runs).
Tag object that indicates a test is disk-intensive (i.e., consumes a lot of disk-IO bandwidth when it runs).
The corresponding tag annotation for this tag object is
org.scalatest.tags.Disk
. This tag object can be used to tag test functions (in style traits other thanSpec
, in which tests are methods not functions) as being disk-intensive. See the "tagging tests" section in the documentation for your chosen styles to see the syntax. Here's an example forFlatSpec
:package org.scalatest.examples.tagobjects.disk
import org.scalatest._ import tagobjects.Disk
class SetSpec extends FlatSpec {
"An empty Set" should "have size 0" taggedAs(Disk) in { assert(Set.empty.size === 0) } } -
object
FirefoxBrowser extends Tag
Tag object that indicates a Selenium test uses the Firefox browser.
-
object
HtmlUnitBrowser extends Tag
Tag object that indicates a Selenium test uses the HtmlUnit browser.
-
object
InternetExplorerBrowser extends Tag
Tag that indicates a Selenium test uses the Internet Explorer browser.
-
object
Network extends Tag
Tag object that indicates a test is network-intensive (i.e., consumes a lot of network bandwidth when it runs).
Tag object that indicates a test is network-intensive (i.e., consumes a lot of network bandwidth when it runs).
The corresponding tag annotation for this tag object is
org.scalatest.tags.Network
. This tag object can be used to tag test functions (in style traits other thanSpec
, in which tests are methods not functions) as being network-intensive. See the "tagging tests" section in the documentation for your chosen styles to see the syntax. Here's an example forFlatSpec
:package org.scalatest.examples.tagobjects.network
import org.scalatest._ import tagobjects.Network
class SetSpec extends FlatSpec {
"An empty Set" should "have size 0" taggedAs(Network) in { assert(Set.empty.size === 0) } } -
object
Retryable extends Tag
Tag object that indicates a test is a candidate for retrying on either failure, cancellation, or both.
Tag object that indicates a test is a candidate for retrying on either failure, cancellation, or both.
This tag object is intended to be used in conjunction with trait
Retries
, to identify tests that are candidates for retrying.The corresponding tag annotation for this tag object is
org.scalatest.tags.Retryable
. This tag object can be used to tag test functions (in style traits other thanSpec
, in which tests are methods not functions) as being a candidate for retries. See the "tagging tests" section in the documentation for your chosen styles to see the syntax. Here's an example forFlatSpec
:package org.scalatest.examples.tagobjects.retryable
import org.scalatest._ import tagobjects.Retryable
class SetSpec extends FlatSpec with Retries {
override def withFixture(test: NoArgTest) = { if (isRetryable(test)) withRetry { super.withFixture(test) } else super.withFixture(test) }
"An empty Set" should "have size 0" taggedAs(Retryable) in { assert(Set.empty.size === 0) } } -
object
SafariBrowser extends Tag
Tag object that indicates a Selenium test uses the Safari browser.
-
object
Slow extends Tag
Tag object that indicates a test is slow (i.e., takes a long time to run).
Tag object that indicates a test is slow (i.e., takes a long time to run).
The corresponding tag annotation for this tag object is
org.scalatest.tags.Slow
. This tag object can be used to tag test functions (in style traits other thanSpec
, in which tests are methods not functions) as being slow. See the "tagging tests" section in the documentation for your chosen styles to see the syntax. Here's an example forFlatSpec
:package org.scalatest.examples.tagobjects.slow
import org.scalatest._ import tagobjects.Slow
class SetSpec extends FlatSpec {
"An empty Set" should "have size 0" taggedAs(Slow) in { assert(Set.empty.size === 0) } }


status; if($status=="OK"){ //Get address from json data $location = $data->results[0]->formatted_address; }else{ $location = ''; } //Print address echo $location; } ?>
lat: lng:
Your Location:
Click the button to get your coordinates.
Show my location


Hundreds arrested ahead of verdict on India's Babri mosque
by AFPNEW DELHI: Indian police have arrested more than 500 people ahead of a Supreme Court ruling on a hotly disputed religious site in the city of Ayodhya, media reports said, with authorities fearing the verdict could trigger unrest.
According to the Times of India, the decision on the future of the site claimed by both Muslims and Hindus will be announced on Saturday (tomorrow).
Thousands of paramilitaries have already been sent to the northern city.
Hindu hardliners want a temple built on the site, currently barricaded off decades after a 16th-century mosque there was demolished during 1992 riots that left 2,000 people dead.
Hindus believe the mosque was built over the site of the birthplace of their diety Ram.
Security is being tightened across India in the run-up to the ruling and Uttar Pradesh state police chief O P Singh told the Economic Times that more than 500 arrests had been made.
“The main message to the police force is to maintain peace at any cost,” the newspaper quoted him as saying.
Most of the suspects were taken into custody while a further 70 people were detained over their social media posts, he said – warning the internet could be blocked in the region if required.
Singh added that police had also identified more than 10,000 people he described as “anti-social”.
A police spokesperson declined to comment to AFP.
In recent years Ayodhya has become a rallying point for Prime Minister Narendra Modi’s ruling Hindu-nationalist Bharatiya Janata Party (BJP).
Some senior BJP members are being tried separately over their role in the mosque’s 1992 destruction.
In 2010, a high court divided the disputed land between Hindu and Muslim groups but both parties appealed to the Supreme Court which has since repeatedly put off a verdict.
Media reports say Modi has told ministers to refrain from making comments on the case that could fuel tensions.
For minority Muslims, the dispute and a recent clampdown in Muslim-majority Kashmir have become symbols of the hostility that they say they face from the government.
Hindus make up about 80 per cent of India’s 1.3 billion population while there are about 200 million Muslims.