|
gtsam
4.0.0
gtsam
|
EliminateableFactorGraph is a base class for factor graphs that contains elimination algorithms.
Any factor graph holding eliminateable factors can derive from this class to expose functions for computing marginals, conditional marginals, doing multifrontal and sequential elimination, etc.
Public Member Functions | |
| boost::shared_ptr< BayesNetType > | eliminateSequential (OptionalOrdering ordering=boost::none, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex=boost::none, OptionalOrderingType orderingType=boost::none) const |
| Do sequential elimination of all variables to produce a Bayes net. More... | |
| boost::shared_ptr< BayesTreeType > | eliminateMultifrontal (OptionalOrdering ordering=boost::none, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex=boost::none, OptionalOrderingType orderingType=boost::none) const |
| Do multifrontal elimination of all variables to produce a Bayes tree. More... | |
| std::pair< boost::shared_ptr< BayesNetType >, boost::shared_ptr< FactorGraphType > > | eliminatePartialSequential (const Ordering &ordering, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex=boost::none) const |
Do sequential elimination of some variables, in ordering provided, to produce a Bayes net and a remaining factor graph. More... | |
| std::pair< boost::shared_ptr< BayesNetType >, boost::shared_ptr< FactorGraphType > > | eliminatePartialSequential (const KeyVector &variables, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex=boost::none) const |
Do sequential elimination of the given variables in an ordering computed by COLAMD to produce a Bayes net and a remaining factor graph. More... | |
| std::pair< boost::shared_ptr< BayesTreeType >, boost::shared_ptr< FactorGraphType > > | eliminatePartialMultifrontal (const Ordering &ordering, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex=boost::none) const |
Do multifrontal elimination of some variables, in ordering provided, to produce a Bayes tree and a remaining factor graph. More... | |
| std::pair< boost::shared_ptr< BayesTreeType >, boost::shared_ptr< FactorGraphType > > | eliminatePartialMultifrontal (const KeyVector &variables, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex=boost::none) const |
Do multifrontal elimination of the given variables in an ordering computed by COLAMD to produce a Bayes net and a remaining factor graph. More... | |
| boost::shared_ptr< BayesNetType > | marginalMultifrontalBayesNet (boost::variant< const Ordering &, const KeyVector & > variables, OptionalOrdering marginalizedVariableOrdering=boost::none, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex=boost::none) const |
| Compute the marginal of the requested variables and return the result as a Bayes net. More... | |
| boost::shared_ptr< BayesTreeType > | marginalMultifrontalBayesTree (boost::variant< const Ordering &, const KeyVector & > variables, OptionalOrdering marginalizedVariableOrdering=boost::none, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex=boost::none) const |
| Compute the marginal of the requested variables and return the result as a Bayes tree. More... | |
| boost::shared_ptr< FactorGraphType > | marginal (const KeyVector &variables, const Eliminate &function=EliminationTraitsType::DefaultEliminate, OptionalVariableIndex variableIndex=boost::none) const |
| Compute the marginal factor graph of the requested variables. More... | |
Public Types | |
| typedef EliminationTraits< FactorGraphType > | EliminationTraitsType |
| Typedef to the specific EliminationTraits for this graph. | |
| typedef EliminationTraitsType::ConditionalType | ConditionalType |
| Conditional type stored in the Bayes net produced by elimination. | |
| typedef EliminationTraitsType::BayesNetType | BayesNetType |
| Bayes net type produced by sequential elimination. | |
| typedef EliminationTraitsType::EliminationTreeType | EliminationTreeType |
| Elimination tree type that can do sequential elimination of this graph. | |
| typedef EliminationTraitsType::BayesTreeType | BayesTreeType |
| Bayes tree type produced by multifrontal elimination. | |
| typedef EliminationTraitsType::JunctionTreeType | JunctionTreeType |
| Junction tree type that can do multifrontal elimination of this graph. | |
| typedef std::pair< boost::shared_ptr< ConditionalType >, boost::shared_ptr< _FactorType > > | EliminationResult |
| The pair of conditional and remaining factor produced by a single dense elimination step on a subgraph. More... | |
| typedef boost::function< EliminationResult(const FactorGraphType &, const Ordering &)> | Eliminate |
| The function type that does a single dense elimination step on a subgraph. | |
| typedef boost::optional< const Ordering & > | OptionalOrdering |
| Typedef for an optional ordering as an argument to elimination functions. | |
| typedef boost::optional< const VariableIndex & > | OptionalVariableIndex |
| Typedef for an optional variable index as an argument to elimination functions. | |
| typedef boost::optional< Ordering::OrderingType > | OptionalOrderingType |
| Typedef for an optional ordering type. | |
| typedef std::pair<boost::shared_ptr<ConditionalType>, boost::shared_ptr<_FactorType> > gtsam::EliminateableFactorGraph< FACTORGRAPH >::EliminationResult |
The pair of conditional and remaining factor produced by a single dense elimination step on a subgraph.
| boost::shared_ptr< typename EliminateableFactorGraph< FACTORGRAPH >::BayesTreeType > gtsam::EliminateableFactorGraph< FACTORGRAPH >::eliminateMultifrontal | ( | OptionalOrdering | ordering = boost::none , |
| const Eliminate & | function = EliminationTraitsType::DefaultEliminate , |
||
| OptionalVariableIndex | variableIndex = boost::none , |
||
| OptionalOrderingType | orderingType = boost::none |
||
| ) | const |
Do multifrontal elimination of all variables to produce a Bayes tree.
If an ordering is not provided, the ordering will be computed using either COLAMD or METIS, dependeing on the parameter orderingType (Ordering::COLAMD or Ordering::METIS)
Example - Full Cholesky elimination in COLAMD order:
Example - Full QR elimination in specified order:
Example - Reusing an existing VariableIndex to improve performance, and using COLAMD ordering:
| std::pair< boost::shared_ptr< typename EliminateableFactorGraph< FACTORGRAPH >::BayesTreeType >, boost::shared_ptr< FACTORGRAPH > > gtsam::EliminateableFactorGraph< FACTORGRAPH >::eliminatePartialMultifrontal | ( | const Ordering & | ordering, |
| const Eliminate & | function = EliminationTraitsType::DefaultEliminate , |
||
| OptionalVariableIndex | variableIndex = boost::none |
||
| ) | const |
Do multifrontal elimination of some variables, in ordering
provided, to produce a Bayes tree and a remaining factor graph.
This computes the factorization \( p(X) = p(A|B) p(B) \), where \( A = \) variables
, \( X \) is all the variables in the factor graph, and \( B = X\backslash A \).
| std::pair< boost::shared_ptr< typename EliminateableFactorGraph< FACTORGRAPH >::BayesTreeType >, boost::shared_ptr< FACTORGRAPH > > gtsam::EliminateableFactorGraph< FACTORGRAPH >::eliminatePartialMultifrontal | ( | const KeyVector & | variables, |
| const Eliminate & | function = EliminationTraitsType::DefaultEliminate , |
||
| OptionalVariableIndex | variableIndex = boost::none |
||
| ) | const |
Do multifrontal elimination of the given variables
in an ordering computed by COLAMD to produce a Bayes net and a remaining factor graph.
This computes the factorization \( p(X) = p(A|B) p(B) \), where \( A = \) variables
, \( X \) is all the variables in the factor graph, and \( B = X\backslash A \).
| std::pair< boost::shared_ptr< typename EliminateableFactorGraph< FACTORGRAPH >::BayesNetType >, boost::shared_ptr< FACTORGRAPH > > gtsam::EliminateableFactorGraph< FACTORGRAPH >::eliminatePartialSequential | ( | const Ordering & | ordering, |
| const Eliminate & | function = EliminationTraitsType::DefaultEliminate , |
||
| OptionalVariableIndex | variableIndex = boost::none |
||
| ) | const |
Do sequential elimination of some variables, in ordering
provided, to produce a Bayes net and a remaining factor graph.
This computes the factorization \( p(X) = p(A|B) p(B) \), where \( A = \) variables
, \( X \) is all the variables in the factor graph, and \( B = X\backslash A \).
| std::pair< boost::shared_ptr< typename EliminateableFactorGraph< FACTORGRAPH >::BayesNetType >, boost::shared_ptr< FACTORGRAPH > > gtsam::EliminateableFactorGraph< FACTORGRAPH >::eliminatePartialSequential | ( | const KeyVector & | variables, |
| const Eliminate & | function = EliminationTraitsType::DefaultEliminate , |
||
| OptionalVariableIndex | variableIndex = boost::none |
||
| ) | const |
Do sequential elimination of the given variables
in an ordering computed by COLAMD to produce a Bayes net and a remaining factor graph.
This computes the factorization \( p(X) = p(A|B) p(B) \), where \( A = \) variables
, \( X \) is all the variables in the factor graph, and \( B = X\backslash A \).
| boost::shared_ptr< typename EliminateableFactorGraph< FACTORGRAPH >::BayesNetType > gtsam::EliminateableFactorGraph< FACTORGRAPH >::eliminateSequential | ( | OptionalOrdering | ordering = boost::none , |
| const Eliminate & | function = EliminationTraitsType::DefaultEliminate , |
||
| OptionalVariableIndex | variableIndex = boost::none , |
||
| OptionalOrderingType | orderingType = boost::none |
||
| ) | const |
Do sequential elimination of all variables to produce a Bayes net.
If an ordering is not provided, the ordering provided by COLAMD will be used.
Example - Full Cholesky elimination in COLAMD order:
Example - METIS ordering for elimination
Example - Reusing an existing VariableIndex to improve performance, and using COLAMD ordering:
| boost::shared_ptr< FACTORGRAPH > gtsam::EliminateableFactorGraph< FACTORGRAPH >::marginal | ( | const KeyVector & | variables, |
| const Eliminate & | function = EliminationTraitsType::DefaultEliminate , |
||
| OptionalVariableIndex | variableIndex = boost::none |
||
| ) | const |
Compute the marginal factor graph of the requested variables.
| boost::shared_ptr< typename EliminateableFactorGraph< FACTORGRAPH >::BayesNetType > gtsam::EliminateableFactorGraph< FACTORGRAPH >::marginalMultifrontalBayesNet | ( | boost::variant< const Ordering &, const KeyVector & > | variables, |
| OptionalOrdering | marginalizedVariableOrdering = boost::none , |
||
| const Eliminate & | function = EliminationTraitsType::DefaultEliminate , |
||
| OptionalVariableIndex | variableIndex = boost::none |
||
| ) | const |
Compute the marginal of the requested variables and return the result as a Bayes net.
| variables | Determines the variables whose marginal to compute, if provided as an Ordering they will be ordered in the returned BayesNet as specified, and if provided as a KeyVector they will be ordered using constrained COLAMD. |
| marginalizedVariableOrdering | Optional ordering for the variables being marginalized out, i.e. all variables not in variables . If this is boost::none, the ordering will be computed with COLAMD. |
| function | Optional dense elimination function, if not provided the default will be used. |
| variableIndex | Optional pre-computed VariableIndex for the factor graph, if not provided one will be computed. |
| boost::shared_ptr< typename EliminateableFactorGraph< FACTORGRAPH >::BayesTreeType > gtsam::EliminateableFactorGraph< FACTORGRAPH >::marginalMultifrontalBayesTree | ( | boost::variant< const Ordering &, const KeyVector & > | variables, |
| OptionalOrdering | marginalizedVariableOrdering = boost::none , |
||
| const Eliminate & | function = EliminationTraitsType::DefaultEliminate , |
||
| OptionalVariableIndex | variableIndex = boost::none |
||
| ) | const |
Compute the marginal of the requested variables and return the result as a Bayes tree.
| variables | Determines the variables whose marginal to compute, if provided as an Ordering they will be ordered in the returned BayesNet as specified, and if provided as a KeyVector they will be ordered using constrained COLAMD. |
| marginalizedVariableOrdering | Optional ordering for the variables being marginalized out, i.e. all variables not in variables . If this is boost::none, the ordering will be computed with COLAMD. |
| function | Optional dense elimination function, if not provided the default will be used. |
| variableIndex | Optional pre-computed VariableIndex for the factor graph, if not provided one will be computed. |


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{% extends "profiles/base_profile_edit.html" %} {% load static %} {% load i18n %} {% block profile-admin-gold-edit %}active{% endblock %} {% block title %}{% trans "Gold Projects" %}{% endblock %} {% block edit_content %}
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--- layout: post esbma_id: 1523 title: Comments on Hatteras date: 2004-05-24 14:45:06 keywords: cornsharp sourcegear teaser: These announcements are causing lots of people to ask questions about SourceGear Vault. This blog posting is intended to start answering those questions. ---
Most people in blogspace already know that today Microsoft announced a full suite of lifecycle development tools called the Visual Studio Team System. One piece of this system is a new enterprise-class source control system which is code-named "Hatteras". These announcements are causing lots of people to ask questions about SourceGear Vault. This blog posting is intended to start answering those questions. We appreciate Microsoft for putting us under NDA and sharing the news about Hatteras in advance. We've known about this for over nine months, and we are glad to finally be able to publicly talk about what this news means for SourceGear and Vault.
Is Hatteras based on technology from Vault?
No. Just like Vault, Hatteras is built on SQL Server as the underlying repository store. Because of this, I have already heard a number of people asking if perhaps Microsoft might have licensed some technology from SourceGear. Alas, this is not the case. Microsoft's new enterprise-class source control tool was written from scratch by a team in the Raleigh-Durham area. The team is being led by Brian Harry, the guy who originally developed SourceSafe at OneTree before it was acquired by Microsoft.
Does this mean SourceGear Vault is dead?
We certainly don't think so. Obviously we do not consider this to be good news. Over time, every source control vendor will be impacted by Microsoft's decision to finally take source control seriously. We would all certainly prefer that Microsoft stay out of this market segment. But we don't get everything we want.
Hatteras is not the end of SourceGear Vault. There is plenty of room for SourceGear and other vendors to differentiate and continue to have a healthy market.
Different Positioning
The positioning of Hatteras is simply different from Vault. Hatteras appears to be designed to kill Rational ClearCase, not Vault. In fact, it seems clear to me that the entire new suite of lifecycle tools is Microsoft's reaction to IBM's acquisition of Rational. I admit that SourceGear will be impacted, but the damage is basically incidental. If you think Microsoft is out to beat SourceGear, you don't understand Microsoft. We are quite successful for a small company, but we are not even close to being on Microsoft's radar screen. Microsoft is firing its weapons at Rational, and SourceGear is simply a little too close to the battlefield to remain completely unharmed.
Most of the damage will happen in terms of very large customers. I'm talking about teams with several hundred developers or more. Those kinds of companies aren't really looking seriously at Vault anyway. Vault is currently selling very well with teams of 10, 25 and 50 developers. We don't see that changing anytime soon. That kind of small team doesn't need the sheer size of something like ClearCase or Hatteras.
And they probably can't afford it either. Pricing for Hatteras has not yet been announced, but folks are getting the impression that it will be very expensive. I don't think this tool is going to be "basically free" like SourceSafe. If you're thinking about Hatteras, you should probably look at the pricing for ClearCase and assume it may end up in a similar ballpark.
Different Features
The source control market has always been highly fragmented. No player dominates this arena, and Hatteras is not going to change that. If you can afford it, Hatteras will eventually be the preferred tool for teams which develop only on Windows using only Visual Studio. But despite all of Microsoft's efforts, there are plenty of teams which don't fit neatly inside that box.
Do you need clients on Unix or MacOS? Do you want integration with other development environments besides Visual Studio? Do you have a limited budget? Microsoft is not likely to meet these needs, and that will leave opportunities open for other vendors. Granted, these are niche opportunities. I'm not saying that the source control market will spawn a bunch of high-flying IPOs. I am merely pointing out just a couple of examples why there will be plenty of vendors who continue to exist and thrive in this space. Unless we screw something up badly, SourceGear will be one of them.
Looking Forward
SourceGear has been in business for over seven years. We have learned how to put plywood over our windows when the storm is approaching. This storm will not be fatal for us.
Microsoft was kind enough to give us nine months advance notice before today's announcement. We probably have at least another nine months before Hatteras 1.0 is shipping. Vault 2.0 is available now. It's selling extremely well and our customers love it. We plan to stay focused on our product and our customers. Our plans did not change today.
Still, Hatteras has probably ruined my chances of becoming a billionaire by age 40. I definitely blame Brian Harry for that.
But SourceGear and Vault are going to be okay.


Code1240 Assessment 3
The Completed Design
Nelson Chen z5219157
The Cinema
Concept
Movie
Idea
The Resident
Completed Design
Renders


Hey there! My name is Courtney and I am a Software and Web Developer. In my free time I love taking snapshots
of
anything and everything. Initially I decided to take up coding as a way to live my dream
life. Living in a van by the river. I love to travel and hope to see as much of this beautiful world as
possible.
Take a look at my resume by clicking here.
Gambit is my Pembroke Welsh Corgi. He isn't the greatest assistant. He sleeps on the job and barks at some of my clients. I keep him around because his adorable smile brings in more clients than he scares off.


Tasks requiring tight coordination between groups of robots are very challenging. Typical approaches use groups with fixed hierarchies or the same simple algorithms on all robots, which may not work for unknown environments and complex problems. However, humans are very good at coordinating and adapting to changes in the environment. A major reason for this is their diversity. In a group, humans take different roles and work together to reach a better solution than they would individually.
To understand the behaviors humans use to cooperate, we have developed an online multiplayer game in which a group of players must arrange themselves in a formation using only a limited view of their immediate surroundings, simulating a robot with a lidar sensor. We have observed that humans learn to complete this task efficiently, and take on diverse roles to ensure group success. Every group has some players who take a leading role and direct others into place, some who collaborate by joining groups and forming lines, and some who create anchors for the group to form around. Over several consecutive games, players maintain the same roles, and the group learns to tightly coordinate their actions. We plan to train diverse groups of autonomous agents and robots to use these same behaviors to coordinate on shape formation and other complex tasks.
The video below shows a group playing the cooperative game with instructions to form a rectangle. Each participant has only the neighborhood view so can only see the area directly around their position. The players initially form several parts of shapes before finding the rest of the group, and show different roles and communication as they coordinate to form the final rectangle.


Welcome to my Portfolio site. I am Lino Francis. I love graphic design. Designing is some thing I have done for as long as I remember. I do what I enjoy. Quite a bit of which you can see on this site..
I apply strategic thinking, design insight and practically to each of my work. I am well versed in Adobe Photoshop, Adobe Illustrator, Adobe XD, Adobe Flash & Coral Draw.


@* * Copyright 2010-2013 Artima, Inc. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @import controllers.Application.latestScaladocFor192 @userGuidePage("Philosophy and design") {
by Bill Venners, creator of ScalaTest
My main goal for creating ScalaTest was the same goal I had for promoting Scala in the first place: to help programmers become more productive. Increased productivity means releasing new features more quickly, with better accuracy and quality. Better accuracy means building the right features. Better quality means fewer defects.
Although writing tests can slow you down in the short term compared to just writing the production code, the time spent writing tests can pay back in increased quality and reduced time in the debugger. Over the long term tests can provide a safety net that enables you to make changes to existing code with greater speed and confidence, though during major refactors, fixing the many broken tests can add a significant extra cost. You can consider this cost another investment in testing, weighing the up-front cost of fixing the broken tests against the potential for returns in increased velocity and quality over time.
Accuracy, building the right features, requires fluid communication between developers and other stakeholders such as management, testers, and customers. Tests can potentially facilitate such communication because tests represent a highly accurate view of the state of the system.
My goal with ScalaTest is to improve the return on investment of testing. I've tried to minimize the cost of testing by making test code concise, plainly obvious, quickly understandable. A guiding design principle of ScalaTest is that different people on a team should be able look at each others test code and know immediately what's going on. I've also tried to bring that clarity out of the code and into the artifacts generated as tests run. My goal with the generated artifacts is to facilitate communication between all stakeholders—developers certainly, but also management, testers, and customers.
Lastly, ScalaTest is not a one-size-fits-all testing framework, it is a platform that can host different styles of testing. Why? Because different people with different problems need different tools to solve them. Even the same people need different tools in different situations. ScalaTest is designed to make it easy for you to customize your testing tool to meet your current needs, and for the built-in traits at least, make it easy for anyone who comes along later to read and understand your code.
ScalaTest is the “scalable test framework.” Like the Scala language on which it is built, ScalaTest is designed to grow with the demands of its users.
Scala is a scalable language in two ways. First, Scala can be molded through library and DSL design to fit widely different tasks. Second, Scala
scales both down to small tasks and up to large ones. It feels as natural to use for small tasks like scripting as it does for large tasks like major software projects built
by large teams. ScalaTest is scalable in similar ways. First, it can be easily molded by overriding its lifecycle methods to address
special testing needs when they arise. Second, it is designed facilitate both small tasks in the Scala interpreter (see Assertions
, ShouldMatchers
,
and the ScalaTest Shell
) and to scale up to testing
very large software projects built by large teams.
The design of ScalaTest can be summed up in one sentence:
ScalaTest allows you to ask a suite of tests to run itself.
The terms suite and test are defined abstractly in ScalaTest to
enable a wide variety of implementations. A test in ScalaTest is anything with a name that
can start and either succeed or fail. A suite is a collection of zero to many tests. ScalaTest's core trait
Suite
, which represents the concept of a suite of zero to many tests, declares
a run
method. To ask a Suite
of tests to run itself, you invoke its run
method.
The run
method is one of many lifecycle methods in trait Suite
. These lifecycle methods, shown in the table below, define a
default way to run a suite of tests and serve as extension points for customization.
| Lifcycle method | Responsibility |
|---|---|
run |
Runs a suite of tests |
runNestedSuites |
Runs suites nested inside this one |
runTests |
Runs tests belonging to this suite |
nestedSuites |
Returns a list of this suite's nested suites |
testNames |
Returns a list of names of this suite's tests |
tags |
Returns a data structure defining how tests have been tagged |
runTest |
Runs one named test |
withFixture |
Runs the passed test function |
suiteName |
Returns a user-friendly name for this suite |
expectedTestCount |
Returns the number of tests expected to run when this suite's run method is invoked |
Trait Suite
is a “functional object” in that it contains no mutable state. What's more, it contains no
immutable state either: trait Suite
is pure behavior. Its run
method does two things, invokes runNestedSuites
and runTests
. The runNestedSuites
method invokes nestedSuites
, then invokes run
on each element
in the returned list. The runTests
method invokes testNames
and
tags
, to get a list of test names and their tags. The testNames
method uses reflection to discover
methods starting with "test"
, and tags
uses reflection to find any tag annotations on the test methods.
Given this data, runTests
filters the tests by their tags, and for each test to run passes the test name to runTest
.
The runTest
method wraps the named test method in a function that invokes the method via reflection and passes the function to withFixture
,
which simply invokes the function.
How does this design enable ScalaTest to address widely different testing needs? You need look no further than ScalaTest itself
for examples. First, ScalaTest facilitates several different styles of testing through its style traits such as
FunSuite
,
FunSpec
, and
FeatureSpec
. One way to think of these style traits is as examples of ScalaTest using
its own extension points to customize itself. All three override the same five lifecycle methods: run
, runTests
, testNames
,
tags
, and runTest
. They inherit the remaining lifecycle methods as is from their supertrait Suite
.
ScalaTest also provides many stackable traits that can be mixed into a style trait, such as
BeforeAndAfterEach
,
OneInstancePerTest
, and
ParallelTestExecution
.
Trait BeforeAndAfterEach
declares methods beforeEach
and afterEach
, then
overrides just one lifecycle method, runTest
, so that it invokes beforeEach
before,
and afterEach
after, invoking super.runTest
. Both OneInstancePerTest
and
ParallelTestExecution
override runTests
. OneInstancePerTest
's implementation of
runTests
ensures each test is run in its own instance of the suite class, and ParallelTestExecution
's
implementation ensures tests are run in parallel.
There is no magic happening behind the scenes. In Scala you could implement something that looks and behaves like a while loop as a curried function that takes a by-name. Similarly you could implement any of ScalaTest's style or stackable traits yourself outside of ScalaTest. As a result, when you do have a special testing need, you will be able to easily accommodate it by overriding one or more of ScalaTest's lifecycle methods in a trait, and mixing that into your test classes. Need to run each test in their own class loader? Need to treat a directory full of files as one suite of tests per file? Need to define a particular order of test or suite execution? Any of these would be easy to implement by overriding lifecycle methods.
Another design focus of ScalaTest is to facilitate the productivity of teams. Teams in the real world are made up of many different kinds of people. These people are often very busy, sometimes tired or under stress. Many are not fans of testing, and even those who think testing is a good idea may not have much time to invest in becoming an expert in a test framework.
One way in which ScalaTest is designed for teams is that it is easy to get started with because it offers styles
familiar to many users. Users of JUnit or TestNG, for example, can
continue using those tools with a few productivity enhancements from ScalaTest sprinkled in. Or if they use
FunSuite
with Assertions
, they'll find an even nicer DSL for testing that still makes sense
given their xUnit background. Users of RSpec will find FunSpec
familiar and easy to get into. Users of Cucumber
will find FeatureSpec
familiar. In short, one of ScalaTest's design goals is that anyone
who has used a test framework in the past should be able to get going with ScalaTest quickly with minimum effort.
Another way ScalaTest is designed for teams is that it is very thoroughly documented in its user guide and Scaladoc, both of which include many examples. The goal is that you should never need to look through ScalaTest source code to figure out what is going on.
Another major design focus of ScalaTest is to use the flexibility of the Scala language to make testing code clear. Because some people working in a team will inevitably be casual, non-expert users of the test framework, ScalaTest's design is focused on making client code “guessable.” The goal is to make client code so plainly obvious that even someone completely unfamiliar with ScalaTest could understand client code written by others without looking anything up in ScalaTest documentation.
In addition to trying to make ScalaTest's syntax as clear and meaningful as possible, the design is also focused on making ScalaTest's behavior as simple as possible. Keeping the test framework simple helps make your test code easier to reason about. The less you need to worry about what ScalaTest is doing behind the scenes, the more you can focus on what your own code is doing.
Lastly, in addition to trying to make code easy to read and understand, ScalaTest is designed to make the code as easy as possible
to write. To write ScalaTest code, you will need to look at the documentation for examples, but
the library is designed to be easy to remember so that you don't have to look again each time you want to write something.
Besides obviousness of code, one other way in which ScalaTest tries to achieve this is through consistency. Once you learn how
BeforeAndAfter
works, for example, you can use that knowledge when using different style traits, because it
is consistent. It works the same everywhere. This “rememberability” is important for teams, because some people on a team will
inevitably have many other duties besides writing ScalaTest code, so they will have a tendency to forget what they learned.
The upshot is that ScalaTest is designed to facilitate productivity of teams by being:
Next, find out about Selecting a style.


From Alban Berg Kammermusik II, Musik-Konzepte 9, 1979, p.49
The main problem that Berg set himself with the Pieces for Clarinet, was to compose in a small form with materials that clearly originate in a large form; he wanted to discover what would happen if this contrary tendancy were displayed, rather than being resolved. Following his inclination to use a difference of viewpoints as the starting-point, he solved the problem by applying, at the same time, two contrasted procedures:
The musician might hope that these problems - particularly in those two examples - will solve themselves if he just plays "exactly as written". This hope is vain, even given Berg's fantastically detailed score. If it is not understood that Berg ties notes into a following rest to indicate a metrical connection - the rests functioning as the downbeat to the previous phrase - then the clarinettist will not be able to play the f# in the first bar as if it were a note on the 4th beat, and the incomplete paraphrase will remain unheard. On the other hand, if the clarinettist, when practising the first two bars, starts an open g on the fourth beat and connects it to the written G#, and then plays the g more softly every time until it vanishes, then he will achieve the right effect. In bar 9, the fermata must be just long enough to break the momentum of the poco accel, and just short enough that the a in the piano is still sounding stongly when the left hand restarts: this a connects the widening intervals in the right hand of bars 8-9 with the opening motif of the piece. Also, the correct performance of this pivotal moment, when time is in suspense, is not possible unless the three voices are carefully balanced. In general, the left and right hands of the piano part have distinct dynamic markings. The crescendo in the right hand in bar 7 begins fff. In the third beat of bar 8 the right hand must already have reached ffff while the left hand is "only" fff. The word "dimin." in the right hand contrasted with the diminuendo sign in the left hand gives them distinct meanings: "make a diminuendo" as against "follow the fading out of the piano's notes". The diminuendo in the left hand should be huge, whereas the right hand should still be strong when it reaches the fermata, to match the clarinet. The pianist can practice this by omitting the last five of the seven chords leading up to the fermata, listening to the piano's natural diminuendo and duplicating it with the given right-hand rhythm.
William DeFotis, born 1953 in Illinois, USA, studied composition at the University of Illinois (Urbana) with Herbert Brün, and in Munich with Wolf Rosenberg as a Fullbright scholar. At that time he was active as clarinetist, private teacher and occasionally as conductor and guest-lecturer. His compositions include: Variation for Piano and Percussion (1973); Septet (1974) for 8 instruments; Wind Quintet (1975); The Charm of Trivia (1975); Tableau (1977); Erasers (1979) (electronic pieces); Inconsequences (1975/6) for speaker and chamber orchestra; String Quartet (1977); Continuous Showing (1978) for 3 percussionists.


Buttons Buttons and their variations Button Button .btn--secondary A link that's sized like a button Button .btn--link A link that's sized like a button Button


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"The Mexican"
Jerry Welbach is a hapless bagman forced to do the bidding of a prominent mob boss.  The only problem is his girlfriend Samantha (Julia Roberts). She wants him to move to Las Vegas with her, now!, or get out of her life forever in "The Mexican."
This much anticipated partnering of Julia Roberts and Brad Pitt has come under some criticism because, for most of "The Mexican," they aren't even together. And, when they are, much of the dialogue consists of Samantha badgering Jerry in a shrew-like and shrill manner, making me wonder why they are even together. Things change for the better when they go their separate ways, but the film then suffers from its real problem - it's too darn long for a light romantic comedy.
This is a problem that is not the fault of the two mega-stars. Roberts and Pitt (along with a fine perf by James Gandolfini) do well enough, but this major star power almost forces the filmmakers, led by director Gore Verbinski, to create a two plus hour tome. As such, there is much padding involved to stretch things out to the detriment of a tight, well-paced story. In Hollywood, it is believed that the people want stars, not a tightly told story, and that's what we get here.
When Jerry heads to Mexico for his one last job to locate the coveted pistol, he is a real fish out of water. The unlucky mob courier sees his little mission as a hit and run, where he does the deed and can get back to Samantha to beg forgiveness. Once he lands, though, things could not go more wrong as he gets the gun, loses it, gets it again, but loses his passport, keeping him in country with no way out. His puppet master back in the States, mob accountant Bernie Nayman (Bob Balaban), for insurance, sets one of his thugs to find Samantha and put her on ice until the gun is delivered. Unknown to Jerry, Bernie is planning his own scam to wrest the gun from his boss, Arnold Margolese (Gene Hackman in an uncredited cameo), the man that Jerry caused to go to prison.
Paralleling Jerry's plight is the strong-willed Samantha's undertaking to move to Las Vegas, get a job as a waitress and become a croupier at a casino. When she hits the road, she finds herself in the clutches of a vicious hit man named Leroy (Gandolfini). This becomes one of the movie's brightest moment as the bad guy and his victim begin to bond, becoming fast friends and confiding their deepest secrets. The chemistry between Roberts and Gandolfini is palpable, sweet and entertaining.
Brad Pitt's Jerry is left to his own simple devices as he makes a journey of both confusion and enlightenment. Pitt has always had a comic flair (check his funny stoner perf in "True Romance") and is up to the task of carrying better than half of the movie alone - well, he does have a bonding relation, himself, with a big, scruffy dog whose favorite toy is a beat up, deflated football. When he finally locates the prized pistol, in the hands of Margolese's grandson, Beck (David Krumholtz), things go bad when the kid is killed in a bizarre shooting accident. They get worse when Jerry's El Camino, the body and the Mexican are stolen.
When Bernie learns of Jerry's dilemma, he thinks he is trying to pull a fast one and sends a hit man, Jerry's friend Ted (J.K. Simmons), to bag the bagman. The two stories - Jerry's plight and Samantha and Leroy's own journey - start to cross paths, frequently, as poor Jerry struggles to get himself and the gun back to the US while trying to save Sam from the mobster.  There's a lot more going on leading to the requisite happy ending.
As one expects with a big star vehicle like "The Mexican." No expense is denied in the making. The production team essentially renovated the small Mexican town of Real de Catorce to fit the film's needs and the look and feel put you right with Jerry, south of the border. Stylish lenser Dariusz Wolski ("The Crow") gives a separate color palate to each of the sequences, with the flashback scenes to the making of the legend of the Mexican handled particularly well. They have a look that gives the sequence a nice period feel. The rest of the production equals the photography.
"The Mexican" is another case where the star power overwhelms the story, making more not necessarily better. There is a logical ending at the 96-minute mark that would have, with a little work, tightened up things considerably and made a better movie. As it is, the stars are entertaining, Gandolfini is terrific and there is some good fun along the way. I give it a B.
For more Reeing reviews visit www.reelingreviews.com
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"x" "1" "SHUBBER,EK, JACOBSON-KRAM,D AND WILLIAMS,JR, COMPARISON OF THE AMESASSAY AND THE INDUCTION OF SISTER CHROMATID EXCHANGES: RESULTS WITH TENPHARMACEUTICALS AND FIVE SELECTED AGENTS, CELL BIOL. TOXICOL. __ 2(3):379-399,1986"


Javascript DOM Exercise
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Guess an integer from 1 to 10
Test Your Guess
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ARTICLE 1. GENERAL PROVISIONS
Note
Article heading reenacted by L 2015, c 77, pt of §1.
§576B-101 Short title. This chapter may be cited as the Uniform Interstate Family Support Act. [L 2015, c 77, pt of §1]
Note
This section is new. Former §576B-101 renumbered as §576B-102.


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cds
2.3.1
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Skip list node. More...
#include <cds/intrusive/details/skip_list_base.h>
Public Types | |
| typedef GC | gc |
| Garbage collector. | |
| typedef Tag | tag |
| tag | |
| typedef cds::details::marked_ptr< node, 1 > | marked_ptr |
| marked pointer | |
| typedef gc::template atomic_marked_ptr< marked_ptr > | atomic_marked_ptr |
| atomic marked pointer specific for GC | |
Public Member Functions | |
| void | make_tower (unsigned int nHeight, atomic_marked_ptr *nextTower) |
Constructs a node's tower of height nHeight . | |
| atomic_marked_ptr & | next (unsigned int nLevel) |
| Access to element of next pointer array. | |
| atomic_marked_ptr const & | next (unsigned int nLevel) const |
| Access to element of next pointer array (const version) | |
| atomic_marked_ptr & | operator[] (unsigned int nLevel) |
Access to element of next pointer array (synonym for next() function) | |
| atomic_marked_ptr const & | operator[] (unsigned int nLevel) const |
Access to element of next pointer array (synonym for next() function) | |
| unsigned int | height () const |
| Height of the node. | |
| void | clear () |
| Clears internal links. | |
Skip list node.
Template parameters:
GC
- garbage collectorTag
- a tag


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Lili Reinhart Talks About Why She's So Open With Her Depression
Lili Reinhart stuns on the November 2019 digital cover of Glamour UK.
The 23-year-old Riverdale star opens up about her struggles with depression, hitting the big screen and body insecurity. Check it:
On depression: “Depression has affected me in so many ways. It’s something that never goes away. I’ve experienced depression and anxiety. Not constantly, but I’m still experiencing it. I have spells of time where I feel completely unmotivated, I don’t want to do anything and I question myself. I don’t know how to handle stress very well. I find that talking about it and sharing my experience with other people, and reminding myself that I’m not alone has been incredibly therapeutic.”
On how Jennifer Lopez has taught her about the power of hustling: “Jennifer Lopez has said about herself, ‘I’m always the hardest worker in the room and I never stop,’ I admire that and that’s what I’ve been doing. At least this past year has been trying to take advantage of where I am in my life. I don’t have kids, I’m young, in my 20′s, I can take the time and energy to put into my career.”
On how cellulite should not be seen as a body insecurity and that real skin is beautiful: “It’s this weird thing where people think that it’s unnatural or a symbol of being fat. It’s so f***ed up because cellulite is just a part of the human body. It’s just genetic, it’s like having freckles on your face. It’s something that is there, you’re born with it, and it’s become this disgusting thing. We’re told: ‘We need to laser this away, no one wants to see that.’ There’s nothing more beautiful than when I see stretch marks, or cellulite, and people’s real skin.”
Read more from Lili‘s interview at GlamourUK.com.


Japanese hospitals struggled with profitability in fiscal 2018, survey finds
Hospitals in Japan faced continuing financial difficulties in fiscal 2018, with institutions across the country registering a 2.7 percent negative profit margin on average, a government survey has shown.
The negative profit margin for those hospitals with 20 or more beds was 0.3 percentage point better than in the previous year, while clinics with up to 19 beds remained in the black, according to a biennial health ministry survey released Wednesday.
Profit margins of health clinics, dental clinics and pharmacies stood at 12.9 percent, 20.5 percent, and 5.5 percent, respectively.
The results of the survey by the Health, Labor and Welfare Ministry will be used to determine the size of medical service payments for health institutions under the public social insurance system. The fees will be reviewed in April.
In the year through March, about ¥42.6 trillion ($390 billion) in total was paid to health care institutions in the country.
Among hospitals, public institutions struggled in particular, with those run by local governments incurring a negative profit margin of 13.2 percent. National hospitals logged negative 2.3 percent.
Private hospitals were in the black with a profit margin of 0.9 percent, with their directors earning about ¥30.42 million annually on average, down 0.5 percent from fiscal 2017.
Heads of clinics earned about ¥28.07 million annually on average, while doctors at private hospitals earned about ¥16.41 million a year, according to the survey.
Medical service fees are funded by taxes, insurance premiums and out-of-pocket payments by patients, and increased service fees lead to inflated medical spending and a heavier contribution burden.
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I recently published my first R package to The Comprehensive R Archive Network (CRAN). It was very exciting and also quite easy. Let me walk you through my process.
First a description of my brand new package: badgecreatr, then a description of steps to take for submission.
Package description
When you go around github looking at projects you often see these interesting images in the readme
The ones you see above are from ggplot2.
What are these? These are what we call badges or shields and they are often actively generated to indicate if software is actually working or not.
Travis-ci, a continous integration service, creates the first one: the one that says build | passing. Everytime a new commit is pushed to Github a new version of the package will be build and tested. Of that test passes without problem the badge will remain green, but if the latest build fails, the green part will turn red like so <- this is my develop branch of badgecreatr that fails while I write this. It could turn green if your read this later on.
The point of these badges is that they quickly signal some quality aspects of your package to new users. I think these badges are incredibly useful, but I found it tedious to copy them to every new project I had, with some small tweaks for different packages (I’m making this a big deal but I have only build 3 packages). Since you will use almost the same badges for many projects I thought it would be interesting to create a package that would copy them in your readme file with one command. I was not the first one with this idea, someone created the briljant badgerbadgerbadger which does the same thing for ruby on rails projects.
After some thoughts I decided to submit it to CRAN so that people can download it from within R without the use of devtools. So that was the why.
Why would you submit a package to CRAN?
The main advantage to getting your package on CRAN is that it will be easier for users to install (with install.packages). Your package will also be tested daily on multiple systems 1.
How do you submit a package to CRAN?
Well, the good people of CRAN have put it on the bottom of their main page:
To “submit” a package to CRAN, check that your submission meets the CRAN Repository Policy and then use the web form.
Mainly you need to run r cmd-check on your package and make sure there are no warnings, and no notes. Or if there are notes, you have to explain them to the people of CRAN. So what does r-cmd-check actually do? It checks if your package can be build and installed and removed without problems, it checks for common problems in naming and if accurate documentation is provided. In other words, it checks if your package will play nice with R and possible other packages, and if people can actually read what you said about your functions in the package. r-cmd-check also checks if your DESCRIPTION and NAMESPACE files are correctly formatted.
You also have to do some effort to check if your package will work on other systems (solaris, FreeBSD, Linux, Windows, MacOS, more?). Since I work on a windows computer, I know it works on my computer. But since I push all my code to Github, and I use Travis-CI, after each commit the entire package is checked on linux (or more, I could even put several mac OS versions in the test I believe).
Furthermore I checked the functionality of most of the code with unit tests. So I was pretty sure it worked on all systems, also because I haven’t used complicated programs that could differ in systems.
The submission process is quite easy, you have to give your name, your email and then you can add your package (the tarball). It is a simple point and click form.
The website reads a part of your package, you have to check if the information is correct. Then you have to promise that you read the information and you hit submit.
What happens after I submit a package?
You receive a confirmation mail with everything you typed in the website. And someone looks over the submission to see if you missed something and if the package works.
I recieved an e-mail back (really fast!), to tell me that I mistyped some things (too bad that devtools or R-cmd-check doesn’t check for these things). I was very excited and quickly did a resubmission (which is identical to a first submission, but I explained what I changed according to their comments )
Things that I should have done that I forgot or didn’t notice:
- single quote software names so ‘Windows 8.1’
- write R and CRAN with caps, not small letters
- I wrote github, projectstatus, travis, rmd etc which should have been:
- ‘Github’, Projectstatus, ‘Travis-CI’ etc.
- linked to a URL that didn’t exist yet
- used a link with http, should have been https
That was it, it took me 2 rounds but then it was finished, I received an email with: “on CRAN now. Best (name of the reviewer)”. that’s it.
And it’s on CRAN now: https://cran.r-project.org/web/packages/badgecreatr/index.html
General remarks
CRAN submission used to be really hard, you had to manually write all the documentation in a seperate file, had to hand check everything, you had to search for computers to test your package on, etc. But with devtools and the excellent manuals online, starting and building a package is really easy. The final step of submission takes almost no work if you followed best practices beforehand.
References:
I had a lot of help from the following sources about writing r packages:
- a very short but extremely useful example of writing packages by Hillary Parker https://hilaryparker.com/2014/04/29/writing-an-r-package-from-scratch/
- Hadley Wickham’s book about writing packages http://r-pkgs.had.co.nz/
- Karl Broman about writing packages http://kbroman.org/pkg_primer/
- You actually have to search for things in the policies, which is a bit dense, but quite ok. https://cran.r-project.org/web/packages/policies.html
- the ropensci organisation has some excellent advise on writing packages https://github.com/ropensci/packaging_guide
Notes
- This I outright copied from Karl Broman found here http://kbroman.org/pkg_primer/pages/cran.html [return]


Maybe you want to join the Big Data world? Or maybe you are already there and want to validate your knowledge? Or maybe you just want to know what Big Data Engineers do and what skills they use? If so, you may find the following article quite useful.
Why ?
Half a year ago I left BigData world after 3 years of creating various data pipelines. Now I would like to share the knowledge I accumulated in this period before it becomes completely obsolete. This article tries to point out the things that I would like you to know if you were to become my coworker. These were also the things I asked candidates for Big Data Engineer position in my previous company.
I will try to not only point out relevant questions but also give brief answers to some of them. It’s important to remember that these answers are very far from comprehensive and to really understand the topic you need to research it on your own.
Don’t be afraid if you don’t know answers to some of these questions. Complete understanding of these topics is a requirement for a senior developer. For mid or junior positions the subset is enough.
Analysis vs Science vs Engineering
It’s a very popular question: what is the difference between data science, data analysis and data engineering. It’s also quite difficult to answer this question. Here is my understanding:
- Data Analyst - a person who looks at the data and tries to answer some business questions based on it. He typically works with business intelligence tools or just MS Excel.
- Data Scientist - a person who plays with raw data, joins various data sources, do basic data processing, feeds data into machine learning models, tunes these models and in general, tries to get as much value from raw data as possible. He typically uses tools such as Python, R, Matlab, tensorflow and similar.
- Data Engineer - a software developer specialized in data transformation. His job is to take the data and make it useful to the business, but also make it in a way that will be maintainable in a long term. This broad description covers fetching data from various sources (ELK, Splunk, Hadoop, Kafka, Rest Apis, object storage and everything else), transforming it and serving to fulfil the business needs. He often works closely with data scientists to know how the data pipeline should look like, which ML model to use and so on.
For more detailed comparison as well as introduction of “Machine Learning Engineer” you can check out this awesone article.
Following paragraphs describe the knowledge needed by data engineers but it’s also relevant to data scientists as they use similar toolset but in a different way.
Programming language
To efficiently develop data pipelines you need to be proficient with at least one of the following languages: Scala, Java, Python.
The choice really depends on your previous experience and on your workplace preference. If you’re wondering which one to learn right now I would go with Scala. It’s the main language for Spark development, has the biggest number of examples and is the most expressive. Because of it’s functional and strongly-typed nature its also the safest of them.
Regardless of your chosen language, you have to have a basic understanding of the JVM, as it is the most popular platform for data processing. You will need to be able to diagnose and debug problems occurring on the JVM. Here is a couple of things to know:
- How would you handle
OutOfMemeryException
s being thrown?  What garbage collectors are available and what are the differences between them?
- How to use a profiler to find performance bottlenecks?
- How to connect debugger to a remote JVM?
Hadoop ecosystem
Apache Hadoop is the most basic toolset for BigData Engineer. You don’t need to know all the caveats of each of the tools but it is important to have a brief understanding of how they work, so you can communicate effectively and know what to study when needed.
- What is Apache Hadoop? - It’s set of a few different tools, not a single thing you can use. Most popular parts of this ecosystem are HDFS, Yarn, Hive, HBase and Oozie.
- What is HDFS? - It’s a distributed filesystem used to store data across the cluster.
- What is a namenode and a datanode?
- What is a replication factor?
- What is a block size? How it affects performance and app development?
- What security mechanisms can we use to restrict data access on the cluster? What is Kerberos?
- What is Yarn? - It’s a resource manager responsible for assigning memory and cores to computing jobs.
- What are schedulers and what schedulers are available by default?
- What are queues?
- What is Hive? - It’s a SQL layer on top of HDFS.
- What is the connection between hive, hiveserver2, metastore and beeline.
- What is the difference between hive, pig and Impala?
- What are UDFs?
- What is HBase? - It’s a NoSQL database built on top of HDFS.
- How it compares to Cassandra?
- Hot it fits into CAP theorem?
- What is Oozie? - It’s a workflow manager used to create data pipelines
- What are alternatives to it?
- What serialization formats do you know? - The most popular ones are Avro, orc, parquet, json and Hadoop object and text files.
- What is a column-based format? How it differs from row-based format?
Apache Spark
Apache Spark has become a go-to tool when doing data processing. It’s good to have some experience with it but it’s not critical. For me, it’s just another tool that exposes some API that you will have to learn. An experienced developer has learned hundreds of APIs and should not have a problem with learning yet another one. Still, there are few things worth knowing about it, even without real experience.
- What are actions and transformations?
- What deployment modes are available?
- How is serialization and deserialization handled when crossing JVM boundaries? How it differs between RDD, Dataframe and Dataset APIs?
- What are shuffles? When they occur and how they affect performance?
- How do you do the stateful processing? Both in batch and streaming.
- What are Spark alternatives? - it’s probably the most important questions. You should have a basic understanding of MapReduce, Flink, Kafka Streams, Cascading/Scalding as well as not-clustered data processing tools like bash or some simple libraries(e.g. fs2 or Akka Streams).
Non-hadoop tools
Sometimes its even more important to know the tools not directly connected to BigData. One of the most critical skills of data engineer is to know when NOT to use BigData and go with much simpler tooling.
- What is Apache Kafka?
- Kafka has become the go-to data streaming solution. You will hit it sooner or later.
- What is a topic?
- What is an offset?
- What is Redis?
- It’s good to know at least on in-memory DB. Once you created your final dataset you need to know how to use it and in many cases, you will need to serve it fast.
- What is ELK stack?
- Sonner or later you will need some search engine or dashboarding solution. Elasticsearch and Kibana are a good start.
- What is your experience with bash?
- In many cases you will need a tolling that will allow you to play with your dataset in most flexible and least time-consuming way. You should be fluent with tools such as grep, cat, jq or awk that will allow you to filter, modify and aggregate your data without any overhead. They are not suitable for production usages but are perfect for experimentation and finding answers quickly.
- What good practices do you know when creating bash scripts?
- These area of expertise can be boosted with knowledge of other scripting technologies like python, ruby or ammonite.
- What are probabilistic data structures?
- When the volume of the data exceeds certain amounts its no longer feasible to make calculations with exact precisions. In such cases, you should know some basic probabilistic data structures such as a bloom filter or hyperloglog that will calculate things such as cardinality or set membership with controlled error rate.
- What SQL databases do you know? What are their limitations?
- It’s good to be familiar with at least one good old sql database. I can bet at least 30% of big data projects could just use postgres and throw all their spark/hadoop code away.
Summary
This list is not comprehensive but it’s most probably not possible to create a comprehensive one. The exact requirements of your potential employer may differ from this list significantly, but being able answer those questions will give you a good base for future development.
If someone would like to complete this article with answers to those questions or add more topics (inline or via links) feel free to open a PR through GitHub repo.


I've worked solo and on teams to come up with ideas to make websites easy to use.
I build mobile first sites that look good on screens of all sizes.
I love building sites using HTML5 and CSS3. I'm also very experienced with front-end frameworks like Bootstrap.
I'm passionate about building great websites that are both easy to use and mobile friendly. I love working as part of a team as well as on my own on various projects. I'm always trying to learn new things and always want to be challenged. I do mostly independent contracting work as well as some freelance projects.
When I'm not working, I love to be home with my wife and daughter. We love going to the park and playing outside together. I'm constantly listening to music and various podcasts. I also enjoy slappin' the bass at church.


Class Transparency
Represents the transparency of a layer or an entity.
Inheritance
Inherited Members
Namespace: netDxf
Assembly: CADability.dll
Syntax
public class Transparency : ICloneable, IEquatable<Transparency>
Remarks
When the transparency of an entity is ByLayer the code 440 will not appear in the dxf, but for comparison purposes the ByLayer transparency is assigned a value of -1.
Constructors
| Improve this Doc View SourceTransparency()
Initializes a new instance of the Transparency
class.
Declaration
public Transparency()
Transparency(Int16)
Initializes a new instance of the Transparency
class.
Declaration
public Transparency(short value)
Parameters
| Type | Name | Description |
|---|---|---|
| System.Int16 | value | Alpha value range from 0 to 90. |
Remarks
Accepted transparency values range from 0 (opaque) to 90 (almost transparent), the reserved values -1 and 100 represents ByLayer and ByBlock transparency.
Properties
| Improve this Doc View SourceByBlock
Gets the ByBlock transparency.
Declaration
public static Transparency ByBlock { get; }
Property Value
| Type | Description |
|---|---|
| Transparency |
ByLayer
Gets the ByLayer transparency.
Declaration
public static Transparency ByLayer { get; }
Property Value
| Type | Description |
|---|---|
| Transparency |
IsByBlock
Defines if the transparency is defined by block.
Declaration
public bool IsByBlock { get; }
Property Value
| Type | Description |
|---|---|
| System.Boolean |
IsByLayer
Defines if the transparency is defined by layer.
Declaration
public bool IsByLayer { get; }
Property Value
| Type | Description |
|---|---|
| System.Boolean |
Value
Gets or sets the transparency value range from 0 to 90.
Declaration
public short Value { get; set; }
Property Value
| Type | Description |
|---|---|
| System.Int16 |
Remarks
Accepted transparency values range from 0 to 90, the reserved values -1 and 100 represents ByLayer and ByBlock.
Methods
| Improve this Doc View SourceClone()
Creates a new transparency that is a copy of the current instance.
Declaration
public object Clone()
Returns
| Type | Description |
|---|---|
| System.Object | A new transparency that is a copy of this instance. |
Equals(Transparency)
Check if the components of two transparencies are equal.
Declaration
public bool Equals(Transparency other)
Parameters
| Type | Name | Description |
|---|---|---|
| Transparency | other | Another transparency to compare to. |
Returns
| Type | Description |
|---|---|
| System.Boolean | True if their indexes are equal or false in any other case. |
FromAlphaValue(Int32)
Gets the Transparency object from a transparency value.
Declaration
public static Transparency FromAlphaValue(int value)
Parameters
| Type | Name | Description |
|---|---|---|
| System.Int32 | value | A transparency value. |
Returns
| Type | Description |
|---|---|
| Transparency |
ToAlphaValue(Transparency)
Gets the transparency value from a Transparency object.
Declaration
public static int ToAlphaValue(Transparency transparency)
Parameters
| Type | Name | Description |
|---|---|---|
| Transparency | transparency | A Transparency. |
Returns
| Type | Description |
|---|---|
| System.Int32 | A transparency value. |
ToString()
Converts the value of this instance to its equivalent string representation.
Declaration
public override string ToString()
Returns
| Type | Description |
|---|---|
| System.String | The string representation. |


Analog signals, such as sound, are continuously variable, both in the number of possible values of the signal at a given time, as well as in the number of points in the signal in a given period of time. However, digital signals (the way things are send/stored in computers) are discrete in both of those respects – generally a finite sequence of integers – therefore a digitization can, in practical terms, only ever be an approximation of the signal it represents.
Digitalization occurs in two parts:
Digitization
The reading of an analog signal A, and, at regular time intervals (frequency), sampling the value of the signal at the point. Each such reading is called a sample and may be considered to have infinite precision at this stage;
Quantization
Samples are rounded to a fixed set of numbers (such as integers), a process known as quantization.
Now that we have seen about digitization we know that it is important to represent continuous signals with discrete variables but, how do we use it in practice?
Sampling
Quantization
Codification
Sound sampling is a way of converting real sounds into a form that a computer can store, and replay. Natural sound is in analogue form this means it is a sound wave and sound waves have a frequency. This frequency is measured in Hertz (Hz). If the sound oscillates at 50 times a second, then its frequency is 50Hz, and so on. The higher the frequency, the higher the pitch of the sound.
A computer is a digital device. This means that it needs definite values (0s and 1s in the case of a computer) so we need a converter. A converter works by "sampling" the wave at regular intervals (in this case the sound wave). At each sampling it samples the wave and turns it into a number for the computer. It does this a fixed number of times per second. This is called the sample rate.
This method of conversion is called "successive approximation", because it keeps taking approximations of the wave.
A typical approach of storing an image digitally on a computer, is by sampling the image at a rectangular grid. The color, or intensity, at each of these points is converted into a numeric value and stored in the computer. Apart from the color/intensity at those specific points, everything else is discarded when the image is stored in the computer.
When displaying this digital image, we again form a continuous image by interpolating between the stored samples.
Now that we defined the needed samples we must defined how to quantify them
the process of converting a continuous range of values into a finite range of discreet values.[1]
As number of bits to represent a pixel intensity (assume Gray scale image for convenience) is limited, quantization is needed. Suppose 8 bit is used for a pixel, it’s equivalent value ranges from 0 to 255 (discrete values). 0 is assigned to pure Black, and 255 is assigned to pure White. Intermediate values are assigned to gray scales as shown in this image. This process is quantization. For 8 bit pixels, quantization level is 256.
Every sample is represented by an approximation of its real value. This is where the conversion into digital of an analogue signal introduces a degradation. The voltage-value gets picked up by an appropriate circuit and must then be stored on a digital support system, therefore it needs to be first converted into binary form. To do this we need to add a quantization operation which identifies a binary number as an approximation of the real value introducing an error called: quantization noise. The latter's maximum amplitude is identified with the amplitude of the quantization-interval.
The Nyquist frequency is the bandwidth of a sampled signal, and is equal to half the sampling frequency of that signal. If the sampled signal should represent a continuous spectral range starting at 0 Hz (which is the most common case for speech recordings), the Nyquist frequency is the highest frequency that the sampled signal can unambiguously represent.
If a speech signal is sampled at 22050 Hz, the highest frequency that we can expect to be present in the sampled signal is 11025 Hz. This means that to heed this expectation, we should run the continuous signal through a low-pass filter with a cut-off frequency below 11025 Hz; otherwise, we would experience the phenomenon of aliasing.


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Introduction to Free, Libre and Open Source Software
for Geospatial Analysis and Modeling
August, 2015
Four freedoms
There are four freedoms that user should have:
the freedom to use the software for any purpose,
the freedom to change the software to suit your needs,
the freedom to share the software,
the freedom to share the changes you make.
Four freedoms
the free software definition
by Richard Stallman and Free Software Foundation
GNU logo, a free operating system by FSF
Gratis versus Libre
for free (at no cost) is not enough
free software is free to run, study, modify and distribute
free is a matter of liberty not price
free as in freedom (as opposed to free as in free beer )
libre as opposed to gratis
also: free software != freeware
More than Open Source Code
having source code is not enough
Stallman's four freedoms require having the source code
but just having the source code doesn't imply the freedoms
open source means also open development, open community, open discussions, …
not only the possibility to see the source code
however, source code is publicly and easily accessible
Free, Libre and Open Source
free software (FS), open source software (OSS), free and open source software (FOSS), free, libre and open source software (FLOSS)
the term open source is often used for simplicity
often used by developers as it refers to a development model
the term FOSS is often used in geospatial community
free refers to user's freedom
open source refers to open development model
FOSS4G (free and open source software for geospatial)
Commercial and FOSS
FOSS can be used commercially
FOSS can be commercial but not proprietary
Famous examples:
Why industry uses open source
vendor neutrality
proprietary software has a single vendor (vendor lock-in)
open source software is independent on particular companies
open source is not influenced by vendor's current business goals
flexibility (use where you want, when you want)
interoperability
open source aims to support standards
proprietary software often uses propriety formats
cost (no license fees, no license management)
low total cost of ownership
(includes cost to upgrade or migrate)
support (many free options available by default)
money not spent on license fees used for support or features
influence in development (direct access to developers)
Types of FOSS licenses
copyleft licenses
GNU GPL
free software licenses (according to four freedoms by FSF)
code cannot be combined with proprietary (and closed) code into one program
examples: Linux, R, GRASS GIS, QGIS
Types of FOSS licenses
permissive licenses
BSD, MIT
source code can be modified, closed and sold as proprietary
more options for the developer
can combine the source code and software more freely
can make the new software proprietary
less freedoms for the user (might not get the source code, thus freedoms)
example: GDAL used in QGIS as well as in ArcGIS
Users and FOSS licenses
licenses are mostly important for the developers
users don't have to worry about the license
as long as they know that the software is under some known license
lists of known licenses:
this is different from proprietary software
user must read and agree to EULA or similar type of agreement
license is used in the meaning license to use
often paid or limited (or both) for proprietary software
applies to paid software, freeware, online services, …
FOSS development
open community of developers and users
may include individuals and companies
flexible, interoperable and portable
extensive supporting software tools for code management, bug tracking, documentation, collaboration
browse the source code on-line
anybody can modify but only selected changes go to the original project
Code sprints
developers and users working together at one place
open participation
self organized through wiki
software development, documentation, data
often associated with conferences
code sprint, community sprint, developer meeting, hackfest, hack day, hackathon, codefest, meetup
Examples:
OSGeo Bolsena 2010 ,
GRASS GIS Prague 2011 ,
OSGeo Vienna 2014 ,
QGIS Copenhagen 2015 ,
GRASS GIS Como 2015
Standardization
Standardization and open source are different but often related.
OSGeo different from OGC - Open Geospatial Consortium
sets standards for interoperability (similar to OpenGL)
both proprietary and open source software follows these
standards where it is good for them
OSGeo and OGC coordinate their activities.
OSGeo
Open Source Geospatial Foundation
By 2005 there was more than 200 FOSS4G projects: need for selection and organization
Founding projects (February 2006):
GDAL/OGR, GeoTools, GRASS, Mapbender, MapBuilder, MapGuide Open Source (Autodesk), MapServer, OSSIM
Mission: To support the development of open source
geospatial software and promote its widespread use.
OSGeo projects
Web Mapping
deegree ,
geomajas ,
GeoServer ,
MapServer ,
OpenLayers ,
PyWPS ,
ZOO-Project
Desktop Applications
GRASS GIS ,
Marble ,
QGIS ,
gvSIG ,
Optics
Geospatial Libraries and Databases
FDO ,
GDAL ,
GEOS ,
GeoTools ,
OSSIM ,
PostGIS ,
Orfeo Toolbox ,
rasdaman
Metadata Catalogs
GeoNetwork ,
pycsw
Some projects are in incubation (code and license cleanup, small community).
FOSS4G Landscape
OSGeo
global organization, local chapters
and related projects like GeoForAll
OpenStreetMap
data, applications, services
FOSS4G Landscape
LocationTech
working group at Eclipse Foundation
FOSS4G conferences
global and local conferences
Individual projects and communities
projects and communities exist on their own
Example Desktop System
GIS
QGIS, GRASS GIS
statistics
R (geostatistics, predictive modeling, …)
database, attribute data
SQLite, PostgreSQL with PostGIS, LibreOffice
graphics
Inkscape, Gimp, ImageMagic
Portability: works on Linux, Mac, Windows
Interoperability: programs designed to work with others
Example WebGIS System
backend
GRASS GIS, R, SAGA GIS
front end
OpenLayers, Django
database
PostgreSQL with PostGIS
server
ZOO-Project or PyWPS (OGC WPS, native support for GRASS GIS)
Flexibility: standards and open formats allows to switch different components
Scalability: can be copied to more machines at no cost
GRASS GIS history
1983 started at US Army CERL
as land management support system,
see an old promotional video
evolved into general purpose GIS
1999 GNU GPL, international team of developers
30 years of GRASS GIS development in 2013
check out last change
(probably just few hours ago)
last release Dec 28, 2016 [last checked Feb 2017]
GRASS GIS
portable: different OS, 32/64bit, code in C and Python
web-based infrastructure
Subversion code repository with online source code browser, feature requests, bug reports,
users and programmers manual, wiki, IRC
support
community
commercial
Faunalia, Tekmap Consulting, GDF Hannover, GIS Mentors, … freelancers
sponsors
Municipality of Trento, Fondazione Edmund Mach, Google via OSGeo, … individuals
GRASS GIS spatial data types
2D raster (imagery, DEMs, …)
2D/3D topological vector
3D raster (voxels, volumes, soil properties, …)
time series (of anything above)
GRASS GIS functionality
current version 7
almost 500 modules
more modules in GRASS GIS Addons repository
wide range of functions
raster and 3D raster
map algebra, DEM, interpolation, flow, neighborhood, solar, cost surfaces
imagery
rectification, multispectral, classification, segmentation, FFT
vector and database
buffers, overlays, conversions, networks, attribute management, SQL
spatio-temporal
temporal topology, algebra, sampling, statistics, aggregation
visualization
2D display, 3D visualization, temporal visualization, PS and PDF maps
interoperability
WMS support, export to different formats
GRASS GIS Basics
database structure
based on cartographic projections and user's need to organize data
example
north_carolina_state_plane_meters (location)
new_highway_project (mapset)
elevation (raster)
streets (vector)
wake_county (mapset)
usa_latlon (location)
interfaces
Graphical User Interface (now wxGUI, other GUIs in the past)
Command Line Interface (examples in manual)
Application Programming Interface (C for hard work, Python for scripting)
designed for easy transition between the interfaces
computational region
extent and resolution for raster computations
GRASS GIS Learning Resources
Summary
FOSS, FOSS4G, open source
software that is free to run, study, modify and distribute
solutions that are not depended on a particular company
open development, code sprints, GSoC, …
driven by user needs, not vendor needs
OSGeo
different combinations of software can be used
GRASS GIS
most established general purpose open source GIS
large portfolio of functions


I personally really appreciate the InstallR package from Tal galilli, since it lets you install a great number of tools needed for working with R just running a function.
Among these functions one of my favourite is the **_updateR()*_** function which checks for new versions of R language and in case of positive response installs it on your machine.
The only issue with this function is that it works only on Windows Operating systems.
Mac version of updateR function: the UpdateR package
That is why I developed updateR, a package that let’s you update your R version from R itself just running the following line of code:
updateR(admin_password = "os_admin_user_password")
that is to say you will only have to provide admin user password. This password is required because some of the command executed by updateR() are run as super user (for advanced readers we are talking of our old “sudo” friend).
short paragraphs which follow shows you how to install and use the package.
how to install the updateR package
UpdateR package is hosted on Github. You will therefore have to install it leveraging the install_github() function from devtools package:
require(devtools)
install_github('andreacirilloac/updateR')
how to update R version using the updateR package
As seen, updating R version from R is actually a breeze: you just need to run a function.
all you will need to do is retrieve your MAC admin user password and then run the two following lines of code:
updateR(admin_password = "os_admin_user_password")
Since every command launched in background (see below for more infos) is passed with verbose option on, you will suddenly see your console being populated of output messages informing you on how things are going on.
A the end of the installation process those two lines should appear:
everything went smoothly open a Terminal session and run 'R' to assert that latest version was installed
that is all you need to know to use updateR. By the way If you want to know more on how the function works, have a look at the following paragraphs.
behind the scenes: how updateR works
as experimented with Ramazon package, which allows you to publish your shiny app on amazon AWS with a function, I developed updateR mainly leveraging the system() function, which lets you pass commands to the command line directly from the R environment.
That said, the updateR function workflow can be summarized into the following steps:
verify that user is running a unix machine
as said, updateR is a package thought as a OSX twin of Tall galili installr package and can therefore be run only on Mac OSX machines.
Following this updateR takes care of verifying that you are launching it from a unix OS, running the stopifnot() function in order to alt the execution on non-unix systems:
stopifnot(.Platform$OS.type == "unix")
get last R version from CRAN
How would you programmatically check for the latest R version?
updateR does it importing in R the html code of the rstudio CRAN mirror page (https://cran.rstudio.com/bin/macosx/) and extracting from it the version number.
page <- xml2::read_html(page_source)
version_block <- rvest::html_nodes(page,"h1+ p a+ a , table:nth-child(8) tr:nth-child(1) td > a")
filename <- rvest::html_text(version_block) %>% strsplit("\n", fixed = TRUE) # the resulting value is a list
filename <- filename[[2]] # we take the second element, containing the name of the file
filename_quoted <- paste("'",filename,"'",sep = "")
Be aware that resulting filename object will be a string similar to the following :“R-xx.xx.xx.pkg”, since we are looking to compose a download URL for the version package on our machine.
This URL is composed pasting the CRAN mirror url togheter with the filename object:
url <- paste(page_source,filename, sep = "")
one thing still missing here is the comparison between version installed on the maching calling the function and the retrieved version on CRAN.
I will probably (hopefully) develop this within next releases, and collaboration on this would be really welcome.
After defining the download URL we can actually proceed with downloading the package and install it. the first of those two steps will be performed leveraging built-in function download.file() :
download.file(url,filename)
run command line commands within R
the last step is the actual installation of the R version package. this activity will be performed using the command line command installer .
we are first going to put toghethet our line of command with paste() function and then submit to the terminal calling system() function on our command string:
command <- paste("echo " , admin_password, "| sudo -S installer -pkg ", filename_quoted , " -target / -verbose")
system(command)
feel free to complain with me
I shared updateR as a package since I think it could be a really useful tool for the R commuynity.
As R itself, updateR package comes with absolute no warranty ( ;)) , nevertheless I will be really pleased to read your comments and suggestions on it, as well as I will be pleased if you would like to further improve it through the Github repo


The easiest way of starting a new project using ABP with ASP.NET Core with Angular is to create a template on templates page. Remember to check "Include module zero". After creating and downloading your project, follow below steps to run your application.
In this template, multi-tenancy is enabled by default. You can disable it in Core project's module class if you don't need.
If you have problems with running the application, please try close and open your Visual Studio again. It sometimes fail on first package restore.
Angular application needs to following tools be installed:
We used angular-cli to develop the Angular application.
Open a command prompt, navigate to angular folder which contains *.sln file and run the following command to restore npm packages:
npm install
Notice that npm install may show some warn messages, which is not related to our solution and generally it's not a problem. The solution can also configured to work with yarn and we suggest to use it if available on your computer.
In your opened command prompt, run the following command:
npm start
Once the application compiled, you can browse http://localhost:4200 in your browser. Be sure that Web.Host application is running at the same time. When you open the application, you will see the login page:
Angular client app has also HMR (Hot Module Replacement) enabled. You can use the following command (instead of npm start) to enable HMR on development time:
npm run hmr
Now you can login the application using default credentials. User name is 'admin' and password is '123qwe' as default. If you want to login as a tenant, first switch to that tenant in login page. There is a tenant named "Default" by default. Once you login successfully, you will see a dashboard:
This dashboard is just for demonstration and to be a base for your actual dashboard.
We used angular-cli tooling to build Angular solution. You can use ng build command to publish your project. It publishes to dist folder by default. Then you can host this folder on IIS or any web server you like.
If you want to consume APIs/application services from a mobile application, you can use token based authentication mechanism just like we do it for Angular client. Startup template includes JwtBearer token authentication infrastructure.
Here, Postman (chrome extension) will be used to demonstrate requests and responses.
Just send a POST request to http://localhost:21021/api/TokenAuth/Authenticate with Context-Type="application/json" header as shown below:
We sent values usernameOrEmailAddress and password. As seen above, result property of returning JSON contains the token and expire time (which is 24 hours by default and can be configured). We can save it and use for next requests.
About Multi Tenancy
API will work as host users by default. You can send
Abp.TenantId header value to work with a
specified tenant. It's an integer value and 1 for default tenant
by default.
After authenticate and get the token, we can use it to call any authorized action. All application services are available to be used remotely. For example, we can use the User service to get a list of users:
Just made a GET request to http://localhost:21021/api/services/app/user/getAll with Content-Type="application/json" and Authorization="Bearer your-auth-token ". All functionality available on UI is also available as API.
Startup template includes a tool, Migrator.exe, to easily migrate your databases. You can run this application to create/migrate host and tenant databases.
This application gets host connection string from it's own appsettings.json file. It will be same in the appsettings.json in the .Web.Host project at the beggining. Be sure that the connection string in config file is the database you want. After getting host connection sring, it first creates the host database or apply migrations if it does already exists. Then it gets connection strings of tenant databases and runs migrations for those databases. It skips a tenant if it has not a dedicated database or it's database is already migrated for another tenant (for shared databases between multiple tenants).
You can use this tool on development or on product environment to migrate databases on deployment, instead of EntityFramework's own tooling (which requires some configuration and can work for single database/tenant in one run).
Startup template includes test infrastructure setup and a few tests under the .Test project. You can check them and write similar tests easily. Actually, they are integration tests rather than unit tests since they tests your code with all ASP.NET Boilerplate infrastructure (including validation, authorization, unit of work...).
This project template is developed as open source and free under Github: https://github.com/aspnetboilerplate/module-zero-core-template


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IMC 7.0 E0202
6 May 2014 · Filed in Networking • 0 CommentsSince my change to HP networking, I´ve been working steadily on gaining experience on every aspect of HP networking products. One product in particular is HP´s IMC which is their solution to network management. In the past I have solely worked on Solarwinds and hold a SCP. But i´m pretty impressed with IMC and the roadmap that HP is showing. So far they have been able to stick to a 6 month update turnaround, which have shown good progress. Now with the latest version E0202 they have bumped IMC upwards with some enhancements and lots of bugfixes. Enhancements
- Topology HTML 5 is now an option on the drop down menu and not hidden away.
- IPv6 management, starting with Comware.
- Virtualization (Vmware, Hyper-v/KVM / XEN get support for more template driven provisioning and other cool stuff.
- Custom view gets some more love and care with better support for backup plans and alarm filtering, and dynamic rules for selecting devices.
Fixes
- Devices adapters.
- Device support (ASA 5520 / 40).
- SSL support for LDAP.
- Link Aggregation alarms now actually work.
- Annoying pop-up removed if you´re not on a officially supported browser.
Modules
- UAM gets some much needed fixes and love.
- APM gets support for postgreSQL, Hyper-V, Vmware.
- Aruba wireless now supported.
- Wireless topology now gets some HTML5 support, but were still stuck on Java for the moment.
- SDN management now includes app manegement. First step towards the new SDN app store!
Summary It´s safe to upgrade and I would advise that you do it as soon as possible. You can upgrade directly from 5.0 E0401. Read the release notes here...
Tags: HP networking · IMC Previous Post: The starting point of my new blog Next Post: IMC operator LDAP / SSL authentication


Game Developer with over 10 years of industry experience in Game Design and Engineering. Looking to expand
my technical skillset while exploring new and innovative gameplay experiences.
I enjoy exploring new technologies in a variety of domains. My current side projects
take to me to wonderful world of web projects and server applications.
Audit and assist Content Designers in using correct systems and augmenting with script when necessary.
Extended our proprietary C-based scripting language and tools to better suit the needs of the design
department.
Worked as a cross-department intemediary to ensure that expectations were clearly and correctly
communicated between
departments.
Handled large, complex, and high-profile scripts in production and live to ensure optimal server
performance
Trained designers to use our scripting language and tools. Was first line of technical assistance when
designers incurred
tools, systems, build, or source control issues.
Provided Tools Engineering support as needed.
Provided Gameplay Systems Engineering support as needed.
Provided UI Lua scripting support as needed.
Game Designer
May 2011 - November 2014
Created Quests, Challenges, Paths, Achievements in multiple overworld content zones. Also worked on
spawning and
staging of NPCs.
Created multiple Normal and Veteran difficulty Adventures, which are 5-player instances that each
provide a unique
PvE experience unline anything in the game. Handled the scripted framework, gameplay setpieces and
boss encounters.
Created complex AI encounters for bosses as well as player-like behavior for NPCs in a MOBA-style
Adventure.
Worked directly with other departments to clearly communicate the needs of the design department
Created prototypes of new systems and outlined the design requirements for engineering
Assisted and trained other designers in understanding our tools, systems, best practices and optimal
workflow
Phaser Typescript VSCode Boilerplate
Boilerplate code project for getting up and running using Phaser and Typescript quickly using Visual
Studio Code.
Use VSCode to run a server and debug instead of downloading other software to host your project.
Press
Ctrl+Shift+B to compile the typescript files in
src/ to
bin/js/.
Press
F5 to start the webserver.
Open localhost:5858 in your favorite web browser. To modify this port, make sure to update the port
number in
.vscode/launch.json
and
server/server.js.


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| | |
- builtins.object
-
- Ray
class Ray(builtins.object) |
| |
Ray(y=0, theta=0, z=0, isBlocked=False)
A vector and a light ray as transformed by ABCD matrices
The Ray() has a height (y) and an angle with the optical axis (theta).
It also has a position (z), the diameter of the aperture at that point
when it propagated through, and a marker if it has been blocked by the
aperture.
Simple static functions are defined to obtain a group of rays: fans
originate from the same height but sweep a range of angles; fan groups
are fans originating from different heights. |
| |
Methods defined here:
- __eq__(self, other)
- Return self==value.
- __init__(self, y=0, theta=0, z=0, isBlocked=False)
- Initialize self. See help(type(self)) for accurate signature.
- __str__(self)
- String description that allows the use of print(Ray())
Static methods defined here:
- fan(y, radianMin, radianMax, N)
- A list of rays spanning from radianMin to radianMax to be used
with Matrix.trace() or Matrix.traceMany()
- fanGroup(yMin, yMax, M, radianMin, radianMax, N)
- A list of rays spanning from yMin to yMax and radianMin to
radianMax to be used with Matrix.trace() or Matrix.traceMany()
Data descriptors defined here:
- __dict__
- dictionary for instance variables (if defined)
- __weakref__
- list of weak references to the object (if defined)
- isNotBlocked
- Opposite of isBlocked. Convenience function for readability
Data and other attributes defined here:
- __hash__ = None
| |


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See the Pen Bee My Valentine Animation by Caitlin Haaf (@caitlinhaaf) on CodePen.
| Bitmap | Vector |
|---|---|
|
|
<svg width="100" height="100">
<circle cx="50" cy="50" r="40" stroke="green"
stroke-width="4" fill="yellow" />
</svg>
See the Pen Basic SVG Shapes by Caitlin Haaf (@caitlinhaaf) on CodePen.
<svg width="100" height="100">
<path d="M0,0 L50,50 C50,100 100,50, 100,100"
stroke="green" stroke-width="4" />
</svg>
Drawing curved SVG lines gets a bit more complicated - you have to include the positions of anchor points which will define the direciton of your curve.
See the Pen SVG Paths by Caitlin Haaf (@caitlinhaaf) on CodePen.
p{
color: red;
background-color: orange;
}
Used to scale, move, rotate, or even use 3D effects
#square{
transform: rotate(60deg);
}
#circle{
transform: scale(2);
}
We can combine CSS transformations by having them all on one line:
#triangle{
transform: translate(100px, 200px);
}
See the Pen Basic SVG Shapes by Caitlin Haaf (@caitlinhaaf) on CodePen.
It is possible to animate between different styles in CSS.
Before adding an animation to an element, we have to define the animation.
@keyframes fly {
0% {
transform: translateY(0%);
}
50% {
transform: translateY(-5%);
}
100% {
transform: translateY(0%);
}
}
When applying an animation to an element, we can define:
You can also define:
#bee {
animation: fly 4s infinite alternate;
}
See the Pen Basic SVG Shapes by Caitlin Haaf (@caitlinhaaf) on CodePen.
#bee {
offset-path: path('M20,20 C20,100 200,0 200,100');
animation: fly 3000ms ease-in-out;
}
@keyframes fly {
0% {
offset-distance: 0%;
}
100% {
offset-distance: 100%;
}
}
See the Pen Bee My Valentine Animation - Starter by Caitlin Haaf (@caitlinhaaf) on CodePen.
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