模型-视图-控制器 (MVC)
什么是 MVC?
MVC 是一个通过它在应用程序中发挥什么作用分离代码. In the 应用程序流程中，首先加载一个控制器. 控制器通过执行某方法从模型取得数据. 一旦完成，控制器决定加载视图，让访问者可以看到输出结果.
控制器
Fuel's routing decides based on the requested URL what controller to load and what method to call upon it. This is where your application starts working. The Controller decides what actions to take, what to do with any user input, what data gets manipulated and which View is shown to the user. The Controller does none of these things itself however; it calls upon Models and Classes to do the work.
查看更多关于 控制器.
模型
Whenever data needs to be retrieved, manipulated or deleted this should always be done by a model. A Model is a representation of some kind of data and has the methods to change them. For example: you never put SQL queries in a Controller, those are put in the Model and the Controller will call upon the Model to execute the queries. This way if your database changes you won't need to change all your Controllers but just the Model that acts upon it.
查看更多关于 模型.
视图
Views contain your HTML, which should never be found in your Controllers or any other class that is not
specifically meant to create output.
Views should thus contain little more than echo and foreach usage of PHP.
查看更多关于 视图.
视图模型
一旦应用程序变得越来越复杂，你会发现，很难去区别一段逻辑代码是否属于控制器, 当它具有定义视图以及少量逻辑代码? 这就是为什么会有 ViewModels; 它们粘合于你的控制器和视图之间.
查看更多关于 视图模型.


Have you ever gone down the wrong isle in the grocery store? Have you always wondered if other stores nearby have lower prices to make sure your getting the best deal? With Smart Shopping these problems are a thing of the past! Read more below to find out how!
Crowdsourcing data relys on as much data as possible so the more users that have our app the better the data and services will be.
This application must, in addition to all it's super cool connected features, function as an easy to use shopping list application.
Crowdsourcing data relys on as much data as possible so the more users that have our app the better the data and services will be.
While users are using our application we want them to have the best experience possible and want to reduce or eliminate any negative impact resulting from using our phone.
While there are other solutions to some of the problems mentioned before there are key features that we implemented that set us apart from the rest:
React Native JS is how we style and structure our application as it serves as the framework we develop and expand on.
Firebase realtime database is how we are able to crowdsource all our user data in a fast, secure and efficent manner.
To facilitate source control and the sharing of code between very different platforms we chose to use a private GitHub repository.


The glossaries bundle is supplied with the package mfirstuc which provides the command:
This makes the first object of <stuff> uppercase unless <stuff> starts with a control sequence followed by a non-empty group, in which case the first object in the group is converted to uppercase. Examples:
produces ABC (first object is {\em abc} so equivalent to \MakeUppercase{\em abc}), and
produces abc (\em doesn’t have an argument therefore first object is \em so equivalent to {\MakeUppercase{\em}abc}).
Note that non-Latin or accented characters appearing at the start of the text must be placed in a group (even if you are using the inputenc package) due to expansion issues.
Note also that
produces: ABC. This is because the first object in the argument of \makefirstuc is \abc, so it does \MakeUppercase{\abc}. Whereas:
produces: Abc. There is a short cut command which will do this:
This is equivalent to \expandafter\makefirstuc\expandafter{<stuff>}. So
produces: Abc.
If you use mfirstuc without the glossaries package, the standard \MakeUppercase command is used. If used with glossaries, \MakeTextUppercase (defined by textcase the package) is used instead. If you are using mfirstuc without the glossaries package and want to use \MakeTextUppercase instead, you can redefine
For example:
Remember to also load textcase (glossaries loads this automatically).
New to mfirstuc v1.06:
This command apply \makefirstuc to each word in <text> where the space character is used as the word separator. Note that it has to be a plain space character, not another form of space, such as ~ or \space. Note that no expansion is performed on <text>.
This is a short cut for \expandafter\capitalisewords\expandafter{<text>}.
If you want to provide an alternative for the PDF bookmark, you can use hyperref’s \texorpdfstring command. See the hyperref manual for further details.
Examples:
produces: A Book Of Rhyme.
produces: A Book of Rhyme.
produces: A BOOK OF RHYME. (No expansion is performed on \mytitle, so <text> consists of just one “word”.) Compare with next example:
produces: A Book of Rhyme.


Yahoo Media RSS supplements the enclosure capabilities of RSS 2.0 to handle additional information and media types.
"Media RSS" is a new RSS module that supplements the enclosure capabilities of RSS 2.0 to allow for more robust media syndication.
RSS enclosures are already being used to syndicate audio files and images.
Media RSS extends enclosures to handle other media types, such as short films or TV, as well as provide additional metadata with the media.
Media RSS extension enables content publishers and bloggers to syndicate multimedia content such as TV and video clips, movies, images, and audio.
It also allows content to be represented in multiple formats.
See also FeedForAll.


2022-04-17
Two weeks ago, I had the opportunity to attend StaffPlus in New York, one of the very first in-person events for senior individual contributors (in tech). Being pretty fresh in a Staff level role myself, this sounded like the perfect event for me.
The event was hosted by Tanya Reilly, author of Being Glue and the upcoming The Staff Engineer’s Path. The talks were grouped into blocks of three to four, with a mix of shorter and longer talks in each block.
Between the blocks, there were breaks with catered goodness and “office hours” to chat to the presenters of the previous block, though they were around the entire day.
Clearly, a lot of thought had gone into the details:
The “Recording” links require a digital pass to the StaffPlus event.
This was an overview of what it means to be a senior individual contributor (IC), though at the Principal level. In Silvia’s view, being a senior IC means making technical decisions and leading, i.e., no doing everything yourself.
A senior IC is more effective if they report to a senior leader and partner with managers; they have input on the performance evaluation of teams.
They translate from product strategy to technical strategy, and from there to technical execution; they identify one-way (“trap”) doors, while avoiding micro-planning.
Silvia used an analogy of steering a ship into the “bay of appropriate futures”, and there was a lighthouse, but the details elude me now.
A method to avoid becoming an architecture astronaut is to be involved in handling incidents; generally, Silvia advises to keep contacts outside of engineering.
A lightning talk about a process to help focus on the most important thing to work on.
There are bad strategies to decide what to work on, for example: inertia, decibels, and guilt.
Ryan described his ritual: at the beginning of the week, he empties his inbox, reflects on the last week, and then chooses his work. This reminds me of my own process, based on Getting Things Done, though my cadence is daily instead of weekly.
To account for biases, Ryan asks himself questions:
Amy talked about getting up to speed quickly when starting a new job as a Staff+ Engineer by way of asking yourself (and others) questions. Some of these would be very relevant for the interview process as well, and to me, they make sense in general to better understand expectations for you position, even without starting at a new company.
Why were you hired, and why was the position filled externally? What’s the problem you’re supposed to solve? The range of possible answers here included “internally, nobody wanted the job”—which probably would be valuable to know during interviewing already.
To build your network, try to understand organizational context; focus on relations that violate Conway’s law. Amy has a script to use for “getting to know you” conversations.
What’s your job? Don’t make assumptions, set expectations. Clarify where on the archetype spectrum people see your position. How do different people define “Staff level”? How do they know somebody is ready for it? What’s top of mind for your manager?
What (wrong) lessons did you learn? Don’t over-index on past experience. As an example: an acquisition doesn’t have to kill innovation; this was interesting to hear, seeing that Amy works at GitHub, where innovation indeed hasn’t come to a grinding halt since the Microsoft acquisition in 20181.
What’s the first thing you should tackle? Agree on a timeline.
What are you, as a new person, uniquely qualified to do? You have the opportunity to identify under-documented areas, you could tackle paper cuts, you could re-examine assumptions; overall: be kind.
When do you know enough? Do you learn through experimentation, or doing? Is there a mismatch between your position on the spectrum and the company’s? (Is it the reason you were hired?)
To gain broad knowledge: draw infrastructure and architecture diagrams, map out a request path; to gain deep knowledge, look at the most complex piece of code, and figure out where its logs are.
Eventually, though, move beyond using these questions, and forget about them.
Amy’s talk is also available as a post on LeadDev.
This lightning talk was technical, i.e., not specifically about Staff+ work, but happened to be very relevant for me. Michelle described the lifecycle of an API design:
An impressive bit was how, as part of designing an API for checkout, the team looked at every single checkout implementation they could find online.
Katie (of Oh Shit, Git?!? fame) talked about running a long term project: turning Etsy into a progressive web app (PWA). The main alternatives were rebuilding from scratch, or refactoring; the latter took longer and was the harder sell, but that’s what they ended up doing.
The whole talk was anchored around a comparison to Mars missions: “to get to Mars, you have to get to the moon first.”
The advice was grouped into four strategies:
This was probably my favourite talk. Short and sweet, to the point, and actionable. Mattie is a director and not a Staff+ Engineer, so they were talking from the perspective on the “other side of the table”.
The topic of the talk was a framework for when “the brilliance of your ideas alone no longer gets commitment”.
I found this talk confusing, and maybe my Helveto–Canadian sensitivities for humility (and perceived absence of it) got the better of me, but the self-promotion got in the way of the message for me.
I did learn that Twitter builds their own hardware, and Bobby was involved in saving tons of money through realizing that you could use cgroups and namespaces to combine storage and compute into single nodes instead of separate ones, more efficiently using spare CPU capacity of storage nodes.
Right after what I found to be an overly self-promoting talk, Leslie
talked about… how to be a more effective self-promoter
influencer by taking cues from social media influencers.
She started off with bragging about her accomplishments, which was shortly thereafter revealed as a technique to improve your name recognition, along with being “known for things” (building your personal brand).
People should want to work with you; build trust by gaining a reputation for actually finishing things. This requires focus and saying “no”; set realistic expectations regarding your involvement, and point to other people who might be able to help better than you.
Listen and give people space; be an idea aggregator.
Share you influence, be a sponsor or mentor, and learn to delegate (“work to put yourself out of a job”).
Diversify: be involved in as many projects as you can (while still not over-committing, I assume).
Get involved outside of the office, give back to the community. Opportunities can crop up everywhere.
This was another technical talk, and slightly less relevant to me, though still very interesting.
Alice talked about how accessibility can be framed as “solve for one, extend to many”; for example, ramps designed for wheelchairs can also be used for strollers.
Semantic HTML supports screen readers; for a while, “divitis” introduced by CSS made this more difficult. Landmarks (navigable regions) and language attributes further support screen reader usage.
Dark mode might be easier on the eyes than light mode, but more importantly, respect user preferences.
There was quite some overlap between Scott’s talk and Katie’s or Mattie’s; I guess ambiguity is a common theme!
An “ambiguous project” can be one where you can’t even see the proper outline, that deals with unknown tech, or where new team dynamics are involved. Ambiguity is also an opportunity; you can make the project what you want it to be. Generally, the closer to the level “mission statement” you get, the less clear things are.
Before even starting, clarify the project goals. This helps avoid situations where the wrong thing is built, or where goals are “suspiciously generalized”. Work backwards by starting with a product brief (PR/FAQ) to provide specificity and clarity.
Who needs to be involved? Be specific, leverage frameworks such as RACI. Have your stakeholders review the product brief; aim for a tight group of visionaries (think “heist team assembly montage”).
Define intent early and iterate; prefer a “known mediocre” proposal to trying to be perfect. If you get lost talking about the project, workshop the right words and be very consistent (write glossaries!).
For execution, accelerate out of the unknown, and keep an eye out toward surprises. Socialize and get feedback, log and broadcast decisions; relevant articles and talks:
Produce frequent incremental value (see Katie’s talk!).
Know your exit criteria: pick your finish line, and repeat it a lot (see Landing projects successfully).
Manage yourself: protect the team from uncertainty, radiate resilience and calm when you hear bad news. Bend and adjust with pressure. Exploring the frontier of what you’re comfortable with can lead to tremendous growth.
John talked about the analytic hierarchy process (AHP) to make decisions. It is based on the insight that multiple independent judgments outperform an individual expert judgment.
The basic idea is to define criteria for the decision, and then weight them pairwise; then, the different solutions are compared pairwise per criterion. Every participant does that individually, or in a discussion similar to how you’d assign story points. The discussion itself might be more valuable than the result (though John said that they always ended up thinking the result made sense for them).
The outcome is a weighted decision, and if all the criteria and weights are published, anybody looking at the decision can see what the criteria were, and how they were weighted. John talked about a decision that was made using the process, but the criteria weren’t published—and the acceptance of the decision was very low.
The graphs coming out of using the process can, for example, be used in ADRs.
The talk certainly didn’t lack slides with tables and numbers, but thankfully, John has published a tool that can support the process. I’m looking forward to try and use this process sometime soon!
This talk was highly entertaining, but I’m not too sure what I’m taking away from it.
After establishing that there is no road (“it’s all swamp”) and no how-to, Bryan did provide some guidance around working at the Staff level:
David talked about establishing a peer group, specifically for Staff+ developers. He recommends providing a low barrier of entry: Slack channel, Google group, biweekly Zoom calls, the occasional dinner.
When meeting, they would usually just chat, bring up issues, and identify common problems. More recently, they have started a tech talk series, and a mentorship program (not limited to, but run by Staff+).
Of the 50 Staff+ at his company, about ten attend the Zoom calls. (For comparison: at my company, there are eight Staff+ in total).
The group should be self-organized, and provide value for its members.
David provides a collection of resources on his website.
We just started our own Staff+ peer group at work, so we’ll see how it goes!
The level of presentation skills was impressive; the range was from strong to very strong.
For myself, I’ve taken away things mostly around improving clarity for my own role, and what questions to ask to get there. I definitely want to try out AHP, and I feel better equipped to tackle ambiguity.
Certainly an event I’d consider attending again!


The SmartRoutes SDK is a tool designed for software developers to easily implement public transit routing logic in their own applications. To be clear, this documentation is not intended for the end-user of an application.
The SmartRoutes SDK cannot be used in all situations. To get started, make sure your situation matches all of these requirements:
The SmartRoute graph search algorithm is writtin in C# and can be used by any .NET version 4.5 application. The most popular .NET languages are C# and Visual Basic .NET. All example code will be in C#.
GTFS stands for Google Transit Feed Specification. It is a data format made popular by Google to express all sorts of public transportation schedules (e.g. bus schedules and routes). Google has fully documented the specification so that transit organizations can expose their own routes and schedules in a format that Google Maps (and other applications, such as SmartRoutes) can understand. Many transit organizations have already exposed their data in this format. If you are developing an application for your own locality, check the official website of your local transit organization or check the GTFS Data Exchange website.
To use GTFS data with SmartRoutes, all you need is the standard ZIP file containing the supported GTFS text files on your local hard drive or at a URL.
SmartRoutes uses latitude and longitude to place your destinations in a 3 dimensional space: latitude, longitude, and time. A graph is suspended in this space to express the user's passage through time and space. More simply, SmartRoutes needs to know where destinations are so that the proper transit routes can be used! It is recommended that the location's latitude and longitude are precomputed and cached so improve the performance of the searching algorithm.
To use the SmartRoutes SDK in your project, you must follow the following steps:
IDestination
interface.NuGet is used add the SmartRoutes assemblies to your project. Make sure that you understand NuGet before getting started.
The core of the searching logic is in SmartRoutes.Graph.dll
, available on NuGet under the name SmartRoutes.Graph
. This assembly has a single dependency on the SmartRoutes.Model
package. This package will be automatically pulled in by NuGet upon installation of SmartRoutes.Graph
.
IDestination
interface.Each of your destinations should be represented by a concrete class that implements the IDestination
interface. The full type name for this interface is SmartRoutes.Model.IDestination
and is found in the SmartRoutes.Model.dll
assembly. This interface itself implements the ILocation
interface. In total, this requires you to implement the following read-only (only a get
method is required) properties:
double Name { get; }
- a string representing the name of the destination, for display only.double Latitude { get; }
- a double representing the latitude of the location.string Longitude { get; }
- a double representing the longitude of the location.In addition to these core properties, open hours properties must be implemented for each day of the week. These properties are used to improve the performance of the search algorithm by removing possible destinations if the destination is not open for business, in turn improving the quality of the results. The <day name>Reported
boolean indicates whether or not valid open hours are available for this particular destination on that day of the week. If <day name>Reported
is true
, non-null
values for <day name>Begin
and <day name>End
indicate the open and close times for that destination, respectively.
If your destination does not have open and close times or this information is not available, simply return false
from all <day name>Reported
properties and return null
for the open and close times. SmartRoutes will interpret this as a destination that is open at all times of the day.
C# representations of the GTFS entities are provided as part of the SmartRoutes.Model.dll
assembly. The following GTFS entities are currently used by SmartRoutes:
SmartRoutes.Model.Gtfs.Route
)SmartRoutes.Model.Gtfs.Stop
)SmartRoutes.Model.Gtfs.StopTime
)SmartRoutes.Model.Gtfs.Trip
)Since the GTFS text files are in an agreed upon format, the SmartRoutes SDK includes an additional assembly used for reading a GTFS zip file and instantiating the associated entities in memory. This parser is available in the SmartRoutes.Reader
NuGet package. The parser can be used as follows to parse a ZIP file at an arbitrary location on the local file system into a connect object graph.
// create the parser, with child parsers for each supported text files var parser = new GtfsCollectionParser( new AgencyCsvStreamParser(), new RouteCsvStreamParser(), new ServiceCsvStreamParser(), new ServiceExceptionCsvStreamParser(), new ShapePointCsvStreamParser(), new StopTimeCsvStreamParser(), new StopCsvStreamParser(), new TripCsvStreamParser() ); // create the reader, for instantiating from the file system var reader = new EntityCollectionReader<GtfsArchive, GtfsCollection>(parser); // read the ZIP file from the file system GtfsCollection gtfsObjectGraph = reader.Read( @"C:\my_gtfs_archive.zip", null).Result;
Loading from an arbitrary URL is just as easy.
// create the downloader, for instantiating from the web var downloader = new EntityCollectionDownloader<GtfsArchive, GtfsCollection>(parser); // read the ZIP file from a URL GtfsCollection gtfsObjectGraph = downloader.Download(new Uri( "http://www.example.com/my_gtfs_archive.zip"), null).Result;
You must have a list of destinations in memory. As mentioned above, all of your destination instances must implement the IDestination
interface. The most likely source of these instances are from some data access layer, such as Entity Framework or NHibernate.
To build the graph (the data structure used for performing searches), instantiate a GraphBuilder
instance (found in the SmartRoutes.Graph
namespace) and call the BuildGraph
method.
// the entites must be in memory GtfsCollection gtfsObjectGraph = ... IEnumerabledestinations = ... // build the graph var graphBuilder = new GraphBuilder(); IGraph graph = graphBuilder.BuildGraph( gtfsObjectGraph.StopTimes, destinations, GraphBuilderSettings.Default);
Important: the graph holds references to the provided destination entities so that searches can operate on your actual concrete entities (not just on the properties made available in IDestination
).
After the graph is created, you are almost ready to execute a search. Suppose your destination's concrete class is GroceryStore
with a boolean property HasPharmacy
, and you want to search for the most convenient grocery store that has a pharmacy. This kind of search can be accomplished with criteria. Criteria are functions that you, as the developer, write to match destinations.The type of all criteria is:
Func<IDestination, bool>
A single criterium is used to match a single destination. If you conduct a search with multiple criteria, one or can destinations can be matched. It is possible for one destination to be returned from a search if that destination matches all criteria.
For our example, you must instantiate a Func<IDestination, bool>
that only returns true
for GroceryStore
instances with HasPharmacy == true
.
Func<IDestination, bool> criterion = destination => { // exclude any destinations that are not grocery stores GroceryStore groceryStore = destination as GroceryStore; if(groceryStore == null) { return false; } // exclude grocery stores that do not have pharmacies return groceryStore.HasPharmacy == true; }
If you want to match any destination that was provided to the graph builder, simply create a criterion that always returns true:
Func<IDestination, bool> criterion = destination => { return true; }
Since the type signature of a criterion is Func<IDestination, bool>
, criteria can be easily generated based off of user-input, meaning the SmartRoutes SDK can be integrated into a web application, console application, etc.
To search the graph, you need the following pieces of information:
ILocation
interface. SmartRoutes.Model.Location
has been provided for convenience.System.DateTime
instance.SmartRoutes.Graph.TimeDirection
enumeration. Forwards
means the provided time is a start time. Backwards
means the provided time is an end time.Execute the search as follows:
// create the graph IGraph graph = ... // the start and end locations ILocation start = new Location { Latitude = 38.8893, Longitude = -77.0501 }; ILocation end = new Location { Latitude = 38.8977, Longitude = -77.0366 }; // arrive at the destination by 10 AM at the latest DateTime time = new DateTime(1970, 1, 1, 10, 0, 0); TimeDirection direction = TimeDirection.Backwards; // some criteria... IEnumerable<IDestination, bool> criteria = ... // how many results we want -- fewer than this number could be returned int n = 10; // perform the search IEnumerable<SearchResult> results = graph.Search(start, end, time, direction, criteria, n);
This is an area where the SDK could be improved to become more usable for developers that are unfamiliar with the inner workings of SmartRoutes.
The return type of the Search
method is IEnumerable<SearchResult>
. Each SearchResult
represents a unique itinerary to one or more destinations that collectively match all of the provided criteria. The SearchResult
class has three important properties:
IEnumerable<NodeInfo> LongResults
- every single step in the itinerary. Each NodeInfo
represents one of three things: arriving at a bus stop, arriving at a destination, and arriving at the final destination.IEnumerable<NodeInfo> ShortResults
- a condensed version of LongResults
. The only GTFS nodes that are includes are where the user is boarding and leaving the transit medium.IEnumerable<IDestination> Destinations
- all of the destinations include in this itinerary.The order of NodeInfo
instances in LongResults
and ShortResults
is in ascending chronological order.
A NodeInfo
is a linked list representing a path through the internal SmartRoutes graph. The two important properties on the NodeInfo
class are:
INode node
- the node at this step in the itinerary, either a GTFS stop time, a destination, or the final destination.NodeInfo parent
- the next NodeInfo
in the path. Note: if you follow the linked list represented by this property, you will get the contents of the LongResults
property above. The order is either in ascending or descending chronological order, determined by the TimeDirection
parameter to the Search
function.The node
property is of type DestinationNode
(with a pointer to your concrete destination class), GtfsNode
(with GTFS stop information), or LocationGoalNode
(which is not available to SDK users). Each node has the following properties:
string Name
- a name of the node, for display.DateTime Time
- the time that the user would arrive at that node.For example, to find the time to travel between two nodes, simply subtract the node.Time
property on a NodeInfo
from its parent
.
Important: manually changing the values on any INode
instance can cause problems with the correctness of the graph.


Balotelli left out of Italy squad by Mancini
Rome - Mario Balotelli was not included in Roberto Mancini's squad on Friday for Italy's final Euro 2020 qualifiers, despite the president of the country's football federation (FIGC) calling for him to be selected.

But the 29-year-old missed out on a return to the international fold for the first time in a year.
Balotelli has scored two goals in six league appearances since returning to Italy with Brescia after a spell at French club Marseille.
He has found the net 14 times in 36 international appearances since making his Azzurri debut in 2010.
Italy, who have already qualified for next year's European Championship, face Bosnia and Herzegovina on November 15 before hosting Armenia three days later.
Mancini has handed maiden call-ups to Brescia defender Andrea Cistana, Fiorentina midfielder Gaetano Castrovilli and Bologna forward Riccardo Orsolini.


Welcome to Notify!
After you set up a notification binding, go ahead and send a notification!
Binding Setup
Read the Twilio Notify guide


|
Lucid
|
#include "glm.hpp"
#include "gtx/string_cast.hpp"
#include "gtc/matrix_transform.hpp"
Go to the source code of this file.
Enumerations | |
| enum class | WindowType { Hierarchy , Project , Console , Animation , Animator , Inspector , Services , Assets , Scene , GameCamera , ToolBar , DevDebug , DefaultAssets , GameConfiguration , None } |
| enum class | DefaultAssetsType { Cube , Sphere , Line } |
Variables | |
| const char | GLSL_VERSION [] = "#version 330" |
| const int | MOUSE_LEFT = 0 |
| const int | MOUSE_RIGHT = 1 |
| const int | MOUSE_MIDDLE = 2 |
| const float | CAMERA_SPEED = 15.0f |
| const float | SCROLL_SPEED = 50.0f |
| const float | CAMERA_SENSITIVITY = 0.25f |
| const int | SCREEN_WIDTH = 1440 |
| const int | SCREEN_HEIGHT = 900 |
| const char | TRIANGLE_VERTEX_SHADER [] = "../src/shaders/triangle.vert" |
| const char | TRIANGLE_FRAGMENT_SHADER [] = "../src/shaders/triangle.frag" |
| const char | MODEL_VERTEX_SHADER [] = "../src/shaders/model.vert" |
| const char | MODEL_ANIMATED_VERTEX_SHADER [] = "../src/shaders/modelAnimated.vert" |
| const char | MODEL_FRAGMENT_SHADER [] = "../src/shaders/model.frag" |
| const char | PRIMITIVE_VERTEX_SHADER [] = "../src/shaders/primitive.vert" |
| const char | PRIMITIVE_LINE_SHADER [] = "../src/shaders/line.vert" |
| const char | PRIMITIVE_INSTANCED_SHADER [] = "../src/shaders/primitiveInstanced.vert" |
| const char | PRIMITIVE_FRAGMENT_SHADER [] = "../src/shaders/primitive.frag" |
| const char | FONT_VERTEX_SHADER [] = "../src/shaders/text.vert" |
| const char | FONT_FRAGMENT_SHADER [] = "../src/shaders/text.frag" |
| const char | MICROPHONE_MODEL [] = "../assets/microphone/scene.gltf" |
| const char | SCIFIHELMET_MODEL [] = "../assets/helmet/ScifiHelmet.gltf" |
| const char | FOX_MODEL [] = "../assets/fox/Fox.gltf" |
| const char | AVOCADO_MODEL [] = "../assets/avocado/Avocado.gltf" |
| const char | POLYBIRD_MODEL [] = "../assets/low-poly-bird-animated/source/Bird_Asset.fbx" |
| const char | POLYFOX_MODEL [] = "../assets/low-poly-fox-by-pixelmannen-animated/source/animations.fbx" |
| const char | SHIBA_INU_MODEL [] = "../assets/animated_dog_shiba_inu_gltf/scene.gltf" |
| const char | KING_BOO_MODEL [] = "../assets/king_boo_idle_animation/scene.gltf" |
| const char | HAPPY_SHARK_MODEL [] = "../assets/happy_shark_swimming/scene.gltf" |
| const char | CHEESE_CAKE_MODEL [] = "../assets/cheesecake/scene.gltf" |
| const char | CAMERA_MODEL [] = "../assets/vhs_camera/scene.gltf" |
| const char | HELVETICA_FONT [] = "../assets/fonts/Helvetica.ttf" |
| const char | AIRMAN_IMAGE [] = "../assets/images/air-man.png" |
| const char | GRUNT_SOUND [] = "../assets/audio/grunt.wav" |
| const char | PIANO_MUSIC [] = "../assets/audio/piano.wav" |
| const float | PI = static_cast<float>(3.14159265359) |
| constexpr float | twoPi = glm::two_pi<float>() |
| const float | PADDLE_MOVE_SPEED = 0.05f |
|
strong |
| Enumerator | |
|---|---|
| Cube | |
| Sphere | |
| Line |
Definition at line 92 of file constants.h.
|
strong |
| Enumerator | |
|---|---|
| Hierarchy | |
| Project | |
| Console | |
| Animation | |
| Animator | |
| Inspector | |
| Services | |
| Assets | |
| Scene | |
| GameCamera | |
| ToolBar | |
| DevDebug | |
| DefaultAssets | |
| GameConfiguration | |
| None |
Definition at line 73 of file constants.h.
| const char AIRMAN_IMAGE[] = "../assets/images/air-man.png" |
Definition at line 59 of file constants.h.
| const char AVOCADO_MODEL[] = "../assets/avocado/Avocado.gltf" |
Definition at line 45 of file constants.h.
| const char CAMERA_MODEL[] = "../assets/vhs_camera/scene.gltf" |
Definition at line 53 of file constants.h.
| const float CAMERA_SENSITIVITY = 0.25f |
Definition at line 20 of file constants.h.
| const float CAMERA_SPEED = 15.0f |
Definition at line 18 of file constants.h.
| const char CHEESE_CAKE_MODEL[] = "../assets/cheesecake/scene.gltf" |
Definition at line 51 of file constants.h.
| const char FONT_FRAGMENT_SHADER[] = "../src/shaders/text.frag" |
Definition at line 39 of file constants.h.
| const char FONT_VERTEX_SHADER[] = "../src/shaders/text.vert" |
Definition at line 38 of file constants.h.
| const char FOX_MODEL[] = "../assets/fox/Fox.gltf" |
Definition at line 44 of file constants.h.
| const char GLSL_VERSION[] = "#version 330" |
Definition at line 12 of file constants.h.
| const char GRUNT_SOUND[] = "../assets/audio/grunt.wav" |
Definition at line 62 of file constants.h.
| const char HAPPY_SHARK_MODEL[] = "../assets/happy_shark_swimming/scene.gltf" |
Definition at line 50 of file constants.h.
| const char HELVETICA_FONT[] = "../assets/fonts/Helvetica.ttf" |
Definition at line 56 of file constants.h.
| const char KING_BOO_MODEL[] = "../assets/king_boo_idle_animation/scene.gltf" |
Definition at line 49 of file constants.h.
| const char MICROPHONE_MODEL[] = "../assets/microphone/scene.gltf" |
Definition at line 42 of file constants.h.
| const char MODEL_ANIMATED_VERTEX_SHADER[] = "../src/shaders/modelAnimated.vert" |
Definition at line 30 of file constants.h.
| const char MODEL_FRAGMENT_SHADER[] = "../src/shaders/model.frag" |
Definition at line 31 of file constants.h.
| const char MODEL_VERTEX_SHADER[] = "../src/shaders/model.vert" |
Definition at line 29 of file constants.h.
| const int MOUSE_LEFT = 0 |
Definition at line 14 of file constants.h.
| const int MOUSE_MIDDLE = 2 |
Definition at line 16 of file constants.h.
| const int MOUSE_RIGHT = 1 |
Definition at line 15 of file constants.h.
| const float PADDLE_MOVE_SPEED = 0.05f |
Definition at line 70 of file constants.h.
| const float PI = static_cast<float>(3.14159265359) |
Definition at line 66 of file constants.h.
| const char PIANO_MUSIC[] = "../assets/audio/piano.wav" |
Definition at line 63 of file constants.h.
| const char POLYBIRD_MODEL[] = "../assets/low-poly-bird-animated/source/Bird_Asset.fbx" |
Definition at line 46 of file constants.h.
| const char POLYFOX_MODEL[] = "../assets/low-poly-fox-by-pixelmannen-animated/source/animations.fbx" |
Definition at line 47 of file constants.h.
| const char PRIMITIVE_FRAGMENT_SHADER[] = "../src/shaders/primitive.frag" |
Definition at line 36 of file constants.h.
| const char PRIMITIVE_INSTANCED_SHADER[] = "../src/shaders/primitiveInstanced.vert" |
Definition at line 35 of file constants.h.
| const char PRIMITIVE_LINE_SHADER[] = "../src/shaders/line.vert" |
Definition at line 34 of file constants.h.
| const char PRIMITIVE_VERTEX_SHADER[] = "../src/shaders/primitive.vert" |
Definition at line 33 of file constants.h.
| const char SCIFIHELMET_MODEL[] = "../assets/helmet/ScifiHelmet.gltf" |
Definition at line 43 of file constants.h.
| const int SCREEN_HEIGHT = 900 |
Definition at line 24 of file constants.h.
| const int SCREEN_WIDTH = 1440 |
Definition at line 23 of file constants.h.
| const float SCROLL_SPEED = 50.0f |
Definition at line 19 of file constants.h.
| const char SHIBA_INU_MODEL[] = "../assets/animated_dog_shiba_inu_gltf/scene.gltf" |
Definition at line 48 of file constants.h.
| const char TRIANGLE_FRAGMENT_SHADER[] = "../src/shaders/triangle.frag" |
Definition at line 27 of file constants.h.
| const char TRIANGLE_VERTEX_SHADER[] = "../src/shaders/triangle.vert" |
Definition at line 26 of file constants.h.
|
constexpr |
Definition at line 67 of file constants.h.


title: “pins: Pin, Discover and Share Resources” output: github_document: fig_width: 8 fig_height: 4 —
You can use the pins
package from R, or Python, to:
pin()
, work offline and cache results with ease.pin_find()
.board_register()
.To start using pins
, install this package as follows:
You can pin remote files with pin()
to cache those files locally, such that, even if the remote resource is removed or while working offline, your code will keep working by using a local cache:
library(tidyverse)
library(pins)
retail_sales <- read_csv(pin("https://raw.githubusercontent.com/facebook/prophet/master/examples/example_retail_sales.csv"))
You can also cache intermediate results to avoid having to recompute expensive operations:
retail_sales %>%
group_by(month = lubridate::month(ds, T)) %>%
summarise(total = sum(y)) %>%
pin("sales_by_month")
## # A tibble: 12 x 2
## month total
## <ord> <dbl>
## 1 Jan 6896303
## 2 Feb 6890866
## 3 Mar 7800074
## 4 Apr 7680417
## 5 May 8109219
## 6 Jun 7451431
## 7 Jul 7470947
## 8 Aug 7639700
## 9 Sep 7130241
## 10 Oct 7363820
## 11 Nov 7438702
## 12 Dec 8656874
You can also discover remote resources using pin_find()
which can search CRAN packages and Kaggle. Kaggle requires to configure it by running once board_register("kaggle", token = "<path-to-kaggle.json>")
. Then we can search resources mentioning “seattle” in CRAN packages and Kaggle with ease:
## # A tibble: 4 x 4
## name description type board
## <chr> <chr> <chr> <chr>
## 1 hpiR/seattle_sales Seattle Home Sales from hpiR package. table packa…
## 2 microsynth/seattl… Data for a crime intervention in Seattle… table packa…
## 3 vegawidget/data_s… Example dataset: Seattle daily weather f… table packa…
## 4 vegawidget/data_s… Example dataset: Seattle hourly temperat… table packa…
Notice that all pins are referenced as <owner>/<name>
and even if the <owner>
is not provided, each board will assign an appropriate one. While you can ignore <owner>
and reference pins by <name>
, this can fail in some boards if different owners assign the same name to a pin.
You can then retrieve a pin as a local path through pin_get()
,
## # A tibble: 43,313 x 16
## pinx sale_id sale_price sale_date use_type area lot_sf wfnt
## <chr> <chr> <int> <date> <chr> <int> <int> <dbl>
## 1 ..00… 2013..…<PHONE_NUMBER>-02-06 sfr 79 9295 0
## 2 ..00… 2013..…<PHONE_NUMBER>-07-11 sfr 18 6000 0
## 3 ..00… 2010..…<PHONE_NUMBER>-12-29 sfr 79 7200 0
## 4 ..00… 2016..…<PHONE_NUMBER>-03-17 sfr 79 7200 0
## 5 ..00… 2012..…<PHONE_NUMBER>-05-02 sfr 79 5662 0
## 6 ..00… 2014..…<PHONE_NUMBER>-03-11 sfr 79 5830 0
## 7 ..00… 2012..…<PHONE_NUMBER>-09-20 sfr 18 12700 0
## 8 ..00… 2015..…<PHONE_NUMBER>-07-21 sfr 79 7000 0
## 9 ..00… 2014..…<PHONE_NUMBER>-02-19 townhou… 79 3072 0
## 10 ..00… 2015..…<PHONE_NUMBER>-03-19 townhou… 79 3072 0
## # … with 43,303 more rows, and 8 more variables: bldg_grade <int>,
## # tot_sf <int>, beds <int>, baths <dbl>, age <int>, eff_age <int>,
## # longitude <dbl>, latitude <dbl>
Finally, you can also share resources with others by publishing to particular to Kaggle, GitHub and RStudio Connect. We can easily publish iris
to Kaggle as follows:
And use all the functionality available in pins
from Python as well:
There are other boards you can use or even create custom boards as described in the Understanding Boards article; in addition, pins
can also be used with RStudio products which we will describe next.
You can use RStudio to discover and pin remote files and RStudio Connect to share content within your organization with ease.
To discover remote resources, simply expand the “Addins” menu and select “Find Pin” from the dropdown:
Notice that, the RStudio connections pane helps you track your pins by providing each board as a connection you can explore:
You can share local files and content using the RStudio Connect board. Lets use dplyr
and the hpiR_seattle_sales
pin to analyze this further and then pin our results in RStudio Connect:
pin_get("hpiR/seattle_sales") %>%
group_by(baths = ceiling(baths)) %>%
summarise(sale = floor(mean(sale_price))) %>%
pin("sales-by-baths", board = "rstudio")
## Preparing to deploy data...DONE
## Uploading bundle for data: 5308...DONE
## Deploying bundle: 12734 for data: 5308 ...
## Building static content...
## Launching static content...
## Data successfully deployed to https://beta.rstudioconnect.com/content/5308/
## # A tibble: 8 x 2
## baths sale
## <dbl> <dbl>
## 1 1 413950
## 2 2 516480
## 3 3 638674
## 4 4 939602
## 5 5 1748859
## 6 6 3384514
## 7 7 3063043
## 8 8 4550750
You can now set the appropriate permissions in RStudio Connect, and voila! From now on, those with access can make use of this remote file locally!
For instance, a colleague can reuse the sales-by-baths
pin by retrieving it from RStudio Connect and visualize its contents using ggplot2
:
pin_get("sales-by-baths") %>%
ggplot(aes(x = baths, y = sale)) +
theme_light() + geom_point() +
geom_smooth(method = 'lm', formula = y ~ exp(x))
Please make sure to pin visit rstudio.github.io/pins to find detailed documentation and additional resources.


Six started far placing saw respect females old. Civilly why how end viewing attempt related enquire visitor. Man particular insensible celebrated conviction stimulated principles day. Sure fail or in said west. Right my front it wound cause fully am sorry if. She jointure goodness interest debating did outweigh. Is time from them full my gone in went. Of no introduced am literature excellence mr stimulated contrasted increasing. Age sold some full like rich new. Amounted repeated as believed in confined juvenile. Unpacked reserved sir offering bed judgment may and quitting speaking. Is do be improved raptures offering required in replying raillery. Stairs ladies friend by in mutual an no. Mr hence chief he cause. Whole no doors on hoped. Mile tell if help they ye full name. Do greatest at in learning steepest. Breakfast extremity suffering one who all otherwise suspected. He at no nothing forbade up moments. Wholly uneasy at missed be of pretty whence. John way sir high than law who week. Surrounded prosperous introduced it if is up dispatched. Improved so strictly produced answered elegance is. Speedily say has suitable disposal add boy. On forth doubt miles of child. Exercise joy man children rejoiced. Yet uncommonly his ten who diminution astonished. Demesne new manners savings staying had. Under folly balls death own point now men. Match way these she avoid see death.  Unfeeling so rapturous discovery he exquisite. Reasonably so middletons or impression by terminated. Old pleasure required removing elegance him had. Down she bore sing saw calm high. Of an or game gate west face shed. no great but music too old found arose. Quick six blind smart out burst. Perfectly on furniture dejection determine my depending an to. Add short water court fat. Her bachelor honoured perceive securing but desirous ham required. Questions deficient acuteness to engrossed as. Entirely led ten humoured greatest and yourself. Besides ye country on observe. She continue appetite endeavor she judgment interest the met. For she surrounded motionless fat resolution may. So if on advanced addition absolute received replying throwing he. Delighted consisted newspaper of unfeeling as neglected so. Tell size come hard mrs and four fond are. Of in commanded earnestly resources it. At quitting in strictly up wandered of relation answered felicity. Side need at in what dear ever upon if. Same down want joy neat ask pain help she. Alone three stuff use law walls fat asked. Near do that he help. Of on affixed civilly moments promise explain fertile in. Assurance advantage belonging happiness departure so of. Now improving and one sincerity intention allowance commanded not. Oh an am frankness be necessary earnestly advantage estimable extensive. Five he wife gone ye. Mrs suffering sportsmen earnestly any. In am do giving to afford parish settle easily garret. Style too own civil out along. Perfectly offending attempted add arranging age gentleman concluded. Get who uncommonly our expression ten increasing considered occasional travelling. Ever read tell year give may men call its.  To do noisy downs round an happy books. Certain but she but shyness why cottage.  Pretended exquisite see cordially the you. Weeks quiet do vexed or whose. Motionless if no to affronting imprudence no precaution. My indulged as disposal strongly attended. Parlors men express had private village man. Discovery moonlight recommend all one not. Indulged to answered prospect it bachelor is he bringing shutters. Pronounce forfeited mr direction oh he dashwoods ye unwilling.


Locations
Stardust Video & Coffee
DVDs, coffee, tea & booze meet sandwiches & other light fare at this hip vegan-friendly cafe.
Austin's Coffee
Bohemian cafe with coffee roasted on-site, light bites, beer, wine & live entertainment nightly.
Downtown Credo
Donations-only nonprofit coffee shop with an unpretentious space, live music & charitable events.
Drunken Monkey
House-roasted coffee & veggie-friendly sandwiches & snacks in a low-key, bohemian setting.
Vespr Craft Coffee & Allures
Contemporary coffeehouse serving single-origin coffees & organic, fair-trade teas.


The Rocket League Play Analyzer is a consumer facing web app built using Python, Flask, Pandas, Scikit-Learn, Random Forest Classifiers, and K-Nearest Neighbors to provide personalized training recommendations to accelerate gamer rank advancement using 20 GB of performance data collected using a web API.
Rocket League is a popular e-sports video game. Gameplay is essentially virtual soccer in rocket propelled cars. Rocket league boasts over 57 million registered players, and due to its popularity an entire ecosystem has arisen all focused around helping players level up in the game ranking system from YouTube videos, to coaching services, to paid training content.
The Rocket League Play Analyzer analyzes an individual player's game data, identifies which skills will help that player rank up in the in-game ranking system fastest, and then recommends specific training resources to help players do so.
The data was collected from https://calculated.gg, a site which allows players to upload their saved replays, and then will analyze the players stats compared to other players. They have a lot of great features on their site, but not a training resource recommender.
The calculated.gg web api was used to collect every 3v3 Standard ranked match played from January 1 to April 9, 2019. The total data included over 200,000 matches and was over 20 GB in size.
The raw data was in the form of 2 sets of json objects. One with the metadata for the games, and another with the performance statistics for the match. The data contained much unneeded information for this application. The unneeded information was discarded, two pandas dataframes were created and then manipulated with user defined functions. Finally, the dataframes were merged with an inner join to produce a dataframe of performance statistics per player per game.
After cleaning the data, the data size was reduced to about 500 MB. Something that could easily fit in memory, and eventually be deployed in the heroku platform.
For this project, the recommended skill to work on should be both:
This section deals with #1 in the list above. Relative feature importance can be used with some caveats to determine which skills are most needed in order to increase in rank.
Unfortunately, calculating the variance inflation factor (VIF) showed a high degree of collinearity amongst the features of data. Collinearity can affect the relative feature importance. In order to mitigate this I iteratively removed the feature with the highest VIF until all VIFs were below 5. This left me with 13 remaining features.
Now that only features with an acceptable amount of multicollinearity remain, I can proceed. Rocket League ranks go from 1-19. It is entirely possible and likely that different skills are going to be critical if a player is going from rank 1 to rank 2 than if they are going from rank 18 to rank 19. In order to determine which skills are most important based on what class a player is currently I trained 18 random forest binary classifiers to predict a player’s rank based on their game stats. Each individual classifier was trained with only a subset of the data and predicts whether a player is in rank i or i+1 only where i is an integer from 1-18.
For this project, the recommended skill to work on should be both:
This section deals with #2 in the list above. There are various ways one might determine which skill the player is deficient in relative to the players at the rank he is trying to achieve. One way might be to compare his average stats to the average stats of the group he is trying to achieve. There could be players who rank up because they are really good at scoring while others could rank up because they are exceptional at stealing the ball from their opponents, as a simplified example. While improving in both of these stats are good, we’re looking for which are the best skills to practice.
This app compare a player’s average stats not to the entire population at the next rank, but rather only the part of the population most similar to the where the player already is by looking at the k-nearest neighbors.
While precomputing the nearest neighbors for every player in my dataset would unnecessarily waste storage space, I did need to normalize the data and then store the dataset in a ball tree in order to readily find the nearest neighbors for any potential user of the app. The k-nearest neighbors can readily be found from the ball tree dataset.
We now have the relative feature importance and the relative deficiencies of the player, but how to average these two priorities? I went with a geometric average, because if a skill was not very important I didn’t want to recommend it to the player even if they are very “deficient” in that statistic. There are other logical ways to average these 2 quantities, but geometric averaging seems like a good bet.
Flask was used for the backend for the web app. The front end of the app was built with bootstrap 4. The app is deployed under heroku’s free tier.


--- layout: default nav: attributes > ic-scroll-offset ---
ic-scroll-offset
- The Scroll Offset AttributeThe ic-scroll-offset
attribute tells Intercooler to apply an offset when scrolling to a given target.
This can be useful when you need an element to be positioned offest from the top of the viewport when using
the ic-scroll-to-target
attribute.
The value of the attribute should a valid integer
ic-scroll-offset
has no dependency implications.


NumPy Real World Examples¶
\begin{exercise} Write a NumPy program to get the numpy version and show numpy build configuration. \end{exercise}
import numpy as np
# NumPy Version
np.__version__
'1.19.2'
# NumPy build config
np.show_config()
blas_mkl_info:
NOT AVAILABLE
blis_info:
NOT AVAILABLE
openblas_info:
libraries = ['openblas', 'openblas']
library_dirs = ['/usr/local/lib']
language = c
define_macros = [('HAVE_CBLAS', None)]
blas_opt_info:
libraries = ['openblas', 'openblas']
library_dirs = ['/usr/local/lib']
language = c
define_macros = [('HAVE_CBLAS', None)]
lapack_mkl_info:
NOT AVAILABLE
openblas_lapack_info:
libraries = ['openblas', 'openblas']
library_dirs = ['/usr/local/lib']
language = c
define_macros = [('HAVE_CBLAS', None)]
lapack_opt_info:
libraries = ['openblas', 'openblas']
library_dirs = ['/usr/local/lib']
language = c
define_macros = [('HAVE_CBLAS', None)]
\begin{exercise} Write a NumPy program to get help on the add function. \end{exercise}
# get help from NumPy
np.info(np.add)
add(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])
Add arguments element-wise.
Parameters
----------
x1, x2 : array_like
The arrays to be added.
If ``x1.shape != x2.shape``, they must be broadcastable to a common
shape (which becomes the shape of the output).
out : ndarray, None, or tuple of ndarray and None, optional
A location into which the result is stored. If provided, it must have
a shape that the inputs broadcast to. If not provided or None,
a freshly-allocated array is returned. A tuple (possible only as a
keyword argument) must have length equal to the number of outputs.
where : array_like, optional
This condition is broadcast over the input. At locations where the
condition is True, the `out` array will be set to the ufunc result.
Elsewhere, the `out` array will retain its original value.
Note that if an uninitialized `out` array is created via the default
``out=None``, locations within it where the condition is False will
remain uninitialized.
**kwargs
For other keyword-only arguments, see the
:ref:`ufunc docs <ufuncs.kwargs>`.
Returns
-------
add : ndarray or scalar
The sum of `x1` and `x2`, element-wise.
This is a scalar if both `x1` and `x2` are scalars.
Notes
-----
Equivalent to `x1` + `x2` in terms of array broadcasting.
Examples
--------
>>> np.add(1.0, 4.0)
5.0
>>> x1 = np.arange(9.0).reshape((3, 3))
>>> x2 = np.arange(3.0)
>>> np.add(x1, x2)
array([[ 0., 2., 4.],
[ 3., 5., 7.],
[ 6., 8., 10.]])
\begin{exercise} Write a NumPy program to test whether none of the elements of a given array is zero. \end{exercise}
# Solution: if zero not exist
A1 = np.array([2, 3, 4, 5 , 6])
np.all(A1)
True
# Solution: if zero exist in array
A2 = np.array([0, 0, 2, 3, 4, 5 , 6])
np.all(A2)
False
\begin{exercise} Write a NumPy program to test whether any of the elements of a given array is non-zero. \end{exercise}
# Solution: if zero not exist
X1 = np.array([2, 3, 4, 5 , 6])
np.any(X1)
True
# Solution: if zero exist in array
X2 = np.array([0, 0, 0, 0, 0, 0])
np.all(X2)
False
np.info(np.any)
any(*args, **kwargs)
Test whether any array element along a given axis evaluates to True.
Returns single boolean unless `axis` is not ``None``
Parameters
----------
a : array_like
Input array or object that can be converted to an array.
axis : None or int or tuple of ints, optional
Axis or axes along which a logical OR reduction is performed.
The default (``axis=None``) is to perform a logical OR over all
the dimensions of the input array. `axis` may be negative, in
which case it counts from the last to the first axis.
.. versionadded:: 1.7.0
If this is a tuple of ints, a reduction is performed on multiple
axes, instead of a single axis or all the axes as before.
out : ndarray, optional
Alternate output array in which to place the result. It must have
the same shape as the expected output and its type is preserved
(e.g., if it is of type float, then it will remain so, returning
1.0 for True and 0.0 for False, regardless of the type of `a`).
See `ufuncs-output-type` for more details.
keepdims : bool, optional
If this is set to True, the axes which are reduced are left
in the result as dimensions with size one. With this option,
the result will broadcast correctly against the input array.
If the default value is passed, then `keepdims` will not be
passed through to the `any` method of sub-classes of
`ndarray`, however any non-default value will be. If the
sub-class' method does not implement `keepdims` any
exceptions will be raised.
Returns
-------
any : bool or ndarray
A new boolean or `ndarray` is returned unless `out` is specified,
in which case a reference to `out` is returned.
See Also
--------
ndarray.any : equivalent method
all : Test whether all elements along a given axis evaluate to True.
Notes
-----
Not a Number (NaN), positive infinity and negative infinity evaluate
to `True` because these are not equal to zero.
Examples
--------
>>> np.any([[True, False], [True, True]])
True
>>> np.any([[True, False], [False, False]], axis=0)
array([ True, False])
>>> np.any([-1, 0, 5])
True
>>> np.any(np.nan)
True
>>> o=np.array(False)
>>> z=np.any([-1, 4, 5], out=o)
>>> z, o
(array(True), array(True))
>>> # Check now that z is a reference to o
>>> z is o
True
>>> id(z), id(o) # identity of z and o # doctest: +SKIP
(191614240, 191614240)
References¶
https://numpy.org/
https://www.edureka.co/blog/python-numpy-tutorial/
https://github.com/enthought/Numpy-Tutorial-SciPyConf-2019
This notebook was created by Jubayer Hossain | Copyright © 2020, Jubayer Hossain


lmmlasso: Linear mixed-effects models with Lasso
This package fits (gaussian) linear mixed-effects models
for high-dimensional data (n<<p) using a Lasso-type approach
for the fixed-effects parameter.
| Version: |
0.1-2 |
| Depends: |
methods, emulator, miscTools, penalized |
| Published: |
2011-04-15 |
| Author: |
Juerg Schelldorfer |
| Maintainer: |
Juerg Schelldorfer <schell at stat.math.ethz.ch> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL] |
| NeedsCompilation: |
no |
| CRAN checks: |
lmmlasso results |
Downloads:
Reverse dependencies:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=lmmlasso
to link to this page.


If you have a question about a particular problem you are having, please call our direct number at<PHONE_NUMBER>. Or please feel free to email us below.
 Then my doctor also told me she was the best and referred me to her for head, neck and TMJ pain. She really IS the best!!! Every week she not only worked hard to find the areas that caused pain but relieved that pain AND taught me ways to relieve the pain (or lessen it) on my own. I can’t imagine ever going to another therapist again. I feel so, so much better than the 1st day I came in!” — Lisa B.


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This web page/application is an interactive visualization of a graph algorithm. Unfortunately your browser does not seem to support Javascript. Please enable Javascript if possible, or switch to a browser which does. Sorry.


Review: Alesso stuns on refreshing 'In The Middle' music video
On November 15, electronic music star Alesso released his new music video for "In The Middle," his collaboration with SUMR CAMP.
by Markos PapadatosThe music video, directed by Andrew Sandler, for this mid-tempo and catchy house tune has become a quest for an underground electronic dance party in the desert.
"In The Middle" is available on Spotify, Amazon Music and on Apple Music.
This past February, as Digital Journal reported, Alesso performed his hit songs at Venezuela Aid Live.
The Verdict
Overall, Alesso's "In The Middle" music video is a stunning accompaniment for this vivacious track, which helps elevate the song to a higher level. The song and the video for "In The Middle" both garner an A rating.
To learn more about Alesso and his music, check out his official Facebook page.
Read More: In September of 2018, Markos Papadatos chatted with Alesso at Electric Zoo in New York.


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`+ listHTML +`


Get the Best Out of the Deals on Chromebooks
by Review TeamChromebooks bear that name since they run on the Google Chrome OS instead of the usual Windows OS. They can handle practically all the computing tasks that a regular laptop or notebook can do and they cost much less. Some of the known brands like Acer and Lenovo have adopted this Chromebook model in their range of offerings. Just check out the other specs like the processor etc. before deciding on the right Chromebook for your use. There are two-in-ones available too in the same range.
Lenovo Chromebook S330 Laptop
The Lenovo Chromebook S330 comes equipped with a 14-inch full HD display. A MediaTek MTK8173C processor supported by 4GB LPDDR3 RAM and 64GB eMMC 5 storage offer processing support. You can make use of an additional 100GB from the cloud via Google Drive. Lenovo has taken great care in designing this Chromebook with just one inch in thickness and 330lbs in weight. The Chrome OS takes care of most functions of this laptop making computing activity a breeze. The connectivity options include Wi-Fi and Bluetooth.
This Lenovo Chromebook S330 laptop can be picked up now for $219.99, $68.19 off the regular price.
Acer Chromebook Spin 13 CP713-1WN-53NF 2-in-1 Convertible
This Acer Chromebook Spin 13 CP713-1WN-53NF 2-in-1 convertible has some great specs. The display is a 13.5-inch touchscreen one with a screen resolution of 2256×1504 pixels. The display is made of Corning Gorilla Glass encased in an aluminum chassis. The main processor is an 8th generation Intel Core i5-8250U. The RAM support is 8GB and the onboard eMMC type storage is 128GB. Acer claims a battery life of 10 hours on this Chromebook. With the Chrome OS running the device, you have access to your favorite apps.
This Acer Chromebook Spin 13 CP713-1WN-53NF 2-in-1 convertible is now selling at $599.99, a discount of $124.91 over its normal price.
Lenovo Chromebook C330 2-in-1 Convertible Laptop
This Lenovo Chromebook C330 2-in-1 convertible laptop has an 11.6-inch HD display with a screen resolution of 1366×768 pixels. The processor is the MediaTek MT8173C and gets 4GB LPDDR3 RAM support. The onboard storage is 64GB eMMC type. Obviously, the Chrome OS runs this 2-in-1 convertible. Google ensures you get the best security protection for the device. You get access to all the apps from the Google Play Store.
This Lenovo Chromebook C330 2-in-1 Convertible Laptop is now priced at $246.99, down $53 from the price of $299.99 it was being sold at.


{% extends "layout.html" %} {% block meta %} {{ super() }} {% endblock %} {% block head %} {{ super() }} {% endblock %} {% block content %}
The Stanford Cable TV Analyzer enables you to write queries that compute the amount of time people appear and the amount of time words are heard in cable TV news. In this tutorial we will go over the basics of how to use the tool to write simple queries. For information about our research publications, visit our about us or paper page.
Writing a query using the Stanford Cable TV Analyzer involves specifying filters that determine what video segments to include in a screen time computation. One of the most important filters is the name filter, which selects video segments where a specified individual appears on screen. For example, below is a query that computes the amount of time Kamala Harris's face appeared on screen in every month since {{ start_date }}. One obvious feature of the graph is the spike in coverage in July 2019 after the first Democratic primary debate. More recently we see the rightmost spike in August 2020 after Ms. Harris was named Joe Biden’s running mate.
The people page provides the full list of names that can be used in a name filter. We encourage you to change the name used in the query above to compute the screen time for other individuals. (The query box is editable!)
Many questions about cable TV news content require comparisons between different screen time computations. To add new lines to the graph, use the + button to add more query boxes. The following graph compares the screen time of Kamala Harris to that of Elizabeth Warren.
{% if not hide_gender %}In addition to filtering by a person's name, it is possible to filter by other tags associated with faces that appear in the dataset. For example, each face detected in our dataset is automatically tagged with a prediction of the individual's presenting gender. In the graph below, we use the tag filter to compare the total time male-presenting individuals are on screen with that of female-presenting individuals. (Please see the discussion in our FAQ about the decision to include binary presenting gender tags in the dataset.)
A query can specify multiple tags that a face must match. For example, adding the "{{ global_tags.host }}" tag to the face tag filter from the prior graph yields this graph that compares the screen time of male news presenters with that of female news presenters. (We consider news presenters to be program hosts, anchors, key on-air staff.) Among news presenters, the trend toward gender parity appears to have reversed since 2015.
{% if hide_person_tags %}The tags page contains the full list of tags that can be used in a face tag filter. Details about how Stanford Cable TV News Analyzer attributes tags to faces is provided on our methodology page.
{% else %}The tags page contains the full list of tags that can be used in a face tag filter. You'll see tags like "musician", "politician", "journalist" and we encourage you to play around with the query above to plot the screen time of people associated with different tags.
As you write queries it is important to be mindful that tags are not exhaustive. For example, a query using the filter "tag=politician" computes the screen time of all faces in the dataset that have been identified as politicians. However, since not every politician in the dataset is correctly identified by automatic face recognition methods, the true screen time of people on the news who are actually politicians is certainly higher. In some cases, tags are present for all faces in the data (e.g., gender, see the tags page for a list). Details about how Stanford Cable TV News Analyzer attributes tags to faces is provided on our methodology page.
{% endif %} {% endif %}To allow more precise control over what video segments are included in a screen time computation, queries may contain multiple filters connected by AND's and OR's. For example, the graph below extends our previous Kamala Harris vs. Elizabeth Warren graph with a third line that plots the amount of time where both Ms. Harris AND Ms. Warren are on screen.
A common use of multiple filters is to limit screen time computations to certain channels, news programs, or specific times of day or days of the week. For example, in the graph below we compare the screen time of Kamala Harris between 6-10pm (US Eastern time) on CNN, with that of Fox News and MSNBC. (Ms. Harris receives the most screen time during this time period on MSNBC.)
Queries may also filter video content based on the text contents of video captions. For example, if you were interested in how much certain foreign countries are discussed on the news, you could use text filters to compare the amount of time country names are mentioned in the captions. For example:
The advanced documentation page describes additional features of text filters, such the ability to specify of lists of words in a filter (e.g., topic lexicons) or perform searches that match inflections of a word.
It is important to notice that the above graph plots measurements of time. It does not count the number of times the specified phrase is found in the captions. All queries run by the Stanford Cable TV Analyzer, regardless of the filters used, compute the time spanned by video segments that match the specified filters. By default, each instance is treated as an interval of textwindow={{ default_text_window }} second{% if default_text_window != 1 %}s{% endif %}. If textwindow is set to 0, then the time when the word is said is used; in this case, users should be mindful that longer text phrases may contribute more time per instance than shorter phrases.
Since text filters (like all other filters) are used to select video segments, they can be combined with all other filters in queries. Combining text and face filters (e.g, name or tag) can yield interesting queries. For example, the following graph computes the time that Hillary Clinton is on screen AND the word "email" is spoken. (We observe that word "email" is sometimes written as "e mail" in the captions, so the text search filter matches both "email" or "e mail".)
Observe that the above graph computes the amount of time where Ms. Clinton is on screen AND the word "email" is spoken, but this query does not mean that the word "email" was spoken by Ms. Clinton. It is possible the speaker was another person on screen or even an off-screen voice. At this time the Stanford Cable TV Analyzer does not provide the ability to identify who spoke the words in the text caption.
In many situations it is useful to view examples of video clips that pass a query's filters. For example, you might wish to inspect the matching video clips to get of sense of whether automated face identification is robustly recognizing the person specified in the query. Another common task is to view selected clips when debugging a more complex query to ensure the query is selecting the types of videos intended. When viewing a graphs on the main Stanford Cable TV News Analyzer site, clicking on the graph will display a random selection of video clips that matched query's filters at that point in time. You can further investigate these results by playing the video clips as well as viewing their captions.
A sampling of results from our earlier query {{ search_keys.face_name }}="Kamala Harris" are given below. These thumbnails serve as a good reminder that a person can appear on screen in many different ways: as a guest on a show, in B-roll footage, or as a static image used as part of an infographic.
You've now seen a few basic examples of writing screen time queries. We now invite you to try creating more of your own! We're excited to see what analyzes are possible.
To learn more about more advanced query features not discussed in this tutorial, please take a look at the advanced queries page. You may also want to browse the dataset page to learn about what people and tags available for use in queries.


gae-ip-geolocation
This is an instance of the
gae-ip-geolocation
project.
No content
There is no content at this URL. Go
home
.


xdriinfo
Section: User Commands (1)Updated: xdriinfo 1.0.2
Index Return to Main Contents
NAME
xdriinfo - query configuration information of DRI driversSYNOPSIS
xdriinfo [-display displayname] [command]DESCRIPTION
xdriinfo can be used to query configuration information of direct rendering drivers. If no command argument is specified it lists the names of the direct rendering drivers for all screens.Valid commands are:
- nscreens
- Print the number of screens.
- driver screen
- Print the name of the direct rendering driver for screen.
- options screen|driver
- Print the XML document describing the configuration options of a driver. The driver can be specified directly by driver name or indirectly by screen number. If the driver name is specified directly then no X connection is needed.
ENVIRONMENT
- DISPLAY
- The default display.
SEE ALSO
X(7), xdpyinfo(1), xwininfo(1), xvinfo(1), glxinfo(1), xprop(1)AUTHOR
Felix Kuehling
Index
This document was created by man2html, using the manual pages.
Time: 05:29:12 GMT, December 24, 2015


git clone https://github.com/talentedunicorn/zest
cd zest
npm start
We accept code contributions and donations


Centurion Orinak:
I, Orinak, am a Centurion.
Let me give you a free word of advice... You may soon meet someone in the company of his minions... Don't get distracted by them! Got it? No matter what, only attack the leader!
"Who is the Lord of the manor and what is the tax rate?"
Quest


The Duodenal Switch Tracker (TDST)
Protein:
Fat:
Simple Carbs:
Complex Carbs:
Total Calories:
Total Absorbed:
gm
gm
gm
gm
Total Carbs:


Essence Collection Loop01
Essence Collection Collect04
IntroResponse Dismissive00
Essence Collection Collect02
Burrow ToxicEruption End 01
Essence Collection Collect03
Essence Collection Collect01
BrushStalker Allied Finish01
Burrow ToxicEruption Launch 01
Isolation Player Impact 01
Essence Collection Regular Activate01
Isolation Victim Impact 01
BrushStalker Allied Buildup01
Essence Collection Max Activate01
Burrow Launch Sweetener 02
Burrow Launch Sweetener 03
Burrow Numbing Eruption Impact 04
Burrow Launch Sweetener 01
Burrow Numbing Eruption Impact 01
Burrow Launch Sweetener 04
Burrow Numbing Eruption Impact 02
Burrow Numbing Eruption Impact 03


Rolling Updates and Versions
Akka upgrades
Akka supports rolling updates between two consecutive patch versions unless an exception is mentioned on this page. For example updating Akka version from 2.5.15 to 2.5.16. Many times it is also possible to skip several versions and exceptions to that are also described here. For example it’s possible to update from 2.5.14 to 2.5.16 without intermediate 2.5.15.
It’s not supported to have a cluster with more than two different versions. Roll out the first update completely before starting next update.
Rolling update from classic remoting to Artery is not supported since the protocol is completely different. It will require a full cluster shutdown and new startup.
Change log
2.5.0 Several changes in minor release
See migration guide when updating from 2.4.x to 2.5.x.
2.5.10 Joining regression
Issue: #24622
Incompatible change was introduced in 2.5.10 and fixed in 2.5.11.
This means that you can’t do a rolling update from 2.5.9 to 2.5.10 and must instead do update from 2.5.9 to 2.5.11.
2.5.10 Joining old versions
Issue: #25491
Incompatibility was introduced in in 2.5.10 and fixed in 2.5.15.
That means that you should do rolling update from 2.5.9 directly to 2.5.15 if you need to be able to join 2.5.9 nodes during the update phase.
2.5.14 Distributed Data serializer for ORSet[ActorRef]
Issue: #23703
Intentional change was done in 2.5.14.
This change required a two phase update where the data was duplicated to be compatible with both old and new nodes.
- 2.5.13 - old format, before the change. Can communicate with intermediate format and with old format.
- 2.5.14, 2.5.15, 2.5.16 - intermediate format. Can communicate with old format and with new format.
- 2.5.17 - new format. Can communicate with intermediate format and with new format.
This means that you can’t update from 2.5.13 directly to 2.5.17. You must first update to one of the intermediate versions 2.5.14, 2.5.15, or 2.5.16.
2.5.22 ClusterSharding serializer for ShardRegionStats
Issue: #25348
Intentional change was done in 2.5.22.
Changed serializer for classes: GetShardRegionStats
, ShardRegionStats
, GetShardStats
, ShardStats
This change required a two phase update where new serializer was introduced but not enabled in an earlier version.
- 2.5.18 - serializer was added but not enabled,
JavaSerializer
still used - 2.5.22 -
ClusterShardingMessageSerializer
was enabled for these classes
This means that you can’t update from 2.5.17 directly to 2.5.22. You must first update to one of the intermediate versions 2.5.18, 2.5.19, 2.5.20 or 2.5.21.
2.6.0 Several changes in minor release
See migration guide when updating from 2.5.x to 2.6.x.
2.6.2 ClusterMessageSerializer manifests change
Issue: #23654
In preparation of switching away from class based manifests to more efficient letter codes the ClusterMessageSerializer
has been prepared to accept those shorter forms but still emits the old long manifests.
- 2.6.2 - shorter manifests accepted
- 2.6.5 - shorter manifests emitted
This means that a rolling update will have to go through at least one of 2.6.2, 2.6.3 or 2.6.4 when upgrading to 2.6.5 or higher or else cluster nodes will not be able to communicate during the rolling update.
2.6.5 JacksonCborSerializer
Issue: #28918. JacksonCborSerializer was using plain JSON format instead of CBOR.
If you have jackson-cbor
in your serialization-bindings
a rolling update will have to go through 2.6.5 when upgrading to 2.6.5 or higher.
In Akka 2.6.5 the jackson-cbor
binding will still serialize to JSON format to support rolling update from 2.6.4. It also adds a new binding to be able to deserialize CBOR format when rolling update from 2.6.5 to 2.6.6. In Akka 2.6.6 the jackson-cbor
binding will serialize to CBOR and that can be deserialized by 2.6.5. Old data, such as persistent events, can still be deserialized.
You can start using CBOR format already with Akka 2.6.5 without waiting for the 2.6.6 release. First, perform a rolling update to Akka 2.6.5 using default configuration. Then change the configuration to:
akka.actor {
serializers {
jackson-cbor = "akka.serialization.jackson.JacksonCborSerializer"
}
serialization-identifiers {
jackson-cbor = 33
}
}
Perform a second rolling update with the new configuration.


?Name:
Bumelia
nigra
Sw.
Locality:
1.5
miles
due
southeast
of
SLIG0VILLE.
Altitude:
18O0-190O
ft.
Date:
April
16,
1971
Habitat:
Wooded
limestone
hillside*
Notes:
Tree
18
m.
tall*
Collector:
George
Proctor
No.
51726
INSTITUTE
OF
JAMAICA
FLORA
OF
JAMAICA
Parish:
Si.
CATHERINE
01443727
Legend -
Level of confidence that token is an accurately-transcribed word
extremely low
very low
low
undetermined
medium
high
very high


The content you are looking for couldn't be found. Please check your connection or URL and try again.


#include <camera.h>
Public Attributes | |
| VX_ipoint3 | position |
| double | rotation_matrix [4][4] |
| VX_fpoint3(* | rg )(struct VX_camera *self, VX_uint32 x, VX_uint32 y) |
| VX_surface * | surface |
| VX_field * | workers |
| void(* | draw )(struct VX_camera *self, void *model) |
| void(* | destroy )(struct VX_camera *self) |
| VX_fpoint3 * | buffer |
| void * | data |
Structure representing camera in raytracing model.
| VX_fpoint3* VX_camera::buffer |
Commonly used buffer for storing rays used by rg function
| void* VX_camera::data |
Other user data
| void(* VX_camera::destroy)(struct VX_camera *self) |
Frees camera object.
| self |
| void(* VX_camera::draw)(struct VX_camera *self, void *model) |
Runs raytracing for camera on model.
| self | |
| model | model to run raytracing on |
| VX_ipoint3 VX_camera::position |
holds position of camera
| VX_fpoint3(* VX_camera::rg)(struct VX_camera *self, VX_uint32 x, VX_uint32 y) |
Function that generates rays for given x and y coordinates
| self | |
| x | x-position of camera |
| y | y-position of camera |
| double VX_camera::rotation_matrix[4][4] |
stores 4x4 viewing matrix, last row and column should be 0
| VX_surface* VX_camera::surface |
surface output surface - framebuffer (read_only)


| Valet Service | No |
| Reserve Parking Service | No |
| Mon - Fri (Before 5/6pm) | |
| 8:00AM - 9:59AM | $1.40 per 30 mins |
| 10:00AM - 9:59PM | $2.00 per hour |
| Mon - Fri (After 5/6pm) | |
| 10:00PM - 7:59AM | $3.00 per entry |
| Saturday | |
| 8:00AM - 9:59PM | $1.40 per hour |
| 10:00PM - 7:59AM | $4.00 per entry |
| Sunday/ Public Holiday | |
| 8:00AM - 9:59PM | $1.40 per hour |
| 10:00PM - 7:59AM | $4.00 per entry |


Branches
INVIRON
We are the explorers. We journey across the galaxy for any discoveries
Neuron
We are the investigators. We document new data and analyze every suspicious nook and crany.
Regex
We are the suppliers. We prepare all the necessary stuff for all projects and maintain the facility.


Background
In September 2011 I had a 3.92kWp PV system installed. My house is heated by oil and I was attracted by the idea of an EMMA unit, but not by the price...
Inspired by the OpenEnergyMonitor project and specifically by pauldreed's Solar Power Manager I thought I'd have a go at an Arduino microcontroller based system myself...
The basic idea is simple - when generating more solar power than the house is using, rather than export the surplus energy I send it to the water tank immersion heater. In the summer this is much more efficient than firing up the oil boiler and heating the long indirect pipework - despite hot boiler efficiency claimed at >80%, I worked out the overall efficiency in heating small amounts of water is more like 25% for my system. In the winter when the oil boiler is already heating radiators it's probably better than that, but still a waste of precious oil!
The system has been up and running for about 10 months without a hitch - recently I've moved on to the final phase which is internet data posting to Pachube (now called Cosm) and a revised 'daily boost' function linked to measured cylinder water temperature to top up the hot water on cloudy days.
I thought it was about time I documented what I've been up to:
a) in case it helps anyone else
b) in case I forget what I've done and can't fix it if it breaks!
The Project Build
I made life a little difficult by choosing a rather small aluminium project box, but have managed to squeeze it all in...
Front cover removed for access to the FTDI programming pins on the Nanode (an internet-enabled microcontroller based on Arduino). I also have to pull the black and grey Rx/Tx wires out when programming.
On the left is the Freeduino (an Arduino clone) with a useful prototyping shield on top and BV4236 real time clock.
Top right is the Nanode, below that is the stripboard for the 3 current transformer inputs (Solar, Immersion and House), 9V AC sampling circuit for real and apparent power calcs and current direction measurement (import/ export) and a bridge rectifier with 8V L7808CV voltage regulator - I use this to power the Freeduino and Nanode. I've since seen various threads on the problems with powering the Arduino from the same supply used to sample the AC, but haven't really noticed a problem other than needing to re-calibrate the power measurements once the circuits are all live. Perhaps my separate voltage regulator helps?
The vertically mounted stripboard has two micro-switches (single rocker button robbed from an old modem) and 2 tri-state LED's:
LED1 - RED when the timed immersion 'boost' function is on.
LED2 - GREEN when exporting to the grid, RED when importing from the grid, ORANGE when exporting and diverting power to the immersion heater.
On the LHS end cover I have the 3 CT connections (Solar, House, Immersion), the Freeduino USB port and a co-axial F-socket for the Dallas DS18B20 one-wire temperature chip. This is great as the only connections needed are Ground and Data line (connected to 5V through a 5K pull-up resistor). I used standard co-ax as I had a roll spare and thought it would be good for data transmission and easy to add more sensors in the future with splitters:
On the RHS end cover I have the RJ45 Nanode connection - this goes straight to a power line network adaptor. The jack plugs are 9V AC input to the sampling/rectifying board and 0-5V DC output to the Phase Angle Controller (4kW industrial triac controller). This needed a little careful matching as the input resistance is only 5000 Ohms:
Dallas 1-wire DS18B20 temperature sensor installed in airing cupboard - approx 10m of co-axial cable to the Nanode and no communication problems:
Incredibly simple wiring - data line (grey) to centre core of co-ax, ground and power wire joined to outer shield of co-ax. The 1-wire chips are really cheap and fantastic for networked sensor set-ups (each has a unique identifying address) - I may add more just because I can!
All fitted to my consumer unit cupboard - Current Transformers (CT's) on solar feed, house tail and immersion wires (plus an extra CT on solar that goes to the EON Current Cost transmitter for display in another room). The Phase Angle Controller is the metal box on the LHS of the consumer unit - complete with semi-conductor fast blow triac fuse and integral heatsink. The PAC actually stays cooler than the 9V wall wart... I fitted another metal box (RHS top) for a Schaffner EMC filter, but to be honest I haven't seen any interference on any other appliances, or on the power line network, so not sure I need to bother with this.
Features
- Arduino (Freeduino) and Nanode communicating via serial link on Dig 0/1 pins
- Real time data posted to Cosm for Solar, House and Immersion W and Wh (reset at midnight) and hot water cylinder temperature
- 3 day summary of total Solar, House (Import and Export), Immersion power written to Arduino EEPROM
- Real Time Clock with battery back-up for Arduino
- One-wire DS18B20 temperature logging support on Nanode (I ran out of pins on the Arduino)
- One-hour immersion 'boost' function from menu button at 2500W target power (less than the full 3000W rating as I guessed it might be more durable)
- British Summer Time (BST) adjustment from menu system
- 0-5VDC output control of 4kW United Automation Phase Angle Controller using a PID controller tuned around target export of 30W. This keeps the 240V circuitry limited to commercially approved products.
- 9 different menus using 16x2 LCD display with time-out return to base menu
- 2 buttons for menu and function control
- 2 tri-state LEDs to quickly show status (and blind anyone staying in the guest bedroom...)
Menu screens
Performance
Overall performance has been fantastic - the main limitation being that on sunny days I can only dump about 3-4kWh to the immersion before the water is scalding. I'm thinking of swapping the hot water cylinder to a larger one with an immersion in the bottom rather than the traditional top-mounted one I currently have. Other good suggestions from forum members have been to fit a 36inch heater or to use a recirculating pump to mix the water in the tank - I've just started trying this.
I find that the immersion control is stable to approx +-20W and that the solar readings (W and kWh) agree to within approx 2% of my Diehl 3800S inverter stats.
A typical day's Cosm feed looks like this:
Emoncms
I have modified the Nanode sketch to upload to Emoncms as well as Cosm - I love the dashboard feature. Here is my current dashboard layout which works really well on smartphone and laptop:
I like this Emoncms dashboard so much I think an android tablet for the kitchen may be needed next...
Costs
| Arduino development shield | Ciseco | 4.99 |
| Arduino Freeduino | Jungletronics | 14.20 |
| RTC | Jungletronics | 6.49 |
| Case | Maplins | 11.99 |
| 12mm CT | Current Cost | 10.74 |
| AVR programmer and component bundle | Oomlout | 29.00 |
| 12mm CT pack of 2 | Current Cost | 17.99 |
| Components | Spiratronics | 6.55 |
| Components | Spiratronics | 16.30 |
| DC Power Jack sockets | ebay | 5.44 |
| Nylon bolts | ebay | 2.45 |
| DC Power Jacks | ebay | 1.49 |
| Nanode | Earthshine | 22.38 |
| LCD display | ebay | 2.90 |
| Schaffner filter | ebay | 14.00 |
| PAC 4kW heater controller | United Automation | 107.00 |
| Connectors and solder | Maplins | 16.99 |
| Mains box | Toolstation | 4.06 |
| Dallas DS18B20 | ebay | 3.50 |
| Cable | Wilkinsons | 2.15 |
| Total | 300.61 |
Certainly not the cheapest project around (three hundred pounds) but it's been a great learning experience and the industrial PAC means the 240V power electronics should be safe and reliable - all my tinkering is low voltage!
This was my first microcontroller project, but I know it won't be my last.
Mike Rowland
Update 18/10/12
I have been suffering from unexplained Nanode hangs about once per day recently - no idea why as it was previously stable for weeks. This is a real pain as the Nanode has to be physically reset to recover, but as usual Google had the answer - Trystan has solved this already by using a watchdog timer from the AVR library.
All I had to do was update Nanode/ Arduino bootloader to Uno Optiboot (I used a USBTiny AVR programmer and soldered the ISCP pins onto the Nanode board). Then add the watchdog library #include <avr/wdt.h> and add wdt_enable(WDTO_8S); in the setup() portion of code and wdt_reset(); at the start of the loop()- then if 8secs expires without a reset, the Atmega chip is reset. I have tested this with several different types of error and it works perfectly - no more frozen feeds!


We love what we do – we bring more to you than our knowledge and experience. We understand that your business is unique and your requirements are therefore unique also, therefore we take the time to get the details to ensure your platform is one of a kind


Node.js Client Extension Interface - CLEXI.js - Test-Page
Connection
Host:
Server ID:
Ping CLEXI server
Connect
Close
Xtensions
Broadcast/HTTP-Events:
Broadcast Ping
Send Test HTTP-Event
Bluetooth BLE Beacon:
Start BLE Beacon Scanner
Stop BLE Beacon Scanner
Get BLE Beacon Scanner State
Runtime Commands:
Call 'echo Hello World' in 3s
Xtension: GPIO Interface (Raspberry Pi):
Get All Registered
Release All
Button - Pin:
Register
Release
LED - Pin:
Register
Release
1
0
ReSpeaker Mic HAT LEDs:
Register
Release
Get
(1) Green
(1) Yellow
(1) Red
All Random
All Off
Client log:
clear
Welcome - Please connect.


NNoisy sheets is an add-on that works in google spreadsheets, which allows users to: add noise to a dataset; share the unique datasets with a student cohort; and calculate the results of functions for the students' versions of the data.
To use the "Noisy sheets" add-on:
Do not show the message again


Key takeaways
Conclusions
Germans
The majority of art historians in Artchive come from Germany.
However, Italian art historians researched more artistic periods.
Renassaince
Is the center of attention.
It is the most researched artistic style in all countries followed by the 16th century and the baroque.
Roma
Is the city with the most art institutes (2).
80% of the institutes are in Italy, there is one institute in the US.
Italy
Is the country with the richest collections.
They researched the most artistic styles.
The most important factor for choosing a place for research in art history...


---
layout: member-page
title: George Whitfield
class_of: 2022
bio: "Hello! I'm George and I am a senior majoring in Electrical and Computer Engineering with a minor in Game Design. I am fascinated by the intersection of computer science and art, which has motivated my interest in game development. I've worked on games as a musician, story writer, and a programmer. I'm pursuing a career in video game software development.
I've made a several games with Unity in and outside of GCS. I've also created a few games in C++ with OpenGL. I've dabbled a little bit with Unreal here and there.
Here's a short list of my favorite games: Dance Dance Revolution, Minecraft, Starcraft II, Outlast 2, Amnesia: The Dark Descent, Skyrim, Oblivion"
last_updated: January 27, 2022
# Optional
website: "https://gwhitfieldjr.com"
social_links: {
SoundCloud: "https://soundcloud.com/poshonmusic",
LinkedIn: "https://www.linkedin.com/in/georgewhitfieldjr/",
GitHub: "https://github.com/gcwhitfield",
Twitter: "https://twitter.com/gcwhitfie"
}
email<EMAIL_ADDRESS>profile_pic: GeorgeWhitfieldJr.jpg
resume: "Resume - George Whitfield Jr.pdf"
---


In this example, one company contain multiple departments while each department contain multiple employee.
Note: the "Sub total" and "Total" are excel formula. After collection data populated, they have been changed.


Capital Work Readiness Analyst (Career) - San Ramon, CA 94583
Requisition ID # 23405
Job Category : Business Operations / Strategy
Job Level : Individual Contributor
Business Unit: Electric Operations
Job Location : San Ramon
Department Overview
The men and women of Electric Operations ensure the delivery of safe and reliable electric service to our customers. Electric Operations is responsible for every aspect of PG&E’s electric distribution and transmission operations, including planning, engineering, maintenance and construction, asset management, business planning, restoration and emergency response.
Position Summary
In Transmission Operations, the capital work portfolio consists mainly of multi-year projects. The Analyst Capital Work Readiness Lead is responsible for providing transparency for both Transmission Operations and Electric Business Operations related to our readiness to execute our work, including reporting, line-of-business action plan review processes, and related day-to-day interactions with personnel responsible for both planning and executing the work.
Job Responsibilities
-
Communicates with peers and management inside and outside of the department exchanging ideas or gathering information.
-
Determines data needs, collects data, selects from different analytical techniques to conduct root cause analysis and determine issues.
-
May participate on project teams
-
Understands business process needs and develops new reports, performs custom and ad-hoc reports working independently. Presents findings to various audiences.
-
Monthly reporting for TO Work Plan Review (WPR) on key units, executability and construction readiness and ad hoc analysis
-
Administrative duties for Transmission Operations (TO) partner coordination call, Capital to Investment Execution (CIE) forum and process improvement workstreams
-
Backup reporting for integrated change request routing and associated reporting
Qualifications
Minimum:
-
Bachelor’s Degree in Business, Statistics, Mathematics of related field or equivalent work experience.
-
Job-related experience, 2 years
Desired:
-
Knowledge of different qualitative and quantitative analytical methods approaches and tactics.
-
Complete understanding of end-to-end process (work flow) knowledge.
-
Full understanding of root cause analysis and process improvement concepts.
-
Organizational, prioritization and multi-tasking skills.
-
Adaptable to changing business conditions and ambiguity.
-
Presentation skills.
-
Proficiency in Microsoft Office, Project, Visio, or similar software.


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I was born and raised in a cold Kibera, Went to Olimpic primary school the the largest school in the area after that went to Kibage high school located at Thika there is where I did my Kenya Certificate Secondary Education.
After school I had passion in doing computer studies inorder to find solution in some problems in the society,Joined an organisation that was teaching compure packages my intrest grew more and more in learning computer there is where a friend introduced me to Moringa.
I'm a guy of adventure I like tours visiting new places,love storts too playing football,swimming,playing guitar and rugby.


Money is a form of currency that is given a determined value for the purpose of transactions.
All societies have some form of currency in order to perform equivalent exchange. There are
requirements for money: it has some value, it is accepted universally, and it maintains the same meaning with time.
Money is effective because it is generally accepted, scarce, and portable. The three funtions
of money are: medium of exchange, store of value, and unit of account.
There is commodity money and fiat money. Commodity money is based on the material it was manufactured with.
Fiat money does not have an intrinsic value; instead, it is given a value dependent upon that which the government
deems.
In order to obtain money from objects, they need to be liquidated. Liquidity is the availability
of value in the market. Cash has the most liquid value. For example, you need to liquidate (or increase the liquidity) of
gold in order to convert into something that can be used in commercial exchange.
The levels of liquidity are M1, M2, and M3. M1 has the most liquidity and M3 has the least.
Stocks and bonds are certificates that are used for starting or expanding a company. Stocks include the purchase of a share in the
company. Bonds do not mean this. That is the difference between stocks and bonds.
The value of stocks and bonds change depending upon the capital value or success of the company.
When the Federal Reserve (or fed) purchases bonds, the price of bonds in the market will increase bond prices.
On the other hand, open market sales will lower the price of bonds.
The fractional reserve system is the policy that is set for banks. When deposits are made, they are required
to take a certain percentage of money per deposit, known as the reserve requirement. The way of determining the
monetary expansion created by the fractional reserve system is: amount of deposit ÷ reserve requirment.
For example, if the amount of deposit is $1,000 and the reserve requirement is 10%, then the monetary expansion
will be $10,000 = $1,000 ÷ 0.1.
The money market compares the money supply to the demand for money. In the money market,
the supply of money is perfectly inelastic. When the fed expands the money supply, the supply curve shifts to the right.
As a result, the quantity of money increases and the real interest rate decreases.
When the demand for money increases, the quantity of money stays the same due to the inelasticity of the money supply.
This makes sense because a shift in money demand should have no effect on the amount in the market. Open market
operations can shift the money supply. Buying government bonds/securities increases the money supply and selling them decreases
the supply of money. As a result, this leads to a shift along the investment demand curve which in turn causes a shift in aggregate demand.
The fed can also decrease the reserve requirement to create expansionary policy. Lowering the reserve requirement increases
the amount of reserves and therefore increases the money supply. Another method of expansionary policy is lowering the discount rate, which
refers to the interest rate on loans given to banks from the federal reserve. Lowering the discount rate performs the same functionality as
lowering the reserve requirement.
The fed may want to expand the money supply to meet a recession and contract the money supply to meet inflation.
We have already discussed the means by which the Federal Reserve can create expansionary or contractionary monetary policies using
open market operations. But let's discuss what open market operations even are. Open market operations consist of the
buying or selling of government bonds from/to commercial banks and the general public.
A key term in open market operations is the federal funds rate. The Federal Reserve uses open market ops
to set a target for this value. The fed has no control over the rate itself, as it is the interest rate that banks charge one another
on overnight loans of reserves held at the Federal Reserve Banks.
Unlike the supply of money which is affected by monetary policy, fiscal policy is what influences the demand for money.
Fiscal policy is government actions such as controlling tax levels or changing the amount of government spending.
The loanable funds market represents the relationship between the demand and supply of money that can
be lent or borrowed. The downward sloping demand D indicates that businesses will borrow more at lower interest
rates. The upward sloping supply curve S reflects the idea that at higher interest rates, households will
defer more of their present consumption (save more), making more funds available for lending.
The loanable funds theory of interest explains the interest rate not in terms of the total supply of and demand
for money but, rather, in terms of the supply and demand for these loanable funds.
Anything that prompts households to save their money will result in greater saving, shifting the supply curve rightward.
For instance, if the government offers a tax exemption for interest earned on savings, the supply of loanable funds would increase.
On the demand side, anything that increases the rate of return on potential investments will increase the demand for loanable funds, such
as productivity, consumer demand, etc.


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