| Data Type |
Frequency |
Format |
Description |
| Surface
Observations |
Hourly |
METAR
|
Airport
observations which contain hourly surface weather conditions including temperature,
dewpoint, winds, cloud cover, pressure, visibility and current weather. |
| Synoptic
Observations |
3/6
hourly |
SYNOP
|
General
surface observations reported every 6 hours (some more frequent) which includes
temperature, dewpoint, winds, cloud cover, pressure and climatic data. This
includes land, ship, CMAN and buoy data. |
| Upper
Air Observations |
12
hourly |
UA
|
Twice
a day balloon sounding data. |
| Terminal
Forecasts |
Roughly
6 hours |
TAF
|
Terminal
Aerodrome Forecasts which gives 12-24 hour aviation forecasts at airports. |
| Pilot
Reports |
Continuous |
UA/PIREP
|
Pilot
reports give upper air information including turbulence and icing at a location
and flight level. |
| Data Type |
Frequency |
Format |
Description |
| Radar
Data |
Hourly |
MDR
|
Manually
Digitized Radar data from Nexrad sites. This is low resolution reflectivity
data for legacy systems. |
| Radar
Data |
30
minutes |
RCM/GRIB
|
Radar
Coded Messages from the Nexrad sites. This is a limited base reflectivity
product with storm attribute information. There is also a GRIB mosaic. |
| Model
Output Data |
Variable / 12 hourly |
Tabular
|
Various
types of coded model output including station based foreasts (MOS), upper
air, trajectory and other forecasts. |
| Hydrology
Data |
Continuous |
SHEF/Text
|
A
variety of climatic and hydrology data reported in text or SHEF format. This
includes river observations, climatic precipitation and exteme temperature
data, etc. |
| Satellite
Data |
Continuous |
Various |
A set
of tabular information from the GOES and POES satellites. |
| Data Type |
Frequency |
Format |
Description |
| Text
Forecasts |
Variable |
Free
text |
This
includes state and zone forecasts issued by forecast offices. This includes
NWS issued surface, upper air and recreational forecasts. |
| Weather
Summaries |
Hourly
/ Variable |
Free
Text |
General
weather summaries issued by forecast offices describing current weather conditions. |
| Climatic
Data |
Daily
/ Monthly |
Free
Text / CLIMAT |
Climatic
data issued daily or monthly which includes max/min temperatures, precipitation,
snowfall, averages and deviations from normal. |
| Severe
Weather Alerts |
Variable |
Free
Text |
Severe
weather watches, warnings and advisories. |
| Admin
Messages |
Variable |
Free
Text |
NWS
administrative messages and notices |
| Data Type |
Frequency |
Format |
Description |
| NGM
Model |
12
hourly |
GRIB |
The
NCEP NGM model which runs to 48 hours. |
| Eta
Model |
6
hourly |
GRIB |
The
NCEP Eta model which runs to 72/60 hours. |
| Global
Forecast Model (GFS) |
6
hourly |
GRIB | The
NCEP Global model which runs to 120 hours (5 days). |
| Global
Forecast Model - Long range |
Daily
(00Z) |
GRIB |
The
NCEP Global model run at 00Z to 10 days. |
| RUC
Model |
Hourly |
GRIB |
The
NCEP Rapid Update Cycle model run hourly to 12/3 hours. |
| Wind
Wave Model |
12
Hourly |
GRIB |
The
NCEP Ocean Wind Wave model run to 72 hours. |
| Sea
Surface Temperature Analysis |
Daily |
GRIB |
|
| ECMWF
Model |
Daily
(12Z) |
GRIB |
The
European Center for Medium Range Forecasting model 12Z model run that goes
to 10 days. |
| UK
Met Model |
12
Hourly |
GRIB |
United
Kingdom Met Office model run to 72 hours. |
| Data Type |
Frequency |
Format |
Description |
| Profiler
Data |
Hourly |
BUFR |
Wind
profiler data put in one hour summary files. |
| GOES
Sounder Data |
BUFR |
||
| Model
Output Data |
12
Hourly |
BUFR |
BUFR
encoded MOS data |
| Quantitative
Precipitation Forecasts |
GRIB |
||
| Flash
Flood Guidance |
GRIB |
| Data Type |
Frequency |
Format |
Description |
| Base
reflectivity |
6/10
minutes |
NIDS |
Base
reflectivity (prod 19) at 4 tilts and prod 20 |
| Composite
reflectivity |
6/10
minutes |
NIDS |
Composite
reflectivity (prod 37) |
| Velocity |
6/10
minutes |
NIDS |
Base
velocity (prod 27) and storm relative velocity (prod 56) |
| VIL |
6/10
minutes |
NIDS |
Vertically
Integrated Liquid (prod 57) |
| Echo
Tops |
6/10
minutes |
NIDS |
Echo
tops (prod 41) |
| Vertical
Wind Profile |
6/10
minutes |
NIDS |
Profiler
data (prod 48) |
| Precipitation
|
6/10
minutes |
NIDS |
One
hour and storm total precipitation (prod 78 and 80) plus digital precip array (prod 81). |
| Admin
Messages |
Variable |
NIDS/Text |
General
Status Messages and Free Text Messages |
| Data Type |
Frequency |
Format |
Description |
| NLDN |
1
minute |
Proprietary |
Lightning
Detection Network data |
| Data Type |
Frequency |
Format |
Description |
| Redbook |
Variable |
Redbook |
Various
Fax like charts encoded in Redbook format |
| Images |
6/10
minutes |
NIDS |
Composite
reflectivity (prod 37) |
| Data Type |
Frequency |
Format |
Description |
| Eta
Model |
6
hourly |
GRIB2 |
The
NCEP Eta model which runs to 72/60 hours. |
| Global
Forecast Model (GFS) |
6
hourly |
GRIB2 | The
NCEP Global model which runs to 120 hours (5 days). |
| DGEX
Model |
6 |
GRIB2 |
The
Downscaled GFS with Eta eXtensions (out to 192 hours) . |
| NDFD
Data |
12
Hourly |
GRIB2 |
National
Digital Forecast Database. |
| Data Type |
Frequency |
Format |
Description |
| Various BUFR Products |
Various |
BUFR |
|
| Data Type |
Frequency |
Format |
Description |
| East
CONUS Satellite |
15
minutes |
GINI |
East
CONUS view from GOES East which includes 5 channels (Vis, IR 3.4, 6.7, 11,
12 micron) at 1 and 4 km resolution |
| West
CONUS Satellite |
15
minutes |
GINI |
West
CONUS view from GOES West which includes 5 channels (Vis, IR 3.4, 6.7, 11,
12 micron) at 1 and 4 km resolution |
| National
Composite |
30
minutes |
GINI |
Composite
of GOES East and West including all 5 channels at 8 km resolution for CONUS
regions. |
| Northern
Hemisphere Composite |
30
minutes |
GINI |
Composite
of GOES East and West including all 5 channels at 24 km resolution covering
the northern/western hemisphere |
| Data Type |
Frequency |
Format |
Description |
| Surface
Hydrology |
Continous |
Various/SHEF |
| Data Type |
Frequency |
Format |
Description |
| Eta
Model |
6
hourly |
GRIB |
The
NCEP Eta model which runs to 72/60 hours over Alaska. |
| Global
Forecast Model (GFS) |
6
hourly |
GRIB | The
NCEP Global model which runs to 120 hours (5 days) over Pacific and Alaska. |
| Data Type |
Frequency |
Format |
Description |
| Northern
Hemisphere Composite (5 Sat) |
3
hourly |
GINI |
Composite
of five geostationary satellite including 3 channels at 24 km resolution covering
the northern hemisphere. |
| Alaska
Satellite |
15
minutes |
GINI |
GOES
West view over Alaska inculding all 5 channels at 1 and 4 km resolution. |
| Alaska
National |
15
minutes |
GINI |
GOES
West view over Alaska inculding 3 channels at 8 km resolution. |
| Hawaii
Satellite |
15
minutes |
GINI |
GOES
West view over Hawaii inculding all 5 channels at 1 and 4 km resolution. |
| Hawaii
National |
15
minutes |
GINI |
GOES
West view over Hawaii inculding 3 channels at 8 km resolution. |
| Puerto
Rico Satellite |
15
minutes |
GINI |
GOES
East view over Puerto Rico inculding all 5 channels at 1 and 4 km resolution. |
| Puerto
Rico National |
15
minutes |
GINI |
GOES
East view over Puerto Rico including 3 channels at 8 km resolution. |
| Data Type |
Frequency |
Format |
Description |
| Redbook |
Variable |
Redbook |
Various
Fax like charts encoded in Redbook format |


History graduate and life-long learner seeking new opportunities in the web development field. Always deepening my understanding of new technologies and wanting to learn more. Additionally, fluent in Spanish, German and conversational French.
Check out some of the cool projects I've made and contributed to on Github!
Video Game project built using Unity3D and C#. Built the front-end and back-end for user login, validation and registration in React.js
Close ProjectLIRI is a Language Interpretation and Recognition Interface. LIRI is a command line node app that takes in parameters and gives you back data.
Close ProjectThis application demonstrates a simple full stack application with a front end implemented with HTML/CSS and the backend implemented with Node.js and Express. HTML templating is done with the help of Handlebars.
Close ProjectMultiplayer car-game built using phaser.js and socket.io to handle the multiplayer aspect of the game. Responsible for game mechanics and controls along with physics.
Close ProjectThis application implements a simple command line based storefront using the npm inquirer package and the MySQL database backend together with the npm mysql package.
Close ProjectSimple Hangman Game running in a web browser using vanilla Javascript and bootstrap.
Close Project


long-drag: In the Toolkit, touching a {_control}, waiting at least half a second, and then {_drag dragging}. It is used to swap the {_parameter parameters} in a {_cluster} with those of another compatible cluster.
Also: {_tap}, {_long-press}, {_drag}


This will convert the events which coming from Siddhi in to wso2Events according to the mapping configurations.
Hence WSO2Event has three data types (Meta, Correlation and Payload), the CEP back end runtime event attributes can be mapped to any one of them by adding the mapping configuration under their type names.
1. Log in to the CEP management console and select the Main menu in the left hand side of the screen. Then select the Add menu item under Event Receiver menu.
2. The Create a New Event Receiver window opens, using which you can configure new event receivers. Enter the following details in the form to create a wso2Event mapping based event receiver as shown in the screenshot below.
Here, there are several properties that need to be filled when creating a event receiver. User interface is divided in to 3 main components (From, Mapping and To) except the event receiver name to give more understanding to the user.
WSO2Event contains three types of data properties. They are Meta Data, Correlation Data and Payload Data. Each data property needs to have
Editing Event Receiver Configuration through the Management Console
By Clicking the Edit button of the relevant event receiver, you will able to edit the event receiver configuration and redeploy it. When you click the Edit button, it will redirect to a xml based editor window which allow you to edit the event receiver configuration from UI without opening the configuration file in the file system.
You can specify a event receiver configuration through in a xml file and deploy it in the deployment directory in the server. Event Receiver deployment directory is available at <CARBON_HOME>/repository/deployment/server/eventreceivers . This is an hot deployment directory, then it is easy to deploy and undeploy an event receiver configuration.
1. Create a xml file and enter the following WSO2Event mapping event receiver configurations inside the above mentioned hot deployment directory. Event receiver implementation needs to be start with "eventreceiver" root element.
<eventReceiver name="StatisticsReceiver" xmlns="http://wso2.org/carbon/eventreceiver"> <from streamName="statisticsStream" version="1.0.0"/> <mapping type="wso2event"> <metaData> <property> <from name="ipAddress"/> <to name="ipAdd" type="string"/> </property> </metaData> <payloadData> <property> <from name="user"/> <to name="username" type="string"/> </property> </payloadData> </mapping> <to eventAdapterType="wso2EventSender" eventAdapterType="wso2event"> <property name="stream">statisticsOutStream</property> <property name="version">1.0.0</property> </to> </eventReceiver>
For more details on creating different types of event receivers, Please refer the official documentation Here.


Time: 03:24:02 GMT, April 01, 2003


--- layout: post title: Swift with Windows App SDK date: 2022-07-07 12:00:00 keywords: swift sgbridge teaser: Demos of my work toward a Swift projection for WinRT ---
My exploration of interop between Microsoft Things and Other Things has brought me to work on creating a Swift projection for WinRT.
This effort is currently taking place in the following repo:
https://github.com/ericsink/SwiftWinRT
The code for the generated bindings, support libraries, and samples is all available there.
Modern Windows APIs are based on WinRT, which is based on COM, which has a long history at Microsoft. The nice thing about WinRT is that it is designed to be used from a variety of programming languages. A few years ago, Microsoft created the "xlang" project as "the hub for the constellation of tools that enable development of Windows applications across a variety of programming languages":
https://github.com/microsoft/xlang
Nowadays, the broader aspects of xlang don't seem to have a lot of momentum. The languages getting the most traction are C++, C#, and Rust, while some others appear to have run out of steam. But the underpinnings remain solid, and it is quite feasible to develop a WinRT projection for other languages.
Swift is a language mostly associated with Apple platforms, but its cross-platform presence is growing, and its use of Automatic Reference Counting (ARC) makes it a natural fit for WinRT, which also uses reference counting for memory management. Like C++ and Rust, Swift compiles to native code. Swift is also considered very pleasant to use, comparable to C# in terms of overall developer experience.
No discussion of Swift on Windows would be complete without mention of Saleem Abdulrasool (GitHub: @compnerd), member of the Swift Core Team. Basically everything about Swift on Windows is built on his efforts. In fact, my working repo linked above is a fork of his repo where he did some experimental work in this area.
I am accumulating a collection of samples in the repo. Here in this blog post, I want to highlight four of them:
HttpClient is a console app, demonstrating the use of Windows.Web.Http
, and showing how async WinRT calls are projected using Swift's async/await features.
MazeGame is a port of https://github.com/microsoft/Win2DMazeGame, demonstrating the use of Win2D from Swift.
Physics is a little app that simply brings up a window with 3 balls bouncing around, further demonstrating the use of Win2D, and also showing C interop with the Chipmunk2D physics engine.
Calc is a WinUI 3 app, demonstrating the use of controls such as TextBox and ListView with Windows App SDK 1.1.1.
This sample is short enough to just include all the code right here:
import WinRT import WindowsSdk @main class HttpClientDemo { public static func main() async throws { try RoInitialize() let httpClient = try Windows.Web.Http.HttpClient(); let requestUri = try Windows.Foundation.Uri(uri: "https://ericsink.com"); let httpResponse = try await httpClient.Get(uri: requestUri)!; let _ = try httpResponse.EnsureSuccessStatusCode(); let httpResponseBody = try await httpResponse.Content!.ReadAsString(); print(httpResponseBody); } }
The projection for asynchronous WinRT methods uses Swift await
,
resulting in a code that is rather similar to C#,
which I consider to be a pleasant outcome.
As mentioned above, this one is just a straight port of a Microsoft sample from C# to Swift.
One thing that turns out very nice in Swift is projecting WinRT delegates as closures, and then using Swift's idiomatic "trailing closure" syntax:
_ = try self.Timer.add_Tick { (_, _) in try canvas.Invalidate(); }
This sample was me experimenting with Swift's C interop (which is lovely) while learning how to use a 2D physics engine (Chipmunk2D).
The typical way to use a C library with Swift is to allow Swift to "import" it, and then write a Swift wrapper which presents things in more idiomatic ways. But that wrapper isn't required, and I didn't write one here. I'm just using the Chipmunk2D API in its Swift form, pointers and all.
func canvasControl_Draw(sender : CanvasControl?, args: CanvasDrawEventArgs?) throws { // The screen is cleared, so we need to redraw everything // call chipmunk to update // doing multiple Step calls here prevents things going too fast // and passing through the static body barrier lines. for _ in 0..<10 { cpSpaceStep(space, ms_to_s(ms: 3)) } // then draw the bouncing ball at its current position let pos1 = cpBodyGetPosition(body1) try args!.DrawingSession!.FillCircle( x: Float(pos1.x), y: Float(pos1.y), radius: Float(ballRadius), color: Microsoft.UI.Colors.Orange );
Looking at the call to
Win2D's FillCircle
method, note that the generated
projections use Swift parameter labels. Most of the time,
in the Swift world, This is the Way.
This sample is an RPN-style calculator, with delusions of grandeur. It is my attempt to write a sample that (1) uses WinUI controls, and (2) is a bit more than just a demo, perhaps even useful.
Note that this sample uses controls from the Microsoft.UI.Xaml
namespace,
but it does not use XAML syntax. For example, here is part of the code
to setup the Grid layout for the window:
let grid = try Microsoft.UI.Xaml.Controls.Grid(); let c0 = try Microsoft.UI.Xaml.Controls.ColumnDefinition() try c0.put_Width(value: GridLength(Value: 1, GridUnitType: GridUnitType.Star)) let c1 = try Microsoft.UI.Xaml.Controls.ColumnDefinition() try c1.put_Width(value: GridLength(Value: 120, GridUnitType: GridUnitType.Pixel)) try grid.ColumnDefinitions!.Append(value: c0) try grid.ColumnDefinitions!.Append(value: c1)
One complication here is that using WinUI controls from the Windows App SDK is not quite as simple as I expected it to be. Instead of sidetracking this blog entry with a long discussion of the details, let me summarize with the bottom line: Currently, using WinUI controls from Swift requires building as a DLL which then gets hosted in a small bootstrap program written in C++.
Full API coverage. Currently, I only generate Swift bindings for certain things, because if I generate them all, the resulting DLL fails to link because it has too many symbols. I need to break things into pieces.
Integrate the C++/Swift build. For the WinUI sample, I manually copy the Swift build outputs into the output directory of the boostrap program. That is Not Very Friendly.
Generalize the reg-free activation stuff. The support for Win2D should be made general-purpose, so it can support wrapping other third party WinUI controls.
And plenty more...
If you have questions or feedback on any of this, or are simply interested in cross-platform Swift, feel free to contact me using GitHub discussions in the repo at the top of this post, or on the Swift Forums:
https://forums.swift.org/c/development/windows/67
Cheers!


--- title: Fastai2 Audio keywords: fastai sidebar: home_sidebar summary: "An audio module for v2 of fastai. We want to help you build audio machine learning applications while minimizing the need for audio domain expertise. Currently under development." description: "An audio module for v2 of fastai. We want to help you build audio machine learning applications while minimizing the need for audio domain expertise. Currently under development." ---
In the future we will offer conda and pip installs, but as the code is rapidly changing, we recommend that only those interested in contributing and experimenting install for now. Everyone else should use Fastai audio v1
To install:
pip install packaging
pip install git+https://github.com/rbracco/fastai2_audio.git
If you plan on contributing to the library instead, you will need to do a editable install:
pip install packaging nbdev --upgrade
git clone https://github.com/rbracco/fastai2_audio
cd fastai2_audio
nbdev_install_git_hooks
pip install -e .
We are looking for contributors of all skill levels. If you don't have time to contribute, please at least reach out and give us some feedback on the library by posting in the v2 audio thread or contact us via PM @baz or @madeupmasters
Create issues, write documentation, suggest/add features, submit PRs. We are open to anything. A good first step would be posting in the v2 audio thread introducing yourself.
The first step to create a new PR is to install jupyter notebook
or jupyterlab
. This library is build using nbdev, meaning that all of the code, documentation and test is written into notebooks, and the .py
python files, online docs and CI tests are created by nbdev based on those notebooks.
All of the files that you will change are present into the nbs/
folder. Your general workflow while developing will be:
make
and nbdev will update the library files, documentation and run the tests all at oncemake
againThe command nbdev_diff_nbs
can let you know if there is a difference between the local library and the notebooks.
If you made a change to the notebooks in one of the exported cells, you can update the library with nbdev_build_lib
or make fastai2
. Note that this command will only update the library code, so before any commit, you'll need to run make
as usual to update the docs and run the tests.
If you made a change to the library, you can export it back to the notebooks with nbdev_update_lib
.


Cytoscape is an open source software platform for integrating, visualizing, and analyzing measurement data in the context of networks.
This tutorial presents a scenario of how expression data and network data can be combined to tell a biological story and includes these concepts:
Visualizing networks using expression data.
Filtering networks based on expression data.
Assessing expression data in the context of a biological network.
Loading Network
To get started, install and launch the latest version of Cytoscape.
We will use NDEx to find a relevant network. In the Network Search interface in the Control Panel, select NDEx from the drop-down, and type in "GAL1 GAL4 GAL80".
In the search results, find the galFiltered network with data. Click the Import network to Cytoscape (green arrow) button to the left of the network name.
The network will open with the default style, similar to the network on the right:
Probably the most common use of expression data in Cytoscape is to set the visual properties of the nodes (color, shape, border) in a network according to expression data. This creates a powerful visualization, portraying functional relation and experimental response at the same time. Here, we will show an example of doing this.
The data used in this example is from yeast, and represents an experiment of perturbations of the genes Gal1, Gal4, and Gal80, which are all yeast transcription factors.
Visualizing Expression Data on Networks
For this tutorial, the experimental data was part of the Cytoscape network file you loaded earlier, and is visible in the Node Table:
Selecting nodes in the network (Shift + Click or Click-and-Drag) will update the Node Table to show only data for those nodes.
Similarly, you can select one or more rows in the Node Table, right-click on the selected rows and click Select nodes from selected rows to highlight the corresponding nodes in the network.
We can now use the data to manipulate the visual properties of the network by mapping specific data columns to visual style properties:
The gal80Rexp expression values will be mapped to Node Fill Color; nodes with low expression will be colored blue, nodes with high expression will be colored red.
Significance of expression values, gal80Rsig, will be mapped to Node Border Width; nodes with significant changes will appear with a thicker border.
Set Node Fill Color
Click on the Style tab in the Control Panel.
Find Fill Color in the list of Node properties and expand it to view the mapping options.
Click the -- select value -- cell in the Column section, and select gal80Rexp in the drop-down.
Click the -- select value-- cell in the Mapping Type section, and select Continuous Mapping in the drop-down.
This produces a default ColorBrewer gradient ranging from blue to red for expression values. For our purposes, this gradient works so we don't need to edit it.
The nodes in the network are now colored based on the gal80RExp data column:
Set Default Node Color
Some nodes in the network don't have any data, and for those nodes, the default color applies. In our case, the default color is blue, which falls within the spectrum of our blue-red gradient. This is not ideal for data visualization, so a useful trick is to choose a color outside the gradient spectrum to distinguish nodes with no defined expression value.
Still in the Style tab, under Node Fill Color, click the Def. (leftmost) cell next to Fill Color.
In the Colors interface, you can click on any single color tile in a color palette to choose it. In this case, choose a light gray color.
Set Default Node Color
Note that at least one node in the network is now colored grey:
Set Node Border Width
Click on the Map. cell for the Node Border Width property in the Style panel.
Click the -- select value -- cell in the Column section, and select gal80Rsig in the drop-down.
Click the -- select value-- cell in the Mapping Type section, and select Continuous Mapping in the drop-down.
Double-click on the gradient, which defines the node border width over the range of p values.
First, we will define the min/max of the range of p values we are interested in, that is anything under 0.05. Click on Set Min and Max... and set the max to 0.05. Click OK to save.
Click on the handle for the max value (black triangle on the right-most end of the gradient) and set the value to 5 in the Node Border Width field. Repeat with the handle for the min value. Click OK to update the mapping.
Click the Def. value for Node Border Paint and select a dark grey color.
Click on the Map. cell for the Node Border Paint property in the Style panel.
Click the -- select value -- cell in the Column section, and select gal80Rsig in the drop-down.
Click the -- select value-- cell in the Mapping Type section, and select Continuous Mapping in the drop-down.
Double-click on the gradient.
Similar to the last step, we will define the min/max of the range of p values we are interested in, that is anything under 0.05. Click on Set Min and Max... and set the max to 0.05. Click OK to save.
Click on the large triangle handle at the right-most end of the gradient. This defines the border color for nodes with gal80Rsig > 0.05, so let's set it to dark grey.
Next, set all the other handles to the same dark red color. We can delete the middle handle since we want the same color for anything under 0.05, simply select it and click Delete.
Set Node Border Width
All nodes with a gal80RSig p-value of 0.05 or less will now have a thicker red border:
Set Node Label
The network nodes are by default labeled with yeast ORF identifiers. We can change the label to something more readible, like the gene symbol.
For the Node Label, change the Column selection for the mapping from name to COMMON.
Click the -- select value-- cell in the Mapping Type section, and select Passthrough Mapping in the drop-down.
Set Node Label
Zoom in to see the updated labels:
Set the Node Table Style
In addition to creating a visualization for the network, you can also add a style to the Node Table. The Column tab of the Style interface has default and mapping style options for a set of table cell attributes.
Next, we can add the column we want to style. Click the plus sign, and in the dialog select Node in the Table drop-down. Also select the gal80Rexp column from the Column drop-down.
In the mapping column of Cell Background Paint select the gal80Rexp column and select a default red-blue continuous mapping.
Set the Node Table Style
The background of the cells in the gal80RExp column is now colored in the same way that the nodes in the network are colored:
Creating the Legend
The Cytoscape app Legend Creator allows you to create a customized legend for your visualization. You can install Legend Creator from the App Store.
In the Control Panel, go to the Legend Panel. This is the interface for the Legend Creator app, and will list multiple options for creating a legend.
Note that the two attributes that we used for mapping, gal80Rexp and gal80Rsig, are selected by default. For our purposes this is actually what we want. Click Refresh Legend to continue. The legend will appear in the lower left of the network view.
Moving a Legend
To move the legend, first click the Toggle Annotation Selection icon at the bottom of the Network View Window. You can now move the legend like you would any object, for example by click and drag.
To learn more about creating legends, see the Legend Creator.
Layouts
An important aspect of network visualization is the layout, meaning the positioning of nodes and edges. Our network had a preset layout in the original file you imported, but this can be changed.
Let's change the layout to Degree Sorted Circle Layout by selecting Layout → Degree Sorted Circle Layout. In this layout, nodes are sorted by degree (connectedness), with the highest degree node at the 6 o'clock position, and remaining nodes are sorted counter clock-wise based on decreasing degree.
For this network, a degree-sorted circle layout may not be the most effective. Instead, let's try a force-directed layout.
By default, Prefuse Force-directed Layout is set as the Preferred Layout in Cytoscape, so an easy way to apply the Prefuse Force-directed Layout is by clicking the Preferred Layout in the toolbar. The preferred layout algorithm can be changed under Layout → Settings....
Cytoscape supports many different layout algorithms, described in detail in the
Cytoscape manual.
Filtering Nodes
Cytoscape allows you to easily filter and select nodes and edges based on data attributes. Next, we will select a subset of nodes with high expression in the gal80 knockout:
Go to the Filter tab in the Control Panel.
Click on the + button and select Column Filter.
Under Choose column..., select Node: gal80Rexp. Notice how all the nodes in the network are selected at first.
Using the slider or the input fields, specify values of 2 or higher by changing the minimum value to 2.
You should now see only a few nodes in the network selected, highlighted yellow.
We have now selected only the few top expressing nodes. To see the context of these nodes in the larger network, we can expand the selection of nodes to include the nodes connecting to the selected nodes, i.e. the first neighbors. Once we have that larger selection, we can create a new network.
Select the first neighbors of selected nodes by clicking the First Neighbors of Selected Nodes → Undirected button in the toolbar.
To expand the selection to second-degree nodes, repeat the last step of clicking the First Neighbors of Selected Nodes → Undirected button again.
Create a new network from the current selection by clicking the New Network From Selection button in the toolbar and choosing New Network From Selected Nodes, All edges.
Click on the Preferred Layout button in the toolbar to apply a force-directed layout.
Your new network will look similar to this:
Exploring Nodes
Right-click on any node (for example GAL4).
Select the menu External Links → Sequences and Proteins → Ensembl Gene View → yeast.
This will launch a Cytoscape Web Browser window showing the Ensembl database entry for YPL248C, the name of the node.
Interpretation
Digging into the biology of this network, it turns out that GAL4 is repressed by GAL80. Both GAL4 and GAL11 show fairly small changes in expression, and neither change is statistically significant: they are pale blue with thin borders. These slight changes in expression suggest that the critical change affecting the red nodes might be somewhere else in the network, and not either of these nodes.
GAL4 interacts with GAL80, which shows a significant level of repression: it is medium blue with a thicker border.
Note that while GAL80 shows evidence of significant repression, most nodes interacting with GAL4 show significant levels of induction: they are rendered as red rectangles.
GAL11 is a general transcription co-factor with many interactions.
Putting all of this together, we see that the transcriptional activation activity of Gal4 is repressed by Gal80. So, repression of Gal80 increases the transcriptional activation activity of Gal4. Even though the expression of Gal4 itself did not change much, the Gal4 transcripts were much more likely to be active transcription factors when Gal80 was repressed. This explains why there is so much up-regulation in the vicinity of Gal4.
Summary
In summary, we have:
Explored a yeast interactome from a transcription factor knockout experiment
Created a visual style using expression value as node color and with border width mapped to significance
Selected high expressing genes and their neighbors and created a new network
Finally, we can now export this network as a publication-quality image....
Saving Results
Cytoscape provides a number of ways to save results and visualizations:
As a session: File → Save Session, File → Save Session As...
As an image: File → Export → Network to Image...
To the web: File → Export → Network to Web Page... (Example)
To a public repository: File → Export → Network to NDEx
As a graph format file: File → Export → Network to File. Formats:


Nearly 20pc of Japan households using e-money but cash still king
TOKYO, Nov 18 — Almost a fifth of Japanese households use electronic money for small purchases, a survey by a central bank-affiliated research institute showed, up from a year ago and a sign the country’s cash-hoarding culture is changing.
In the survey published today and conducted between June and July, 18.5% of households said they use electronic money, such as smartphone apps and debit card payments, on shopping trips where ¥1,000 (US$9.17) or less is spent, up from 15.4% in the previous year.
Among single-person households — 43% of whom are in their 20s and 30s — the ratio was much higher at 35.6%, suggesting government efforts to prod Japanese to go cashless may be paying off, at least among the younger generation.
Despite the growth in electronic payments, Japan’s “cash-is-king” mentality remains entrenched with the survey showing 84% still use notes and coins for small purchases.
And for payments exceeding ¥10,000 and up to ¥50,000, 48.5% of households said they pay by cash and 3.4% by electronic money, the survey showed.
A low crime rate, years of ultra-low interest rates and a nationwide network of automatic-teller machines (ATMs) have long made cash appealing in Japan, giving people few reasons to shift to cashless payments.
Prime Minister Shinzo Abe is pushing to make more Japanese switch to cashless payments to allow stores to automate sales estimates and banks to cut back on costly ATMs.
Shoppers have recently been encouraged to ditch cash for e-money after the government introduced a rebate programme to ease the pain of a sales tax hike on October 1. — Reuters


Una Notte a North Beach is a collection of free instrumental songs available via Josh's YouTube page, for artists and creators to use and help inspire their own works. The songs set scenes inspired by locations in the San Francisco Italian neighborhood North Beach.
Blending the sounds of 70s euro crime soundtracks with San Francisco psych rock, Una Notte a North Beach creates a soundtrack to a movie that hasn't been made, but is lived every night on the streets of North Beach.
Manzanillo St., his second full length began while Josh was living in Mexico City. Josh finished the album on a break between tours pulling a four day stand at Different Fur studios, literally sleeping on the couch in the control room to put all the pieces together.
With inspirations from early Brit Rock, 50’s doo-wop, Mexican culture, observations of life on the road & the changes at his home, Manzanillo St. is a further exploration into Lippi's Rock & Roll influences.
"The Girl With The Tambourine" by Josh Lippi & The Overtimers from the upcoming record Manzanillo St.
Released February 2, 2018 Written & Performed by Josh Lippi additional guitar by Eva "Mother Cuffer" Treadway Recorded & Mixed by Sean Paulson & Josh Lippi Mastered by Brian Lucey
Art by Josh Lippi & Brit O'Brien
Lippi released his first solo record Now Open Sundays under Josh Lippi & The Overtimers in 2015, as a return to the garage rock sound that he grew up on.
Recorded by Josh Lippi at The Torta Oven in San Francisco, CA except "Game Called Home" and "Bombing Hills" recorded by Tommy Guerrero at TG's Spot in San Francisco, CA. Mixed by Patrick Brown for Different Fur Studios and Josh Lippi. Mastered by Joe Bagale. Album Photo by Trevor Traynor.
Produced and recorded by Josh Lippi at The Torta Oven San Francisco, CA. All instruments Josh Lippi. Mastered by Patrick Brown for Different Fur Studios San Francisco, CA.
Released April 11, 2014
released April 1, 2014 Produced and recorded by Josh Lippi at The Torta Oven San Francisco, CA. All instruments Josh Lippi. Mastered by Patrick Brown for Different Fur Studios San Francisco, CA.
Josh Lippi is an American multi-instrumentalist, songwriter, producer from Sacramento, CA. He currently is the touring bassist/guitarist for K.Flay and skateboard legend and musician Tommy Guerrero . His studio credits include recordings for Logic, Ray Barbee, Alice Russell & more. He is a group member of The Park, Blktop Project and Rock & Roll band Lee Bob & the Truth.


Lebanese Parties Agree on Naming Next PM - Reports
Former Lebanese Prime Minister Saad al-Hariri stepped down on 29 October following demonstrations comprising about half of the country's population and which have continued up to the present.
Lebanese parties have agreed on naming former finance minister Mohammad Safadi as the next prime minister, the Lebanese broadcasters LBCI and MTV reported on Thursday.
The decision was made by Lebanon’s major political parties — the Future Movement of former Prime Minister Saad Hariri, the Free Patriotic Movement, the Amal Movement and Hezbollah, according to the LBCI broadcaster.
The MTV broadcaster has confirmed this information and added that the new government would be formed by technocrats and politicians. The outlet added that the Future Movement would be represented in the government.
The protests in Lebanon started on 17 October and were triggered by the government's intention to raise taxes on online calls, including those via Whatsapp, as well as by the discontent with the overall socio-economic situation in the country.


Chile January 2015
11th
Arrive
Flights:
SFO 2:05PM to ATL 9:38PM
ATL 10:27PM to SCL 9:55AM
12th to 18th
Pucon
Head straight to Pucon
Hike then sled volcano
Bike ride to the falls
Kayaking On the lake
Check out national parks
18th to 19th
Santiago
Dinner at Delaveau Grand parents house.
20th to 22nd
Vina Del Mar
23rd
Sanitago with Pinedas
24th
Wedding
25th
Rancagua or Sanitago
26th
Depart


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Tony is an invigorating artist based in Gloucestershire. His work celebrates the pleasure of movement and colour through an original and vibrant style. He creates work which is ambitious, unrestrained, determined and exciting.
Tony's figurative "Body Language" paintings and drawings have been shown in exhibitions in Berlin (Germany) and in the UK , Bristol, Birmingham, Cheltenham and London.
Works on the carousel opposite are detailed in Paintings-Life and Drawings-Life in the "About" section of this website as is the full list of exhibitions.


|
DigitalOwl
|
Generic repository. More...
Public Member Functions | |
| GenericRepository (IDbContext dbContext) | |
| Generic repository constructor. More... | |
| IQueryable< TEntity > | GetAll () |
| Method for getting all instances of an entity from database. More... | |
| IQueryable< TEntity > | GetAll (Expression< Func< TEntity, bool >> predicate) |
| Method for getting all instances of an entity on certain condition. More... | |
| virtual async Task< IEnumerable< TEntity > > | GetAllAsync () |
| Asynchronous method for getting all instances of an entity from database. More... | |
| virtual TEntity | Get (int id) |
| Method for getting entity of given id from database. More... | |
| virtual async Task< TEntity > | GetAsync (int id) |
| Asynchronous method for getting entity of given id from database. More... | |
| virtual TEntity | Create (TEntity t) |
| Method for creating instance of the entity in database. More... | |
| virtual IEnumerable< TEntity > | Create (IEnumerable< TEntity > t) |
| Method for creating IEnumerable of instances of the entity in database. More... | |
| virtual TEntity | Find (Expression< Func< TEntity, bool >> match) |
| Method for finding element in database fulfilling predicate. More... | |
| virtual async Task< TEntity > | FindAsync (Expression< Func< TEntity, bool >> match) |
| Asynchronous method for finding element in database fulfilling predicate. More... | |
| IEnumerable< TEntity > | FindAll (Expression< Func< TEntity, bool >> match) |
| Method for finding all elements in database fulfilling predicate. More... | |
| async Task< IEnumerable< TEntity > > | FindAllAsync (Expression< Func< TEntity, bool >> match) |
| Asynchronous method for finding all elements in database fulfilling predicate. More... | |
| virtual void | Delete (TEntity entity) |
| Method for deleting instance of an object in database. More... | |
| void | DeleteRange (IEnumerable< TEntity > entities) |
| Method for deleting objects in database. More... | |
| virtual TEntity | Update (TEntity t, object key) |
| Method for updating an object in database. More... | |
| int | Count () |
| Method counting number of object in database. More... | |
| async Task< int > | CountAsync () |
| Asynchronous method counting number of object in database. More... | |
| virtual IQueryable< TEntity > | FindBy (Expression< Func< TEntity, bool >> predicate) |
| Find an object in database fulfilling the predicate. More... | |
| IQueryable< TEntity > | GetAllIncluding (params Expression< Func< TEntity, object >>[] includeProperties) |
| Get all objects from database including. More... | |
| void | Dispose () |
| IEnumerable< TEntity > | FindAll (List< Expression< Func< TEntity, bool >>> filters) |
| Find all objects filtered. More... | |
| async Task< IEnumerable< TEntity > > | FindAllAsync (List< Expression< Func< TEntity, bool >>> filters) |
| Asynchronous method Find all objects filtered. More... | |
| IQueryable< TEntity > | FindBy (List< Expression< Func< TEntity, bool >>> predicate) |
| Find objects fulfilling predicate. More... | |
Protected Member Functions | |
| virtual void | Dispose (bool disposing) |
Protected Attributes | |
| readonly IDbContext | _dbContext |
| Access to database. More... | |
Generic repository.
| TEntity |
| TEntity | : | class | |
| TEntity | : | IEntity |
Definition at line 16 of file GenericRepository.cs.
| DigitalOwl.Repository.Repositories.Base.GenericRepository< TEntity >.GenericRepository | ( | IDbContext | dbContext | ) |
Generic repository constructor.
| dbContext |
Definition at line 27 of file GenericRepository.cs.
| int DigitalOwl.Repository.Repositories.Base.GenericRepository< TEntity >.Count | ( | ) |
Method counting number of object in database.
Implements DigitalOwl.Repository.Interface.Base.IGenericRepository< TEntity >.
Definition at line 183 of file GenericRepository.cs.
| async Task<int> DigitalOwl.Repository.Repositories.Base.GenericRepository< TEntity >.CountAsync | ( | ) |
Asynchronous method counting number of object in database.
Implements DigitalOwl.Repository.Interface.Base.IGenericRepository< TEntity >.
Definition at line 192 of file GenericRepository.cs.
|
virtual |
Method for creating instance of the entity in database.
| t | Entity to be created. |
Implements DigitalOwl.Repository.Interface.Base.IGenericRepository< TEntity >.
Definition at line 85 of file GenericRepository.cs.
|
virtual |
Method for creating IEnumerable of instances of the entity in database.
| t | IEnumerable of entities to be created. |
Implements DigitalOwl.Repository.Interface.Base.IGenericRepository< TEntity >.
Definition at line 96 of file GenericRepository.cs.
|
virtual |
Method for deleting instance of an object in database.
| entity | Object to be deleted. |
Implements DigitalOwl.Repository.Interface.Base.IGenericRepository< TEntity >.
Definition at line 146 of file GenericRepository.cs.
| void DigitalOwl.Repository.Repositories.Base.GenericRepository< TEntity >.DeleteRange | ( | IEnumerable< TEntity > | entities | ) |
Method for deleting objects in database.
| entities | Objects to be deleted. |
Implements DigitalOwl.Repository.Interface.Base.IGenericRepository< TEntity >.
Definition at line 155 of file GenericRepository.cs.
|
protectedvirtual |
| disposing |
Definition at line 233 of file GenericRepository.cs.
| void DigitalOwl.Repository.Repositories.Base.GenericRepository< TEntity >.Dispose | ( | ) |
Implements DigitalOwl.Repository.Interface.Base.IGenericRepository< TEntity >.
Definition at line 249 of file GenericRepository.cs.
|
virtual |
Method for finding element in database fulfilling predicate.
| match | Predicate to be fulfilled. |
Implements DigitalOwl.Repository.Interface.Base.IGenericRepository< TEntity >.
Definition at line 107 of file GenericRepository.cs.
| IEnumerable<TEntity> DigitalOwl.Repository.Repositories.Base.GenericRepository< TEntity >.FindAll | ( | Expression< Func< TEntity, bool >> | match | ) |
Method for finding all elements in database fulfilling predicate.
| match | Predicate to be fulfilled. |
Implements DigitalOwl.Repository.Interface.Base.IGenericRepository< TEntity >.
Definition at line 127 of file GenericRepository.cs.
| IEnumerable<TEntity> DigitalOwl.Repository.Repositories.Base.GenericRepository< TEntity >.FindAll | ( | List< Expression< Func< TEntity, bool >>> | filters | ) |
Find all objects filtered.
| filters |
Implements DigitalOwl.Repository.Interface.Base.IGenericRepository< TEntity >.
Definition at line 260 of file GenericRepository.cs.
| async Task<IEnumerable<TEntity> > DigitalOwl.Repository.Repositories.Base.GenericRepository< TEntity >.FindAllAsync | ( | Expression< Func< TEntity, bool >> | match | ) |
Asynchronous method for finding all elements in database fulfilling predicate.
| match | Predicate to be fulfilled. |
Implements DigitalOwl.Repository.Interface.Base.IGenericRepository< TEntity >.
Definition at line 137 of file GenericRepository.cs.
| async Task<IEnumerable<TEntity> > DigitalOwl.Repository.Repositories.Base.GenericRepository< TEntity >.FindAllAsync | ( | List< Expression< Func< TEntity, bool >>> | filters | ) |
Asynchronous method Find all objects filtered.
| filters |
Implements DigitalOwl.Repository.Interface.Base.IGenericRepository< TEntity >.
Definition at line 273 of file GenericRepository.cs.
|
virtual |
Asynchronous method for finding element in database fulfilling predicate.
| match | Predicate to be fulfilled. |
Implements DigitalOwl.Repository.Interface.Base.IGenericRepository< TEntity >.
Definition at line 117 of file GenericRepository.cs.
|
virtual |
Find an object in database fulfilling the predicate.
| predicate | Predicate for an object to be fulfilled. |
Implements DigitalOwl.Repository.Interface.Base.IGenericRepository< TEntity >.
Definition at line 202 of file GenericRepository.cs.
| IQueryable<TEntity> DigitalOwl.Repository.Repositories.Base.GenericRepository< TEntity >.FindBy | ( | List< Expression< Func< TEntity, bool >>> | predicate | ) |
Find objects fulfilling predicate.
| predicate |
Implements DigitalOwl.Repository.Interface.Base.IGenericRepository< TEntity >.
Definition at line 286 of file GenericRepository.cs.
|
virtual |
Method for getting entity of given id from database.
| id | Id of requested entity. |
Implements DigitalOwl.Repository.Interface.Base.IGenericRepository< TEntity >.
Definition at line 65 of file GenericRepository.cs.
| IQueryable<TEntity> DigitalOwl.Repository.Repositories.Base.GenericRepository< TEntity >.GetAll | ( | ) |
Method for getting all instances of an entity from database.
Implements DigitalOwl.Repository.Interface.Base.IGenericRepository< TEntity >.
Definition at line 36 of file GenericRepository.cs.
| IQueryable<TEntity> DigitalOwl.Repository.Repositories.Base.GenericRepository< TEntity >.GetAll | ( | Expression< Func< TEntity, bool >> | predicate | ) |
Method for getting all instances of an entity on certain condition.
| predicate | Condition which specifies request. |
Implements DigitalOwl.Repository.Interface.Base.IGenericRepository< TEntity >.
Definition at line 46 of file GenericRepository.cs.
|
virtual |
Asynchronous method for getting all instances of an entity from database.
Implements DigitalOwl.Repository.Interface.Base.IGenericRepository< TEntity >.
Definition at line 55 of file GenericRepository.cs.
| IQueryable<TEntity> DigitalOwl.Repository.Repositories.Base.GenericRepository< TEntity >.GetAllIncluding | ( | params Expression< Func< TEntity, object >> [] | includeProperties | ) |
Get all objects from database including.
| includeProperties | Properties to be included. |
Implements DigitalOwl.Repository.Interface.Base.IGenericRepository< TEntity >.
Definition at line 213 of file GenericRepository.cs.
|
virtual |
Asynchronous method for getting entity of given id from database.
| id | Id of requested entity. |
Implements DigitalOwl.Repository.Interface.Base.IGenericRepository< TEntity >.
Definition at line 75 of file GenericRepository.cs.
|
virtual |
Method for updating an object in database.
| t | Updated version of an object. |
| key | Key of an object to be updated. |
Implements DigitalOwl.Repository.Interface.Base.IGenericRepository< TEntity >.
Definition at line 166 of file GenericRepository.cs.
|
protected |
Access to database.
Definition at line 21 of file GenericRepository.cs.


Weekend Links: 30 November – 1 December 2019
by GunnamattaThe arrival of implacable gifts, 1985, James Gleeson, Art Gallery of NSW
China – Asia
- ‘China spy’ spills secrets that rattle Taiwan and Hong Kong – Nikkei Asian Review
- China Financial Warning Signs Are Flashing Almost Everywhere – Bloomberg
- Sri Lanka’s new government wants to undo Hambantota port lease to Chinese venture – SCMP
- Japan’s factory output posts largest fall in almost two years – Reuters
- China says 2019 pork imports to exceed 3 million tonnes – Reuters
- Doubts grow over Hong Kong’s future as financial centre amid protests – FT.com
Europe – UK
- Germany’s difficult choice could be whether to alienate the US or China – CNBC
- A US-UK free trade agreement will bring benefits on both sides of the Atlantic – Spectator
- Wealthy US donors gave millions to rightwing UK groups – Guardian
- Europe’s Social Democrats Could Suffer Mass Extinction – Bloomberg
Americas – US
- American jobs are getting worse, new economic index shows – CBS
- Big Coal Trashed the Climate and Got Away With It – Rolling Stone
- US economy growing modestly, labor market still tight, according to Fed’s Beige Book report – CNBC
- Trade war: US soybean farmers anxiously await China deal, but Brazil, Argentina exporters hope for ‘no deal’ – SCMP
Terraspecufestorus
- Little Christmas cheer for school leavers looking for holiday work, with fewer positions and more competition – ABC
- Bardot goes into administration as it struggles in ‘highly cluttered’ retail sector – ABC
- Business hurting customers with ‘weapons of mass influence’: ASIC – Ninefax
- Alleged Chinese spy target pictured sitting next to Liberal MP Gladys Liu at her home – Ninefax
- Suddenly, the Chinese Threat to Australia Seems Very Real – NY Times
- The Private Health Insurance industry is a scam subsidised by taxpayers – Pearls and Irritations, Menadue
- Calls for Reserve Bank as Peoples Bank as farm credit dries up – Michael West
Macro & Markets
- Leading Rare Earth Miner Looks To Seal Mega Deal In The U.S. – Oilprice.com
- Bold Bets That Gold Could Triple to $4,000 Trade in New York – Bloomberg
- High-grade iron ore outperforms as China steel margins recover – Reuters, Clyde Russell
- How investment analysts became data miners – FT.com
- Are we in for a repeat of the ‘Long Depression’? – MarketWatch…interesting read…
…and furthermore…
- Chinese astronomers discover ‘impossibly massive’ black hole LB-1 – SCMP
- How US immigration officials set up a fake university and arrested 250 with illegal visas – SCMP
- The horror of sameness – Aeon
- If a monopoly gives away free services is it a problem? – FT.com
- The Next Wave of Debt Monetization Will Be a Disaster – Epoch Times
- Demographics and technology explain secular stagnation and more – VoxEU
- Beyond the Phillips curve: Understanding low wage growth in the euro area – VoxEU
- The Web Might Be a Monopolist’s Best Friend – Bloomberg, Smith
Gunnamatta
General all round punter
Latest posts by Gunnamatta (see all)
- Weekend Links: 30 November – 1 December 2019 - November 30, 2019
- A night at the charts – 28 November 2019 - November 28, 2019
- A night at the charts – 15 October 2019 - October 15, 2019


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CHAPTER 841
INQUESTS, CORONERS
Section
841-1 Who are coroners
841-2 Appointment of deputies
841-3 Duties
841-4 Testimony under oath reduced to writing
841-5 Form of oath
841-6 Subpoenas; penalty for failure to comply
841-7 Reports of investigation
841-8 Duty to forward copy of reports to any county or
prosecuting attorney and to person in charge of
disposition of body
841-9 Fees
841-10 Decent burial
841-11 Witness fees and mileage; expenses
841-12 Removal of corpse
841-13 Disposition of personal effects
841-14 Autopsies and further investigations
841-14.5 Performance of autopsy by pathologist
841-14.6 Assistance among counties
841-15 Notice to next of kin, expense
841-16 Disposal of decomposed remains
841-17 Hospital records
841-18 Coroner's physician; laboratory facilities
841-19 Notification of this chapter


| Modifier and Type | Field and Description |
|---|---|
Constraint |
Solver.FALSE
Two basic constraints TRUE and FALSE, cached to avoid multiple useless occurrences
|
Constraint |
Solver.TRUE
Two basic constraints TRUE and FALSE, cached to avoid multiple useless occurrences
|
| Modifier and Type | Method and Description |
|---|---|
Constraint[] |
Solver.getCstrs()
Returns the array of declared
Constraint objects defined in this Solver . |
| Modifier and Type | Method and Description |
|---|---|
void |
Solver.post(Constraint... cs)
Post constraints
cs permanently in the constraints network of this :
- add them to the data structure,
- set the fixed idx,
- checks for restrictions |
void |
Solver.post(Constraint c)
Post permanently a constraint
c in the constraints network of this :
- add it to the data structure,
- set the fixed idx,
- checks for restrictions |
void |
Solver.postTemp(Constraint c)
Post a constraint temporary, that is, it will be unposted upon backtrack.
|
void |
Solver.unpost(Constraint c)
Remove permanently the constraint
c from the constraint network. |
| Modifier and Type | Class and Description |
|---|---|
class |
Arithmetic
A constraint dedicated to arithmetic operations.
|
class |
ReificationConstraint
Implication constraint: boolean b => constraint c
Also known as half reification
|
| Modifier and Type | Field and Description |
|---|---|
protected Constraint |
Propagator.constraint |
| Modifier and Type | Method and Description |
|---|---|
static Constraint |
IntConstraintFactory.absolute(IntVar VAR1,
IntVar VAR2)
Enforces VAR1 = |VAR2|
|
static Constraint |
IntConstraintFactory.alldifferent_conditionnal(IntVar[] VARS,
Condition CONDITION)
Alldifferent holds on the subset of VARS which satisfies the given CONDITION
|
static Constraint |
IntConstraintFactory.alldifferent_conditionnal(IntVar[] VARS,
Condition CONDITION,
boolean AC)
Alldifferent holds on the subset of VARS which satisfies the given CONDITION
|
static Constraint |
IntConstraintFactory.alldifferent_except_0(IntVar[] VARS)
Variables in VARS must either be different or equal to 0
|
static Constraint |
IntConstraintFactory.alldifferent(IntVar[] VARS)
Ensures that all variables from VARS take a different value.
|
static Constraint |
IntConstraintFactory.alldifferent(IntVar[] VARS,
String CONSISTENCY)
Ensures that all variables from VARS take a different value.
|
static Constraint |
IntConstraintFactory.among(IntVar NVAR,
IntVar[] VARS,
int[] VALUES)
NVAR is the number of variables of the collection VARIABLES that take their value in VALUES.
|
static Constraint |
LogicalConstraintFactory.and(BoolVar... BOOLS)
Make a and constraint
|
static Constraint |
LogicalConstraintFactory.and(Constraint... CONS)
Make a and constraint
|
static Constraint |
IntConstraintFactory.arithm(IntVar VAR,
String OP,
int CSTE)
Ensures: VAR OP CSTE, where OP in {"=", "!
|
static Constraint |
IntConstraintFactory.arithm(IntVar VAR1,
String OP,
IntVar VAR2)
Ensures: VAR1 OP VAR2, where OP in {"=", "!
|
static Constraint |
IntConstraintFactory.arithm(IntVar VAR1,
String OP1,
IntVar VAR2,
String OP2,
int CSTE)
Ensures: VAR1 OP VAR2, where OP in {"=", "!
|
static Constraint |
IntConstraintFactory.atleast_nvalues(IntVar[] VARS,
IntVar NVALUES,
boolean AC)
Let N be the number of distinct values assigned to the variables of the VARS collection.
|
static Constraint |
IntConstraintFactory.atmost_nvalues(IntVar[] VARS,
IntVar NVALUES,
boolean STRONG)
Let N be the number of distinct values assigned to the variables of the VARS collection.
|
static Constraint[] |
IntConstraintFactory.bin_packing(IntVar[] ITEM_BIN,
int[] ITEM_SIZE,
IntVar[] BIN_LOAD,
int OFFSET)
Bin Packing formulation:
forall b in [0,BIN_LOAD.length-1],
BIN_LOAD[b]=sum(ITEM_SIZE[i] | i in [0,ITEM_SIZE.length-1], ITEM_BIN[i] = b+OFFSET
forall i in [0,ITEM_SIZE.length-1], ITEM_BIN is in [OFFSET,BIN_LOAD.length-1+OFFSET],
|
static Constraint |
IntConstraintFactory.bit_channeling(BoolVar[] BITS,
IntVar VAR)
Ensures that VAR = 20*BIT_1 + 21*BIT_2 + ... 2n-1*BIT_n.
|
static Constraint |
IntConstraintFactory.boolean_channeling(BoolVar[] BVARS,
IntVar VAR,
int OFFSET)
Maps the boolean assignments variables BVARS with the standard assignment variable VAR.
|
static Constraint |
IntConstraintFactory.circuit(IntVar[] VARS,
int OFFSET)
Creates a circuit constraint which ensures that
the elements of vars define a covering circuit
where VARS[i] = OFFSET+j means that j is the successor of i.
|
static Constraint |
IntConstraintFactory.circuit(IntVar[] VARS,
int OFFSET,
CircuitConf CONF)
Creates a circuit constraint which ensures that
the elements of vars define a covering circuit
where VARS[i] = OFFSET+j means that j is the successor of i.
|
static Constraint |
IntConstraintFactory.clause_channeling(IntVar VAR,
BoolVar[] EVARS,
BoolVar[] LVARS)
Link each value from the domain of VAR to two boolean variable:
one reifies the equality to the i^th value of the variable domain,
the other reifies the less-or-equality to the i^th value of the variable domain.
|
static Constraint |
IntConstraintFactory.cost_regular(IntVar[] VARS,
IntVar COST,
ICostAutomaton CAUTOMATON)
Ensures that the assignment of a sequence of variables is recognized by CAUTOMATON, a deterministic finite automaton,
and that the sum of the costs associated to each assignment is bounded by the cost variable.
|
static Constraint |
IntConstraintFactory.count(int VALUE,
IntVar[] VARS,
IntVar LIMIT)
Let N be the number of variables of the VARIABLES collection assigned to value VALUE;
Enforce condition N = LIMIT to hold.
|
static Constraint |
IntConstraintFactory.count(IntVar VALUE,
IntVar[] VARS,
IntVar LIMIT)
Let N be the number of variables of the VARIABLES collection assigned to value VALUE;
Enforce condition N = LIMIT to hold.
|
static Constraint |
IntConstraintFactory.cumulative(Task[] TASKS,
IntVar[] HEIGHTS,
IntVar CAPACITY)
Cumulative constraint: Enforces that at each point in time,
the cumulated height of the set of tasks that overlap that point
does not exceed a given limit.
|
static Constraint |
IntConstraintFactory.cumulative(Task[] TASKS,
IntVar[] HEIGHTS,
IntVar CAPACITY,
boolean INCREMENTAL)
Cumulative constraint: Enforces that at each point in time,
the cumulated height of the set of tasks that overlap that point
does not exceed a given limit.
|
static Constraint[] |
IntConstraintFactory.diffn(IntVar[] X,
IntVar[] Y,
IntVar[] WIDTH,
IntVar[] HEIGHT,
boolean USE_CUMUL)
Constrains each rectanglei, given by their origins Xi,Yi
and sizes WIDTHi,HEIGHTi, to be non-overlapping.
|
static Constraint |
IntConstraintFactory.distance(IntVar VAR1,
IntVar VAR2,
String OP,
int CSTE)
Ensures:
|VAR1-VAR2| OP CSTE where OP can take its value among {"=", ">", "<", "! |
static Constraint |
IntConstraintFactory.distance(IntVar VAR1,
IntVar VAR2,
String OP,
IntVar VAR3)
Ensures:
|VAR1-VAR2| OP VAR3 where OP can take its value among {"=", ">", "<"} |
static Constraint |
IntConstraintFactory.element(IntVar VALUE,
int[] TABLE,
IntVar INDEX)
Build ELEMENT constraint: VALUE = TABLE[INDEX]
|
static Constraint |
IntConstraintFactory.element(IntVar VALUE,
int[] TABLE,
IntVar INDEX,
int OFFSET,
String SORT)
Build ELEMENT constraint: VALUE = TABLE[INDEX-OFFSET]
|
static Constraint |
IntConstraintFactory.element(IntVar VALUE,
IntVar[] TABLE,
IntVar INDEX,
int OFFSET)
Build an ELEMENT constraint: VALUE = TABLE[INDEX-OFFSET] where TABLE is an array of variables.
|
static Constraint |
IntConstraintFactory.eucl_div(IntVar DIVIDEND,
IntVar DIVISOR,
IntVar RESULT)
Ensures DIVIDEND / DIVISOR = RESULT, rounding towards 0 -- Euclidean division
Also ensures DIVISOR !
|
static Constraint |
IntConstraintFactory.FALSE(Solver solver)
Ensures the FALSE constraint
|
Constraint |
Propagator.getConstraint() |
Constraint |
Constraint.getOpposite()
Get/make the opposite constraint of this
The default opposite constraint does not filter domains but fails if this constraint is satisfied
|
static Constraint |
IntConstraintFactory.global_cardinality(IntVar[] VARS,
int[] VALUES,
IntVar[] OCCURRENCES,
boolean CLOSED)
Global Cardinality constraint (GCC):
Each value VALUES[i] should be taken by exactly OCCURRENCES[i] variables of VARS.
|
static Constraint |
LogicalConstraintFactory.ifThen_reifiable(BoolVar BVAR,
Constraint CSTR)
Same as ifThen, but:
+ can be reified
- may be slower
|
static Constraint |
LogicalConstraintFactory.ifThen_reifiable(Constraint IF,
Constraint THEN)
Same as ifThen, but:
+ can be reified
- may be slower
|
static Constraint |
LogicalConstraintFactory.ifThenElse_reifiable(BoolVar BVAR,
Constraint THEN,
Constraint ELSE)
Same as ifThenElse, but:
+ can be reified
- may be slower
|
static Constraint |
LogicalConstraintFactory.ifThenElse_reifiable(Constraint IF,
Constraint THEN,
Constraint ELSE)
Same as ifThenElse, but:
+ can be reified
- may be slower
|
static Constraint |
IntConstraintFactory.inverse_channeling(IntVar[] VARS1,
IntVar[] VARS2,
int OFFSET1,
int OFFSET2)
Make an inverse channeling between VARS1 and VARS2:
VARS1[i-OFFSET2] = j <=> VARS2[j-OFFSET1] = i
Performs AC if domains are enumerated.
|
static Constraint |
IntConstraintFactory.knapsack(IntVar[] OCCURRENCES,
IntVar TOTAL_WEIGHT,
IntVar TOTAL_ENERGY,
int[] WEIGHT,
int[] ENERGY)
Ensures that :
- OCCURRENCES[i] * WEIGHT[i] ≤ TOTAL_WEIGHT - OCCURRENCES[i] * ENERGY[i] = TOTAL_ENERGY and maximizing the value of TOTAL_ENERGY. |
static Constraint |
IntConstraintFactory.lex_chain_less_eq(IntVar[]... VARS)
For each pair of consecutive vectors VARSi and VARSi+1 of the VARS collection
VARSi is lexicographically less or equal than than VARSi+1
|
static Constraint |
IntConstraintFactory.lex_chain_less(IntVar[]... VARS)
For each pair of consecutive vectors VARSi and VARSi+1 of the VARS collection
VARSi is lexicographically strictly less than than VARSi+1
|
static Constraint |
IntConstraintFactory.lex_less_eq(IntVar[] VARS1,
IntVar[] VARS2)
Ensures that VARS1 is lexicographically less or equal than VARS2.
|
static Constraint |
IntConstraintFactory.lex_less(IntVar[] VARS1,
IntVar[] VARS2)
Ensures that VARS1 is lexicographically strictly less than VARS2.
|
Constraint |
Constraint.makeOpposite()
Make the opposite constraint of this
BEWARE: this method should never be called by the user
but it can be overridden to provide better constraint negations
|
Constraint |
Arithmetic.makeOpposite() |
static Constraint |
IntConstraintFactory.maximum(BoolVar MAX,
BoolVar[] VARS)
MAX is the maximum value of the collection of boolean variables VARS
|
static Constraint |
IntConstraintFactory.maximum(IntVar MAX,
IntVar[] VARS)
MAX is the maximum value of the collection of domain variables VARS
|
static Constraint |
IntConstraintFactory.maximum(IntVar MAX,
IntVar VAR1,
IntVar VAR2)
Ensures: MAX = MAX(VAR1, VAR2)
(Bound Consistency)
|
static Constraint |
IntConstraintFactory.mddc(IntVar[] VARS,
MultivaluedDecisionDiagram MDD)
Create a constraint where solutions (tuples) are encoded by a multi-valued decision diagram.
|
static Constraint |
IntConstraintFactory.member(IntVar VAR,
int[] TABLE)
Ensures VAR takes its values in TABLE
|
static Constraint |
IntConstraintFactory.member(IntVar VAR,
int LB,
int UB)
Ensures VAR takes its values in [LB, UB]
|
static Constraint |
IntConstraintFactory.minimum(BoolVar MIN,
BoolVar[] VARS)
MIN is the minimum value of the collection of boolean variables VARS
|
static Constraint |
IntConstraintFactory.minimum(IntVar MIN,
IntVar[] VARS)
MIN is the minimum value of the collection of domain variables VARS
|
static Constraint |
IntConstraintFactory.minimum(IntVar MIN,
IntVar VAR1,
IntVar VAR2)
Ensures: VAR1 = MIN(VAR2, VAR3)
(Bound Consistency)
|
static Constraint |
IntConstraintFactory.mod(IntVar X,
IntVar Y,
IntVar Z)
Ensures X % Y = Z,
i.e. |
static Constraint |
IntConstraintFactory.multicost_regular(IntVar[] VARS,
IntVar[] CVARS,
ICostAutomaton CAUTOMATON)
Ensures that the assignment of a sequence of VARS is recognized by CAUTOMATON, a deterministic finite automaton,
and that the sum of the cost vector COSTS associated to each assignment is bounded by the variable vector CVARS.
|
static Constraint |
IntConstraintFactory.not_member(IntVar VAR,
int[] TABLE)
Ensures VAR does not take its values in TABLE
|
static Constraint |
IntConstraintFactory.not_member(IntVar VAR,
int LB,
int UB)
Ensures VAR does not take its values in [LB, UB]
|
static Constraint |
LogicalConstraintFactory.not(Constraint CONS)
Get the opposite of a constraint
Works for any constraint, but the associated performances might be weak
|
static Constraint[] |
IntConstraintFactory.nvalues(IntVar[] VARS,
IntVar NVALUES)
Let N be the number of distinct values assigned to the variables of the VARS collection.
|
static Constraint |
LogicalConstraintFactory.or(BoolVar... BOOLS)
Make an or constraint
|
static Constraint |
LogicalConstraintFactory.or(Constraint... CONS)
Make an or constraint
|
static Constraint[] |
IntConstraintFactory.path(IntVar[] VARS,
IntVar START,
IntVar END,
int OFFSET)
Creates a path constraint which ensures that
the elements of VARS define a covering path from START to END
where VARS[i] = OFFSET+j means that j is the successor of i.
|
static Constraint |
IntConstraintFactory.regular(IntVar[] VARS,
IAutomaton AUTOMATON)
Enforces the sequence of VARS to be a word
recognized by the deterministic finite automaton AUTOMATON.
|
static Constraint |
LogicalConstraintFactory.reification_reifiable(BoolVar BVAR,
Constraint CSTR)
Same as reification, but:
+ can be reified
- may be slower
|
static Constraint |
IntConstraintFactory.scalar(IntVar[] VARS,
int[] COEFFS,
IntVar SCALAR)
Enforces that ∑i in |VARS|COEFFSi * VARSi = SCALAR.
|
static Constraint |
IntConstraintFactory.scalar(IntVar[] VARS,
int[] COEFFS,
String OPERATOR,
IntVar SCALAR)
A scalar constraint which ensures that Sum(VARS[i]*COEFFS[i]) OPERATOR SCALAR
|
static Constraint |
IntConstraintFactory.sort(IntVar[] VARS,
IntVar[] SORTEDVARS)
Creates a sort constraint which ensures that the variables of SORTEDVARS correspond to the variables
of VARS according to a permutation.
|
static Constraint |
IntConstraintFactory.square(IntVar VAR1,
IntVar VAR2)
Enforces VAR1 = VAR2^2
|
static Constraint |
IntConstraintFactory.subcircuit(IntVar[] VARS,
int OFFSET,
IntVar SUBCIRCUIT_SIZE)
Creates a subcircuit constraint which ensures that
the elements of vars define a single circuit of subcircuitSize nodes where
VARS[i] = OFFSET+j means that j is the successor of i.
|
static Constraint[] |
IntConstraintFactory.subpath(IntVar[] VARS,
IntVar START,
IntVar END,
int OFFSET,
IntVar SIZE)
Creates a subpath constraint which ensures that
the elements of VARS define a path of SIZE vertices, leading from START to END
where VARS[i] = OFFSET+j means that j is the successor of i.
|
static Constraint |
IntConstraintFactory.sum(BoolVar[] VARS,
IntVar SUM)
Enforces that ∑i in |VARS|VARSi = SUM.
|
static Constraint |
IntConstraintFactory.sum(BoolVar[] VARS,
String OPERATOR,
IntVar SUM)
Enforces that ∑i in |VARS|VARSi OPERATOR SUM.
|
static Constraint |
IntConstraintFactory.sum(IntVar[] VARS,
IntVar SUM)
Enforces that ∑i in |VARS|VARSi = SUM.
|
static Constraint |
IntConstraintFactory.sum(IntVar[] VARS,
String OPERATOR,
IntVar SUM)
Enforces that ∑i in |VARS|VARSi OPERATOR SUM.
|
static Constraint |
IntConstraintFactory.table(IntVar[] VARS,
Tuples TUPLES,
String ALGORITHM)
Create a table constraint, with the specified algorithm defined ALGORITHM
- GAC2001: Arc Consistency version 2001 for tuples,
- GAC2001+: Arc Consistency version 2001 for allowed tuples, - GAC3rm: Arc Consistency version AC3 rm for tuples, - GAC3rm+ (default): Arc Consistency version 3rm for allowed tuples, - GACSTR+: Arc Consistency version STR for allowed tuples, - STR2+: Arc Consistency version STR2 for allowed tuples, - FC: Forward Checking. |
static Constraint |
IntConstraintFactory.table(IntVar VAR1,
IntVar VAR2,
Tuples TUPLES,
String ALGORITHM)
Create a table constraint over a couple of variables VAR1 and VAR2:
- AC2001: table constraint which applies the AC2001 algorithm, - AC3: table constraint which applies the AC3 algorithm, - AC3rm: table constraint which applies the AC3 rm algorithm, - AC3bit+rm (default): table constraint which applies the AC3 bit+rm algorithm, - FC: table constraint which applies forward checking algorithm. |
static Constraint |
IntConstraintFactory.times(IntVar X,
int Y,
IntVar Z)
Ensures: X * Y = Z
|
static Constraint |
IntConstraintFactory.times(IntVar X,
IntVar Y,
IntVar Z)
Ensures: X * Y = Z
|
static Constraint |
IntConstraintFactory.tree(IntVar[] SUCCS,
IntVar NBTREES,
int OFFSET)
Partition SUCCS variables into NBTREES (anti) arborescences
SUCCS[i] = OFFSET+j means that j is the successor of i.
|
static Constraint |
IntConstraintFactory.TRUE(Solver solver)
Ensures the TRUE constraint
|
static Constraint[] |
IntConstraintFactory.tsp(IntVar[] SUCCS,
IntVar COST,
int[][] COST_MATRIX)
A constraint for the Traveling Salesman Problem :
Enforces SUCCS to form a hamiltonian circuit of value COST
|
| Modifier and Type | Method and Description |
|---|---|
static Constraint |
LogicalConstraintFactory.and(Constraint... CONS)
Make a and constraint
|
void |
Propagator.defineIn(Constraint c)
Informs this propagator the (unique) constraint it filters.
|
static Constraint |
LogicalConstraintFactory.ifThen_reifiable(BoolVar BVAR,
Constraint CSTR)
Same as ifThen, but:
+ can be reified
- may be slower
|
static Constraint |
LogicalConstraintFactory.ifThen_reifiable(Constraint IF,
Constraint THEN)
Same as ifThen, but:
+ can be reified
- may be slower
|
static void |
LogicalConstraintFactory.ifThen(BoolVar BVAR,
Constraint CSTR)
Imply a constraint: BVAR => CSTR
Also called half reification constraint
Ensures:
- BVAR = 1 => CSTR is satisfied, - CSTR is not satisfied => BVAR = 0 Example : - ifThen(b1, arithm(v1, "=", 2)); :
b1 is equal to 1 => v1 = 2, so v1 ! |
static void |
LogicalConstraintFactory.ifThen(Constraint IF,
Constraint THEN)
Post a constraint ensuring that if IF is satisfied, then THEN is satisfied as well
BEWARE : it is automatically posted (it cannot be reified)
|
static Constraint |
LogicalConstraintFactory.ifThenElse_reifiable(BoolVar BVAR,
Constraint THEN,
Constraint ELSE)
Same as ifThenElse, but:
+ can be reified
- may be slower
|
static Constraint |
LogicalConstraintFactory.ifThenElse_reifiable(Constraint IF,
Constraint THEN,
Constraint ELSE)
Same as ifThenElse, but:
+ can be reified
- may be slower
|
static void |
LogicalConstraintFactory.ifThenElse(BoolVar BVAR,
Constraint THEN,
Constraint ELSE)
Implication constraint: BVAR => THEN && not(B) => ELSE.
|
static void |
LogicalConstraintFactory.ifThenElse(Constraint IF,
Constraint THEN,
Constraint ELSE)
Post a constraint ensuring that if IF is satisfied, then THEN is satisfied as well
Otherwise, ELSE must be satisfied
BEWARE : it is automatically posted (it cannot be reified)
|
static Constraint |
LogicalConstraintFactory.not(Constraint CONS)
Get the opposite of a constraint
Works for any constraint, but the associated performances might be weak
|
static Constraint |
LogicalConstraintFactory.or(Constraint... CONS)
Make an or constraint
|
static Constraint |
LogicalConstraintFactory.reification_reifiable(BoolVar BVAR,
Constraint CSTR)
Same as reification, but:
+ can be reified
- may be slower
|
static void |
LogicalConstraintFactory.reification(BoolVar BVAR,
Constraint CSTR)
Reify a constraint with a boolean variable: BVAR <=> CSTR
Ensures:
- BVAR = 1 <=> CSTR is satisfied, BEWARE : it is automatically posted (it cannot be reified) |
| Constructor and Description |
|---|
ReificationConstraint(BoolVar bVar,
Constraint consIfBoolTrue,
Constraint consIfBoolFalse) |
| Modifier and Type | Class and Description |
|---|---|
class |
DistanceXYC
Ensures:
|X-Y| OP C where OP can take its value among {=, >, <, =/=} |
| Modifier and Type | Class and Description |
|---|---|
class |
AllDifferent
Ensures that all variables from VARS take a different value.
|
| Modifier and Type | Class and Description |
|---|---|
class |
CostRegular
COST_REGULAR constraint
|
| Modifier and Type | Class and Description |
|---|---|
class |
SatConstraint
|
| Modifier and Type | Class and Description |
|---|---|
class |
Cumulative
Cumulative constraint
|
| Modifier and Type | Class and Description |
|---|---|
class |
GlobalCardinality
Global Cardinality constraint
|
| Modifier and Type | Method and Description |
|---|---|
static Constraint[] |
GlobalCardinality.reformulate(IntVar[] vars,
IntVar[] card,
Solver solver) |
| Modifier and Type | Class and Description |
|---|---|
class |
NogoodConstraint
Created by cprudhom on 21/01/15.
|
| Modifier and Type | Class and Description |
|---|---|
class |
Scalar
Bounds Consistency Techniques for Long Linear Constraint" W. |
| Modifier and Type | Method and Description |
|---|---|
static Constraint |
ScalarFactory.reduce(IntVar[] VARS,
int[] COEFFS,
String OPERATOR,
IntVar SCALAR) |
static Constraint |
ScalarFactory.reduce(IntVar[] VARS,
String OPERATOR,
IntVar SUM) |
| Modifier and Type | Class and Description |
|---|---|
class |
IntEqRealConstraint
Channeling constraint between integers and reals, to avoid views
|
class |
RealConstraint
A constraint on real variables, solved using IBEX.
|
| Constructor and Description |
|---|
PropConditionnal(Variable[] vars2observe,
Constraint[] condTrue,
Constraint[] condFalse) |
PropConditionnal(Variable[] vars2observe,
Constraint[] condTrue,
Constraint[] condFalse) |
PropOpposite(Constraint original,
Variable[] vars) |
PropReif(Variable[] allVars,
Constraint consIfBoolTrue,
Constraint consIfBoolFalse) |
| Modifier and Type | Method and Description |
|---|---|
static Constraint |
SetConstraintsFactory.all_different(SetVar[] SETS)
Sets in SETS are all different (not necessarily disjoint)
Note that there cannot be more than two empty sets
|
static Constraint |
SetConstraintsFactory.all_disjoint(SetVar[] SETS)
Sets in SETS are all disjoint
Note that there can be multiple empty sets
|
static Constraint |
SetConstraintsFactory.all_equal(SetVar[] SETS)
SETS are all equal
|
static Constraint |
SetConstraintsFactory.bool_channel(BoolVar[] BOOLEANS,
SetVar SET,
int OFFSET)
Channeling between a set variable SET and boolean variables BOOLEANS
i in SET <=> BOOLEANS[i-OFFSET] = TRUE
|
static Constraint |
SetConstraintsFactory.cardinality(SetVar SET,
IntVar CARD)
Cardinality constraint: |SET_VAR| = CARD
|
static Constraint |
SetConstraintsFactory.disjoint(SetVar SET_1,
SetVar SET_2)
SET_1 and SET_2 are disjoint, i.e. they cannot contain the same element.
|
static Constraint |
SetConstraintsFactory.element(IntVar INDEX,
SetVar[] SETS,
int OFFSET,
SetVar SET)
Element constraint over sets
states that SETS[INDEX-OFFSET] = SET
|
static Constraint |
SetConstraintsFactory.int_channel(SetVar[] SETS,
IntVar[] INTEGERS,
int OFFSET_1,
int OFFSET_2)
Channeling between set variables SETS and integer variables INTEGERS
x in SETS[y-OFFSET_1] <=> INTEGERS[x-OFFSET_2] = y
|
static Constraint |
SetConstraintsFactory.int_values_union(IntVar[] VARS,
SetVar VALUES)
Channeling constraint ensuring that
VALUES is exactly the set of values taken by VARS,
|
static Constraint |
SetConstraintsFactory.intersection(SetVar[] SETS,
SetVar INTERSECTION)
Constraint which ensures that the intersection of sets in SET_VAR is equal to the set SET_INTER
|
static Constraint |
SetConstraintsFactory.inverse_set(SetVar[] SETS,
SetVar[] INVERSE_SETS,
int OFFSET_1,
int OFFSET_2)
Inverse set constraint
x in SETS[y-OFFSET_1] <=> y in INVERSE_SETS[x-OFFSET_2]
|
static Constraint |
SetConstraintsFactory.max(SetVar INDEXES,
int[] WEIGHTS,
int OFFSET,
IntVar MAX_ELEMENT_VALUE,
boolean NOT_EMPTY)
Retrieves the maximum element MAX_ELEMENT_VALUE induced by INDEXES
max{WEIGHTS[i-OFFSET] | i in INDEXES} = MAX_ELEMENT_VALUE
|
static Constraint |
SetConstraintsFactory.max(SetVar SET,
IntVar MAX_ELEMENT_VALUE,
boolean NOT_EMPTY)
Retrieves the maximum element MAX_ELEMENT_VALUE of SET
max{i | i in set} = MAX_ELEMENT_VALUE
|
static Constraint |
SetConstraintsFactory.member(IntVar INTEGER,
SetVar SET)
Member constraint over an IntVar and a SetVar
states that INTEGER is included in SET
|
static Constraint |
SetConstraintsFactory.member(SetVar[] SETS,
SetVar SET)
Member constraint over sets
states that SET belongs to SETS
|
static Constraint |
SetConstraintsFactory.min(SetVar INDEXES,
int[] WEIGHTS,
int OFFSET,
IntVar MIN_ELEMENT_VALUE,
boolean NOT_EMPTY)
Retrieves the minimum element MIN_ELEMENT_VALUE induced by INDEXES
min{WEIGHTS[i-OFFSET] | i in INDEXES} = MIN_ELEMENT_VALUE
|
static Constraint |
SetConstraintsFactory.min(SetVar SET,
IntVar MIN_ELEMENT_VALUE,
boolean NOT_EMPTY)
Retrieves the minimum element MIN_ELEMENT_VALUE of SET:
min{i | i in SET} = MIN_ELEMENT_VALUE
|
static Constraint |
SetConstraintsFactory.nbEmpty(SetVar[] SETS,
IntVar NB_EMPTY_SETS)
Restricts the number of empty sets in SETS
|{s in SETS such that |s|=0}| = NB_EMPTY_SETS
|
static Constraint |
SetConstraintsFactory.not_member(IntVar INTEGER,
SetVar SET)
NotMember constraint over an IntVar and a SetVar
states that INTEGER is not included in SET
|
static Constraint |
SetConstraintsFactory.notEmpty(SetVar SET)
Prevents SET to be empty
|
static Constraint |
SetConstraintsFactory.offSet(SetVar SET_1,
SetVar SET_2,
int OFFSET)
links SET_1 and SET_2 with OFFSET
x in SET_1 <=> x+OFFSET in SET_2
|
static Constraint |
SetConstraintsFactory.partition(SetVar[] SETS,
SetVar UNIVERSE)
Partitions UNIVERSE into disjoint sets, SETS
|
static Constraint |
SetConstraintsFactory.subsetEq(SetVar[] SETS)
Constraint which ensures that i
|
static Constraint |
SetConstraintsFactory.sum(SetVar INDEXES,
int[] WEIGHTS,
int OFFSET,
IntVar SUM,
boolean NOT_EMPTY)
Sums weights given by a set of indexes INDEXES:
sum{WEIGHTS[i-OFFSET] | i in INDEXES} = SUM
|
static Constraint |
SetConstraintsFactory.sum(SetVar SET,
IntVar SUM,
boolean NOT_EMPTY)
Sums elements of a SET
sum{i | i in set} = SUM
|
static Constraint |
SetConstraintsFactory.symmetric(SetVar[] SETS,
int OFFSET)
Symmetric sets constraint
x in SETS[y-OFFSET] <=> y in SETS[x-OFFSET]
|
static Constraint |
SetConstraintsFactory.union(SetVar[] SETS,
SetVar UNION)
Constraint which ensures that the union of sets in SET_VARS is equal to the set SET_UNION
|
| Modifier and Type | Class and Description |
|---|---|
class |
DistanceXYZ
|X-Y| OP Z |
class |
Max
X = MAX(Y,Z)
|
class |
Min
X = MIN(Y,Z)
|
class |
Times
X*Y = Z
|
| Modifier and Type | Class and Description |
|---|---|
class |
Member
|
class |
NotMember
|
| Modifier and Type | Method and Description |
|---|---|
Constraint |
NotMember.makeOpposite() |
Constraint |
Member.makeOpposite() |
| Constructor and Description |
|---|
FailPerPropagator(Constraint[] constraints,
Solver solver) |
| Modifier and Type | Field and Description |
|---|---|
protected Constraint[] |
MeasuresRecorder.cstrs |
Copyright © 2015. All rights reserved.


bag
banana
bathroom
Welcome to Bus-Mall survey project!
You will see three products at a time. Click on the one the you like the most. Voting goes on for 25 rounds. At the end of the survery the most popular products will be included in our next edition's BusMall catalogue.
You can press the view results buttoon to see the current results!


Currently finishing a fullstack software engineering program with the Flatiron School, I work full-time, remotely.
Through coursework and projects, I am developing fluency in: React, Javascript, Node, Ruby, Rails, Sinatra, SQL, HTML, CSS, Rack,
jQuery, and AJAX, as well as industry standards and best practices, while logging over 1000+ hours of coding time.
I have several years of experience using R and SAS statistical modeling languages to build, analyze, and model large
climate and environmental datasets as an ecologist/hydrologist. I understand the data behind the numbers- having
collected, analyzed and modelled data for the last 10 years across scientific disciplines. Having relied on technology
to analyze large datasets during this period, I started diving deeper into the 'ecology' and systems-design aspects of
the software that I was using. Ultimately, I am a systems-thinker who enjoys solving problems with the best tools
available, maintaining agility and improving my capacity and learning in both data science and software engineering.
This Ruby GEM provides the user a CLI to view "12 Global Food Festivals Worth the Trip" as described on Fodor's Travel
website. This program will list all 12 festivals and the user can choose which festival, by number, they would like
more information on. Details include a short description of the festival and the festival location.
[Source Code]
An examp anythtion.


Oracle JET FixItFast is a field service demo application. This application shows several common mobile UX patterns used in phone-based applications. Demonstrated patterns include sign in, navigation drawer, dashboard, list view features, profiles, search, activity feeds and more.
Device services such as camera, dialer, email and more are made available. Selected animations are used for transitions, component effects and graphic fade animations. This demo is available for different mobile themes (Android and iOS) and for a web theme.
To learn more visit Oracle JET website.
Oracle JET FixItFast is a field service demo application. This application shows several common mobile UX patterns used in phone-based applications. Demonstrated patterns include sign in, navigation drawer, dashboard, list view features, profiles, search, activity feeds and more.
Device services such as camera, dialer, email and more are made available. Selected animations are used for transitions, component effects and graphic fade animations. This demo is available for different mobile themes (Android and iOS) and for a web theme.
To learn more visit Oracle JET website.


{{ $name := .Get "name" }} {{ $id := $name | urlize }} {{ $active := cond (eq (.Parent.Get "active" | urlize) $id) "active show" "" }}


Abbreviations
MA—New York City Municipal Archives
BOE—Board of Education of the City of New York Collection
MSRC—Moorland-Spingarn Research Center, Howard University
NYCBOE—New York City Board of Education
NYCIBO—New York City Independent Budget Office
NYCPC—New York City Planning Commission
NYSDOE—New York State Department of Education
Schomburg—Schomburg Center for Research in Black Culture, New York Public Library
Tamiment—Tamiment Library and Robert F. Wagner Labor Archives, New York University
UFT—United Federation of Teachers
USNY—University of the State of New York


Preparation time
less than 30 mins
Cooking time
10 to 30 mins
Serves
Serves 4
This stir fry is super simple and full of flavour, skip the takeaway and try this instead.
Combine the lemongrass, chilli, garlic and fish sauce in a bowl and mix.
Put the pork in another bowl, add half the lemongrass mix and reserve the rest. Cover and place in the fridge to marinate for at least 20 minutes.
Heat the oil in a wok or a large frying pan over a high heat. Add the reserved lemongrass mixture and curry powder and stir-fry for about 1 minute until fragrant.
Add the marinated pork and stir-fry for 4–5 minutes until the pieces have a nice colour on all sides. Pour in the chicken stock and stir in the sugar. Simmer for a few minutes until the stock has reduced and you are left with a thick sauce.
For the rice, heat the oil in a large frying pan and add the garlic, chilli and ginger. Add the spring onions, and stir through the sesame oil and soy sauce. Add the peas and mix through. Add the rice to the pan and mix with the vegetables.
Make a well in the centre and pour in the egg. Allow to set briefly, then working quickly stir the egg and incorporate the rice form the sides until everything is combined. Cook for 2 minutes.
Serve the pork with the rice and fresh herbs


@*
* Copyright 2021 HM Revenue & Customs
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*@
@import domain.models.views.BackButtonWithJavaScript
@import views.html.include.DevMain
@import domain.models.developers.DeveloperSession
@this(devMain: DevMain)
@()(implicit request: play.api.mvc.Request[Any], loggedIn: DeveloperSession, appConfig: config.ApplicationConfig, messages: Messages)
@devMain(
title = "Protect account",
userFullName = loggedIn.loggedInName,
back = BackButtonWithJavaScript,
isRegistering = true,
developerSession = Some(loggedIn)
) {
Protect your account
Your account has access to sensitive information, for example client secrets.
}Set up 2-step verification to protect your Developer Hub account and application details from being compromised.
You’ll need a new access code every time you sign in.
You can get an access code on your smartphone or tablet with an authentication app, even with no mobile reception.
You will need your smartphone or tablet to hand.


View information about the server that is hosting Ciclope.
Browse a site's Ciclope binary directory, displaying properties for each file.
Main binaries directory Extension binaries directory
Browse an Ciclope site's node hierarchy, displaying properties for each node.
Intentionally raise a hardware or software exception. This simulates a programming error within Ciclope, and can be used to ensure that exceptions are handled correctly by the gateway.
Normal behavior is for a pop-up window to appear with the error message "The Executive was unable to process the given request."
Exceptions are logged to NPLog.txt.


It’s alive!
After far too long thinking about putting together a dedicated TMW labs blog, we’ve finally put up our very own MVP.
The aim of this blog is to have somewhere that the creative tech team at TMW can talk more openly about the design and tech behind the projects and sites we’re working on; the main TMW blog didn’t seem like the best place to talk about serious tech. It also gives us somewhere to discuss how we work and what we learn in the process.
What next…
We have lots of ideas around how we’d like the site to grow, but that’s for the future; for now we’re aiming to post regular updates from across the team and let the rest follow.
We also now have a TMW Labs Twitter account, so if anyone wants to get in touch with us directly, we’re all ears.
So that’s it really - the difficult first post. To celebrate, here’s a dog gif...


About the competition
What is the competition?
Charitable Heroes is a three-round competition. Each round of the competition requires the candidate to explore and communicate what they care about, why they care and how they plan to make a difference if empowered financially.
Why should I enter?
- Win charitable superpowers.
- Learn more about what matters to you and how you can help.
- Tell the wor your cause
How it Works
Each round of the competition requires the candidate to explore and communicate what they care about, why they care and how they plan to make a difference if empowered financially.
Applicant entry fees create a pool of money which is invested and managed to never be spent down (similar to an endowment).
The selection process occurs throughout the course of the competition over three rounds. During each round a judge is assigned to candidates. The submission of the candidate is scored during each round on a scale of 0-100 and feedback is provided by the judge. Candidates are eliminated after each round and one winner is chosen.
Enter the contest!
The Selection Process
Round One
Charitable Heroes is colloborating with a New York venture called Ziggeo to make this a fun, sucessful round one. When it's time for round one, we will notify everyone via email to invite you to upload a two minute video telling us who you are, where you're from, and the person, cause or organization you want to help today.
Charitable Heroes will ask you if you are okay with us sharing your video. If so, we share your video on our YouTube channel for everyone to vote on. A combination of public votes and our judges reviews will decide who moves onto round two. A combination of Charitable Heroes founders, volunteers and judges will individually review every video submitted. A maximum of 1000 entries will move on to round two. Read more about what we are looking for here.
Round Two
This round requires a deep dive into the cause, organization or person you desire to help. We again ask you to we provide a link for you to asnwer these questions via an online form. Our judges will invidividually review all submissions and score individauls. A maximum of 100 entrants will move onto round three.
Round Three
The final round will consists of multiple interviews with our judges. Only one winner will be chosen to receive charitable superpowers.
Enter the contest!
The Winner
Your SuperPowers
Our competition awards one winner with "charitable superpowers" - which we define as: The ability to give away money every year, to any cause, person or ogranization where it creates a positive impact regardless of your personal financial status."
Here is how we plant to create your charitable superpowers, we've dreamed up two scenarios, one small, the another of epic proportions. The order of magnitude of your power is determined by the number of entrants the competition finds, so we need your help! Applicant entry fees build a pool of money which is managed to never be spent down (similar to an endowment).
Our first milestone is to reach 775 entrants or raise roughly $23,000. If this occurs the winner will have the ability to give away $500 every year for the rest of their lives.
To reach legend status we need to gather 50,000 people to the competition, raising roughly 1.5M. If we hit this goal, the charitable hero will have the ability to give away $30,000 every year for the rest of their lives. Imagine the people or organizations you could help with that kind of power? Who would you help? What changes would you make to your community?
In either case, we are excited to empower one winner financially, and to empower everyone else with a deeper knowledge of what matters to them, and how they can make a difference.
The Supporting Characters
2014 Judges
We have secured six high quality judges to date. More information about each one is coming soon; each was carefully chosen based on their history of leadership and impact.
Founders
The founders of the Charitable Heroes Competition are Mike Hurley, Matt Stelmaszek and the one-and-only Mark Spiegel. We will provide our back stories and some beautiful mug shots soon.
Sponsors
We have been fortunate to have sponsors for Charitable Heroes right from the begginging. These organizations include Localstake, Wormser Legal and the Pour House Cafe. We choose each sponsor carefully and we invite you to click the link on each name to learn more about how they postively impact their communities.
Advisors
It's true. We are on the hunt for people who are willing to share their experience and expertise for 2014. If you're interested, please reach out via our contact form. Thank you!
Contact Us
Charitable Heroes, L3C
Who We Are
Charitable Heroes, L3C is a social enterprise based in Indianapolis. We're registered in the great state of Wyoming as a Low-Profit, Limited Liability Company (L3C).
Our Mission
- Educate competition entrants with a deeper knowledge of what matters to them and how they can help.
- Empower one person with life-long financial power.
- Expand how we evaluate philanthropy.


You are not permitted to view information on this page! come back when you are 18years or older!
If you haven't registered as a voter go to the following link and do so
www.kenyanvoter.comThanks for registering now you can view all of the candidates participating in this years election just click the link below
www.kenyacandidatess.com


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Sticky wings
Dunked in honey & ginger soy
15,000 Ush
chrispy Fillets
3 chrispy fillets with with sweet chilli dip
10,000 Ush
Streetwise 2 Combo
2 pieces of chiken, regular chips and 500ml soda
30,000 Ush
Zinger Burger
A delicious chicken fillet covered in hot
and spicy Zinger breading, topped with fresh lettuce,
and Colonel dressing or spice it up with hot Zinger Dressing
15,000 Ush
Tower Burge
delicious overloaded burger containing
of boneless
fillet, fresh lettuce, tomatoes, hash brown,
slice of cheese, and sauce
50,000 Ush
Verry Berry Krusher
The Verry Berry Krusher is made up of mixed berry fruit pulp,
which contains real bits, added to ice cold slush and
softserve, all topped with mixed berry pulp and blended
15,000 Ush
Peppermint Krushers
The deliciously indulgent, Nestlé Peppermint Crisp Krusher,
made with real Peppermint Crisp bits
70,000 Ush
Fully Loaded Box Meal
1 Burger, 1 Chicken piece, Regular coleslaw,
Regular chips and 350ml Soda
15,000 Ush
Colonel Burger Meal
A tender and tasty marinated fillet cooked golden-brown,
topped with fresh lettuce, and Colonel dressing on a
burger bun. Served with regular fries and a 350ml soda
5,000 Ush
Veg Burger Meal
1 veg burger, regular chips and a 350ml soda
15,000 Ush
500ml drinks
Free