This extension creates a generic statusbar to showcase the various states of JupyterLab. Different components will render depending on the active context: notebook, console, file editor, and terminal. This extension can be used by other extensions to add custom elements into the statusbar.
Changing Contexts
Component Interactions
jupyter labextension install @jupyterlab/statusbar
For a development install (requires npm version 4 or later), do the following in the repository directory:
npm install
npm run build
jupyter labextension link .
To rebuild the package and the JupyterLab app:
npm run build
jupyter lab build
Generated using TypeDoc


Add Train
Train Name
Destination
First Train Time (HH:mm - military time / 24-hour format)
Frequency (min)


Migrating Existing Containers¶
Titan provides lots of freedom for how you might populate data into a new repository. You might run some scripts to generate data, load data from a database dump, or replicate data into the container. Titan does not try to encode these database-specific mechanisms into the tool, but does provide some platform-agnostic capabilities for loading data. In this section, we show how you can migrate data from a container you already have running on your laptop.
Source Container¶
The source container must be from an image available in a docker registry, and the data corresponding to volume directives (see the Copying Existing Data section) must be mounted from your host system. For example, you must have run:
docker run -d -v /home/henrietta/postgres/data:/var/lib/postgresql/data postgres:11
If you didn’t specify a -v
value, or used a volume not mounted from the
host system, thn migrate
won’t work.
The container must also be stopped. This is both to ensure that the underlying data is not changing, and because the new repository will use the same configuration, potentially creating conflicts with ports and other global resources.
Migrating a Container¶
Assuming the container meets the above criteria, you can create a repository from it by running:
titan mgirate -s somecontainer myrepo
This will get the configuration of the container and use it verbatim for the new repository. It will determine where each volume resides on the host system, and use the equivalent of titan cp to copy the volumes over one by one.
Note
There is no way to alter the configuration of the container at the time it is migrated, you must use the identical configuration as the source container.


Pairing
Giving and Receiving Feedback
April 16, 2015
Pairing is an integral part of this program and one that I thought I may not enjoy when I initially heard about it. However, my goal was always to go in with an open mind and a positive attitude. Hopefully, everything else would take care of itself. My very first pairing session was actually only with a guide during my GPS. This actually made me even more nervous, but I knew that I was prepared based on how much time I spent learning the material the week prior. It ended up going really well. She wanted me to drive the majority of the time which was completely understandable. I thought this would make things more difficult, but it actually provided me with an outlet to focus my nervous energy. After I successfully completed a few tasks, I grew more and more confident. She was very encouraging and even impressed with how quickly I had learned the material (since I had no prior experience). We even got to experiment with a few things that were not in the challenge.
The most fun I've had comes from my second GPS challenge which centered around HTML and CSS. We ended up joking around about how beautiful our design was and how amazing it looked when in reality we knew it was just a basic layout with a header, two columns for the body and a footer. We used easy colors for debugging our elements and I gave my pair a hard time when he took my horribly yellow color away from our finished product. Even the guide was having a good time. The most difficult time I had while pairing was in a discussion that I was not necessarily a part of. My pair was confused about something simple (the basic flow of a conditional statement), and she asked about it. The guide must have misunderstood because she seemed to go off on a tangent about efficiency and how web applications making handling thousands of requests at a time need to have condensed code. This was a perfectly valid point, but my pair got frustrated because all she wanted was a simple explanation.
As for feedback, I can't remember a time where I was nervous to receive it. I told myself from the very beginning that it's all about how I choose to take the feedback. Even if somebody writes something that I don't agree with, I should at least consider it and see if I can use it to make myself better. It turns out that almost all the feedback I've received so far has been helpful. It's good to know how other people perceive you. The most practical piece I've received is that I don't need to comment my code so much unless it's overly-complicated. I can include these comments in my pseudocode instead. Since the goal is the write code that's easy to follow, we generally shouldn't need a comment. I intend on incorporating that feedback in to my remaining assignments. She also challenged me to go further with my solution. In Ruby, there are so many built-in methods that can really condense common constructs such as loops. While still timeboxing, I intend on considering alternative solutions as best I can.
Writing feedback is actually more difficult than I thought it would be. I found that unless I was consciouslly thinking about the feedback during the pairing session, then I had trouble actually giving constructive feedback. Most of what I remember about the sessions is positive, so I've been trying to keep at least one thing in mind that my pair can work on so they can improve. My lowest marks in my meta feedback have come from not being actionable enough, and I completely agree. This happened on the pairing session where I genuinely could not think of anything constructive to say. In my following sessions, I'm going to try and write down a few keywords or notes that will help trigger a talking point on the feedback I write.


A collection of statistical tests for martingale difference hypothesis
| Version: | 0.97 |
| Published: | 2014-08-10 |
| Author: | Jae H. Kim |
| Maintainer: | Jae H. Kim <J.Kim at latrobe.edu.au> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| In views: | Finance |
| CRAN checks: | vrtest results |
| Reference manual: | vrtest.pdf |
| Package source: | vrtest_0.97.tar.gz |
| Windows binaries: | r-devel: vrtest_0.97.zip, r-release: vrtest_0.97.zip, r-oldrel: vrtest_0.97.zip |
| OS X binaries: | r-release: vrtest_0.97.tgz, r-oldrel: vrtest_0.97.tgz |
| Old sources: | vrtest archive |
Please use the canonical form https://CRAN.R-project.org/package=vrtest to link to this page.


05 May 2020
We scrape social networks from Instagram and find interesting results on race and "implicit segregation."
25 Feb 2020
We run a free dating service for Berkeley students and collect a lot of data on (among other things) alcohol consumption, political views, and sex life. We analyze the results here (anonymously of course).
05 Aug 2018
The story of how we put (multiple) pianos on the campus of UC Berkeley.


Hi, I'm Claire Parker-Jones, a UX designer based in the North West of England.
I've been blogging about web development on this site since 2015. Why not:
- read some of my blog posts
- find out what I've been working on recently in my Dev Diary
- find even more coding content in the captions of my Instagram account claire_codes


35.004 Publicizing requirements and expanding research and development sources.
35.006 Contracting methods and contract type.
35.009 Subcontracting research and development effort.
35.010 Scientific and technical reports.
35.014 Government property and title.
35.015 Contracts for research with educational institutions and nonprofit organizations.
35.016 Broad agency announcement.
35.017 Federally Funded Research and Development Centers.
35.017-1 Sponsoring agreements.
35.017-2 Establishing or changing an FFRDC.
35.017-5 Terminating an FFRDC.
35.017-6 Master list of FFRDC’s.
35.017-7 Limitation on the creation of new FFRDC’s.


A typed view into a BufferView.
More...
Inherits glTF::Object.
|
| static const char * | TranslateId (Asset &, const char *id) |
| | Maps special IDs to another ID, where needed. Subclasses may override it (statically) More...
|
| |
|
| Ref< BufferView > | bufferView |
| | The ID of the bufferView. (required) More...
|
| |
| unsigned int | byteOffset |
| | The offset relative to the start of the bufferView in bytes. (required) More...
|
| |
| unsigned int | byteStride |
| | The stride, in bytes, between attributes referenced by this accessor. (default: 0) More...
|
| |
| ComponentType | componentType |
| | The datatype of components in the attribute. (required) More...
|
| |
| unsigned int | count |
| | The number of attributes referenced by this accessor. (required) More...
|
| |
| std::string | id |
| | The globally unique ID used to reference this object. More...
|
| |
| std::vector< double > | max |
| | Maximum value of each component in this attribute. More...
|
| |
| std::vector< double > | min |
| | Minimum value of each component in this attribute. More...
|
| |
| std::string | name |
| | The user-defined name of this object. More...
|
| |
| AttribType::Value | type |
| | Specifies if the attribute is a scalar, vector, or matrix. (required) More...
|
| |
A typed view into a BufferView.
A BufferView contains raw binary data. An accessor provides a typed view into a BufferView or a subset of a BufferView similar to how WebGL's vertexAttribPointer() defines an attribute in a buffer.
◆ Accessor()
| glTF::Accessor::Accessor |
( |
| ) |
|
|
inline |
◆ ExtractData()
template<class T >
| bool glTF::Accessor::ExtractData |
( |
T *& |
outData | ) |
|
◆ GetBytesPerComponent()
| unsigned int glTF::Accessor::GetBytesPerComponent |
( |
| ) |
|
|
inline |
◆ GetElementSize()
| unsigned int glTF::Accessor::GetElementSize |
( |
| ) |
|
|
inline |
◆ GetIndexer()
| Indexer glTF::Accessor::GetIndexer |
( |
| ) |
|
|
inline |
◆ GetNumComponents()
| unsigned int glTF::Accessor::GetNumComponents |
( |
| ) |
|
|
inline |
◆ GetPointer()
| uint8_t * glTF::Accessor::GetPointer |
( |
| ) |
|
|
inline |
◆ IsSpecial()
| virtual bool glTF::Object::IsSpecial |
( |
| ) |
const |
|
inlinevirtualinherited |
Objects marked as special are not exported (used to emulate the binary body buffer)
Reimplemented in glTF::Buffer.
◆ Read()
| void glTF::Accessor::Read |
( |
Value & |
obj, |
|
|
Asset & |
r |
|
) |
| |
|
inline |
◆ TranslateId()
| static const char* glTF::Object::TranslateId |
( |
Asset & |
, |
|
|
const char * |
id |
|
) |
| |
|
inlinestaticinherited |
Maps special IDs to another ID, where needed. Subclasses may override it (statically)
◆ WriteData()
| void glTF::Accessor::WriteData |
( |
size_t |
count, |
|
|
const void * |
src_buffer, |
|
|
size_t |
src_stride |
|
) |
| |
|
inline |
◆ bufferView
The ID of the bufferView. (required)
◆ byteOffset
| unsigned int glTF::Accessor::byteOffset |
The offset relative to the start of the bufferView in bytes. (required)
◆ byteStride
| unsigned int glTF::Accessor::byteStride |
The stride, in bytes, between attributes referenced by this accessor. (default: 0)
◆ componentType
The datatype of components in the attribute. (required)
◆ count
| unsigned int glTF::Accessor::count |
The number of attributes referenced by this accessor. (required)
◆ id
| std::string glTF::Object::id |
|
inherited |
The globally unique ID used to reference this object.
◆ max
| std::vector<double> glTF::Accessor::max |
Maximum value of each component in this attribute.
◆ min
| std::vector<double> glTF::Accessor::min |
Minimum value of each component in this attribute.
◆ name
| std::string glTF::Object::name |
|
inherited |
The user-defined name of this object.
◆ type
Specifies if the attribute is a scalar, vector, or matrix. (required)
The documentation for this struct was generated from the following files:


---
layout: default
---
Aerial photo by a Kite
When I was in Shanghai Jiao Tong University, China, as a college students. My roomate and I developed interest in photography and flying kites. One day we come up with this idea that why don't we do this together? How about putting our mobile phone on the kite and take some photos?
We did this, got many wonderful photos and many funny things happened. It was a long journey and eventually got intense media cover all over China. Here I select some links (unfortunately all in Chinese, including my own menory note).
Weilei's note
press 1
press 2
press 3


Claim your NHS Pension
What is the member's deemed pensionable pay?
This is on top of the member's basic rate of pay. It includes:
- sickness, but not statutory sick pay
- staged pay awards
- bonus payments
- special duty payments
- merit or distinction awards
- london weighting
Give the deemed pensionable pay from the highest period of disallowed days.


Kremlin says Zelensky, Putin may talk gas at Normandy Four summit
Peskov said intensive contacts on gas are underway and it is premature to give estimates.
Russian Presidential Press Secretary Dmitry Peskov Ukrainian President Volodymyr Zelensky and Russian President Vladimir Putin will be able to discuss the natural gas issue as part of a meeting in the Normandy format in Paris.
"In general, there is an understanding that thorough negotiations must first take place at the working level. If there are problematic issues that could not be agreed upon, the presidents will be able to use the summit in Paris, where they will somehow have an opportunity to talk and discuss the gas issue. Time is shrinking, there's time pressure, but intense contacts are underway," Peskov told journalists, according to an UNIAN correspondent.
He said intensive contacts on gas are underway and it is premature to give any assessment . According to him, negotiations are being conducted through the mediation of the European Commission and in a bilateral format, as agreed by the heads of state during the last phone conversation.


Release Notes
Version 1.2.0
- Added guidance for issues #698
- Including PDB files in NuGet Packages #744
- Migrated remaining NUnit specs to MSpec #687
- Introduced GitVersion for getting version for builds #758
- Changed all files from CRLF to LF #732
- Various changes migrated from ProCoSys fork: [#713(https://github.com/dolittle/Bifrost/issues/713)
- Use activator to create instances of Commands when generating proxies
- Limit initialization of AssetsManager once - JavaScript
- Introduce IConvention as a marker interface for finding things that are governed by conventions
- Composed validators improvement
- Security proxies excluded from the application route
- Updated debugging documentation #734
- Documentation for how you could set up Web.config #735
- Documentation for editor config #731
- Added MyGet symbol source #737
- Cleaned up Tabs to Spaces #733
- Ensure CommandHandlerInvoker is only called once #766
- Upgraded to release version of FluentValidation #775
IExceptionSubscriber
for catching errors that can be logged or surfaced #773- Updated to the latest Mongo DB driver #789 #594 #779
- [Breaking Change] Configuration of events for pipeline #787
- [Breaking Change] Changed entire pipeline of events and subscriptions #596 #193
- [Breaking Change] Move to OWIN and not be coupled to System.Web #515
- [Breaking Change] Include Application details instead of system #799
- [Breaking Change] Updated ITenant to use a
dynamic
instead ofWriteOnceExpandoObject
#797 - Fixed so that
ICanPopulateTenant
implementations actually gets called #798 - Using ContentRoot as the actual content root for .NET Core Applications #800
- Provide a workaround for .NET standard of Ninject for default bindings #801
- [Breaking Change] Improved configuration of Events #591
- [Breaking Change] Do not expose default lifecycle for containers #717
- EventStore implementation for Azure Tables #771
- Simple support for Rabbit MQ #741
- DocumentDB support moved to Bifrost.Read.DocumentDB #812
- Introduce logging #816
- Fixing an exception that occured in Ninject during parallell loading #818
- Migrate to MSBuild based projects - away from project.json #815
- [Breaking Change] Systemname not part of configuration anymore #814
- [Breaking Change] Remove Saga implementation till we have a better design #832
Version 1.1.2
- No changes - just fingertrouble when releasing 1.1.1
Version 1.1.1
- Releasing all the projects - not just the core Bifrost project
- Clean up QuickStart #736
- Create contribution guideline for documentation #689
- Detailed coding guidelines for C# #688
- Issues guideline #712
- Create a travis build #710 for cross platform - needs more work - it is failing right now
- Put in place editor.config file #709
Version 1.1.0
- Targetting .NET 4.6.1 as a minimum for desktop framework
- Targetting .NET Standard 1.6 - capable of running on .NET Core 1.x
- Silverlight support removed
- Mimir has been removed as it was not maintained - it will come back in a new form
- License header text updated for all files
- New build mechanism based on FAKE - fully automated
- Removed ASP.NET MVC support - unmaintained
- Documentation is now being generated on build and published
- NullReferenceException in DefaultCallContext (#665, #706)
- Bifrost.debug.js created using an MSBuild task (#707)
- Only call implementations of ICanSpecifyAssemblies once per assembly (#666)
- Discover HBM mappings automatically (#664)
- Compliance with Newtonsoft.JSON guidelines for caching (#676)
- Removed Oracle EventStore support
- Removed Microsoft Unity support
- Removed Yggdrasil support
- Removed Castle Windsor support
- Removed CommonServiceLocator support
- Rmoved WinRT support
- Including Code of Conduct
Version <IP_ADDRESS>
- LocalPath used for assemblies during loading from the URI (#662) - from PR #663
Version <IP_ADDRESS>
- Fixed type discovery performance, both for startup and runtime (#657)
Version <IP_ADDRESS> & <IP_ADDRESS>
- Adding in support for assemblies without a location during assembly discovery
Version <IP_ADDRESS>
- areAllParametersSet() on query now supports observables (#655)
- Fixed NuGet package reference for FluentValidation to the signed version (#654)
- Added a package for the MVC project support (PS: this is MVC3, so good old)
Version <IP_ADDRESS>
- Removing IExecutionEnvironment - introducing ICanProvideAssemblies and rewriting how things work with assemblies. Fixes #650.
Version <IP_ADDRESS> & <IP_ADDRESS>
- Making it possible to override IExecutionEnvironment at startup - as a temporary solution till we get to #650 & #651
Version <IP_ADDRESS> & <IP_ADDRESS>
- Improved weak delegates for Messenger
- Change to WeakDelegate for the CommandFor pipeline
- Ignoring dynamic assemblies for type discovery and assembly loading
- Started work on support for EntityFramework
- Making Proxy generation a part of core as it is needed for more than just clients
- Upgraded NHibernate support to the latest and greatest (#640)
- Fixing how assemblies are loaded and making configuration use the same mechanism. It is now possible to specify assemblies in a fluent way + a new interface that can be implemented for specifying assemblies to include or ignore in discovery mechanisms (ICanSpecifyAssemblies)
- JavaScript dependency resolver now only resolves dependency instances when needed (#646)
- Upgaded to .NET 4.5.1 for all projects
Version <IP_ADDRESS>
- Interface that you can implement to get notified when configuration is done - IWantToKnowWhenConfigurationIsDone (#598)
- ICommandFor callbacks are called per instance (#626)
- Explicit registration of IMessenger -> Messenger removed - marked with singleton
- Making designtime work properly without errors in Visual Studio for the ViewModel markup extension
- Making messenger use WeakReference (#601)
Version <IP_ADDRESS> & <IP_ADDRESS>
Due to some strange behavior of the NuGet servers yielding error messages claiming errors in the Package metadata and also that versions where missing. We ended up releasing a couple of versions thinking our packages were corrupt. Therefor, these versions are exactly the same as <IP_ADDRESS>.
Version <IP_ADDRESS>
- Added process callbacks for ICommandFor<> (#614)
- Configuring messenger for desktop when configuring for desktop (Configure.Frontend.Desktop()) - as a singleton (#617)
Process Callbacks (#614)
public class ViewModel
{
public ViewModel(ICommandFor<MyCommand> commandFor)
{
commandFor.Succeeded((cmd, result) => {
// Things to do when successful
});
commandFor.Failed((cmd, result) => {
// Things to do when failed
});
commandFor.Handled((cmd, result) => {
// Things to do when handled
});
}
}
Version <IP_ADDRESS>
- FromMethod extension in WPF/XAML client can now take an IValueConverter to use for converting parameters (#616)
ParameterConverter (#616)
<Button Command="{interaction:FromMethod NameOfMethod, CanExecuteWhen=NameOfMethodOrProperty, ParameterConverter={StaticResource AValueConverter}}"/>
Version <IP_ADDRESS>
- FromMethod extension in WPF/XAML client now supports CanExecute (#612)
- ICommandFor<> can now be generated from a concrete instance of a command (#611)
- Fix for assembly filtering to filter assemblies delay loaded after initial initialization (#613)
CanExecute for FromMethod
In the Interaction namespace of Bifrost when using the client, you'll find a markup extension called FromMethod. This extension now supports the capability of taking
<Button Command="{interaction:FromMethod NameOfMethod, CanExecuteWhen=NameOfMethodOrProperty}"/>
The CanExecuteWhen property of the extension is optional and can take either a method or a property. If it is a method, the method can be a method without any parameters or with one parameter and the command parameter will then be passed in. The return type needs to be of type bool, same goes for if it is a property. If the declaring type implements INotifyPropertyChanged and you're specifying a property - it will trigger a CanExecuteChanged event for the returning command when the property changed so it can be evaluated by the WPF infrastructure again.
Version <IP_ADDRESS>
- Configure startup now uses same AssemblyProvider as the rest of the system
Version <IP_ADDRESS>
- ICommandFor<> support for client in place with full proxy generation of the frontend concerns. Fully bindable in XAML.
Version <IP_ADDRESS>
- AssemblyFiltering for startup in place. For now accessible from the DiscoverAndConfigure() method on the configure object
Usage
For instance, lets say you have a WPF application, the way you would use this is would be in your App.xaml.cs file:
using Bifrost.Configuration;
using Bifrost.Configuration.Assemblies;
namespace YourApp
{
public partial class App : Application
{
static App()
{
Configure.DiscoverAndConfigure(a=>
a.IncludeAll()
.ExceptAssembliesStartingWith("System","Microsoft"));
}
}
}
In the future there will be other mechanisms as well which would apply better for Web scenarios where one does not call DiscoverAndConfigure manually. Also, the sample above with "System" and "Microsoft" will eventually be something Bifrost itself will filter, this is linked to the future mechanism coming.
Version <IP_ADDRESS>
- XAML Visual Tree extensions
- Adding better check for wether or not an assembly is a .NET assembly during location instead of relying on BadImageException
- Temporarily disabling Windows Phone version of Client - will return in a future release
- DocumentDB strategies for collection storage - default implementation puts all entity types in same collection called entities. Query support for getting correct entities when asking for a specific type.
- Added FromMethod MarkupExtension for XAML clients for pointing directly to a method on your ViewModel for the Command property of things like Button.
- Enabling strong name signing - for same reason this was not enabled
- Introducing a ViewModel MarkupExtension for XAML clients that takes the type of ViewModel and it will use the container to instantiate it. Used for the DataContext property typically.
- Started work on a better way to filter assemblies you don't want to discover types for. Early days.
- Putting in place DesktopConfiguration for the WPF client
- Changing one of the overloads for IOC containers for Bind when binding to a Func. It now takes the type being bound to.
Version <IP_ADDRESS>
- Support for derived query types (#538)
- Required rule more robust
- Fixed generation of Queryable extensions for .Skip and .Take (#540)
- Started work on DocumentDB implementation - EventStore is up and running (#531)
- Started work on new validation and business rules engine based on a new underlying rule engine
- FluentValidation moved into its own components - Bifrost.FluentValidation - available as its own NuGet package
- Improving the naive file system based EventStore and EntityContexts
- Improved QuickStart to showcase more features - this still need to be worked on a lot to actually cover all features.
- Started work on query validation support (#382)
- ICanResolvePrinicpal implementation automatically discovered (#549)
- Added ViewModelService for XAML based clients
- Support for SimpleInjector (#568 - pull request)
- Handling of missing properties for mapping in JavaScript fixed (#585 - pull request)
- Improving XML comments in general
- Added MethodInfoConverter to JSON component
- Started work on mapping support for C# - needed for DocumentDB support, amongst others
- Heavy work on the new control, binding and object model for JavaScript
- Improved isObject() check when checking "undefined" for JavaScript
- Code quality work with the help of NDepend
- Updated 3rd party references
- Newtonsoft Json - 6.0.8
- Ninject - <IP_ADDRESS>
- SignalR - 2.2.0
Version <IP_ADDRESS>
- Making everything compile against .net 4.5
- Updated 3rd party references
- Newtonsoft Json - 6.0.5
- Ninject - <IP_ADDRESS>
- RavenDB - 2.5.2916
- MongoDB - 1.9.2
- SignalR - 2.1.2
- Unity - 3.5.1404.0
- AutoFac - 3.5.2
- Exposing StructureMap IOC support as Nuget package
- Exposing Unity IOC support as Nuget package
- Exposing AutoFac IOC support as Nuget package
- Exposint Windsor IOC support as Nuget package
- Retiring dedicated SignalR component - merged into the Web component. Consequence is SignalR is now front and center of everything moving forward.
- Introducing a simple JSON File based EventStore implementation - mostly for demo purposes
- Introducing a simple JSON File based EntityContext implementation - mostly for demo purposes
- Cleaning up QuickStart
- Easier and more convenient way to import types (#534)
- Fixing bugs related to client mapping of objects (#524, #522)
- Fixed deserialization of Concepts based on Guids (#513)
- Fixing IE issues with mapping and type recognition. IE does not hold a property called name for the constructor property. Regex to the rescue.
- Started work on the new rule engine. First implementation targets queries and validation of these. This is the first step in moving away from FluentValidation.
- Fixing a lot of "system" properties to be prefixed with an underscore - avoiding naming conflicts
- Rules namespace holding a specification implementation is now called Specifications both in C# and JavaScript
- BundleTables routes from System.Web.Optimization are now recognized and will be ignored for the SPA HTTP request handling
More convenient way to import instances of types - #534
In Bifrost we have something called ITypeImporter and ITypeDiscoverer. The importer imports the types as instances, that means the implementation uses the IOC container to resolve an instance when calling the .Import<>() methods. The discoverer is responsible for just discovering the types when calling the Find<>() methods and has nothing to do with instances. With this release, we're introducing something that makes this whole thing more explicit, clearer and easier to use. There is now an interface called IInstancesOf<> that you can take a dependency on. The interface inherits IEnumerable<> and as a consequence the implementation InstancesOf<> will do the instantiation when its enumerated. The way you can use this is:
using Bifrost.Execution;
namespace MyNamespace
{
public class MySystem
{
public MySystem(IInstancesOf<SomeAbsractOrInterfaceType> instances)
{
// The enumerator will now instantiate through the IOC container
// giving you an instance with correct lifecycle
foreach( var instance in instances )
{
}
}
}
}
SignalR support
We are now generating Bifrost proxies for hubs - these are placed in the namespace corresponding to the namespace maps set up for the application. Once you have created a hub like so:
using Microsoft.AspNet.SignalR;
namespace Web.BoudedContext.Module
{
public class MyHub : Hub
{
public int DoSomething(int someInteger, string someString)
{
return 42;
}
}
}
From your JavaScript - for instance a ViewModel, you can now do this:
Bifrost.namespace("Web.BoundedContext.Module", {
myFeature: Bifrost.views.ViewModel.extend(function(myHub) {
myHub.server.doSomething(43,"fourty three")
.continueWith(function(result) {
// Result should be 42...
});
});
});
The "myHub" dependency will automatically be injected by convention. All hubs are placed in the closest matching namespace. If none is found, there is a global namespace called hubs that will hold it. The last dependency resolver in the chain will be the one that recognizes the global namespace and it will always be found.
With this you don't have to think about starting the SignalR Hub connection like one would have to do with a bare SignalR. Bifrost manages this on the first hub being used.
You might notice that instead of what might be expected when working with SignalR for results coming back from calls to the server:
.done(function() {})
We are using the Bifrost promise instead. This is because we want consistency with Bifrost, not with SignalRs proxies. In fact, we are not using the SignalR proxies at all, but our own proxy generation engine as we use for the generation of proxies for Commands, Queries, ReadModels, Security, Validation and more.
If you want to set up client funtions that you want for the particular Hub instances, you can do this by doing the following:
myHub.client(function(client) {
client.doSomething = function(someString, someNumber) {
// Do things in the client called from the server
};
});
This will subscribe to any method calls from the server corresponding to the name of the function. The above code can be called anytime, there is no ordering to think of as with vanilla SignalR were you have to do this prior to starting the Hub connection.


Vanakkam! 🙏 I'm Dinesh Prasanth Moluguwan Krishnamoorthy (yes, it's freakishly long!!!). I go by a friendly nickname MKD and an internet nickname SilleBille. I developed passion for Computers in 2006 and started coding since 2009. I live with the attitude "கற்றது கைமண் அளவு, கல்லாதது உலகளவு" (words from avvaiyar) which translates to "Known is a drop, unknown is an ocean". A person with never ending desire to learn new things, especially when it comes to the field of Computers and Technology.
Completed with an aggregate of 92% and stood top 15 in the school. Secured 99% in Math and 96% in Science.
Completed with an aggregate of 93.92%. Secured 98.5% in Math, 98.5% in Computer Science, 97% in Chem and 94% in Physics.
Started dwelling into the universe of Computer Science with hands-on experience across a huge array of technologies. Graduate with a CGPA of 9.37 out of 10
Crawled into my professional career, developing portlets on IBM WebSphere Portal for a major manufacturing industry.
With a never-ending passion for technology, pursued a Masters degree and graduated with a GPA of 3.93 out of 4
Started living my dream by working for the world's largest open-source organization. Worked with the Dogtag PKI team where I designed, developed and maintained the CI infrastructure (well, I still own the entire infrastructure 😉).
Enjoyed working on wide-array of responsibilities; Fedora liaison for Dogtag PKI, maintaining CI infrastructure, developing CLI tools, acting development lead and working cross-team for smooth integration of Dogtag PKI project
Follow my passion of Android App development by working on official eBay Android App (Selling)
Blindness is now extinct.
A fully customizable android library.
Multitasking by just shaking your mobile.
Filesystem over main memory.
Linux Micro shell.
One click deployment for iTrust and checkbox.
Enhancements to SimpleDB.
Restrict application's run-time permissions.
Pocket IoT application for Bicyclists.
User thread library implemented in C.
First ever cloud backup for contacts.
IoT to measure energy level consumption.
Feel lazy? Type in a message with your info and I'll get back as soon as possible! 😊
Raleigh, NC, USA
mkdineshprasanth
mkdineshprasanth32@gmail.com


Learning to code? We make searches easy, so that you can focus on your goals.
Just type /fccbot to get started.
Get information on freeCodeCamp challenges, right in Slack!
Learning to code? We make searches easy, so that you can focus on your goals.
Just type /fccbot to get started.
Type /fccbot [certificate] to see the challenges for your next freeCodeCamp certificate, then select a challenge to view details.
In a rush? The command /fccbot [project name] takes you straight to information about your current project.


LAPM.example_models¶
Example linear autonomous pool models.
Classes
|
Two-compartment model with feedback. |
|
Two-compartment model with no feedback. |
|
Two-compartment model with no feedback. |
Detailed module content¶
- class LAPM.example_models.TwoPoolsFeedback(alpha_12, alpha_21, u_1, u_2)[source]¶
Bases:
LAPM.linear_autonomous_pool_model.LinearAutonomousPoolModel
Two-compartment model with feedback.
\[\begin{split}u = \begin{pmatrix} u_1 \\ u_2 \end{pmatrix}, \quad A = \begin{pmatrix} -\lambda_1 & \alpha_{12}\,\lambda_2 \\ \alpha_{21}\,\lambda_1 & -\lambda_2 \end{pmatrix}\end{split}\]- A_cum_dist_func(age=None)¶
Return the cumulative distribution function of the system age.
- Parameters
age (nonnegative, optional) – age at which \(F_A\) is to be evaluated, defaults to None: purely symbolic treatment
- Returns
cumulative distribution function of PH(\(\eta\), \(B\)) (evaluated at age)
- Return type
SymPy expression or numerical value
See also
phase_type.cum_dist_func()
: Return the (symbolic) cumulative distribution function of phase-type.
- A_density(age=None)¶
Return the probability density function of the system age.
- Parameters
age (nonnegative, optional) – age at which \(f_A\) is to be evaluated, defaults to None: purely symbolic treatment
- Returns
probability density function of PH(\(\eta\), \(B\)) (evaluated at age)
- Return type
SymPy expression or numerical value
See also
phase_type.density()
: Return the (symbolic) probability density function of the phase-type distribution.
- property A_expected_value¶
Return the (symbolic) expected value of the system age.
- Returns
expected value of PH(\(\eta\), \(B\))
- Return type
SymPy expression or numerical value
See also
phase_type.expected_value
: Return the (symbolic) expected value of the phase-type distribution.
- property A_laplace¶
Return the symbolic Laplacian of the system age.
- Returns
Laplace transform of the probability density of PH(\(\eta\), \(B\))
- Return type
SymPy expression
See also
phase_type.laplace
: Return the symbolic Laplacian of the phase-type distribtion.
- A_nth_moment(n)¶
Return the (symbolic)
n
th moment of the system age.- Parameters
n (positive int) – order of the moment
- Returns
n
th moment of PH(\(\eta\), \(B\))- Return type
SymPy expression or numerical value
See also
phase_type.nth_moment()
: Return the (symbolic)n
th moment of the phase-type distribution.
- A_quantile(q, tol=1e-08)¶
Return a numerical quantile of the system age distribution.
The quantile is computed by a numerical inversion of the cumulative distribution function.
- Parameters
q (between 0 and 1) – probability mass to be left to the quantile q = 1/2 computes the median
tol (float) – tolerance for brentq algorithm of numerical root search, defaults to 1e-8
- Raises
Error – if attempt is made to compute quantiles symbolically
- Returns
smallest \(y\) such that \(F_A(y)\geq q\)
- Return type
float
See also
A_cum_dist_func()
: Return the cumulative distribution function of the system age.
- property A_standard_deviation¶
Return the (symbolic) standard deviation of the system age.
- Returns
standard deviation of PH(\(\eta\), \(B\))
- Return type
SymPy expression or numerical value
See also
phase_type.standard_deviation()
: Return the (symbolic) standard deviation of the phase-type distribution.
- property A_variance¶
Return the (symbolic) variance of the system age.
- Returns
variance of PH(\(\eta\), \(B\))
- Return type
SymPy expression or numerical value
See also
func:.phase_type.variance: Return the (symbolic) variance of the phase-type distribution.
- T_cum_dist_func(time=None)¶
Return the cumulative distribution function of the transit time.
- Parameters
time (nonnegative, optional) – time at which \(F_T\) is to be evaluated, defaults to None: purely symbolic treatment
- Returns
cumulative distribution function of the transit time (evaluated at time)
- Return type
SymPy expression or numerical value
See also
phase_type.cum_dist_func()
: Return the (symbolic) cumulative distribution function of phase-type.
- T_density(time=None)¶
Return the probability density function of the transit time.
- Parameters
time (nonnegative, optional) – time at which \(f_T\) is to be evaluated, defaults to None: purely symbolic treatment
- Returns
probability density function of the transit time (evaluated at time)
- Return type
SymPy expression or numerical value
See also
phase_type.density()
: Return the (symbolic) probability density function of the phase-type distribution.
- property T_expected_value¶
Return the (symbolic) expected value of the transit time.
See also
phase_type.expected_value()
: Return the (symbolic) expected value of the phase-type distribution.
- property T_laplace¶
Return the symbolic Laplacian of the transit time.
- Returns
Laplace transform of the probability density of PH(\(\beta\), \(B\))
- Return type
SymPy expression
See also
phase_type.laplace
: Return the symbolic Laplacian of the phase-type distribtion.
- T_nth_moment(n)¶
Return the (symbolic)
n
th moment of the transit time.- Parameters
n (positive int) – order of the moment
- Returns
\(\mathbb{E}[T^n]\)
- Return type
SymPy expression or numerical value
See also
phase_type.nth_moment()
: Return the (symbolic)n
th moment of the phase-type distribution.
- T_quantile(q, tol=1e-08)¶
Return a numerical quantile of the transit time distribution.
The quantile is computed by a numerical inversion of the cumulative distribution function.
- Parameters
q (between 0 and 1) – probability mass to be left to the quantile q = 1/2 computes the median
tol (float) – tolerance for brentq algorithm of numerical root search, defaults to 1e-8
- Returns
The smallest \(y\) such that \(F_T(y)\geq q\)
- Return type
float
See also
T_cum_dist_func()
: Return the cumulative distribution function of the transit time.
- property T_standard_deviation¶
Return the (symbolic) standard deviation of the transit time.
See also
phase_type.standard_deviation()
: Return the (symbolic) standard deviation of the phase-type distribution.
- property T_variance¶
Return the (symbolic) variance of the transit time.
See also
phase_type.variance()
: Return the (symbolic) variance of the phase-type distribution.
- a_cum_dist_func(age=None)¶
Return the cumulative distribution function vector of the compartment ages.
- Parameters
age (nonnegative, optional) – age at which \(F_a\) is to be evaluated, defaults to None: purely symbolic treatment
- Returns
- \(F_a\) (evaluated at age)
\(F_a(y) = (X^\ast)^{-1}\,B^{-1}\,\) \((e^{y\,B}-I)\,u\)
- Return type
SymPy or numerical dx1-matrix
- a_density(age=None)¶
Return the probability density function vector of the compartment ages.
- Parameters
age (nonnegative, optional) – age at which \(f_a\) is to be evaluated, defaults to None: purely symbolic treatment
- Returns
- \(f_a\) (evaluated at age)
\(f_a(y) = (X^\ast)^{-1}\,\) \(e^{y\,B}\,u\)
- Return type
SymPy or numerical dx1-matrix
- property a_expected_value¶
Return the (symbolic) vector of expected values of the compartment ages.
- Returns
\(\mathbb{E}[a] = -(X^\ast)^{-1}\,\) \(B^{-1}\,x^\ast\)
- Return type
SymPy dx1-matrix
- a_nth_moment(n)¶
Return the (symbolic) vector of the
n
th moments of the compartment ages.- Parameters
n (positive int) – order of the moment
- Returns
\(\mathbb{E}[a^n] = (-1)^n\,n!\,(X^\ast)^{-1}\) \(\ ,B^{-n}\,x^\ast\)
- Return type
SymPy or numerical dx1-matrix
- a_quantile(q, tol=1e-08)¶
Return a vector of numerical quantiles of the pool age distributions.
The quantiles is computed by a numerical inversion of the cumulative distribution functions.
- Parameters
q (between 0 and 1) – probability mass to be left to the quantile q = 1/2 computes the median
tol (float) – tolerance for brentq algorithm of numerical root search, defaults to 1e-8
- Returns
vector \(y=(y_1,\ldots,y_d)\) with \(y_i\) smallest value such that \(F_{a_i}(y_i)\geq q\)
- Return type
numpy.array
See also
a_cum_dist_func()
: Return the cumulative distribution function vector of the compartment ages.
- property a_standard_deviation¶
Return the (symbolic) vector of standard deviations of the compartment ages.
- Returns
\(\sigma(a) = \sqrt{\sigma^2(a)}\) component-wise
- Return type
SymPy dx1-matrix
- property a_variance¶
Return the (symbolic) vector of variances of the compartment ages.
- Returns
\(\sigma^2(a) = \mathbb{E}[a^2]\) \(- (\mathbb{E}[a])^2\) component-wise
- Return type
SymPy dx1-matrix
- property absorbing_jump_chain¶
Return the absorbing jump chain as a discrete-time Markov chain.
The generator of the absorbing chain is just given by \(B\), which allows the computation of the transition probability matrix \(P\) from \(B=(P-I)\,D\) with \(D\) being the diagonal matrix with diagonal entries taken from \(-B\).
- Returns
DTMC
(beta, P)- Return type
DTMC
- property beta¶
Return the initial distribution of the according Markov chain.
- Returns
\(\beta = \frac{u}{\|u\|}\)
- Return type
SymPy or numerical dx1-matrix
- property entropy_per_cycle¶
Return the entropy per cycle.
- Returns
entropy per jump \(\times\) expected number of jumps per cycle
- Return type
SymPy expression or float
See also
entropy_per_jump
: Return the entropy per jump.expected_number_of_jumps
: Return the (symbolic) expected number of jumps before absorption.
- property entropy_per_jump¶
Return the entropy per jump.
- Returns
\(\theta_J=\) \(\sum\limits_{j=1}^{d+1} \pi_j\) \(\sum\limits_{i=1}^{d+1}-p_{ij}\,\log p_{ij}\) \(+\sum\limits_{j=1}^d \pi_j\,(1-\log -b_{jj})\) \(+\pi_{d+1}\,\sum\limits_{i=1}^d\) \(-\beta_i\,\log\beta_i\)
- Return type
SymPy expression or float
Notes
\(\pi\) is the stationary distribution of the ergodic jump chain.
\(\theta_J=\) entropy of ergodic jump chain + entropy of sojourn times (no stay in environmental compartment \(d+1\))
See also
stationary_distribution
: Return the (symbolic) stationary distribution.
- property entropy_rate¶
Return the entropy rate (entropy per unit time).
- Returns
entropy per cycle \(\cdot\frac{1}{\mathbb{E}T}\)
- Return type
SymPy expression or float
See also
entropy_per_cycle
: Return the entropy per cylce.T_expected_value
: Return the (symbolic) expected value of the transit time.
- property ergodic_jump_chain¶
Return the ergodic jump chain as a discrete-time Markov chain.
The generator is given by
\[\begin{split}Q = \begin{pmatrix} B & \beta \\ z^T & -1 \end{pmatrix}\end{split}\]and the corresponding transition probability matrix \(P_Q\) can then be obtained from \(Q=(P_Q-I)\,D_Q\), where \(D_Q\) is the diagonal matrix with entries from the diagonal of \(-Q\).
- Returns
DTMC
(beta_ext, Q) with beta_ext = (beta, 0)- Return type
DTMC
- property eta¶
Return the initial distribution of the Markov chain according to system age.
- Returns
\(\eta = \frac{x^\ast}{\|x^\ast\|}\)
- Return type
SymPy or numerical dx1-matrix
- classmethod from_random(d: int, p: float)¶
Create a random compartmental system.
- Parameters
d – dimension of the matrix (number of pools)
p – probability of having a connection between two pools and of nonzero value in input vector
- Returns
randomly generated compartmental system
beta: from
create_random_probability_vector()
B: from :func:`~create_random_compartmental_matrix
- property r_compartments¶
Return the (symbolic) release vector of the system in steady state.
- Returns
\(r_j = z_j \, x^\ast_j\)
- Return type
SymPy or numerical dx1-matrix
See also
phase_type.z()
: Return the (symbolic) vector of rates toward absorbing state.xss
: Return the (symbolic) steady state vector.
- property r_total¶
Return the (symbolic) total system release in steady state.
- Returns
\(r = \sum\limits_{j=1}^d r_j\)
- Return type
SymPy expression or numerical value
See also
r_compartments
: Return the (symbolic) release vector of the system in steady state.
- property xss¶
Return the (symbolic) steady state vector.
- Returns
\(x^\ast = -B^{-1}\,u\)
- Return type
SymPy or numerical dx1-matrix
- class LAPM.example_models.TwoPoolsFeedbackSimple(alpha, u_1)[source]¶
Bases:
LAPM.linear_autonomous_pool_model.LinearAutonomousPoolModel
Two-compartment model with no feedback.
\[\begin{split}u = \begin{pmatrix} u_1 \\ 0 \end{pmatrix}, \quad A = \begin{pmatrix} -\lambda_1 & \lambda_2 \\ \alpha\,\lambda_1 & -\lambda_2 \end{pmatrix}\end{split}\]Inputs and outputs only through compartment 1.
- A_cum_dist_func(age=None)¶
Return the cumulative distribution function of the system age.
- Parameters
age (nonnegative, optional) – age at which \(F_A\) is to be evaluated, defaults to None: purely symbolic treatment
- Returns
cumulative distribution function of PH(\(\eta\), \(B\)) (evaluated at age)
- Return type
SymPy expression or numerical value
See also
phase_type.cum_dist_func()
: Return the (symbolic) cumulative distribution function of phase-type.
- A_density(age=None)¶
Return the probability density function of the system age.
- Parameters
age (nonnegative, optional) – age at which \(f_A\) is to be evaluated, defaults to None: purely symbolic treatment
- Returns
probability density function of PH(\(\eta\), \(B\)) (evaluated at age)
- Return type
SymPy expression or numerical value
See also
phase_type.density()
: Return the (symbolic) probability density function of the phase-type distribution.
- property A_expected_value¶
Return the (symbolic) expected value of the system age.
- Returns
expected value of PH(\(\eta\), \(B\))
- Return type
SymPy expression or numerical value
See also
phase_type.expected_value
: Return the (symbolic) expected value of the phase-type distribution.
- property A_laplace¶
Return the symbolic Laplacian of the system age.
- Returns
Laplace transform of the probability density of PH(\(\eta\), \(B\))
- Return type
SymPy expression
See also
phase_type.laplace
: Return the symbolic Laplacian of the phase-type distribtion.
- A_nth_moment(n)¶
Return the (symbolic)
n
th moment of the system age.- Parameters
n (positive int) – order of the moment
- Returns
n
th moment of PH(\(\eta\), \(B\))- Return type
SymPy expression or numerical value
See also
phase_type.nth_moment()
: Return the (symbolic)n
th moment of the phase-type distribution.
- A_quantile(q, tol=1e-08)¶
Return a numerical quantile of the system age distribution.
The quantile is computed by a numerical inversion of the cumulative distribution function.
- Parameters
q (between 0 and 1) – probability mass to be left to the quantile q = 1/2 computes the median
tol (float) – tolerance for brentq algorithm of numerical root search, defaults to 1e-8
- Raises
Error – if attempt is made to compute quantiles symbolically
- Returns
smallest \(y\) such that \(F_A(y)\geq q\)
- Return type
float
See also
A_cum_dist_func()
: Return the cumulative distribution function of the system age.
- property A_standard_deviation¶
Return the (symbolic) standard deviation of the system age.
- Returns
standard deviation of PH(\(\eta\), \(B\))
- Return type
SymPy expression or numerical value
See also
phase_type.standard_deviation()
: Return the (symbolic) standard deviation of the phase-type distribution.
- property A_variance¶
Return the (symbolic) variance of the system age.
- Returns
variance of PH(\(\eta\), \(B\))
- Return type
SymPy expression or numerical value
See also
func:.phase_type.variance: Return the (symbolic) variance of the phase-type distribution.
- T_cum_dist_func(time=None)¶
Return the cumulative distribution function of the transit time.
- Parameters
time (nonnegative, optional) – time at which \(F_T\) is to be evaluated, defaults to None: purely symbolic treatment
- Returns
cumulative distribution function of the transit time (evaluated at time)
- Return type
SymPy expression or numerical value
See also
phase_type.cum_dist_func()
: Return the (symbolic) cumulative distribution function of phase-type.
- T_density(time=None)¶
Return the probability density function of the transit time.
- Parameters
time (nonnegative, optional) – time at which \(f_T\) is to be evaluated, defaults to None: purely symbolic treatment
- Returns
probability density function of the transit time (evaluated at time)
- Return type
SymPy expression or numerical value
See also
phase_type.density()
: Return the (symbolic) probability density function of the phase-type distribution.
- property T_expected_value¶
Return the (symbolic) expected value of the transit time.
See also
phase_type.expected_value()
: Return the (symbolic) expected value of the phase-type distribution.
- property T_laplace¶
Return the symbolic Laplacian of the transit time.
- Returns
Laplace transform of the probability density of PH(\(\beta\), \(B\))
- Return type
SymPy expression
See also
phase_type.laplace
: Return the symbolic Laplacian of the phase-type distribtion.
- T_nth_moment(n)¶
Return the (symbolic)
n
th moment of the transit time.- Parameters
n (positive int) – order of the moment
- Returns
\(\mathbb{E}[T^n]\)
- Return type
SymPy expression or numerical value
See also
phase_type.nth_moment()
: Return the (symbolic)n
th moment of the phase-type distribution.
- T_quantile(q, tol=1e-08)¶
Return a numerical quantile of the transit time distribution.
The quantile is computed by a numerical inversion of the cumulative distribution function.
- Parameters
q (between 0 and 1) – probability mass to be left to the quantile q = 1/2 computes the median
tol (float) – tolerance for brentq algorithm of numerical root search, defaults to 1e-8
- Returns
The smallest \(y\) such that \(F_T(y)\geq q\)
- Return type
float
See also
T_cum_dist_func()
: Return the cumulative distribution function of the transit time.
- property T_standard_deviation¶
Return the (symbolic) standard deviation of the transit time.
See also
phase_type.standard_deviation()
: Return the (symbolic) standard deviation of the phase-type distribution.
- property T_variance¶
Return the (symbolic) variance of the transit time.
See also
phase_type.variance()
: Return the (symbolic) variance of the phase-type distribution.
- a_cum_dist_func(age=None)¶
Return the cumulative distribution function vector of the compartment ages.
- Parameters
age (nonnegative, optional) – age at which \(F_a\) is to be evaluated, defaults to None: purely symbolic treatment
- Returns
- \(F_a\) (evaluated at age)
\(F_a(y) = (X^\ast)^{-1}\,B^{-1}\,\) \((e^{y\,B}-I)\,u\)
- Return type
SymPy or numerical dx1-matrix
- a_density(age=None)¶
Return the probability density function vector of the compartment ages.
- Parameters
age (nonnegative, optional) – age at which \(f_a\) is to be evaluated, defaults to None: purely symbolic treatment
- Returns
- \(f_a\) (evaluated at age)
\(f_a(y) = (X^\ast)^{-1}\,\) \(e^{y\,B}\,u\)
- Return type
SymPy or numerical dx1-matrix
- property a_expected_value¶
Return the (symbolic) vector of expected values of the compartment ages.
- Returns
\(\mathbb{E}[a] = -(X^\ast)^{-1}\,\) \(B^{-1}\,x^\ast\)
- Return type
SymPy dx1-matrix
- a_nth_moment(n)¶
Return the (symbolic) vector of the
n
th moments of the compartment ages.- Parameters
n (positive int) – order of the moment
- Returns
\(\mathbb{E}[a^n] = (-1)^n\,n!\,(X^\ast)^{-1}\) \(\ ,B^{-n}\,x^\ast\)
- Return type
SymPy or numerical dx1-matrix
- a_quantile(q, tol=1e-08)¶
Return a vector of numerical quantiles of the pool age distributions.
The quantiles is computed by a numerical inversion of the cumulative distribution functions.
- Parameters
q (between 0 and 1) – probability mass to be left to the quantile q = 1/2 computes the median
tol (float) – tolerance for brentq algorithm of numerical root search, defaults to 1e-8
- Returns
vector \(y=(y_1,\ldots,y_d)\) with \(y_i\) smallest value such that \(F_{a_i}(y_i)\geq q\)
- Return type
numpy.array
See also
a_cum_dist_func()
: Return the cumulative distribution function vector of the compartment ages.
- property a_standard_deviation¶
Return the (symbolic) vector of standard deviations of the compartment ages.
- Returns
\(\sigma(a) = \sqrt{\sigma^2(a)}\) component-wise
- Return type
SymPy dx1-matrix
- property a_variance¶
Return the (symbolic) vector of variances of the compartment ages.
- Returns
\(\sigma^2(a) = \mathbb{E}[a^2]\) \(- (\mathbb{E}[a])^2\) component-wise
- Return type
SymPy dx1-matrix
- property absorbing_jump_chain¶
Return the absorbing jump chain as a discrete-time Markov chain.
The generator of the absorbing chain is just given by \(B\), which allows the computation of the transition probability matrix \(P\) from \(B=(P-I)\,D\) with \(D\) being the diagonal matrix with diagonal entries taken from \(-B\).
- Returns
DTMC
(beta, P)- Return type
DTMC
- property beta¶
Return the initial distribution of the according Markov chain.
- Returns
\(\beta = \frac{u}{\|u\|}\)
- Return type
SymPy or numerical dx1-matrix
- property entropy_per_cycle¶
Return the entropy per cycle.
- Returns
entropy per jump \(\times\) expected number of jumps per cycle
- Return type
SymPy expression or float
See also
entropy_per_jump
: Return the entropy per jump.expected_number_of_jumps
: Return the (symbolic) expected number of jumps before absorption.
- property entropy_per_jump¶
Return the entropy per jump.
- Returns
\(\theta_J=\) \(\sum\limits_{j=1}^{d+1} \pi_j\) \(\sum\limits_{i=1}^{d+1}-p_{ij}\,\log p_{ij}\) \(+\sum\limits_{j=1}^d \pi_j\,(1-\log -b_{jj})\) \(+\pi_{d+1}\,\sum\limits_{i=1}^d\) \(-\beta_i\,\log\beta_i\)
- Return type
SymPy expression or float
Notes
\(\pi\) is the stationary distribution of the ergodic jump chain.
\(\theta_J=\) entropy of ergodic jump chain + entropy of sojourn times (no stay in environmental compartment \(d+1\))
See also
stationary_distribution
: Return the (symbolic) stationary distribution.
- property entropy_rate¶
Return the entropy rate (entropy per unit time).
- Returns
entropy per cycle \(\cdot\frac{1}{\mathbb{E}T}\)
- Return type
SymPy expression or float
See also
entropy_per_cycle
: Return the entropy per cylce.T_expected_value
: Return the (symbolic) expected value of the transit time.
- property ergodic_jump_chain¶
Return the ergodic jump chain as a discrete-time Markov chain.
The generator is given by
\[\begin{split}Q = \begin{pmatrix} B & \beta \\ z^T & -1 \end{pmatrix}\end{split}\]and the corresponding transition probability matrix \(P_Q\) can then be obtained from \(Q=(P_Q-I)\,D_Q\), where \(D_Q\) is the diagonal matrix with entries from the diagonal of \(-Q\).
- Returns
DTMC
(beta_ext, Q) with beta_ext = (beta, 0)- Return type
DTMC
- property eta¶
Return the initial distribution of the Markov chain according to system age.
- Returns
\(\eta = \frac{x^\ast}{\|x^\ast\|}\)
- Return type
SymPy or numerical dx1-matrix
- classmethod from_random(d: int, p: float)¶
Create a random compartmental system.
- Parameters
d – dimension of the matrix (number of pools)
p – probability of having a connection between two pools and of nonzero value in input vector
- Returns
randomly generated compartmental system
beta: from
create_random_probability_vector()
B: from :func:`~create_random_compartmental_matrix
- property r_compartments¶
Return the (symbolic) release vector of the system in steady state.
- Returns
\(r_j = z_j \, x^\ast_j\)
- Return type
SymPy or numerical dx1-matrix
See also
phase_type.z()
: Return the (symbolic) vector of rates toward absorbing state.xss
: Return the (symbolic) steady state vector.
- property r_total¶
Return the (symbolic) total system release in steady state.
- Returns
\(r = \sum\limits_{j=1}^d r_j\)
- Return type
SymPy expression or numerical value
See also
r_compartments
: Return the (symbolic) release vector of the system in steady state.
- property xss¶
Return the (symbolic) steady state vector.
- Returns
\(x^\ast = -B^{-1}\,u\)
- Return type
SymPy or numerical dx1-matrix
- class LAPM.example_models.TwoPoolsNoFeedback(alpha, u_1, u_2)[source]¶
Bases:
LAPM.linear_autonomous_pool_model.LinearAutonomousPoolModel
Two-compartment model with no feedback.
\[\begin{split}u = \begin{pmatrix} u_1 \\ u_2 \end{pmatrix}, \quad A = \begin{pmatrix} -\lambda_1 & 0 \\ \alpha\,\lambda_1 & -\lambda_2 \end{pmatrix}\end{split}\]- Qt¶
Qt = \(e^{t\,A}\) is given
- Type
SymPy matrix
- A_cum_dist_func(age=None)¶
Return the cumulative distribution function of the system age.
- Parameters
age (nonnegative, optional) – age at which \(F_A\) is to be evaluated, defaults to None: purely symbolic treatment
- Returns
cumulative distribution function of PH(\(\eta\), \(B\)) (evaluated at age)
- Return type
SymPy expression or numerical value
See also
phase_type.cum_dist_func()
: Return the (symbolic) cumulative distribution function of phase-type.
- A_density(age=None)¶
Return the probability density function of the system age.
- Parameters
age (nonnegative, optional) – age at which \(f_A\) is to be evaluated, defaults to None: purely symbolic treatment
- Returns
probability density function of PH(\(\eta\), \(B\)) (evaluated at age)
- Return type
SymPy expression or numerical value
See also
phase_type.density()
: Return the (symbolic) probability density function of the phase-type distribution.
- property A_expected_value¶
Return the (symbolic) expected value of the system age.
- Returns
expected value of PH(\(\eta\), \(B\))
- Return type
SymPy expression or numerical value
See also
phase_type.expected_value
: Return the (symbolic) expected value of the phase-type distribution.
- property A_laplace¶
Return the symbolic Laplacian of the system age.
- Returns
Laplace transform of the probability density of PH(\(\eta\), \(B\))
- Return type
SymPy expression
See also
phase_type.laplace
: Return the symbolic Laplacian of the phase-type distribtion.
- A_nth_moment(n)¶
Return the (symbolic)
n
th moment of the system age.- Parameters
n (positive int) – order of the moment
- Returns
n
th moment of PH(\(\eta\), \(B\))- Return type
SymPy expression or numerical value
See also
phase_type.nth_moment()
: Return the (symbolic)n
th moment of the phase-type distribution.
- A_quantile(q, tol=1e-08)¶
Return a numerical quantile of the system age distribution.
The quantile is computed by a numerical inversion of the cumulative distribution function.
- Parameters
q (between 0 and 1) – probability mass to be left to the quantile q = 1/2 computes the median
tol (float) – tolerance for brentq algorithm of numerical root search, defaults to 1e-8
- Raises
Error – if attempt is made to compute quantiles symbolically
- Returns
smallest \(y\) such that \(F_A(y)\geq q\)
- Return type
float
See also
A_cum_dist_func()
: Return the cumulative distribution function of the system age.
- property A_standard_deviation¶
Return the (symbolic) standard deviation of the system age.
- Returns
standard deviation of PH(\(\eta\), \(B\))
- Return type
SymPy expression or numerical value
See also
phase_type.standard_deviation()
: Return the (symbolic) standard deviation of the phase-type distribution.
- property A_variance¶
Return the (symbolic) variance of the system age.
- Returns
variance of PH(\(\eta\), \(B\))
- Return type
SymPy expression or numerical value
See also
func:.phase_type.variance: Return the (symbolic) variance of the phase-type distribution.
- T_cum_dist_func(time=None)¶
Return the cumulative distribution function of the transit time.
- Parameters
time (nonnegative, optional) – time at which \(F_T\) is to be evaluated, defaults to None: purely symbolic treatment
- Returns
cumulative distribution function of the transit time (evaluated at time)
- Return type
SymPy expression or numerical value
See also
phase_type.cum_dist_func()
: Return the (symbolic) cumulative distribution function of phase-type.
- T_density(time=None)¶
Return the probability density function of the transit time.
- Parameters
time (nonnegative, optional) – time at which \(f_T\) is to be evaluated, defaults to None: purely symbolic treatment
- Returns
probability density function of the transit time (evaluated at time)
- Return type
SymPy expression or numerical value
See also
phase_type.density()
: Return the (symbolic) probability density function of the phase-type distribution.
- property T_expected_value¶
Return the (symbolic) expected value of the transit time.
See also
phase_type.expected_value()
: Return the (symbolic) expected value of the phase-type distribution.
- property T_laplace¶
Return the symbolic Laplacian of the transit time.
- Returns
Laplace transform of the probability density of PH(\(\beta\), \(B\))
- Return type
SymPy expression
See also
phase_type.laplace
: Return the symbolic Laplacian of the phase-type distribtion.
- T_nth_moment(n)¶
Return the (symbolic)
n
th moment of the transit time.- Parameters
n (positive int) – order of the moment
- Returns
\(\mathbb{E}[T^n]\)
- Return type
SymPy expression or numerical value
See also
phase_type.nth_moment()
: Return the (symbolic)n
th moment of the phase-type distribution.
- T_quantile(q, tol=1e-08)¶
Return a numerical quantile of the transit time distribution.
The quantile is computed by a numerical inversion of the cumulative distribution function.
- Parameters
q (between 0 and 1) – probability mass to be left to the quantile q = 1/2 computes the median
tol (float) – tolerance for brentq algorithm of numerical root search, defaults to 1e-8
- Returns
The smallest \(y\) such that \(F_T(y)\geq q\)
- Return type
float
See also
T_cum_dist_func()
: Return the cumulative distribution function of the transit time.
- property T_standard_deviation¶
Return the (symbolic) standard deviation of the transit time.
See also
phase_type.standard_deviation()
: Return the (symbolic) standard deviation of the phase-type distribution.
- property T_variance¶
Return the (symbolic) variance of the transit time.
See also
phase_type.variance()
: Return the (symbolic) variance of the phase-type distribution.
- a_cum_dist_func(age=None)¶
Return the cumulative distribution function vector of the compartment ages.
- Parameters
age (nonnegative, optional) – age at which \(F_a\) is to be evaluated, defaults to None: purely symbolic treatment
- Returns
- \(F_a\) (evaluated at age)
\(F_a(y) = (X^\ast)^{-1}\,B^{-1}\,\) \((e^{y\,B}-I)\,u\)
- Return type
SymPy or numerical dx1-matrix
- a_density(age=None)¶
Return the probability density function vector of the compartment ages.
- Parameters
age (nonnegative, optional) – age at which \(f_a\) is to be evaluated, defaults to None: purely symbolic treatment
- Returns
- \(f_a\) (evaluated at age)
\(f_a(y) = (X^\ast)^{-1}\,\) \(e^{y\,B}\,u\)
- Return type
SymPy or numerical dx1-matrix
- property a_expected_value¶
Return the (symbolic) vector of expected values of the compartment ages.
- Returns
\(\mathbb{E}[a] = -(X^\ast)^{-1}\,\) \(B^{-1}\,x^\ast\)
- Return type
SymPy dx1-matrix
- a_nth_moment(n)¶
Return the (symbolic) vector of the
n
th moments of the compartment ages.- Parameters
n (positive int) – order of the moment
- Returns
\(\mathbb{E}[a^n] = (-1)^n\,n!\,(X^\ast)^{-1}\) \(\ ,B^{-n}\,x^\ast\)
- Return type
SymPy or numerical dx1-matrix
- a_quantile(q, tol=1e-08)¶
Return a vector of numerical quantiles of the pool age distributions.
The quantiles is computed by a numerical inversion of the cumulative distribution functions.
- Parameters
q (between 0 and 1) – probability mass to be left to the quantile q = 1/2 computes the median
tol (float) – tolerance for brentq algorithm of numerical root search, defaults to 1e-8
- Returns
vector \(y=(y_1,\ldots,y_d)\) with \(y_i\) smallest value such that \(F_{a_i}(y_i)\geq q\)
- Return type
numpy.array
See also
a_cum_dist_func()
: Return the cumulative distribution function vector of the compartment ages.
- property a_standard_deviation¶
Return the (symbolic) vector of standard deviations of the compartment ages.
- Returns
\(\sigma(a) = \sqrt{\sigma^2(a)}\) component-wise
- Return type
SymPy dx1-matrix
- property a_variance¶
Return the (symbolic) vector of variances of the compartment ages.
- Returns
\(\sigma^2(a) = \mathbb{E}[a^2]\) \(- (\mathbb{E}[a])^2\) component-wise
- Return type
SymPy dx1-matrix
- property absorbing_jump_chain¶
Return the absorbing jump chain as a discrete-time Markov chain.
The generator of the absorbing chain is just given by \(B\), which allows the computation of the transition probability matrix \(P\) from \(B=(P-I)\,D\) with \(D\) being the diagonal matrix with diagonal entries taken from \(-B\).
- Returns
DTMC
(beta, P)- Return type
DTMC
- property beta¶
Return the initial distribution of the according Markov chain.
- Returns
\(\beta = \frac{u}{\|u\|}\)
- Return type
SymPy or numerical dx1-matrix
- property entropy_per_cycle¶
Return the entropy per cycle.
- Returns
entropy per jump \(\times\) expected number of jumps per cycle
- Return type
SymPy expression or float
See also
entropy_per_jump
: Return the entropy per jump.expected_number_of_jumps
: Return the (symbolic) expected number of jumps before absorption.
- property entropy_per_jump¶
Return the entropy per jump.
- Returns
\(\theta_J=\) \(\sum\limits_{j=1}^{d+1} \pi_j\) \(\sum\limits_{i=1}^{d+1}-p_{ij}\,\log p_{ij}\) \(+\sum\limits_{j=1}^d \pi_j\,(1-\log -b_{jj})\) \(+\pi_{d+1}\,\sum\limits_{i=1}^d\) \(-\beta_i\,\log\beta_i\)
- Return type
SymPy expression or float
Notes
\(\pi\) is the stationary distribution of the ergodic jump chain.
\(\theta_J=\) entropy of ergodic jump chain + entropy of sojourn times (no stay in environmental compartment \(d+1\))
See also
stationary_distribution
: Return the (symbolic) stationary distribution.
- property entropy_rate¶
Return the entropy rate (entropy per unit time).
- Returns
entropy per cycle \(\cdot\frac{1}{\mathbb{E}T}\)
- Return type
SymPy expression or float
See also
entropy_per_cycle
: Return the entropy per cylce.T_expected_value
: Return the (symbolic) expected value of the transit time.
- property ergodic_jump_chain¶
Return the ergodic jump chain as a discrete-time Markov chain.
The generator is given by
\[\begin{split}Q = \begin{pmatrix} B & \beta \\ z^T & -1 \end{pmatrix}\end{split}\]and the corresponding transition probability matrix \(P_Q\) can then be obtained from \(Q=(P_Q-I)\,D_Q\), where \(D_Q\) is the diagonal matrix with entries from the diagonal of \(-Q\).
- Returns
DTMC
(beta_ext, Q) with beta_ext = (beta, 0)- Return type
DTMC
- property eta¶
Return the initial distribution of the Markov chain according to system age.
- Returns
\(\eta = \frac{x^\ast}{\|x^\ast\|}\)
- Return type
SymPy or numerical dx1-matrix
- classmethod from_random(d: int, p: float)¶
Create a random compartmental system.
- Parameters
d – dimension of the matrix (number of pools)
p – probability of having a connection between two pools and of nonzero value in input vector
- Returns
randomly generated compartmental system
beta: from
create_random_probability_vector()
B: from :func:`~create_random_compartmental_matrix
- property r_compartments¶
Return the (symbolic) release vector of the system in steady state.
- Returns
\(r_j = z_j \, x^\ast_j\)
- Return type
SymPy or numerical dx1-matrix
See also
phase_type.z()
: Return the (symbolic) vector of rates toward absorbing state.xss
: Return the (symbolic) steady state vector.
- property r_total¶
Return the (symbolic) total system release in steady state.
- Returns
\(r = \sum\limits_{j=1}^d r_j\)
- Return type
SymPy expression or numerical value
See also
r_compartments
: Return the (symbolic) release vector of the system in steady state.
- property xss¶
Return the (symbolic) steady state vector.
- Returns
\(x^\ast = -B^{-1}\,u\)
- Return type
SymPy or numerical dx1-matrix


--- title: Nesterov Adam optimizer type: docs repo: https://github.com/rstudio/keras menu: main: name: Nesterov Adam optimizer - keras parent: keras-reference aliases: - /reference/keras/optimizer_nadam.html - /keras/reference/optimizer_nadam.html - /guide/keras/reference/optimizer_nadam.html - /tools/tools/keras/reference/optimizer_nadam.html - /installation/keras/reference/optimizer_nadam.html - /tutorials/keras/reference/optimizer_nadam.html - /guide/tools/keras/reference/optimizer_nadam.html - /deploy/keras/reference/optimizer_nadam.html - /tools/keras/reference/optimizer_nadam.html - /tutorials/tools/keras/reference/optimizer_nadam.html ---
Much like Adam is essentially RMSprop with momentum, Nadam is Adam RMSprop with Nesterov momentum.
optimizer_nadam(
lr = 0.002,
beta_1 = 0.9,
beta_2 = 0.999,
epsilon = NULL,
schedule_decay = 0.004,
clipnorm = NULL,
clipvalue = NULL
)
| lr | float >= 0. Learning rate. |
| beta_1 | The exponential decay rate for the 1st moment estimates. float, 0 < beta < 1. Generally close to 1. |
| beta_2 | The exponential decay rate for the 2nd moment estimates. float, 0 < beta < 1. Generally close to 1. |
| epsilon | float >= 0. Fuzz factor. If |
| schedule_decay | Schedule deacy. |
| clipnorm | Gradients will be clipped when their L2 norm exceeds this value. |
| clipvalue | Gradients will be clipped when their absolute value exceeds this value. |
Default parameters follow those provided in the paper. It is recommended to leave the parameters of this optimizer at their default values.
On the importance of initialization and momentum in deep learning.
Other optimizers:
optimizer_adadelta()
,
optimizer_adagrad()
,
optimizer_adamax()
,
optimizer_adam()
,
optimizer_rmsprop()
,
optimizer_sgd()


Rental House Management System
- 1.clone repo or download as a zip.
- 2.Extract it to xampp/htdocs/ in windows and /opt/lampp/htdocs/
- 3.in linux give it execution permission by typing root@ORION:/opt/lampp/htdocs# chmod -R 777 rhms
- 4.Create database named martdev1_rhms and import the attached SQL File.
- 5.Run the application by typing http://<IP_ADDRESS>/rhms/
- 6.Admin Credentials: Email<EMAIL_ADDRESS>Password: demo
The DevLan Platform is open-source software crafted with love by MartDevelopers and its licensed under the [MIT license]
.


ksh725.00
ksh755.00
Please fill in this form to create an account.
By creating an account you agree to our Terms & Privacy.


Folders
<parent>
File
File
File
File
File
File
File
File
File
File
ClockAngl...t.java
FractionT...t.java
GCDForMan...t.java
JourneySt...t.java
MinNumber...t.java
NumbersCo...t.java
PrimesTest.java
SayNumberTest.java
Staircase...t.java
VersionTest.java
EggDropTest.java
GasStatio...t.java
Folders: 1
Files: 12
Size of all files: 0.006 MG


NYC 2010 Census Tracts
Polygon color
by Population
[ 0, 1670.5 ]
( 1670.5, 2259.0]
( 2259.0, 2862.88]
( 2862.88, 3426.50]
( 3426.50, 4013.25 ]
( 4013.25, 4814.00 ]
( 4814.00, 6204.25 ]
( 6204.25, 26588.00 ]


Maharashtra alliance plans to give state's share of bullet train cost to farmers
A prospective common minimum programme being prepared by the Shiv Sena, the Nationalist Congress Party (NCP) and the Congress has a major thrust on loan waivers, sources said.
by ,HIGHLIGHTS
- Bullet train's estimated cost is 1.08 lakh crore
- It will be built with help from Japan
- It's expected to be completed by 2023
The three parties working to form a government in Maharashtra plan to give money meant for investment in a bullet train project to farmers instead, sources said.
A prospective common minimum programme being prepared by the Shiv Sena, the Nationalist Congress Party (NCP) and the Congress has a major thrust on loan waivers, sources in the Congress and the NCP said.
Maharashtra's share in the cost of building the Mumbai-Ahmedabad high-speed rail line -- 25 per cent -- will be given to farmers, the sources said.
The project's estimated cost is Rs 1.08 lakh crore; it is being built with help from Japan (through a loan) and is expected to be completed by 2023. Both the Congress and the Shiv Sena have opposed the bullet train project.
POLITICAL INSTABILITY NEARING END?
Maharashtra was placed under President's Rule this month, following a prolonged delay in government formation in the wake of an October state election that the Shiv Sena and the BJP won together.
But the two former allies fell out bitterly, unable to agree that the chief minister's job should be shared using a rotation policy.
Shiv Sena leader Sanjay Raut on Thursday said a final decision on a new Maharashtra government would be taken in two days.


Ocean [az79], ocean, in Crystal Sea
Last updated Turn #108_bad
Ocean [az79], ocean, in Crystal Sea
------------------------------------------------------------------------
13: The Savvy Aardvark [ha5r], ship, 2 hulls, 1 rowing port, 6 sails,
13: 1% damaged, arrived from Ocean [ay79], owner:
13: Phantasm [n4b], Imperial Empire, duke, with two workers,
13: eight sailors
14: The Savvy Aardvark [ha5r], ship, 2 hulls, 1 rowing port, 6 sails,
14: 1% damaged, departed for Ocean [ba79].
Routes leaving Ocean:
North, to Ocean [ay79], 6 days
East, to Ocean [az80], 6 days
South, to Ocean [ba79], 6 days
West, to Ocean [az78], 6 days


< NewBee / Zkcy >
The first step is as good as half over.
>>
Blog
/
Github
<
CFC Studio
|
Windisco
|
Mr.code
|
Dolia
>


How to run the application
In order to install and run the application you need a Browser compatible with CSS3 and D3 javascript library, I suggest the last Chrome version.
You also need to enable javascript in your browser, if you don't know how see this guide.
You need a web server. If you don't have one don't worry, I'll explain how to use a basic Python web server.
You need a Database server that support MySQL, you can download and setup it following one of the following tutorial:
In oreder to download the code you can clone the GitHub repository with the command
git clone https://github.com/gianluca-venturini/bicycle-race.git
If you don't want to use git you can download the bicycle-race.zip and decompress it in a directory of your choice.
This step depends on your environment. I'll describe the procedure for starting the server under Linux environment since is the most common one. For Windows please go here, for Max OSX please go here. Linux command:
/etc/init.d/mysql start
The first time that you start the server you need to create the database using the dump present into the repository. In order to create the DB and the user please refer to the manual. Now you need to download the dump from this address. There are different ways to insert the dump inside the database, this step depends on your environment:
mysql -u username -p -h localhost DATA-BASE-NAME < data.sql
In order to interact with the DB the PHP backend need to be configured. The application can be easily configured, you only need to open the file data/private/mysqli.int and insert your own database, username and password:
$DB_server = "localhost";
$DB_username = "username";
$DB_password = "password";
$DB_database = "db_name";
In this section I explain how to start a basic Python based web server that allows you to see the project in local. If you want to use another webserver please refere to its manual for the setup.
In order to start the Python web server you need Python 2.7 or higher already installed in your system. If you don't have it please refer to this site.
In order to start the web server (in this example on port 8000) use this command:
python -m SimpleHTTPServer
If the server give you an output similar to that everything works fine:
Serving HTTP on <IP_ADDRESS> port 8000 ...
Now you can access the application through your browser at this URL: http://<IP_ADDRESS>:8000/


@*
* 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 uk.gov.hmrc.cataloguefrontend.connector.Team
@import uk.gov.hmrc.cataloguefrontend.DateHelper._
@(teams: Seq[Team], form: Form[_])(implicit messages: Messages, request: Request[_])
@standard_layout("Teams list", "teams") {
Teams
| @team.name.asString | @team.firstActiveDate.map(_.asPattern("yyyy-MM-dd")) | @team.lastActiveDate.map(_.asPattern("yyyy-MM-dd")) |


getExpressionStructure Loads a representation of an experiment from an Excel file (see comments further down) fileName an Excel representation on an experiment experiment an experiment structure data matrix with expression values orfs the corresponding ORFs experiments the titles of the experiments boundNames reaction names for the bounds upperBoundaries matrix with the upper bound values fitNames reaction names for the measured fluxes fitTo matrix with the measured fluxes A very common data set when working with genome-scale metabolic models is that you have measured fermentation data, gene expression data, and some different 'bounds' (for example different carbon sources or genes that are knocked out) in a number of conditions. This function reads an Excel representation of such an experiment. The Excel file must contain three sheets, 'EXPRESSION', 'BOUNDS', 'FITTING'. Below are some examples to show how they should be formatted: -EXPRESSION ORF dsm_paa wisc_paa Pc00e00030 79.80942723 78.14755338 Shows the expression of the gene Pc00e00030 under two different conditions (in this case a DSM strain and a Wisconsin strain of P. chrysogenum with PSS in the media) -BOUNDS Fixed Upper dsm_paa wisc_paa paaIN 0.1 0.2 The upper bound for the reaction paaIN should be 0.1 for the first condition and 0.2 for the second -FITTING Fit to dsm_paa wisc_paa co2OUT 2.85 3.05 glcIN 1.2 0.9 The measured fluxes for CO2 production and glucose uptake for the two conditions. The model(s) can later be fitted to match these values as good as possible. Usage: experiment=getExpressionStructure(fileName) Rasmus Agren, 2014-01-08
0001 function experiment=getExpressionStructure(fileName) 0002 % getExpressionStructure 0003 % Loads a representation of an experiment from an Excel file (see 0004 % comments further down) 0005 % 0006 % fileName an Excel representation on an experiment 0007 % 0008 % experiment an experiment structure 0009 % data matrix with expression values 0010 % orfs the corresponding ORFs 0011 % experiments the titles of the experiments 0012 % boundNames reaction names for the bounds 0013 % upperBoundaries matrix with the upper bound values 0014 % fitNames reaction names for the measured fluxes 0015 % fitTo matrix with the measured fluxes 0016 % 0017 % A very common data set when working with genome-scale metabolic models 0018 % is that you have measured fermentation data, gene expression data, 0019 % and some different 'bounds' (for example different carbon sources 0020 % or genes that are knocked out) in a number of conditions. This function 0021 % reads an Excel representation of such an experiment. 0022 % The Excel file must contain three sheets, 'EXPRESSION', 'BOUNDS', 0023 % 'FITTING'. Below are some examples to show how they should be 0024 % formatted: 0025 % 0026 % -EXPRESSION 0027 % ORF dsm_paa wisc_paa 0028 % Pc00e00030 79.80942723 78.14755338 0029 % Shows the expression of the gene Pc00e00030 under two different 0030 % conditions (in this case a DSM strain and a Wisconsin strain of P. 0031 % chrysogenum with PSS in the media) 0032 % 0033 % -BOUNDS 0034 % Fixed Upper dsm_paa wisc_paa 0035 % paaIN 0.1 0.2 0036 % The upper bound for the reaction paaIN should be 0.1 for the first 0037 % condition and 0.2 for the second 0038 % 0039 % -FITTING 0040 % Fit to dsm_paa wisc_paa 0041 % co2OUT 2.85 3.05 0042 % glcIN 1.2 0.9 0043 % The measured fluxes for CO2 production and glucose uptake for the two 0044 % conditions. The model(s) can later be fitted to match these values as 0045 % good as possible. 0046 % 0047 % Usage: experiment=getExpressionStructure(fileName) 0048 % 0049 % Rasmus Agren, 2014-01-08 0050 % 0051 0052 [type, sheets]=xlsfinfo(fileName); 0053 0054 %Check if the file is a Microsoft Excel Spreadsheet 0055 if ~strcmp(type,'Microsoft Excel Spreadsheet') 0056 EM='The file is not a Microsoft Excel Spreadsheet'; 0057 dispEM(EM); 0058 end 0059 0060 %Check that all sheets are present and saves the index of each 0061 exprSheet=find(strcmp('EXPRESSION', sheets)); 0062 boundSheet=find(strcmp('BOUNDS', sheets)); 0063 fitSheet=find(strcmp('FITTING', sheets)); 0064 0065 if length(exprSheet)~=1 || length(boundSheet)~=1 || length(fitSheet)~=1 0066 EM='Not all required spreadsheets are present in the file'; 0067 dispEM(EM); 0068 end 0069 0070 %Load the expression data 0071 [discard,dataSheet]=xlsread(fileName,exprSheet); 0072 experiment.data=discard; 0073 experiment.orfs=dataSheet(2:size(dataSheet,1),1); 0074 experiment.experiments=dataSheet(1,2:size(dataSheet,2)); 0075 0076 %Loads the maximal boundaries 0077 [discard,dataSheet]=xlsread(fileName,boundSheet); 0078 experiment.boundNames=dataSheet(2:size(dataSheet,1),1); 0079 experiment.upperBoundaries=discard; 0080 0081 %Loads the experimental data to fit to 0082 [discard,dataSheet]=xlsread(fileName,fitSheet); 0083 experiment.fitNames=dataSheet(2:size(dataSheet,1),1); 0084 experiment.fitTo=discard; 0085 0086 %Check to see that the dimensions are correct 0087 if length(experiment.orfs)~=size(experiment.data,1) || (length(experiment.experiments)~=size(experiment.data,2) && ~isempty(experiment.data)) 0088 EM='The expression data does not seem to be formated in the expected manner'; 0089 dispEM(EM); 0090 end 0091 end


This scenario demonstates subscribing to the change event for the DatePicker and TimePicker controls to receive notifications when the user selects a different value. Note, the change event only fires when changed from the UI, it does not fire when a the date or time is changed programmatically.


From
{{name[0]}} {{name[1]}}
{{email}}
Add company info
Invoice
{{invoice_name}}
Add client info
Invoice
No
Invoice
Date
{{invoice_date}}
Due:
Aug 16th, 2021
Add description
Item
HRS/QTY
Rate
Tax
Subtotal
{% for i in range(5) %}
{{invoice_input[i][0]}}
{{invoice_input[i][1]}}
{{invoice_input[i][2]}}
({{invoice_input[i][3]}}%)
{{subtotal[i]}}
{% endfor %}
Invoice
Summary
Total
(
INR
)
INR {{total}}
Save as PDF


Today I noticed that building the iwant tutorial was broken because the sourceforge download link of my descript tool was broken.
So, I moved descript to github. Earlier I had done this to iwant itself. Sourceforge downloads are a bit unreliable, and the worst is that sometimes they don't even fail properly: the download succeeds, but the downloaded file is just an HTML file with an error message.
I think I'll gradually move all my live projects to github. I'm grateful to sourceforge for all the years together, but the pragmatic fact is that it's time to move on.
The actual migration from subversion to git was super easy. This is what I did with descript, for example:
I created the project but left the repository empty, by choosing not to add a license or a readme. Then I migrated and pushed the sourceforge subversion repository — the latter part as instructed by the github project creation wizard:
Published on 2018-04-03


OAS3 is a parser, validator and compiler for dealing with Open API Specification v3. This library provides an interface for working with specifications, and loading and dumping to various locations and formats.


| GanttPlotObject |
| SetCaptionMargin() SetConstrain() SetCSIMAlt() SetCSIMTarget() SetLabelLeftMargin() |
Internal method that can't be instantiated directly. Used to capture the common properties of all objects that can be added to the Ganttgraph, i.e. GanttBar, MileStones GanttVLine.
Specify margin (in pixels) between object and caption string
| Argument | Default | Description |
|---|---|---|
| $aMarg | Margin in pixels |
Example:
Specify a constrain link between this object and another
| Argument | Default | Description |
|---|---|---|
| $aRow | Gantt row where tarhet for constrain is | |
| $aType | Type of constrain | |
| $aColor | 'black' | Color of constrain |
| $aArrowSize | ARROW_S2 | Size of ending arrow |
| $aArrowType | ARROWT_SOLID | Type of arrow |
The possible arrow sizes are
Type of arrow can be either of
Example:
Specify ALT-tag text for hotspot
| Argument | Default | Description |
|---|---|---|
| $aAlt | Alt text |
Note: You must have specified a CSIM atrget for this to have any effect.
Example:
Specify URL when object is used in a CSIM
| Argument | Default | Description |
|---|---|---|
| $aTarget | Target URL | |
| $aAlt | '' | Alt texts |
| $aWinTarget | '' | No description available |
Example:
Specify offset (in pixels) for the title of this object.
| Argument | Default | Description |
|---|---|---|
| $aOff | Offset in pixels |
Example:


|
|
def | __init__ (self, dof_names, scaling_groups=None) |
| |
|
def | init_scalers (self) |
| |
|
def | add_demonstration (self, data) |
| |
|
def | create_models (self, dofs, start_phase, end_phase) |
| |
|
def | get_model_mse (self, model, dofs, sample_start_phase=0.0, sample_end_phase=1.0) |
| |
|
def | get_information_criteria (self, dofs, basis_start_phase=0.0, basis_end_phase=1.0, sample_start_phase=0.0, sample_end_phase=1.0) |
| |
|
def | get_best_model (self, aic, bic) |
| |
|
def | log_likelihood (self, measured, predicted) |
| |
|
def | plot_information_criteria (self, ic, save_name=None) |
| |
|
def | __init__ (self, dof_names, scaling_groups=None) |
| |
|
def | init_scalers (self) |
| |
|
def | add_demonstration (self, data) |
| |
|
def | create_models (self, dofs, start_phase, end_phase) |
| |
|
def | get_model_mse (self, model, dofs, sample_start_phase=0.0, sample_end_phase=1.0) |
| |
|
def | get_information_criteria (self, dofs, basis_start_phase=0.0, basis_end_phase=1.0, sample_start_phase=0.0, sample_end_phase=1.0) |
| |
|
def | get_best_model (self, aic, bic) |
| |
|
def | log_likelihood (self, measured, predicted) |
| |
|
def | plot_information_criteria (self, ic, save_name=None) |
| |