Wadi Drone is a fixed wing data MULE (Mobile Ubiquitous LAN Extension) that automates the collection of camera trap photographs, a critical conservation data. Designed in collaboration with Wadi Wurayah National Park the drone can fly over mountains to camera trap locations and wirelessly download photographs taken by the ground-based camera traps. This technology requires minimal investment and employs off the shelf components, so it can be deployed affordably to document animal population sizes and wildlife diversity. Furthermore, with slight modification, the drone can be used to automate the retrieval of any sensor data.
The Wadi Drone system consists of a modified camera trap system and a drone carrying a small electronics payload. The modified camera can transmit pictures from the camera to the electronics payload and the payload consists of a communication system that can send signals and retrieve the transmitted photographs from the camera.
The drone is composed of a Penguin V2 frame with customized power electronics to maximize flight time and flight distance. It is equipped with autonomous navigation ability through the APM 2.6 flight controller and the ArduPilot Open Source Autopilot. The plane carries a small communication payload that communicates with the modified camera trap and downloads the camera trap photo autonomously.
The camera contains photography transmission system that is made up of two separate systems, a low-power XBee and Arduino based system and a high-power Wi-Fi and Raspberry Pi system. During the time when a photo is not in transmission, the camera trap enters the power saving mode by shutting down the Wi-Fi and Raspberry Pi system, however, when the drone is in the vicinity, the camera turns the high-power system back on to begin photo transmission process.
In February 2015, our team presented the Wadi Drone system live in front of more than 600 people in the Drones for Good competition. Our team came first in the National category and won a prize of AED 1,000,000. The funding allows for the continuation of Wadi Drone development. Currently, with the funding from the Drone for Good competition, we have obtained a new plane, RMRC Anaconda, and successfully conducted autonomous landings and take-offs, and we have also developed the tutorial documents for all the necessary steps needed to build our system from scratch, the whole project will be open sourced very soon, check out our website for more information


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Punisher
Phoebe Bridgers
40:37
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DVD Menu
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Garden Song
3
Kyoto
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Punisher
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Halloween
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Chinese Satellite
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ICU
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Graceland Too
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Before Change
from chaospy.distributions import collection
DISTRIBUTIONS = tuple(
attr for attr in (getattr(collection, name) for name in dir(collection))
if isclass(attr) and issubclass(attr, cp.Dist)
)
@pytest.fixture(params=DISTRIBUTIONS)
def distribution(request):
return request.param
After Change
for name in dir(collection):
attr = getattr(collection, name)
if isclass(attr) and issubclass(attr, cp.Dist):
with suppress(TypeError):
attr()
DISTRIBUTIONS = DISTRIBUTIONS + (attr,)
@pytest.fixture(params=DISTRIBUTIONS)
def distribution(request):
return request.param


Cognifty is segmented into a 3-tier system of code. The current style-du-jour of MVC is a bit overused in the Web world. MVC is a pattern for graphical user interfaces, not for application architecture.
MSE is a structured layout of business logic so that any portion of code is easily found by any member of a development team. By assigning specialized diretory, file, and class layout, much work can be eliminated because the framework follows standard naming conventions.
A Module is, literally, a directory. It can be an entire Web application or a well defined portion of an entire Web application.
A Service is a special class inside a module. Services provide all the business logic of your application. A service can have special templating and authorization hooks, different from other parts of your Web application.
An Event is a single instance of a client request. Events are methods of your service class. Events take SystemRequest objects as input and template arrays for output.
Templating can be thought of as the output phase of your application. All output from a service is named in an output array. This array is then passed to the templating system, which can be configured to suit your needs.
The default templating system is designed to pull in one single template file and put the current MSE's output into the main content area. Other content areas use the Layout system to gather their needed output.
The layout system is built around named areas of your template. Each area is defined in the layout.ini file and is given a specific path, file, class name, object name, and method call. The template designer then simply places a call to the layout engine and passes in the named area, example: box.side1. The layout system then finds the configured object method call for box.side1, calls the method, and passes back the complete output.
The entire system configuration is held in the Object store. The object store eliminates the need for globals.
The object store is essential to customizing a developed Web application. With it, you can insert your own sub-classes throught the system without changing any code! You simply need to reconfigure core components in the supplied ini files, giving your new sub-classes and the system will automatically use them system-wide.


Programmer & Game Developer
My name is Rasmus Nielsen. I have a Bachelor's degree of Science (BSc) in Medialogy. I have always had an interest in programming, even starting to get a grip of it in primary school. Through my programming journey, I have worked mostly within game development. I have by far the most experience with Unity, and know my way around with most of the systems such as: scripting, materials, lighting, audio and much more. I have also been using the Unreal Engine, where I am currently spending time getting to grips with the engine and their blueprint system, along with C++ programming. In my early days I also worked with GameMaker Studio and RPG Maker, both of which are less programming-focused. I have also released a couple of mods for Paradox Interactive games Heart of Iron IV and Imperator: Rome, latter of which, became one of the most popular mods for the game and later, a similar system was officially added to the game by the developers. You can find this mod here.
Tectonicus: On the Edge of War
Verdant City
Interface for Spatial Audio Mixing in Virtual Reality
Imitation of Natural Settings in Virtual Reality for Relaxation
Puzzle Solving Machine Learning Agent
Tectonicus: On the Edge of War is a game currently in development by Gamucatex. Tectonicus is a Card-Based Auto Battler Game. The game has a major focus on history, achieving historical accuracy and narration to the point where the player will after each session have a better understanding of history, i.e. Meaningful Gaming. During my time working with the game (2½ years), I was placed as Lead Developer, at the very beginning of the project. With no other or previous developer lead, I got the opportunity to implement work methodologies, where I went for a Scrum approach, with daily meetings and code reviews, ensuring the best possible implementation of new features. The development team was quite small, and in many instances I was the only one, and with this I created most of the foundational code base, with a SOLID OOP approach, to make sure the code base was easy to work with and expand upon. Since Gamucatex also had other departments, it was important to ensure that these departments could go into the project in Unity and create for example new cards without having to deal with a single line of code. So creating designer-friendly tools was extremely important. During my time at Gamucatex, some of the systems I am especially proud of having developed include:
This game was my bachelor's project, I was the main programmer for this project. You are playing as a robot, with a constantly draining battery. You have to keep yourself alive, and save a heavily polluted uninhabitable city by building certain sustainable solutions. The experience will be guided through an objecitve system, that will help to let the player know their current/next steps towards sustainability. The player will have to carefully manage their own battery life, along with gathering resources, to be able to build solutions to keep themselves alive and to not worsen the pollution to a dangerously high level. Fog reacts dynamically to the amount of pollution in the city. Music is also dynamic, depending on the amount of health the player has, new instruments are introduced to the simple music loop for every 25% decrease in health.
This is a VR interface for spatial audio mixing. It was developed in a group of five, I was mostly working as the Unity developer. This interface, integrates Unity with Max for Live, which we can then use to communicate to the Digital Audio Workstation (DAW): Ableton Live. Furthermore, the program works completely wirelessly, only tested with an Oculus Quest. The VR headset will send and receive data through Open Sound Control (OSC), which works similarly to UDP, but for transferring audio data, that Ableton Live can interpret. Ableton Live has tracks, these tracks can be integrated into Unity, visualized as spheres, where the name of the track also appears above. The VR headset will constantly send the azimuth between the headset and every track independently, Ableton Live will then compute the data through a set of Head-Related Impulse Responses, where finally it will output the appropriate sound for that azimuth.
This was developed in a group of five people, where I was mainly the programmer and level designer. The purpose of the project was to develop a VR experience, that would relieve a person's stress, by putting them into one of two relaxing environments. The two environments had very distinct themes. The environments included wildlife (Fish and birds) with Artificial Intelligence. The experience was developed for the Samsung Gear VR, a mobile VR platform, so visual fidelity was a big hurdle. However, we overcame this hurdle by recording the environment as a 360 degree video. This meant that we could make quite advanced scenes, without factoring in performance for the mobile VR headset.
This was a solo project of mine. Here I taught Artificial Intelligence (AI) agents to solve a puzzle. The task of the agent is to reach the goal, but the goal is only reachable once a pressure plate is being pressed at the same time. However, there is also a block in the level, that the agent can push on top of the pressure plate, if the agent leaves the block on top of the pressure plate, they are able to reach the goal. The agent is rewarded once they push the block on top of the pressure plate, however, if they move it away again, they will lose that reward. They also get rewarded by finally reaching the goal. This project made use of the Unity ML Agents package, which depends on the Machine Learning library, TensorFlow.


- addArmies(int) - Method in class models.Country
-
Adds armies to the country
- addArmySection(BorderPane) - Method in class views.Runner
-
Adds the total armies section to the WD View.
- addBorder(String, String) - Method in class models.GameMap
-
Adds a border between two countries making them neighbors
- addBorder() - Method in class models.GameMapTest
-
check if the selected two countries are neighbors
- addCard(Card) - Method in class models.player.Player
-
Executes add card for this player strategy.
- addCard(Card) - Method in class models.player.PlayerAggressive
-
Adds a card to this player's hand.
- addCard(Card) - Method in class models.player.PlayerBenevolent
-
Adds a card to this player's hand.
- addCard(Card) - Method in class models.player.PlayerCheater
-
Adds a card to this player's hand.
- addCard(Card) - Method in class models.player.PlayerHuman
-
Adds a card to this player's hand.
- addCard(Card) - Method in class models.player.PlayerRandom
-
Adds a card to this player's hand.
- addCard(Card) - Method in interface models.player.PlayerStrategy
-
For adding a card to the Player's hand.
- addCardToView(Label) - Method in class views.Runner
-
Adds a card to the view.
- addCardView(BorderPane) - Method in class views.Runner
-
Adds the card view to the WD View.
- addContinent(String, int) - Method in class models.GameMap
-
Add a continent to the gameMap
- addContinent() - Method in class models.GameMapTest
-
check if the added Continent is part of the map
- addCountry(String, String) - Method in class models.GameMap
-
Adds a country to the game map
- addCountry() - Method in class models.GameMapTest
-
check if the added Country is part of the map
- addGamePlayer(String, String) - Method in class models.GameMap
-
Adds a game player to the current state
- addMapSection(BorderPane) - Method in class views.Runner
-
Adds the continent control section to the WD View.
- addObserver(CardExchangeView) - Method in class models.player.PlayerAggressive
-
Adds an observer by a call to the 'super' implementation of addObserver
- addObserver(CardExchangeView) - Method in class models.player.PlayerBenevolent
-
Registers this class as an observer
- addObserver(CardExchangeView) - Method in class models.player.PlayerCheater
-
Registers this class as an observer
- addObserver(CardExchangeView) - Method in class models.player.PlayerHuman
-
Adds the CardExchangeView as an observer to this class.
- addObserver(CardExchangeView) - Method in class models.player.PlayerRandom
-
Update the CardExchangeView with the context
- addObserver(CardExchangeView) - Method in interface models.player.PlayerStrategy
-
Adds the CardExchangeView as an Observer to the Player.
- appInstance - Variable in class views.CardExchangeView
-
Reference to the Runner instance on the UI thread *
- appInstance - Variable in class views.PhaseView
-
Instance of the Runner class.
- appInstance - Variable in class views.WDView
-
Instance of the Runner class.
- armiesTradedForSet - Static variable in class models.GameMap
-
Number of armies traded in for each set
- armiesTradedForSet - Variable in class models.state.GameState
-
Number of armies traded in for each set
- assignCard() - Method in class models.GameMap
-
Assigns a random card to the player from the deck
- assignedCard - Static variable in class controllers.GameController
-
This boolean is false if a card hasn't been assigned to the current player.
- attack(GameMap, String) - Method in class models.player.Player
-
Executes attack using the current strategy.
- attack(GameMap, String) - Method in class models.player.PlayerAggressive
-
Performs the attack using the Aggressive Strategy
- attack(GameMap, String) - Method in class models.player.PlayerBenevolent
-
The Benevolent player never attacks.
- attack(GameMap, String) - Method in class models.player.PlayerCheater
-
The Cheater's attack will capture all neighbors of owned countries.
- attack() - Method in class models.player.PlayerCheaterTest
-
check if attack will capture all neighbors of countries that belong to the cheater
- attack(GameMap, String) - Method in class models.player.PlayerHuman
-
Starts the battle loop.
- attack(GameMap, String) - Method in class models.player.PlayerRandom
-
The player will attack a random country for a random number of times
- attack(GameMap, String) - Method in interface models.player.PlayerStrategy
-
The attack command allows the user to attack another territory.
- attackArmiesTest() - Method in class controllers.GameControllerTest
-
Test to check whether the attacking country has more than 2 armies
- attackerName - Variable in class controllers.BattleController
-
The name of the attacking player.
- attackingCountry - Variable in class controllers.BattleController
-
The attacking Country object.
- attackingPlayer - Variable in class controllers.BattleController
-
The attacking player.
- attackTest() - Method in class models.player.PlayerRandomTest
-
check if player attacks from any random country that belongs to the Random Player
- attackUtil(GameMap, Map.Entry<String, Country>) - Method in class models.player.PlayerAggressive
-
Utility to perform the attack until no attack possible
- attemptAttack() - Method in class controllers.BattleController
-
Attempts an attack based on the command specified by the attacker.
- autoLoadMap(String) - Method in class utils.MapAdaptor
-
Automatically loads map file based on the content inside of them
- autoSaveMap(GameMap, String) - Method in class utils.MapAdaptor
-
Automatically save map based on the original loaded map type


Pay a Ticket
- Lorem ipsum dolor sit amet, consectetur adipisicing elit. Tempore nihil fugiat distinctio! Eveniet voluptatibus
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- Lorem ipsum dolor sit amet, consectetur adipisicing elit. Tempore nihil fugiat distinctio! Eveniet voluptatibus
The Parking Citation Assistance Center is located at 250 Frank H. Ogawa Plaza, Suite 6300, near Oakland City Hall. It is open Mondays, Tuesdays, Thursdays and Fridays from 9 a.m. to 4:30 p.m. and Wednesdays from 9:30 a.m. to 4:30 p.m. The phone number is<PHONE_NUMBER>.
For your convenience, parking ticket payment kiosks (photo at right) are located at: Dalziel Building lobby, 250 Frank H. Ogawa Plaza, Monday through Friday from 8:30 a.m. to 5 p.m. Eastmont Police Substation, 2651 73rd Avenue, Monday through Saturday from 8:00 a.m. to 4 p.m.


IPApps Admin Workflow
S3 Temp Folder Name
File Name - "Vendor+Ver.zip" - For e.x., "densov1.1.zip"
What do you want to do?
Read a File
Upload a File
Upload File
Get Pre Signed Url
Url Text Goes here ...


The constructor method is a method that is used to create and initialize an object when using the class keyword. There can only be one method that uses the constructor keyword within a class. A SyntaxError exception will be thrown if the class contains more than one constructor method. The constructor can use the super keyword to call the constructor of the parent class
objects
,
let's say this object
are persons it will be very restrictive if we have to create a lot of
persons and we will not respect the DRY. So we can create a constructor
who will be like a
reference where all object
will inherit of his property and methods.constructor
we only create a constructor()
function.this
keyword who point
on this parameter we indicated that the parameter create news property.this
keyword point from the object where he's called,
so in our example the this
keyword point the the window object
. To avoid that when we create a
new object
we simply add the new keyword before the constructor function to create a new empty object
and declare that the parameters are the new property of our object
.constructor
works with property
we can also add
methods
. To do that we can create a method directly in our objects.


StreamLogHandler
public struct StreamLogHandler: LogHandler
StreamLogHandler
is a simple implementation of LogHandler
for directing
Logger
output to either stderr
or stdout
via the factory methods.
-
Factory that makes a
StreamLogHandler
to directs its output tostdout
Declaration
Swift
public static func standardOutput(label: String) -> StreamLogHandler
-
Factory that makes a
StreamLogHandler
to directs its output tostderr
Declaration
Swift
public static func standardError(label: String) -> StreamLogHandler
-
Declaration
Swift
public var logLevel: Logger.Level = .info
-
Declaration
Swift
public var metadata = Logger.Metadata()
-
Declaration
Swift
public subscript(metadataKey metadataKey: String) -> Logger.Metadata.Value?


Version 1.1, 2009-06-21
The Modelica_Fluid library (revisionId = r2625) was included as
Modelica.Fluid in the Modelica Standard Library 3.1.
Version 1.0, 2009-01-28
Modelica_Fluid was refactored and finalized for the release:
- Refactoring of the code
This became necessary as the previous release Modelica.Fluid
Streams Beta3 still reflected the long development history, while
the basic concepts had been crystallized. Please consult the
subversion control (SVN) logs for individual changes.
- Device oriented package names
The former sub-packages Junctions and PressureLosses have been
combined into the new subpackage Fittings. The former Pumps and
Volumes.SweptVolume have become the initial version of fluid
Machines. The former Volumes package is now called Vessels.
- Complete implementation of one-dimensional fluid flow
The balance equations as documented in
UsersGuide.ComponentDefinition.BalanceEquations are now
completely implemented. The implementations with generic boundary
flow and source terms find in:
Specific models combine the balances and define the boundary flow
and source terms as appropriate. For instance
All non-trivial mass and energy balances of Vessels, Machines and
Fittings have been replaced with PartialLumpedVolume. The mass and
energy balances of Pipes are based on
PartialDistributedVolume.
See Examples.BranchingDynamicPipes
for an example utilizing the complete balance equations.
- New approach for the connection of distributed flow
models
The staggered grid approach offers different choices for the
connection approach. So far the preferred modeling was to put full
mass balances into the pipes and expose half momentum balances
through the ports (ModelStructure a_v_b). This resulted in
nonlinear equation systems for pressure/flow correlations in
connection sets. A new default ModelStructure av_vb has been
introduced putting full momentum balances into the models and
exposing half mass balances through the ports (av_vb replaces the
former avb). This way the nonlinear equation systems are avoided.
High-index DAEs need to be treated instead in connection sets.
Alternatively a Fitting like SuddenExpansion can be introduced to
account for different cross flow areas of connected flow
models.
- New Vessels.BaseClasses.PartialLumpedVessel treating the ports,
including hydraulic resistances, for ClosedVolume, SimpleTank and
SweptVolume.
- Clarification of modeling assumptions
The documentation has been extended to better explain the modeling
assumptions made. In particular the section
UsersGuide.ComponentDefinition.FluidConnectors now makes clear
that the ports represent the thermodynamic enthalpy, as opposed to
stagnation enthalpy, and thermodynamic or static pressure, as
opposed to total pressure. An new package Explanatory has been
added to the examples to show the difference between static
pressure and total pressure and possible implications. See
Examples.Explanatory.MomentumBalanceFittings.
- System (former Ambient)
The use of the global System object has been extended towards
common default values for modeling assumptions, initialization, and
advanced settings that are different for each application of the
library but should nevertheless provide default values for reasons
of convenience. In particular steady-state initialization and
complete steady-state simulation can now be specified system-wide.
A new Types.Init.Dynamics has been introduced, combining
steady-state and initial conditions. The former Types.Init has
become obsolete.
See Examples.HeatingSystem
- Extension of pumps for better consideration of zero flow and
heat transfer with environment
The simplified mass and energy balances have been replaced with a
rigorous formulation. Moreover an optional heat transfer model can
be configured for heat exchanged with the environment or the
housing.
See Machines.BaseClasses.PartialPump
- Refinement of valves for flow reversal
All valves now use upstream discretization for reversing flow
conditions.
- Finalization of trace substances
Modelica.Fluid now provides a sound implementation for trace
substances, which can easily be added to existing Media models, in
order to study their evolution in a fluid system.
See
Examples.TraceSubstances.RoomCO2WithControls
- Vectorized ports for volumes
The ports of models that typically have large volumes, like Vessels
and Sources, have been vectorized. Formerly the connection of
multiple flow models to the same port of such volume models
resulted in unintended mixing equations for stream variables in
connection sets outside the volumes. The mixing takes place inside
the volumes when using multiple ports. Moreover a Fittings.MultiPort
has been introduced. It can be attached to components like pipes,
which do not have vectorized ports on their own.
- Inverse parameterization of flow models with nominal
operational conditions
Flow models have been added or extended to support the
parameterization with nominal values (Machines.ControlledPump,
Orifices.SimpleGenericOrifice,
Pipes.BaseClasses.FlowModels.NominalTurbulentFlow). They are
intended for early phases of system modeling, if geometries and
flow characteristics are of secondary interest. As these models use
the same interfaces, base classes and naming conventions, they can
easily be replaced with more detailed models as more information
shall be taken into account later on.
See Examples.InverseParameterization
- Replaceable HeatTransfer models
The Vessels and the Machines now have replaceable HeatTransfer
models, besides the Pipes. All HeatTransfer models are optional.
The heat transfer models are parameterized with the Medium and the
ThermodynamicState of involved flow segments.
See Interfaces.PartialHeatTransfer.
- All examples are working now (using Dymola 7.1).
The number of examples has been extended with the former critical
test cases HeatingSystem and IncompressibleFluidNetwork. Moreover
the HeatExchangers have been moved into Examples.
Version 1.0 Streams Beta 3, 2008-10-12
Modelica.Fluid was further improved:
- Volumes, tanks, junctions
Added asserts to require that ports are connected at most once. If
a user would perform more than one connection, ideal mixing takes
place for the connected components and this is nearly never what
the user would like to have
- Ambient
Renamed Ambient to System, including adaptation of models.
Introduced default values system.flowDirection and as a comment
system.initType. system.flowDirection is used in two port
components as default.
- GenericJunction
Corrected specification of flowDirection.
Added a HeatPort.
- PartialDistributedFlow models
Adapted determination of velocities to usage of upstream properties
at ports.
Corrected and unified initialization of p_start[*] values.
- DistributedPipe models
Changed treatment of port densities and viscosities to the
treatment of the lumped pipe model. This way events are avoided if
the mass flow rate crosses or approaches zero.
Correct determination of Reynolds numbers.
Added test model DistributedPipeClosingValve.
- ControlValves
Changed flowCharacteristic into valveCharacteristic
Removed parameter Kv and added dp_nom, m_flow_nom from linear and
discrete valve interfaces. Added test cases.
Adapted Examples to new LinearValve and DiscreteValve, using
nominal values instead of Kv.
Changed default flow coefficient selection to OpPoint
- Fixed units for Kv and Cv in control valve models.
- Updated tests for valves.
- Bug in Modelica.Fluid.Test.TestComponents.Pumps.TestWaterPump2
corrected (complicated redeclaration issue).
- Adapted AST_BatchPlant so that "Check" is successful.
Simulation fails after 600 s.
- Introduced density_pTX(Medium.p_default, Medium.T_default,
Medium.X_default) as default value for nominal densities
(previously it was a literal such as 1000).
- Pumps
Updated energy balance equations for pumps (no division by zero
anymore, fixed several bugs related to Np).
Added two more test cases for pumps.
Fixed pump initialization options.
- PartialPump
Explanation for the energy balanced added as comment
"h=0" replaced by "h=Medium.h_default" since otherwise an assert is
triggered if "h=0" is not in the medium range.
Fluid ports positioned in the middle line and using the same size
as for all other components.
- Pumps.Pump
Resized input connector, so that it has the same size as the
standard input connectors.
Changed icon text to input connector to "N_in [rpm]".
Added unit rev/min to the external and internal input
connector.
- PartialValve
fillcolor=white added to icon
made line Thickness = Single, since icon does not look nice
sometimes
- All components
Changed %name color from black to blue (is a conversion bug, since
Modelica 2 has blue as default color whereas Modelica 3 has black
and Dymola is not taking care off this).
- Sources
Made icon elements invisible, if corresponding input is
disabled.
- Valves, Pipes, PressureLosses, HeatExchangers, two port
senors
Added an arrow in the icon for the "design flow direction" from
port_a to port_b.
- Moved default initialization in "System" in to a comment, since
no effect yet
- Added the explanation from Francesco for Kv, Cv for valves in
the User's Guide and added links in the corresponding valves to
this description
"Check" for the library is successful. "Check with Simulation"
(i.e., simulating all test models in the library) is successful
with the exceptions:
- Examples.AST_BatchPlant.BatchPlant_StandardWater
Need to be fixed in a later release (requires quite a lot of
work).
- Test.TestOverdeterminedSteadyStateInit.Test5
Test.TestOverdeterminedSteadyStateInit.Test6
These are test cases where too much initial conditions are given.
The goal is to work on methods how this can be handled. So, this is
a principal problem that these models do not simulate.
Version 1.0 Streams Beta 2, 2008-10-08
Modelica.Fluid was transformed to Modelica 3 and to Modelica
Standard library 3.0 (by automatic conversion). Further
changes:
- Emulated enumerations changed to real enumerations.
- Improved ControlValves code
- Introduced stream connectors with stream keyword (was
previously an annotation)
- Introduced inStream() instead of inflow()
- Introduced m_flow*actualStream(h_outflow) instead of
streamFlow() or semiLinear(m_flow, inStream(h_outflow),
medium.h)
- Removed Modelica.Fluid.Media and all references to it (since
now available in Modelica.Media of MSL3.0).
- Fixed PartialLumpedVolume for media with multiple
substances
- New function "Utilities.RegFun3" for regularization with static
head
- Fix density in static head models with the new RegFun3
functions (ticket 7)
- Minor bug in MixingVolume corrected:
V_lumped and Wb_flow have been set as modifiers when extending from
PartialLumpedVolume, although they are not declared as input. This
is not allowed in Modelica 3. Fixed by replacing the modifiers by
equations.
- Modelica.Fluid.Sources.FixedBoundary
Introduced p_default, T_default, h_default as default values, since
otherwise warnings will always be printed because parameter value
is missing.
- Modelica.Fluid.Sources.Boundary_pT
Modelica.Fluid.Sources.Boundary_ph
Modelica.Fluid.Sources.MassFlowSource_T
Changed default values of parameters reference_p, reference_T to
p_default, T_default (some have been xx_default, some reference_xx,
it seems best to always use the same approach)
- Modelica.Fluid.Pipes.BaseClasses.PartialDistributedFlow
Added default value for parameter "rho_nominal" =
Medium.density_pTX(Medium.p_default, Medium.T_default,
Medium.X_default) in order to avoid unnecessary warning messages.
Should be replaced by "Medium.rho_default", once available.
- Modelica.Fluid.Pipes.DistributedPipe
Modelica.Fluid.Pipes.DistributedPipeSb
Modelica.Fluid.Pipes.DistributedPipeSa
Added default value for parameter "mu_nominal" (computed with
default values of p,T,X from dynamicViscosity(..))
-
Modelica.Fluid.Pipes.BaseClasses.PartialDistributedFlowLumpedPressure
Replaced default value "rho_nominal=0.01" by
Medium.density_pTX(Medium.p_default, Medium.T_default,
Medium.X_default)
- Modelica.Fluid.Volumes.OpenTank
Modelica.Fluid.Volumes.Tank
Corrected icons of ports (wrongly sized by automatic conversion
from Modelica 2 to Modelica 3).
- Examples.BranchingDistributedPipes
Modelica.Fluid.Test.TestComponents.Junctions.TestGenericJunction
Modelica.Fluid.Test.TestComponents.Pipes.TestDistributedPipe01
Parameters dp_nom, m_flow_nom are not defined in junction
components. Values provided.
- PressureLosses.BaseClasses.QuadraticTurbulent.BaseModel
No default or start values for "parameter LossFactorData data"
Changed the model to "partial model" to avoid warning messages
Version 1.0 Streams Beta 1, 2008-05-02
Changed connectors to stream connectors and adapted the
following sublibraries:
- Volumes
- PressureLosses
- Sensors
- Sources
- ControlValves
- HeatExchangers
- Junctions
- Pipes
- Pumps
- Test and Examples (most of the examples and tests are
simulating)
Other changes:
- Introduced HeatPorts with vectorized icon in
Modelica.Fluid.Interfaces
- Deleted Modelica.Fluid.WorkInProgress since it seems to be too
much work to convert it to stream connectors
- Added Modelica.Fluid.Media (contains ConstantLiquidWater medium
because functions are missing in Modelica.Media)
- Added two additional test cases with LumpedPipes (to identify
problems with hierarchically connected stream connectors).
- Deleted TestPortVolumes since PortVolumes can no longer be
implemented with stream connectors
- Leakage flow introduced for valves
- DrumBoiler Example corrected
- Regularization for sensors (T,h,...), in order that no
discontinuity for bi-directional flow
- Density computation in static head corrected
- New functions Utilities.regUnitStep, regStep
- New components
(TestComponents.Sensors.TestOnePortSensors1/.TestOnePortSensors2l,
TestRegStep)
- PartialTwoPortTransport
- Introduced port_a.T, port_b.T (for plotting)
- Removed initialization menu
- Introduced dp_start, m_flow_start
- Removed previous start values of PartialTwoPortTransport in all
models
- PartialPump: Removed p_nom, since no longer needed (only
dp_nom)
- Made "%name" in the icons of all components unified (and better
looking)
- Changed default value of leakage flow of valves to zero.
- Fixed Modelica.Fluid.Junctions.MassFlowRatio so that it
compiles (inflow(..) currently only supported for scalars, not for
vectors)
- Added script libraryinfo.mos, in order that Modelica.Fluid
appears in the Dymola library window automatically (provided
library is in MODELICAPATH)
- Replaced semiLinear(..) by streamFlow(..) (not yet at all
places)
- Introduced check-boxes in parameter menu of Sources (is more
convenient to use)
- TwoPortTransport
Computation of V_flow and optionally port_a_T, port_b_T. Error in
temperature calculation corrected
- Tank:
Default of bottom pipe diameter changed from 0 to 0.1, since
otherwise a division by zero (if not connected and not
changed).
- Modelica.Fluid.ControlValves.ValveVaporizing:
Due to changes in PartialTwoPortTransport, port_a_T_inflow does no
longer exist and the usage to it is removed.
-
Modelica.Fluid.Test.TestComponents.Sensors.TestTemperatureSensor:
Due to changes in PartialTwoPortTransport, p_start does no longer
exist and the usage to it is removed.
- VersionBuild introduced, as well as automatic update of
VersionBuild/VersionDate
Version 1.0 Beta 4, 2008-04-26
Changes according to the Modelica Design Meetings since the last
beta version. This version is used to "freeze" the current
development, in order to change to a version with a new connector
design using stream variables.
Version 1.0 Beta 3, 2007-06-05
Changes according to the Modelica Design Meetings since the
Modelica'2006 conference, especially, improved initialization,
changed Source components (input connectors must be enabled),
improved tank component, moved test models from Examples to new
package Test, many more test models, etc. This version is slightly
non-backward compatible to version 1.0 Beta 2.
Version 1.0 Beta 2, 2006-08-28
Package considerably restructured and some new components added.
New examples (ControlledTankSystem, AST_BatchPlant).
Version 0.96, 2006-01-08
- New package Modelica.Fluid.PressureLosses.
- New package Modelica.Fluid.WorkInProgress.
- New components in Modelica.Fluid.Components:
ShortPipe, OpenTank, ValveDiscrete, StaticHead.
- New components in Modelica.Fluid.Examples.
- Improved users guide.
Version 0.910, 2005-10-25
- Changes as decided on 41th-45th Modelica Design Meetings
(details, see minutes).
Version 0.900, 2004-10-18
- Changes as decided on 40th Modelica Design Meeting in Dresden
(see also minutes)
Version 0.794, 2004-05-31
- Sensors.mo, Examples/DrumBoiler.mo: extend sensors with user
choice for measurement unit.
- Components.mo, Types.mo: moved components and types to package
Examples.
- Moved Examples from file
Modelica.Fluid/package.mo to Modelica.Media/Examples
subdirectory and created separate file per
sub-package. This shall simplify the maintenance of examples by
different authors
- Moved Interfaces from file Modelica.Fluid/package.mo to
Modelica.Fluid/Interfaces.mo
Version 0.793, 2004-05-18
- Removed "semiLinear" function since available as Modelica 2.1
built-in operator in Dymola.
- Minor bug in "Components.ShortPipe" corrected.
- Bug in "Components.Orifice" corrected (dp was previously
calculated in Interfaces.PartialTwoPortTransport, but this was
removed and not updated in Orifice).
Version 0.792, 2003-11-07
This is the first consolidated version made up from several
changes for Modelica'2003. Modelica.Fluid is still quite far away
from a library that could be included in the Modelica standard
library.
Previous Releases
- Oct., 2003
by Martin Otter: Adapted to latest design of the Modelica.Media
library.
by Rüdiger Franke: Included sensor components and
Modelica.Fluid.Examples.DrumBoiler example.
- Sept., 2003
by Martin Otter: Changes according to the decisions of the Modelica
design meeting in Dearborn, Sept. 2-4, 2003. Fluid library split
into two packages: Modelica.Media that contains the media models
and Modelica.Fluid that contains fluid flow components.
Modelica.Media is independent of Modelica.Fluid and my be used also
from other packages that may have a different design as
Modelica.Fluid.
- Aug., 2003
by Martin Otter: Improved documentation, PortVicinity (now called
semiLinear) manually expanded, two different volume types, replaced
number of massFractions from n to n-1 in order that usage of model
for single substances is easier and in order that no special cases
have to be treated in the equations (previously the massFraction
equations had to be removed for single substance flow; now they are
removed automatically, since the dimensions are zero, and not one
as previously), included asserts to check the validity of the
medium models, included the dynamic viscosity in the medium models,
adapted the examples and medium models to the changes in
Interfaces, improved menus according to the new features in Dymola
5.1. Added "Components.ShortPipe" that contains a detailed model of
the frictional losses in pipes over a very wide range.
- Feb., 2003
by Martin Otter: Included several elementary components and a model
for moist air. Some elementary components, such as FixedAmbient,
are adapted versions from the SimpleFlow fluid library of Anton
Haumer.
- Dec., 2002
by Hubertus Tummescheit: Improved version of the high precision
water model (Copy from ThermoFluid library, code reorganization,
enhanced documentation, additional functions).
- Nov. 30, 2002
by Martin Otter: Improved the design from the design meeting:
Adapted to Modelica standard library 1.5, added
"choicesAllMatching=true" annotation, added short documentation to
"Interfaces", added packages "Examples" and "Media" (previously
called "Properties") from previous versions and adapted them to the
updated "Interfaces" package.
- Nov. 20-21, 2002
by Hilding Elmqvist, Mike Tiller, Allan Watson, John Batteh, Chuck
Newman, Jonas Eborn: Improved at the 32nd Modelica Design
Meeting.
- Nov. 11, 2002
by Hilding Elmqvist, Martin Otter: improved version.
- Nov. 6, 2002
by Hilding Elmqvist: first version of the basic design.
Generated at 2019-01-25T16:44:03Z by OpenModelica 1.14.0~dev-130-gefa44fb


Air quality in Noida improves, but remains 'very poor'
The India Meteorological Department said winds gusting up to 25 kmph were flushing out the pollutants faster
The air quality in Noida and Greater Noida improved further on November 5. However, it remained in the "very poor" category.
The Air Quality Index (AQI) of Greater Noida was 321 at 9.30 pm on November 5, down from 384 on November 4, while in Noida, it was 335, down from 383 the previous night, according to Central Pollution Control Board (CPCB) data.
The average AQI for the last 24 hours was recorded at 351 at 4.00 pm in Noida and at 340 in Greater Noida.
An AQI between 300 and 400 falls in the "very poor" category, meaning it may cause respiratory illnesses on a prolonged exposure.
Early morning on November 6, major pollutants PM 2.5 at 279 and PM 10 at 250, both in 'poor' category in Lodhi Road area, according to AQI data.
Penalties imposed
In a statement, the Noida Authority said penalties amounting to Rs 5.05 lakh were imposed on those found keeping construction material uncovered, in violation of the law.
The district administration and the local authorities in the twin cities carried out water sprinkling on the roads to check dust, as the region grappled with a "public health emergency".
The Supreme Court of India (SC)-mandated Environment Pollution (Prevention and Control) Authority had last week announced a "public health emergency" in Delhi-NCR.
It had also banned construction activities and bursting of firecrackers, and ordered shutting down of the dirty fuel-based industries, among others, till November 5.
On November 4 night, the EPCA extended the ban till November 8.
Also read: Over 99,000 challans issued, Rs 14-cr penalty levied in Delhi for anti-air pollution laws violation
Wind flushing out pollutants
The India Meteorological Department (IMD) said winds gusting up to 25 kmph were flushing out the pollutants faster.
"There are good chances of rains in northwest India on Wednesday night and Thursday due to a western disturbance. The precipitation will cover Delhi-National Capital Region (NCR), Jammu and Kashmir (J&K), Himachal Pradesh, Uttarakhand, Punjab, Haryana, Rajasthan and western Uttar Pradesh," Kuldeep Srivastava, the head of the IMD's regional weather forecasting centre, said.
(With inputs from PTI)
Special Thursday Expiry on 10th 7th Nov
Early Closure to The Great Diwali Discount. Last Chance to get Moneycontrol PRO for a year @ Rs. 289/- only
Coupon code: DIWALI


Why do corellations matter?
There are several reasons why correlations are important, here a few benefits of tracking them in the markets –
Correlation doesn’t imply causation
A popular saying among the statistics crowd is “correlation does not imply causation.” It comes up often, and it’s important to understand its meaning.
Essentially, correlations can provide valuable insights, but you’re bound to come across situations that might imply a correlation where a relationship does not exist.
As an example, data has shown a sharp rise in Netflix subscribers due to the lockdown that followed the Coronavirus escalation. The idea is that people are forced to stay at home and therefore are more likely to watch tv.
The same scenario has resulted in a rise in electricity bills. People are using more electricity at home compared to when they were at work all day.
If you were blindly comparing the rise in Netflix subscribers versus the rise in electricity usage during the month of lockdown, you might reasonably conclude that the two have a relationship.
However, having some perspective on the manner, it is clear that the two are not related and that it is not likely that fluctuations in one will impact the other moving forward. Rather, the lockdown, an external variable, is the causation for both of these trends.
What is a correlation coefficient?
We’ve discussed that fluctuations in the stock prices of Apple and Microsoft tend to have a relationship. You might then notice other tech companies also correlate well with the two.
But not all relationships are equal, and the correlation coefficient can help assess the strength of a correlation.
There are a few different ways of calculating a correlation coefficient, but the most popular methods result in a number between -1 and +1.
The closer the number is to +1, the stronger the relationship. If the figure is close to -1, it indicates that there is a strong inverse relationship.
In the finance world, an inverse relationship is where one asset rises while the other drops. Stocks and gold prices have a long-standing inverse relationship.
The closer the correlation coefficient is to zero, the more likely it is that the two variables being compared don’t have any relationship to each other.
Breaking down the math to calculate the correlation coefficient
The above formula is what’s used to calculate a correlation coefficient using the Pearson method. We will break down this formula.
There are libraries available that can do this automatically, but the following example will show how we can make the calculation manually.
We will start by creating a dataset. We can use the Numpy library to create some random data for us. Here is the code:
import numpy as np
import pandas as pd
df = pd.DataFrame(np.random.randint(0,10,size=(5, 2)), columns=list('xy'))
The image below shows what my DataFrame looks like. If you’re following along, the data will look different for you as Numpy is filling in random numbers. But the format should look the same.
Now that we have a dataset let’s move on to the formula. We will start by separating the first part of the formula.
We can break this down further.
For the formula above, we need to take each value of x and subtract it by the mean of x.
We can use the mean()
function in Pandas to create the mean for us. Like this:
df.x.mean()
df['step1'] = df.x - df.x.mean()
This is what our DataFrame looks like at this point.
Now that we have the calculations needed for the first step. Let’s keep going.
The second step involves doing the same thing for the y column.
df['step2'] = df.y - df.y.mean()
df['step3'] = df.step1 * df.step2
step4 = df.step3.sum()
Great, we have summed up the values and have stored it in a variable called step4. We will come back to this later. For now, we can start on the second part of the formula.
We have already found in the following in step1, so we can use that data. We will store this data in a new column labeled step5.
df['step5'] = df.step1 ** 2
df['step6'] = df.step2 ** 2
step7 = df.step5.sum() * df.step6.sum()
Let’s keep going, almost there!
The last portion of this part is to simply take the square root of the figure from our previous step. We can use the Numpy library to calculate the square root.
step8 = np.sqrt(step7)
step4/step8
corr()
function which is built into Pandas to calculate the coefficient.
df.x.corr(df.y)
How to calculate the correlation coefficients for a watchlist?
Calculating a correlation coefficient in Python is quite simple as there are several libraries that can do the heavy lifting for you. In this guide, we will be using python and the libraries from this GitHub repository.
Step one – Gathering and cleaning up historical data
We are using the Alpha Vantage library in this step.
import pandas as pd
from alpha_vantage.timeseries import TimeSeries
#grab tickers from csv file
watchlist_df = pd.read_csv('watchlist.csv', header=None)
watchlist = watchlist_df.iloc[0].tolist()
#instantiate TimeSeries class from alpha_vantage library
app = TimeSeries(output_format='pandas')
#itter through watchlist and retrieve daily price data
stocks_df = pd.DataFrame()
for ticker in watchlist:
alphav_df = app.get_daily_adjusted(ticker)
alphav_df = alphav_df[0]
alphav_df.columns = [i.split(' ')[1] for i in alphav_df.columns]
stocks_df[ticker] = alphav_df['adjusted'].pct_change()
Next, we iterate through our Python list of stock tickers and call the Alpha Vantage API for each ticker's data. But before doing that, we’ll create an empty Pandas dataframe that we can append data to.
What we’ve done is taken the ‘adjusted’ column, which is the adjusted daily close, and appended it to our stocks_df
dataframe. Note the additional pct_change()
function. This will normalize our data by converting the price data to a percentage return. This is what our dataframe looks like at this point.
Now we have a nicely formated time-series dataframe in less than 20 lines of code!
Step Two – Calculating the correlation coefficient
Now that we have our data, we can easily check the correlation coefficient between any stocks within our dataframe. Here is how we check the correlation between AAPL and MSFT.
print(stocks_df.AAPL.corr(stocks_df.MSFT))
print(stocks_df.AAPL)
print(stocks_df['AAPL'])
When dealing with a single column we are no longer working with a dataframe. Rather, we are working with a Pandas series. The basic syntax for calculating the correlation between different series is as follows:
Series.corr(other_series)
print(stocks_df.AAPL.corr(stocks_df.NFLX)
print(stocks_df.AAPL.corr(stocks_df.NFLX, method='spearman')
print(stocks_df.AAPL.corr(stocks_df.NFLX, method='kendall'))
Correlation of returns versus prices
We calculated the percentage return between each price point in our dataset and ran our correlation function on that rather than calculating it on the raw data itself. We do this to get a more accurate correlation coefficient.
The reasoning behind it is that it standardizes the data which is beneficial no matter which calculation method you use.
If you’re using the Spearman or Kendall method, which utilizes a ranking system, returns data will remove some of the extremes from your dataset, which can otherwise influence the entire ranking system.
The Pearson method doesn’t use a ranking system but heavily relies on the mean of your data set. Using returns data narrows the range of your dataset, which in turn puts more emphasis on deviations from the mean, resulting in higher accuracy.
values_x = [10, 11, 13, 16, 17, 4, 5, 6]
values_y = [10, 11, 13, 16, 17, 18, 19, 20]
Take a look at the above two datasets as an example.
Notice how they both have almost the same data? The difference is that values_x
dropped off sharply in the third last value from 17 to 4. However, it continued to rise by one in the last two values, the same way values_y
did.
This type of behavior can often happen in the markets. For example, a stock might have reported earnings, which caused a sharp but temporary drop in its price. But aside from the momentary drop, the overall fluctuations in the stock price have not changed much at all compared to other correlated stocks.
The ranking systems used in correlation calculations, however, will view the momentary decline differently. It will assign an arbitrarily low value to the last three values in values_x since they are the lowest in the dataset. At the same time, it will rank the last three values in values_y as the largest.
This creates a major discrepancy that will ultimately cause our correlation coefficient to be much lower than it should be.
In a non-ranking system such as the Pearson method, the last three values will drag down the mean value for the entire dataset.
If we take the returns instead, we are comparing how much one value fluctuated relative to the value before it.
In that case, there would have been a major decline when the values in values_x dropped from 17 to 4, but the divergence in correlation stops there as both the data sets rose in value in the last two places.
How to create a time-series dataset in Pandas?
A time-series is simply a dataset that follows regular, timed intervals. The previous example, where we had data for five stocks, is a good example of a time-series dataset.
Further, Pandas intuitively lined up price data when we merged all five stocks into one dataframe based on the date column, which all of our data had in common. This column then acts as an index for our data.
We can just as easily create a dataframe with a time-series index from scratch. The next example will show how to do that with data we have saved in a CSV file.
import pandas as pd
TSLA_df = pd.read_csv('TSLA.CSV')
print(TSLA_df)
Here we’ve imported price data for TSLA based on 15-minute intervals. In other words, 15-minute bars for TSLA.
Next we will check the data type for our newly-created index.
print(TSLA_df.index[:4])
TSLA_df.index = pd.to_datetime(TSLA_df.index)
When importing a CSV file, we can pass through parse_dates=True
into the pd.read_csv()
function to automatically parse the dates as a DateTime object.
TSLA_df = pd.read_csv('TSLA.CSV', index_col=0, parse_dates=True)
What is a correlation matrix?
The previous examples have shown how to calculate a correlation coefficient for two stocks. But if we have a dataframe full of stocks? Surely there has to be an easier way to get the coefficient for everything in the dataframe?
That’s where the correlation matrix comes in. It is a table or a matrix that will display the correlation coefficient for everything in the dataframe. To create this, simply type your dataframe name, followed by .corr()
. Or in our example, stocks_df.corr()
.
Here we have our correlation matrix. The first column in the first row is the correlation between AAPL and AAPL, which obviously will have the highest correlation when comparing data with itself.
Looking at this matrix, we can easily see that the correlation between Apple (AAPL) and Exxon Mobile (XOM) is the strongest while the correlation between Netflix (NFLX) and AAPL is the weakest.
Further, there is fairly notable negative correlation between AAPL and GLD which is an ETF that tracks gold prices.
We can also create a heatmap. This will allow us to visualize the correlation between the different stocks.
To do this, we will use the Seaborn library, which is a great tool for plotting and charting. It is built on top of the popular matplotlib library and does all the heavy lifting involved in creating a plot.
import seaborn as sns
import matplotlib.pyplot as plt
ax = sns.heatmap(stocks_df.corr())
plt.show()
Here we’ve imported the library and called the heatmap function to display the heatmap. At this stage, we’ve only passed through the correlation matrix dataframe.
We can now assess the strength in correlation based on color, and there is a useful guide on the right-hand side. But since we are used to seeing things in red and green in the finance world, let’s customize it a bit.
ax = sns.heatmap(stocks_df.corr(), cmap='RdYlGn', linewidths=.1)
plt.show()
The above code snippet sets the Red, Yellow, Green values to cmap which defines our colors. We have also passed through a line width of .1 to create a bit of space between the boxes just to improve the visual aesthetics.
There you have it. It is much easier to see that AAPL and NFLX have the weakest correlation. We can also easily see that GLD has a negative correlation with all of the other assets.
What is a correlation matrix?
You can use a correlation matrix to filter out stocks for various reasons quickly. Maybe you’re already in a trade, and you don’t want to trade other instruments with a strong correlation. Another reason might be to check other strongly correlated instruments to ensure your analysis produces a similar signal.
As an example, say you’ve already taken a long position in AAPL. Now your automated trading algo is sending you a signal to buy MSFT. This is very likely to happen since we’ve already determined that the two have a strong correlation with each other.
In this case, you might want to skip that trade because it is only increasing your risk exposure. In other words, when the correlation is that high, it’s not all that different from just doubling up your exposure in AAPL, and that is something to avoid.
In the same way, we can also confirm if our signal is strong enough to act on. For example, let’s say we are trading a breakout strategy, and we buy a stock when it exceeds more than one standard deviation from its average.
We get a signal to buy NFLX. We can see what stock is most closely correlated with NFLX to determine if it has also exceeded one standard deviation from its average. We can use the idxmax()
function from Pandas to figure out the strongest correlation.
nflx_corr_df = stocks_df.corr().NFLX
print(nflx_corr_df.idxmax())
print(nflx_corr_df[ nflx_corr_df < 1 ].idxmax())
The above code returns ‘MSFT’. Now we can check where Microsoft is trading relative to its standard deviation. If it is trading below it, we can even wait until it exceeds it to give us a stronger signal on our original NFLX buy signal.
In the same manner, we can easily check for inverse correlations with NFLX as follows
print(nflx_corr_df.idxmin())


|
cds
1.5.0
|
Type traits for SkipListSet class. More...
#include <cds/intrusive/skip_list_base.h>
Public Types | |
| typedef base_hook | hook |
| Hook used. More... | |
| typedef opt::none | compare |
| Key comparison functor. More... | |
| typedef opt::none | less |
| specifies binary predicate used for key compare. More... | |
| typedef opt::v::empty_disposer | disposer |
| Disposer. More... | |
| typedef atomicity::empty_item_counter | item_counter |
| Item counter. More... | |
| typedef opt::v::relaxed_ordering | memory_model |
| C++ memory ordering model. More... | |
| typedef turbo_pascal | random_level_generator |
| Random level generator. More... | |
| typedef CDS_DEFAULT_ALLOCATOR | allocator |
| Allocator. More... | |
| typedef cds::backoff::Default | back_off |
| back-off strategy used More... | |
| typedef empty_stat | stat |
| Internal statistics. | |
| typedef opt::v::rcu_throw_deadlock | rcu_check_deadlock |
| RCU deadlock checking policy (only for RCU-based SkipListSet) More... | |
Type traits for SkipListSet class.
Allocator.
Although the skip-list is an intrusive container, an allocator should be provided to maintain variable randomly-calculated height of the node since the node can contain up to 32 next pointers. The allocator specified is used to allocate an array of next pointers for nodes which height is more than 1.
back-off strategy used
If the option is not specified, the cds::backoff::Default is used.
Key comparison functor.
No default functor is provided. If the option is not specified, the less
is used.
| typedef opt::v::empty_disposer cds::intrusive::skip_list::type_traits::disposer |
Disposer.
The functor used for dispose removed items. Default is opt::v::empty_disposer.
Hook used.
Possible values are: skip_list::base_hook, skip_list::member_hook, skip_list::traits_hook.
Item counter.
The type for item counting feature. Default is no item counter (atomicity::empty_item_counter)
specifies binary predicate used for key compare.
Default is std::less<T>
.
C++ memory ordering model.
List of available memory ordering see opt::memory_model
Random level generator.
The random level generator is an important part of skip-list algorithm. The node height in the skip-list have a probabilistic distribution where half of the nodes that have level i
pointers also have level i+1
pointers (i = 0..30). So, the height of a node is in range [0..31].
See skip_list::random_level_generator option setter.
RCU deadlock checking policy (only for RCU-based SkipListSet)
List of available options see opt::rcu_check_deadlock


Implement all the components required to triangulate a simple polygon
Divide the polygon into y-monotone pieces
Triangulate all the monotone pieces to get a triangulated polygon
Test Cases
Case #1
8
5 0
10 0
15 5
15 10
10 15
5 15
0 10
0 5
Before Triangulation
After Triangulation
Case #2
6
0 0
5 5
10 0
10 20
5 10
0 20
Before Triangulation
After Triangulation
Closing Comments
Make_Monotone
This routine of our implementation has remained unusable despite multiple efforts to debug. The essential error was tracked down to insertion of helper/deletion of edge operations for certain vertices.
We were unable to determine the exact conditions under which the error definitely shows, but it seems that polygons with a lesser number of split/merge vertices are getting monotonized properly.
Triangulate_Monotone
This routine takes in a list of polygons which are assumed to be monotone and proceeds to triangulate them. Degeneracies:
Floating point error can cause some unexpected results. Complexity:
The triangulation of a monotone polygon is achieved in linear time. The algorithm used is to make a priority queue based on y-coordinates and then adding diagonals while traversing down the chains. Robustness:
Since this method depends on the monotone nature of passed input, the first step is to check whether the polygon is really monotone. Only after verification does the rest of the algorithm proceed.


Yung Baby Tate Previews "CAMP" Album With Title Track Debut
YBT delivers.
by Milca P.CAMP
Yung Baby Tate
Editor Rating: HOTTTTT
User Rating: make it stop - 17% (6)
Yung Baby Tate's time is fast approaching. After gaining considerable traction with her Megatron Challenge entry, bookended by an outstanding GIRLS project and a hefty set of strategic visuals and placements, the Atlanta-bred multihyphenate has carved out an interesting subset in budding pop stardom, effortlessly vacillating between the roles of singer, rapper, producer, and Platinum-recording songwriter.
Now, she is capitalizing off of the momentum with her forthcoming CAMP album, set to be premiered with a companion show during Red Bull Music Festival's appearance in her hometown this month. But just ahead of CAMP's arrival, YBT has touched down with the album's title track, setting the outline for the genre-hopping know-how of Yung Baby Tate.
Get into "CAMP" below.


This project was generated with Angular CLI version 12.2.9.
If you came here looking for older version of the same, I have archived it. Going forward I will be creating newer
angular projects when a newer angular versions are released. Main Branch will always be pointing to the latest angular
version. While older versions will be branched out from main branch with Angular version as name. As said there will
not be any upgrade to the existing project other than bugfixes. If anyone would like to collaborate, please keep in
mind that for next version of angular we will be starting from scratch again.
There are four (for each environment) dev server commands are added to package.json
for ease of use. If you would like
to dig more read NodeJs Custom Run Scripts.
PLEASE NOTE: FOLLOWING PROXY SETUP IS NOT FOR PRODUCTION USE
Run npm run start:local
for a dev server with proxy to local API Service. Or Run npm run start:dev
for a dev server
with dev hosted API Service.
If you are running npm run start:local
, This has an assumption that you have already mapped local api path
in proxy-config/local.config.json
. You may need to restart angular server whenever you are making a change to proxy
configuration.
While customizing proxy-config
follow the instructions
from proxying to backend server from Angular's
Documentation.
{
"/api": {
"target": "https://domain.tld/v2/api/",
"secure": false,
"pathRewrite": {
"^/api": ""
},
"changeOrigin": true
}
}
First you need to register your angular application with your OIDC Provider and get your client Id
and paste it
inside environment.ts
before you can run this application. While registering your angular application keep it is
recommended
to use OAuth 2/OIDC using code flow + PKCE.
Each of the below routes are Master Pages. First empty route accepts next level child by making use of router-outlet
inapp.component.html
. Since it was immediate child of empty route it further acts as Master Page. Each of it again
having router-outlet
inside of it. Which helps resolves child routes provided. An Auth Guard is Provided along with
Private Route this eliminates repeated use of Auth Guard in child routes.
const routes: Routes = [
{
path: '',
children: [
{
path: '',
component: PublicHomeComponent,
children: PUBLIC_ROUTES,
},
{
path: '',
component: PrivateHomeComponent,
canActivate: [AuthGuard],
children: PRIVATE_ROUTES,
},
],
},
{path: AUTH_ROUTES.login, component: LoginComponent},
{path: AUTH_ROUTES.logout, component: LogoutComponent}
];
Run rpm run build
to build the project. The build artifacts will be stored in the dist/
directory. Use the --prod
flag for a production build. Check angular.json
for further customisation of the build configurations.
Following set of run commands build
, build:dev
, build:int
and build:prod
were added to package.json
with CI/CD
pipeline in mind. Use it for build without optimisation
, build for dev server
, build for int server
and build
for Build for Production
.
You can take advantage of Nodejs custom script
and Angular Build Configurations if you
wanted to create additional environments.
All files from app/assets
are declared as assets by config provided in angular.json
. If you have any other assets
that need to be included while building then you have to specify that in angular.json
.
Eg: silent-refresh.html - An asset file which needed by Auth is declared an asset in angular.json therefore it will be
copied to output directory while building.
Angular application can be deployed to domain|sub-domain|sub-folder. For this demo, there is an assumption that API
service is available via base-url/api
.
example: if https://domain.tld
is Angular App
and https://domain.tld/api/v1-21/products/9634/reviews?sort=date&order=desc
can be an API URI.
If you have any other special use
case visit this stackoverflow thread
to learn & customise for your need.
Run ng test
to execute the unit tests via Karma.
NOTE:
NOT WRITTEN YET - BE Careful with That.
Earlier Protractor was first class e2e testing tool in Angular. From Angular 12 onwards e2e is removed from project
source. One among the most used tool is Cypress. Please feel free to use any e2e testing tool
that suites your team.
To get more help please follow Official Documentation from Angular.


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''I'm the hard hitting puncher in boxing history''-Wilder
Deontay Wilder has retained his World Boxing Council heavyweight title.
The 34 year old knocked out Luiz Ortiz in the 7th round on Saturday night in Las Vegas, United States.
“At this point in time, I think I earned my due respect and my credit. I am the hard hitting puncher in boxing history, period. We are still undefeated, we still have our belt, and now we move on to the next phase and chapter of my life. I’m looking to beat undisputed heavyweight champion in the world (Joshua vs Ruiz, Dec 7th in Saudi Arabia, ed.). One champion, one face, one name. I’m going to pursue that and I’m getting closer and closer each and every time”, Wilder said.
Despite been unbeaten in 43 fights, Wilder says he’s still learning.
“I’m still learning. I started boxing late, so you know I’m still learning. I love to learn, I love to continue to learn because if I wasn’t still learning, this would be boring to me already. The knockouts that I have, this is like, it’s boring now”, the WBC heavyweight champion added.
Ortiz won most of the early rounds. The 40-year-old was coming off three straight victories and was trying to become the first Cuban to win the world heavyweight title.
AFP