System Toolkit 1.5.1
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STEPS is a simulation toolkit for powerflow and dynamic simulation of large-scale power systems. It provides detailed models of bus, line, transformer, HVDC, generator, wind turbine generator, load, and fixed shunt. For more information about STEPS, see ( ).
For this reason, when preparing a container Sarus will look forCUDA_VISIBLE_DEVICES in the host environment, and modify accordingly bothNVIDIA_VISIBLE_DEVICES and CUDA_VISIBLE_DEVICES in the container.These modifications ensure that the host resource allocations are respected,while guaranteeing the correct operation of CUDA applications inside thecontainer, even in the case of partial or shuffled devices selection onmulti-GPU systems.
The Dojo Foundation was a non-profit organization created with the goal to promote the adoption of the toolkit. In 2016, the foundation merged with jQuery Foundation to become JS Foundation.
In addition to providing support functions for reading and writing cookies, Dojo formerly supported a local, client-side storage abstraction named Dojo Storage. Dojo Storage allows web applications to store data on the client-side, persistently and securely and with a user's permission. It works across existing web browsers, including Internet Explorer, Firefox, and Safari. When included in a web page, Dojo Storage determines the best method for persistently storing information. Firefox 2 uses native browser persistence; on other browsers, it uses a hidden Flash applet. With Flash 6+ being installed on about 95% of computers connected to the web, this makes the storage mechanism accessible for much of the web's installed base. For a web application loaded from the file system, i.e., from a file:// URL, Dojo Storage will transparently use XPCOM on Firefox and ActiveX on Internet Explorer to persist information. The programmer using Dojo Storage is abstracted from the storage mechanism used and is presented with a simple hash table abstraction, with methods such as put() and get().Dojo Storage is not supported in versions later than the 1.3 release.
SitePen, a Dojo consulting company, has made an Adobe AIR application called \"Dojo Toolbox\" using Dojo. It includes an API viewer and a GUI to Dojo's build system. Normally, the build system is run from within Rhino, but in this AIR application the build system can be run from AIR, without the use of Java.
Early users faced a difficult transition to the 1.0 release after the toolkit was totally rewritten. The move to AMD in recent versions has been similarly problematic. Dojo has taken great pains to maintain backward compatibility despite its rapid evolution, with a large portion of the current API deprecated but still maintained, but users have often found that upgrades did not go as smoothly as hoped.
In 2006, both IBM and Sun Microsystems announced official support for Dojo, including code contributions. A Gartner report in 2009 noted that IBM support Dojo across 30 of their products. Zend Technologies, the company behind the PHP core, announced a partnership with Dojo in 2008, incorporating the toolkit into the Zend Framework.
Yes, the pip wheels and conda binaries ship with their own CUDA runtime (as well as cuDNN. NCCL etc.) so you would only need to install the NVIDIA driver. If you want to build PyTorch from source or a custom CUDA extension, the local CUDA toolkit will be used.
lsim(sys,u,t) plots the simulated time response of the dynamic system model sys to the input history (t,u). The vector t specifies the time samples for the simulation. For single-input systems, the input signal u is a vector of the same length as t. For multi-input systems, u is an array with as many rows as there are time samples (length(t)) and as many columns as there are inputs to sys.
lsim(sys1,sys2,...,sysN,u,t,___) simulates the responses of several dynamic system models to the same input history and plots these responses on a single figure. All systems must have the same number of inputs and outputs. You can also use the x0 and method input arguments when computing the responses of multiple models.
y = lsim(sys,u,t) returns the system response y, sampled at the same times t as the input. For single-output systems, y is a vector of the same length as t. For multi-output systems, y is an array having as many rows as there are time samples (length(t)) and as many columns as there are outputs in sys. This syntax does not generate a plot.
To compute the response of this system to an arbitrary input signal, provide lsim with a vector of the times t at which you want to compute the response and a vector u containing the corresponding signal values. For instance, plot the system response to a ramping step signal that starts at 0 at time t = 0, ramps from 0 at t = 1 to 1 at t = 2, and then holds steady at 1. Define t and compute the values of u.
gensig returns the vector t of time steps and the vector u containing the corresponding values of the input signal. (If you do not specify a sample time for t, then gensig generates 64 samples per period.) Use these with lsim and plot the system response.
To simulate the response of a discrete-time system to a periodic input signal, use the same sample time with gensig to generate the input. For instance, simulate the system response to a sine wave with period of 1 s and a duration of 4 s.
Similarly, the output y(t) computed by lsim is a matrix whose columns represent the signal at each system output. When you use lsim to plot the simulated response, lsim provides separate axes for each output, representing the system response in each output channel to the input u(t) applied at all inputs.
Suppose that you want to allow the system to evolve from a known set of initial states with no input for 2 s, and then apply a unit step change. Specify the vector x0 of initial state values, and create the input vector.
The first half of the plot shows the free evolution of the system from the initial state values [-0.2 0.3]. At t = 2 there is a step change to the input, and the plot shows the system response to this new signal beginning from the state values at that time.
For a MIMO system, the response data is returned in an array of dimensions N-by-Ny-by-Nu, where Ny and Nu are the number of outputs and inputs of the dynamic system. For instance, consider the following state-space model, representing a three-state system with two inputs and three outputs.
lsim uses the same line style for the responses of all entries in the array. One way to distinguish among entries is to use the SamplingGrid property of dynamic system models to associate each entry in the array with the corresponding w0 value.
For multi-input systems, u is an array with as many rows as there are time samples (length(t)) and as many columns as there are inputs to sys. In other words, each row u(i,:) represents the values applied at the inputs of sys at time t(i). Each column u(:,j) is the signal applied to the jth input of sys.
For continuous-time sys, the lsim command uses the time step dT to discretize the model. If dT is too large relative to the system dynamics (undersampling), lsim issues a warning recommending a faster sampling time. For further discussion of the impact of sampling time on simulation, see Effect of Sample Time on Simulation (Control System Toolbox).
For multi-output systems, y is an array with as many rows as there are time samples (length(t)) and as many columns as there are outputs in sys. Thus, the jth column of y, or y(:,j), contains the response at the jth output to u applied at all inputs.
For continuous-time systems, lsim first discretizes the system using c2d, and then propagates the resulting discrete-time state-space equations. Unless you specify otherwise with the method input argument, lsim uses the first-order-hold discretization method when the input signal is smooth, and zero-order hold when the input signal is discontinuous, such as for pulses or square waves. The sample time for discretization is the spacing dT between the time samples you supply in t.
This software package contains the Base Operating System Network Facilities to provide network support for the operating system. It includes Transmission Control Protocol/Internet Protocol (TCP/IP), Point-to-Point Protocol (PPP), Network File System (NFS), Network Information Services (NIS), UNIX-to-UNIX Copy (UUCP), Async Point to Point (PPP), Asynchronous Terminal Emulator (ATE), and Cache File System (CacheFS).
This fileset contains all files and function required to use PPP on AIX. PPP provides AIX with the capability to communicate with other systems over serial lines conforming to IETF RFC's 1661,1662, 1332, and 1334.
This fileset contains the CacheFS file system, which is a general-purpose file system caching mechanism that improves NFS server performance and scalability by reducing server and network load. Designed as a layered file system, CacheFS provides the ability to cache one file system on another. In an NFS environment, CacheFS increases the client-per-server ratio, reduces server and network loads, and improves performance for clients on slow links, such as Point-to-Point Protocol (PPP). CacheFS is in the bos.net.nfs.cachefs fileset.
A large number of third party packages extend and build on Matplotlibfunctionality, including several higher-level plotting interfaces(seaborn, HoloViews, ggplot, ...), and a projection and mappingtoolkit (Cartopy). 153554b96e