Metadata-Version: 1.0
Name: PyOPUS
Version: 0.9
Summary: A simulation-based design optimization library
Home-page: http://fides.fe.uni-lj.si/pyopus/
Author: Árpád Bűrmen
Author-email: arpadb@fides.fe.uni-lj.si
License: GPL V3
Description: 
        PyOPUS is a library for simulation-based optimization of arbitrary systems. 
        It was developed with circuit optimization in mind. The library is the basis 
        for the PyOPUS GUI that makes it possible to setup design automation tasks with 
        ease. In the GUI you can also view the the results and plot the waveforms 
        generated by the simulator. 
        
        PyOPUS provides several optimization algorithms (Coordinate Search, 
        Hooke-Jeeves, Nelder-Mead Simplex, Successive Approximation Simplex, PSADE 
        (global), MADS, ...). Optimization algorithms can be fitted with plugins that 
        are triggered at every function evaluation and have full access to the 
        internals of the optimization algorithm. 
        
        PyOPUS has a large library of optimization test functions that can be used for 
        optimization algorithm development. The functions include benchmark sets by 
        Moré-Garbow-Hillstrom, Lukšan-Vlček (nonsmooth problems), Karmitsa (nonsmooth 
        problems), Moré-Wild, global optimization problems by Yao, Hedar, and Yang, 
        problems used in the developement of MADS algorithms, and an interface to 
        thousands of problems in the CUTEr/CUTEst collection. Benchmark results can 
        be converted to data profiles that visualize the relative performance of 
        optimization algorithms. 
        
        The ``pyopus.simulator`` module currently supports SPICE OPUS, HSPICE, and 
        SPECTRE (supports OP, DC, AC, TRAN, and NOISE analyses, as well as, collecting 
        device properties like Vdsat). The interface is simple can be easily extended to 
        support any simulator.
        
        PyOPUS provides an extensible library of postprocessing functions which
        enable you to easily extract performance measures like gain, bandwidth, rise
        time, slew-rate, etc. from simulation results.
        The collected performance measures can be further post-processed to obtain
        a user-defined cost function which can be used for guiding the optimization
        algorithms toward better circuits.
        
        At a higher elvel of abstraction PyOPUS provides sensitivity analysis, 
        parameter screening, worst case performance analysis, worst case distance 
        analysis (deterministic approximation of parametric yield), and Monte Carlo 
        analysis (statistical approximation of parametric yield). Designs can be 
        sized efficiently across a large number of corners. PyOPUS fully automates 
        the procedure for finding a circuit that exhibits the desired parametric yield. 
        Most of these procedures can take advantage of parallel computing which 
        significantly speeds up the process. 
        
        Parallel computing is supported through the use of the MPI library. A 
        cluster of computers is represented by a VirtualMachine object which
        provides a simple interface to the underlying MPI library. Parallel programs 
        can be written with the help of a simple cooperative multitasking OS. This 
        OS can outsource function evaluations to computing nodes, but it can also 
        perform all evaluations on a single processor. 
        Writing parallel programs follows the UNIX philosophy. A function can be run 
        remotely with the ``Spawn`` OS call. One or more remote functions can be 
        waited on with the ``Join`` OS call. The OS is capable of running a parallel 
        program on a single computing node using cooperative multitasking or on a set 
        of multiple computing nodes using a VirtualMachine object. Parallelism can be 
        introduced on multiple levels of the program (i.e. parallel performance 
        evaluation across multiple corners, parallel optimization algorithms, solving 
        multiple worst case performance problems in parallel, ...). 
        
        PyOPUS provides a plotting mechanism based on MatPlotLib and wxPython with 
        an interface and capabilities similar to those available in MATLAB.
        The plots are handled by a separate thread so you can write your programs
        just like in MATLAB. Professional quality plots can be 
        easily exported to a large number of raster and vector formats for inclusion 
        in your documents. The plotting capability is used in the ``pyopus.visual`` module 
        that enables the programmer to visualize the simulation results after an 
        optimization run or even during an optimization run. 
        
Platform: Linux
Platform: Windows
