Acknowledge4.2软件安装包

Copyright 2006-2010 BIOPAC Systems, Inc. All Rights Reserved This directory contains a Python language binding for the AcqKnowledge network data transfer feature and several example programs illustrating how to use this language binding to process data in Python applications as it is being acquired by AcqKnowledge. The language binding and sample applications are written for the Python 2.x language and tested on Python 2.3 and higher. Programmers using Python 3 will need to convert these files to make them compatible with the incompatible language changes included in that langauge version. The following example programs are available in the source/ directory: simplesample.py Loads a graph template into AcqKnowledge and starts the data acquisition. Does not process any data within Python, rather showing basics of locating an AcqKnoweldge server and issuing rudimentary requests. singleconnection.py MP150 required. Loads a graph template, prints the contents of data received from AcqKnowledge to the console, and writes one channel of data into a raw data file on disk. Illustrates basic communication and data processing using the 'single' transfer mode. Also illustrates usage of variable sampling rates. multiconnect.py MP150 required. Loads a graph template and writes all channels of data to raw binary files on disk. On Mac OS X, prints the received data to the console. Illustrates basic communication and data processing using the 'multiple' transfer mode, that is, one network connection and one thread for each channel. autothreshold.py MP150 required. Connect UIM and 1/8" CBL100 between Analog Out 0 and analog input 1. Loads a graph template and begins acquiring data while monitoring data in Python for amplitude jumps > 1 Volt, at which point the data acquisition is halted. The MP150 analog output is used to change voltages on the input. Illustrates analog output control and may be used as a rudimentary measure of overall latency (which will vary from system to system). plotchan.py Displays a graphical user interface that plots data received for a sinusoidal calculation channel computed during data acquisition. May be used to visually confirm accuracy of data transfer from AcqKnowledge. To run one of these examples, first make sure AcqKnowledge is running, has the networking feature turned on, and is configured to respond to auto-discovery requests in the Networking preference panel. On Mac OS X open a Terminal, cd into the source/ directory, and issue python example.py replacing "example.py" with the naem of the example you wish to run. On Windows you will need to install a Python environment such as ActivePython 2: http://www.activestate.com/activepython/downloads/ Run Python 2.6.4 shell via WIndows menu: Start-->Programs-->Python 2.6-->IDLE (Python GUI) Open one of the NDT samples of p.3.a via Python Shell menu: "File-->Open" Opening sample application You will get another Python shell window with source code of the sample. Execute the sample via menu: "Run-->Run Module (F5)" The language binding is included in source/biopacndt.py and generated documentation is available in the docs/ subdirectory. The general usage pattern for applications is as follows: 1) Create an AcqNdtServer object either by manually entering the address and port, by locating servers with FindAcqNdtServers(), or just connecting to the first available AcqKnoweldge server with AcqNdtQuickConnect(). 2) Load the hardware acquisition settings using the AcqNdtServer.LoadTemplate() function and an AcqKnowledge graph template file. 3) Construct an AcqNdtDataServer object and register the callbacks used to process data. 4) Begin listening for data connections by using AcqNdtDataServer.Start() 5) Begin data acquisition with AcqNdtServer.toggleAcquisition() 6) Wait for the data acquisition to complete using AcqNdtServer.WaitForAcquisitionEnd(). During this time incoming data will continue to be processed on threads. 7) Release resources used and halt data processing with AcqNdtDataServer.Stop(). See the generated documentation and the source code comments within the example Python sample code for more details.