Showing 4 open source projects for "dat file extractor"

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  • 1
    asammdf

    asammdf

    Fast Python reader and editor for ASAM MDF / MF4 (Measurement Format)

    *asammdf* is a fast Python parser and editor for ASAM (Associtation for Standardisation of Automation and Measuring Systems) MDF / MF4 (Measurement Data Format) files. It supports MDF versions 2 (.dat), 3 (.mdf) and 4 (.mf4). *asammdf* works on Python 2.7, and Python >= 3.4
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    Downloads: 59 This Week
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  • 2
    Yet Another Audio Feature Extractor is a toolbox for audio analysis. Easy to use and efficient at extracting a large number of audio features simultaneously. WAV and MP3 files supported, or embedding in C++, Python or Matlab applications.
    Downloads: 0 This Week
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  • 3

    SpiKeDeteKt

    An automatic spike detection program to be used with new KlustaKwik

    This is an automatic spike detection program which takes account of probe geometry and produces a .mask file to be used with the new masked version of KlustaKwik. We recommend you use Python 2.6 or 2.7, e.g. a free academic version can be obtained from Entthought Python. The input files for SpiKeDeteKt are: .dat (raw data file) .probe (probe file, described below - user constructed) parameters.py (optional - otherwise it uses defaultparameters.py) SpiKeDeteKt outputs the following files: .fet.n (feature file) .mask.n (needed for using the new (masked) KlustaKwik) .clu.n (a trivial clue file where everything is put into a single cluster) .fmask.n (trial - float masks instead of binary, we are using this for testing masked KlustaKwik) .spk.n (spike file) .upsk.n (unfiltered spike waveform) .res.n (list of spike times) .xml (an xml file with all the parameters that can subsequently be used by neuroscope or klusters) .fil (highpass filtered data) .h5 (
    Downloads: 0 This Week
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  • 4

    WebSynonymExtractor

    a synonym extractor based on web-corpora and a multilingual translator

    This project is an approach for synonym extraction and extending WordNet by the so found synonyms. The python application is realised as a kind of pipe that starts with a web-corpus-reader which is followed by several workers (tokenizers, lemmatizers, ...) and finally completed by a result writer. In contrast to the state of the art approaches, this implementation is based on single words found in the web used as a corpus and translated to other languages. If translations of different...
    Downloads: 0 This Week
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