Metadata-Version: 1.0
Name: pysdif
Version: 0.1.4
Summary: SDIF for python
Home-page: http://github.com/gesellkammer/pysdif
Author: UNKNOWN
Author-email: UNKNOWN
License: UNKNOWN
Description: 
        Needs Cython >= 0.15
        
        It allows to read and write any kind of SDIF file, to define new
        kinds of frames and matrices and to read and write metadata.
        
        The matrices read from a sdif file are exposed as numpy arrays.
        
        It exposes both a low level and a high level interface.
        
        The low level interface for reading and writing sdif files mirrors the
        sdif library quite transparently so that the example files and
        utilities using it can be directly translated with it. In particular
        it does not create any intermediate objects, even the data of the matrices
        is a numpy array mapped to the c array read from disk, so no allocation takes
        place. Whereas this makes for very fast code, one has to take care to copy the
        data if it will be used longer, since by the time a new matrix is read this
        data is no longer valid.
        
        to read for ex. 1TRC format:
        
        import pysdif
        
        sdif_file = pysdif.SdifFile('filename.sdif')
        sig1TRC = pysdif.str2signature("1TRC")
        while not sdif_file.eof:
        sdif_file.read_frame_header()
        if sdif_file.frame_numerical_signature) == sig1TRC:
        print sdif_file.time
        for n in range(sdif_file.matrices_in_frame):
        sdif_file.read_matrix_header()
        if sdif_file.matrix_numerical_signature == sig1TRC:
        data = sdif_file.get_matrix_data(copy=True) # default is True
        # data is now a numpy array
        # here is what happens under the hood: the SDIF library
        # reads a whole matrix and puts it in its internal buffer
        # This data is memcpy(ed) to create a numpy array
        # If you dont intend to keep this data and there are no
        # chances that a new matrix or frame will be read before
        # you use this data for some calculation, then it is safe
        # to call sdif_file.get_matrix_data(copy=False)
        # in this case no memory is copied, the numpy array does not
        # own its memory and uses the internal buffer of the SDIF runtime
        print data
        
        a more natural way:
        
        from pysdif import SdifFile
        sdif_file = SdifFile('filename.sdif')
        for frame in sdif_file:
        if frame.signature == "1TRC":
        print frame.time
        for matrix in frame:
        if matrix.signature == "1TRC":
        print matrix.get_data() # data will be copied, use .get_data(copy=False) to avoid this
        
        the frames and the matrices resulting from the iteration
        are only guaranteed to be valid as long as no new frames and matrices are read
        
        to write a SdifFile:
        
        f = SdifFile('new_sdif.sdif', 'w')
        # these are optional
        #   add some metadata
        f.add_NVT({
        'name' : 'my name',
        'date' : time.asctime(time.localtime())
        })
        # define new frame and matrix types
        f.add_frame_type('1NEW', '1ABC NewMatrix, 1FQ0 New1FQ0')
        f.add_matrix_type('1ABC', 'Column1, Column2')
        # now you can begin adding frames
        frame = f.new_frame('1NEW', time_now)
        frame.add_matrix('1ABC', array([
        [0,     1.2],
        [3.5,   8.13],
        ...
        ]))
        frame.write()
        
        # say we just want to take the data from an existing
        # sdiffile, modify it and write it back
        in_sdif = SdifFile("existing-file.sdif")
        out_sdif = SdifFile("outfile.sdif", "w")
        out_sdif.clone_definitions(in_sdif)
        for in_frame in in_sdif:
        if in_frame.signature == "1NEW":
        new_frame = out_sdif.new_frame("1NEW", in_frame.time)
        # we know there is only one matrix and we dont need to keep the data
        in_data = in_frame.get_matrix_data(copy=False)
        # multiply it
        in_data[:,1] *= 0.5
        # add it to the stream
        new_frame.add_matrix('1ABC', in_data)
        # only one matrix, so write the frame.
        new_frame.write()
        # along this operation, only the memory used to allocate the original
        # matrix was used, the rest of the operations is performed in place
        # This is only safe where only one thread has access to
        # the sdif entity at once and data is not kept longer than the time
        # read it, transform it and rewrite it.
        
        there are also many utility functions under pysdif.sdiftools
        
        see release notes and changes at http://github.com/gesellkammer/pysdif
Keywords: scientific computing,music,sound analysis,SDIF,IRCAM
Platform: Linux
Platform: Mac OS X
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: MacOS
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Programming Language :: C
Classifier: Programming Language :: Cython
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Topic :: Multimedia
Classifier: Topic :: Multimedia :: Sound/Audio
Classifier: Topic :: Multimedia :: Sound/Audio :: Analysis
Classifier: Topic :: Multimedia :: Sound/Audio :: Sound Synthesis
Classifier: Topic :: Multimedia :: Sound/Audio :: Speech
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Software Development :: Libraries :: Python Modules
