plot.py 文件源码

def view_masks(file_name, t_start=0, t_stop=1, n_elec=0):

    params          = CircusParser(file_name)
    data_file       = params.get_data_file()
    data_file.open()
    N_e             = params.getint('data', 'N_e')
    N_t             = params.getint('detection', 'N_t')
    N_total         = params.nb_channels
    sampling_rate   = params.rate
    do_temporal_whitening = params.getboolean('whitening', 'temporal')
    do_spatial_whitening  = params.getboolean('whitening', 'spatial')
    spike_thresh     = params.getfloat('detection', 'spike_thresh')
    file_out_suff    = params.get('data', 'file_out_suff')
    nodes, edges     = get_nodes_and_edges(params)
    chunk_size       = (t_stop - t_start)*sampling_rate
    padding          = (t_start*sampling_rate, t_start*sampling_rate)
    inv_nodes        = numpy.zeros(N_total, dtype=numpy.int32)
    inv_nodes[nodes] = numpy.argsort(nodes)
    safety_time      = params.getint('clustering', 'safety_time')

    if do_spatial_whitening:
        spatial_whitening  = load_data(params, 'spatial_whitening')
    if do_temporal_whitening:
        temporal_whitening = load_data(params, 'temporal_whitening')

    thresholds       = load_data(params, 'thresholds')
    data = data_file.get_data(0, chunk_size, padding=padding, nodes=nodes)
    data_shape = len(data)
    data_file.close()
    peaks            = {}
    indices          = inv_nodes[edges[nodes[n_elec]]]

    if do_spatial_whitening:
        data = numpy.dot(data, spatial_whitening)
    if do_temporal_whitening: 
        data = scipy.ndimage.filters.convolve1d(data, temporal_whitening, axis=0, mode='constant')

    for i in xrange(N_e):
        peaks[i]   = algo.detect_peaks(data[:, i], thresholds[i], valley=True, mpd=0)


    pylab.figure()

    for count, i in enumerate(indices):

        pylab.plot(count*5 + data[:, i], '0.25')
        #xmin, xmax = pylab.xlim()
        pylab.scatter(peaks[i], count*5 + data[peaks[i], i], s=10, c='r')

    for count, i in enumerate(peaks[n_elec]):
        pylab.axvspan(i - safety_time, i + safety_time, facecolor='r', alpha=0.5)

    pylab.ylim(-5, len(indices)*5 )
    pylab.xlabel('Time [ms]')
    pylab.ylabel('Electrode')
    pylab.tight_layout()
    pylab.setp(pylab.gca(), yticks=[])
    pylab.show()
    return peaks