Python Raw.info['lowpass']方法代码示例

# 需要导入模块: from mne.io import Raw [as 别名]
# 或者: from mne.io.Raw import info['lowpass'] [as 别名]
def test_chpi_subtraction():
    """Test subtraction of cHPI signals"""
    raw = Raw(chpi_fif_fname, allow_maxshield='yes', preload=True)
    with catch_logging() as log:
        filter_chpi(raw, include_line=False, verbose=True)
    assert_true('5 cHPI' in log.getvalue())
    # MaxFilter doesn't do quite as well as our algorithm with the last bit
    raw.crop(0, 16, copy=False)
    # remove cHPI status chans
    raw_c = Raw(sss_hpisubt_fname).crop(0, 16, copy=False).load_data()
    raw_c.pick_types(
        meg=True, eeg=True, eog=True, ecg=True, stim=True, misc=True)
    assert_meg_snr(raw, raw_c, 143, 624)

    # Degenerate cases
    raw_nohpi = Raw(test_fif_fname, preload=True)
    assert_raises(RuntimeError, filter_chpi, raw_nohpi)

    # When MaxFliter downsamples, like::
    #     $ maxfilter -nosss -ds 2 -f test_move_anon_raw.fif \
    #           -o test_move_anon_ds2_raw.fif
    # it can strip out some values of info, which we emulate here:
    raw = Raw(chpi_fif_fname, allow_maxshield='yes')
    with warnings.catch_warnings(record=True):  # uint cast suggestion
        raw = raw.crop(0, 1).load_data().resample(600., npad='auto')
    raw.info['buffer_size_sec'] = np.float64(2.)
    raw.info['lowpass'] = 200.
    del raw.info['maxshield']
    del raw.info['hpi_results'][0]['moments']
    del raw.info['hpi_subsystem']['event_channel']
    with catch_logging() as log:
        filter_chpi(raw, verbose=True)
    assert_true('2 cHPI' in log.getvalue())

# 需要导入模块: from mne.io import Raw [as 别名]
# 或者: from mne.io.Raw import info['lowpass'] [as 别名]

#.........这里部分代码省略.........
    raw_resamp.save(op.join(tempdir, 'raw_resamp-raw.fif'))
    raw_resamp = Raw(op.join(tempdir, 'raw_resamp-raw.fif'), preload=True)
    assert_equal(sfreq, raw_resamp.info['sfreq'] / 2)
    assert_equal(raw.n_times, raw_resamp.n_times / 2)
    assert_equal(raw_resamp._data.shape[1], raw_resamp.n_times)
    assert_equal(raw._data.shape[0], raw_resamp._data.shape[0])
    # test non-parallel on downsample
    raw_resamp.resample(sfreq, n_jobs=1, npad='auto')
    assert_equal(raw_resamp.info['sfreq'], sfreq)
    assert_equal(raw._data.shape, raw_resamp._data.shape)
    assert_equal(raw.first_samp, raw_resamp.first_samp)
    assert_equal(raw.last_samp, raw.last_samp)
    # upsampling then downsampling doubles resampling error, but this still
    # works (hooray). Note that the stim channels had to be sub-sampled
    # without filtering to be accurately preserved
    # note we have to treat MEG and EEG+STIM channels differently (tols)
    assert_allclose(raw._data[:306, 200:-200],
                    raw_resamp._data[:306, 200:-200],
                    rtol=1e-2, atol=1e-12)
    assert_allclose(raw._data[306:, 200:-200],
                    raw_resamp._data[306:, 200:-200],
                    rtol=1e-2, atol=1e-7)

    # now check multiple file support w/resampling, as order of operations
    # (concat, resample) should not affect our data
    raw1 = raw.copy()
    raw2 = raw.copy()
    raw3 = raw.copy()
    raw4 = raw.copy()
    raw1 = concatenate_raws([raw1, raw2])
    raw1.resample(10., npad='auto')
    raw3.resample(10., npad='auto')
    raw4.resample(10., npad='auto')
    raw3 = concatenate_raws([raw3, raw4])
    assert_array_equal(raw1._data, raw3._data)
    assert_array_equal(raw1._first_samps, raw3._first_samps)
    assert_array_equal(raw1._last_samps, raw3._last_samps)
    assert_array_equal(raw1._raw_lengths, raw3._raw_lengths)
    assert_equal(raw1.first_samp, raw3.first_samp)
    assert_equal(raw1.last_samp, raw3.last_samp)
    assert_equal(raw1.info['sfreq'], raw3.info['sfreq'])

    # test resampling of stim channel

    # basic decimation
    stim = [1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0]
    raw = RawArray([stim], create_info(1, len(stim), ['stim']))
    assert_allclose(raw.resample(8., npad='auto')._data,
                    [[1, 1, 0, 0, 1, 1, 0, 0]])

    # decimation of multiple stim channels
    raw = RawArray(2 * [stim], create_info(2, len(stim), 2 * ['stim']))
    assert_allclose(raw.resample(8., npad='auto')._data,
                    [[1, 1, 0, 0, 1, 1, 0, 0],
                     [1, 1, 0, 0, 1, 1, 0, 0]])

    # decimation that could potentially drop events if the decimation is
    # done naively
    stim = [0, 0, 0, 1, 1, 0, 0, 0]
    raw = RawArray([stim], create_info(1, len(stim), ['stim']))
    assert_allclose(raw.resample(4., npad='auto')._data,
                    [[0, 1, 1, 0]])

    # two events are merged in this case (warning)
    stim = [0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0]
    raw = RawArray([stim], create_info(1, len(stim), ['stim']))
    with warnings.catch_warnings(record=True) as w:
        warnings.simplefilter('always')
        raw.resample(8., npad='auto')
        assert_true(len(w) == 1)

    # events are dropped in this case (warning)
    stim = [0, 1, 1, 0, 0, 1, 1, 0]
    raw = RawArray([stim], create_info(1, len(stim), ['stim']))
    with warnings.catch_warnings(record=True) as w:
        warnings.simplefilter('always')
        raw.resample(4., npad='auto')
        assert_true(len(w) == 1)

    # test resampling events: this should no longer give a warning
    stim = [0, 1, 1, 0, 0, 1, 1, 0]
    raw = RawArray([stim], create_info(1, len(stim), ['stim']))
    events = find_events(raw)
    raw, events = raw.resample(4., events=events, npad='auto')
    assert_equal(events, np.array([[0, 0, 1], [2, 0, 1]]))

    # test copy flag
    stim = [1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0]
    raw = RawArray([stim], create_info(1, len(stim), ['stim']))
    raw_resampled = raw.resample(4., npad='auto', copy=True)
    assert_true(raw_resampled is not raw)
    raw_resampled = raw.resample(4., npad='auto', copy=False)
    assert_true(raw_resampled is raw)

    # resample should still work even when no stim channel is present
    raw = RawArray(np.random.randn(1, 100), create_info(1, 100, ['eeg']))
    raw.info['lowpass'] = 50.
    raw.resample(10, npad='auto')
    assert_equal(raw.info['lowpass'], 5.)
    assert_equal(len(raw), 10)

# 需要导入模块: from mne.io import Raw [as 别名]
# 或者: from mne.io.Raw import info['lowpass'] [as 别名]
def filter(l_freq, h_freq, picks=None, filter_length='10s',
           l_trans_bandwidth=0.5, h_trans_bandwidth=0.5, n_jobs=1,
           method='fft', iir_params=None, verbose=None):
    """Filter a subset of channels.
    Applies a zero-phase low-pass, high-pass, band-pass, or band-stop
    filter to the channels selected by "picks". The data of the Raw
    object is modified inplace.
    The Raw object has to be constructed using preload=True (or string).
    l_freq and h_freq are the frequencies below which and above which,
    respectively, to filter out of the data. Thus the uses are:
        * ``l_freq < h_freq``: band-pass filter
        * ``l_freq > h_freq``: band-stop filter
        * ``l_freq is not None and h_freq is None``: high-pass filter
        * ``l_freq is None and h_freq is not None``: low-pass filter
    If n_jobs > 1, more memory is required as "len(picks) * n_times"
    additional time points need to be temporarily stored in memory.
    raw.info['lowpass'] and raw.info['highpass'] are only updated
    with picks=None.
    Parameters
    ----------
    l_freq : float | None
        Low cut-off frequency in Hz. If None the data are only low-passed.
    h_freq : float | None
        High cut-off frequency in Hz. If None the data are only
        high-passed.
    picks : array-like of int | None
        Indices of channels to filter. If None only the data (MEG/EEG)
        channels will be filtered.
    filter_length : str (Default: '10s') | int | None
        Length of the filter to use. If None or "len(x) < filter_length",
        the filter length used is len(x). Otherwise, if int, overlap-add
        filtering with a filter of the specified length in samples) is
        used (faster for long signals). If str, a human-readable time in
        units of "s" or "ms" (e.g., "10s" or "5500ms") will be converted
        to the shortest power-of-two length at least that duration.
        Not used for 'iir' filters.
    l_trans_bandwidth : float
        Width of the transition band at the low cut-off frequency in Hz
        (high pass or cutoff 1 in bandpass). Not used if 'order' is
        specified in iir_params.
    h_trans_bandwidth : float
        Width of the transition band at the high cut-off frequency in Hz
        (low pass or cutoff 2 in bandpass). Not used if 'order' is
        specified in iir_params.
    n_jobs : int | str
        Number of jobs to run in parallel. Can be 'cuda' if scikits.cuda
        is installed properly, CUDA is initialized, and method='fft'.
    method : str
        'fft' will use overlap-add FIR filtering, 'iir' will use IIR
        forward-backward filtering (via filtfilt).
    iir_params : dict | None
        Dictionary of parameters to use for IIR filtering.
        See mne.filter.construct_iir_filter for details. If iir_params
        is None and method="iir", 4th order Butterworth will be used.
    verbose : bool, str, int, or None
        If not None, override default verbose level (see mne.verbose).
        Defaults to raw.verbose.
    See Also
    --------
    mne.Epochs.savgol_filter
    """
    fname='ec_rest_before_tsss_mc_rsl.fif'
    raw = Raw(fname, preload=False)
    raw.preload_data() #  data becomes numpy.float64

    if verbose is None:
        verbose = raw.verbose
    fs = float(raw.info['sfreq'])
    if l_freq == 0:
        l_freq = None
    if h_freq is not None and h_freq > (fs / 2.):
        h_freq = None
    if l_freq is not None and not isinstance(l_freq, float):
        l_freq = float(l_freq)
    if h_freq is not None and not isinstance(h_freq, float):
        h_freq = float(h_freq)

    if not raw.preload:
        raise RuntimeError('Raw data needs to be preloaded to filter. Use '
                           'preload=True (or string) in the constructor.')
    if picks is None:
        if 'ICA ' in ','.join(raw.ch_names):
            pick_parameters = dict(misc=True, ref_meg=False)
        else:
            pick_parameters = dict(meg=True, eeg=True, ref_meg=False)
        picks = pick_types(raw.info, exclude=[], **pick_parameters)
        # let's be safe.
        if len(picks) < 1:
            raise RuntimeError('Could not find any valid channels for '
                               'your Raw object. Please contact the '
                               'MNE-Python developers.')

        # update info if filter is applied to all data channels,
        # and it's not a band-stop filter
        if h_freq is not None:
            if (l_freq is None or l_freq < h_freq) and \
               (raw.info["lowpass"] is None or
               h_freq < raw.info['lowpass']):
                    raw.info['lowpass'] = h_freq
        if l_freq is not None:
#.........这里部分代码省略.........