Python Dataset.fa['timepoints']方法代码示例

# 需要导入模块: from mvpa2.datasets.base import Dataset [as 别名]
# 或者: from mvpa2.datasets.base.Dataset import fa['timepoints'] [as 别名]

#.........这里部分代码省略.........
            raise ValueError("Input looks like a filename, but file"
                                " %s does not exist" % samples)
        with open(samples) as f:
            samples = f.read()

    lines = samples.split('\n')
    samples = []
    cur_sample = None

    for i, line in enumerate(lines):
        if not line:
            continue
        if i == 0:
            # first line contains the channel names
            channel_labels = line.split()
            n_channels = len(channel_labels)
        else:
            # first value is the time point, the remainders the value 
            # for each channel
            values = map(float, line.split())
            t = values[0]  # time 
            eeg = values[1:] # values for each electrode

            if len(eeg) != n_channels:
                raise ValueError("Line %d: expected %d values but found %d" %
                                    (n_channels, len(eeg)))

            if cur_sample is None or t < prev_t:
                # new sample
                cur_sample = []
                samples.append(cur_sample)

            cur_sample.append((t, eeg))
            prev_t = t

    # get and verify number of elements in each dimension
    n_samples = len(samples)
    n_timepoints_all = map(len, samples)

    n_timepoints_unique = set(n_timepoints_all)
    if len(n_timepoints_unique) != 1:
        raise ValueError("Different number of time points in different"
                            "samples: found %d different lengths" %
                            len(n_timepoints_unique))

    n_timepoints = n_timepoints_all[0]

    shape = (n_samples, n_timepoints, n_channels)

    # allocate space for data
    data = np.zeros(shape)

    # make a list of all channels and timepoints
    channel_array = np.asarray(channel_labels)
    timepoint_array = np.asarray([samples[0][i][0]
                                  for i in xrange(n_timepoints)])

    dts = timepoint_array[1:] - timepoint_array[:-1]
    if not np.all(dts == dts[0]):
        raise ValueError("Delta time points are different")

    # put the values in the data array
    for i, sample in enumerate(samples):
        for j, (t, values) in enumerate(sample):
            # check that the time is the same
            if i > 0 and timepoint_array[j] != t:
                raise ValueError("Sample %d, time point %s is different "
                                 "than the first sample (%s)" %
                                 (i, t, timepoint_array[j]))

            for k, value in enumerate(values):
                data[i, j, k] = value

    samples = None # and let gc do it's job

    # make a Dataset instance with the data
    ds = Dataset(data)

    # append a flatten_mapper to go from 3D (sample X time X channel)
    # to 2D (sample X (time X channel))
    flatten_mapper = FlattenMapper(shape=shape[1:], space='time_channel_indices')
    ds = ds.get_mapped(flatten_mapper)

    # make this a 3D array of the proper size
    channel_array_3D = np.tile(channel_array, (1, n_timepoints, 1))
    timepoint_array_3D = np.tile(np.reshape(timepoint_array, (-1, 1)),
                                            (1, 1, n_channels))

    # for consistency use the flattan_mapper defined above to 
    # flatten channel and timepoint names as well
    ds.fa['channelids'] = flatten_mapper.forward(channel_array_3D).ravel()
    ds.fa['timepoints'] = flatten_mapper.forward(timepoint_array_3D).ravel()

    # make some dynamic properties
    # XXX at the moment we don't have propert 'protection' in case
    # the feature space is sliced in a way so that some channels and/or
    # timepoints occur more often than others 
    _eeglab_set_attributes(ds)

    return ds