neurosynth/analysis/reduce.py
def average_within_regions(dataset, regions, masker=None, threshold=None,
                           remove_zero=True):
    """ Aggregates over all voxels within each ROI in the input image.

    Takes a Dataset and a Nifti image that defines distinct regions, and
    returns a numpy matrix of  ROIs x mappables, where the value at each
    ROI is the proportion of active voxels in that ROI. Each distinct ROI
    must have a unique value in the image; non-contiguous voxels with the
    same value will be assigned to the same ROI.

    Args:
        dataset: Either a Dataset instance from which image data are
            extracted, or a Numpy array containing image data to use. If
            the latter, the array contains voxels in rows and
            features/studies in columns. The number of voxels must be equal
            to the length of the vectorized image mask in the regions
            image.
        regions: An image defining the boundaries of the regions to use.
            Can be one of:
            1) A string name of the NIFTI or Analyze-format image
            2) A NiBabel SpatialImage
            3) A list of NiBabel images
            4) A 1D numpy array of the same length as the mask vector in
                the Dataset's current Masker.
        masker: Optional masker used to load image if regions is not a
            numpy array. Must be passed if dataset is a numpy array.
        threshold: An optional float in the range of 0 - 1 or integer. If
            passed, the array will be binarized, with ROI values above the
            threshold assigned to True and values below the threshold
            assigned to False. (E.g., if threshold = 0.05, only ROIs in
            which more than 5% of voxels are active will be considered
            active.) If threshold is integer, studies will only be
            considered active if they activate more than that number of
            voxels in the ROI.
        remove_zero: An optional boolean; when True, assume that voxels
            with value of 0 should not be considered as a separate ROI, and
            will be ignored.

    Returns:
        A 2D numpy array with ROIs in rows and mappables in columns.
    """

    if masker is not None:
        masker = masker
    else:
        if isinstance(dataset, Dataset):
            masker = dataset.masker
        else:
            if not type(regions).__module__.startswith('numpy'):
                raise ValueError(
                    "If dataset is a numpy array and regions is not a numpy "
                    "array, a masker must be provided.")

    if not type(regions).__module__.startswith('numpy'):
        regions = masker.mask(regions)

    if isinstance(dataset, Dataset):
        dataset = dataset.get_image_data(dense=False)

    # If multiple images are passed, give each one a unique value
    if regions.ndim == 2:
        m = regions
        for i in range(regions.shape[1]):
            _nz = np.nonzero(m[:, i])[0]
            if isinstance(threshold, int):
                m[_nz, i] = 1.0
            else:
                m[_nz, i] = 1.0 / np.count_nonzero(m[:, i])

    # Otherwise create an ROI-coding matrix
    else:
        labels = np.unique(regions)

        if remove_zero:
            labels = labels[np.nonzero(labels)]

        n_regions = labels.size

        m = np.zeros((regions.size, n_regions))
        for i in range(n_regions):
            if isinstance(threshold, int):
                m[regions == labels[i], i] = 1.0
            else:
                m[regions == labels[i], i] = 1.0 / \
                    np.sum(regions == labels[i])

    # Call dot() on the array itself as this will use sparse matrix
    # multiplication if possible.
    result = dataset.T.dot(m).T

    if threshold is not None:
        result[result < threshold] = 0.0
        result = result.astype(bool)

    return result
def average_within_regions(dataset, regions, masker=None, threshold=None,
                           remove_zero=True):
    if masker is not None:
        masker = masker
    else:
        if isinstance(dataset, Dataset):
            masker = dataset.masker
        else:
            if not type(regions).__module__.startswith('numpy'):
                raise ValueError(
                    "If dataset is a numpy array and regions is not a numpy "
                    "array, a masker must be provided.")

    if not type(regions).__module__.startswith('numpy'):
        regions = masker.mask(regions)

    if isinstance(dataset, Dataset):
        dataset = dataset.get_image_data(dense=False)

    # If multiple images are passed, give each one a unique value
    if regions.ndim == 2:
        m = regions
        for i in range(regions.shape[1]):
            _nz = np.nonzero(m[:, i])[0]
            if isinstance(threshold, int):
                m[_nz, i] = 1.0
            else:
                m[_nz, i] = 1.0 / np.count_nonzero(m[:, i])

    # Otherwise create an ROI-coding matrix
    else:
        labels = np.unique(regions)

        if remove_zero:
            labels = labels[np.nonzero(labels)]

        n_regions = labels.size

        m = np.zeros((regions.size, n_regions))
        for i in range(n_regions):
            if isinstance(threshold, int):
                m[regions == labels[i], i] = 1.0
            else:
                m[regions == labels[i], i] = 1.0 / \
                    np.sum(regions == labels[i])

    # Call dot() on the array itself as this will use sparse matrix
    # multiplication if possible.
    result = dataset.T.dot(m).T

    if threshold is not None:
        result[result < threshold] = 0.0
        result = result.astype(bool)

    return result
def average_within_regions ( dataset , regions , masker = None , threshold = None , remove_zero = True ) : if masker is not None : masker = masker else : if isinstance ( dataset , Dataset ) : masker = dataset . masker else : if not type ( regions ) . __module__ . startswith ( 'numpy' ) : raise ValueError ( "If dataset is a numpy array and regions is not a numpy " "array, a masker must be provided." ) if not type ( regions ) . __module__ . startswith ( 'numpy' ) : regions = masker . mask ( regions ) if isinstance ( dataset , Dataset ) : dataset = dataset . get_image_data ( dense = False ) # If multiple images are passed, give each one a unique value if regions . ndim == 2 : m = regions for i in range ( regions . shape [ 1 ] ) : _nz = np . nonzero ( m [ : , i ] ) [ 0 ] if isinstance ( threshold , int ) : m [ _nz , i ] = 1.0 else : m [ _nz , i ] = 1.0 / np . count_nonzero ( m [ : , i ] ) # Otherwise create an ROI-coding matrix else : labels = np . unique ( regions ) if remove_zero : labels = labels [ np . nonzero ( labels ) ] n_regions = labels . size m = np . zeros ( ( regions . size , n_regions ) ) for i in range ( n_regions ) : if isinstance ( threshold , int ) : m [ regions == labels [ i ] , i ] = 1.0 else : m [ regions == labels [ i ] , i ] = 1.0 / np . sum ( regions == labels [ i ] ) # Call dot() on the array itself as this will use sparse matrix # multiplication if possible. result = dataset . T . dot ( m ) . T if threshold is not None : result [ result < threshold ] = 0.0 result = result . astype ( bool ) return result
Aggregates over all voxels within each ROI in the input image.

    Takes a Dataset and a Nifti image that defines distinct regions, and
    returns a numpy matrix of  ROIs x mappables, where the value at each
    ROI is the proportion of active voxels in that ROI. Each distinct ROI
    must have a unique value in the image; non-contiguous voxels with the
    same value will be assigned to the same ROI.

    Args:
        dataset: Either a Dataset instance from which image data are
            extracted, or a Numpy array containing image data to use. If
            the latter, the array contains voxels in rows and
            features/studies in columns. The number of voxels must be equal
            to the length of the vectorized image mask in the regions
            image.
        regions: An image defining the boundaries of the regions to use.
            Can be one of:
            1) A string name of the NIFTI or Analyze-format image
            2) A NiBabel SpatialImage
            3) A list of NiBabel images
            4) A 1D numpy array of the same length as the mask vector in
                the Dataset's current Masker.
        masker: Optional masker used to load image if regions is not a
            numpy array. Must be passed if dataset is a numpy array.
        threshold: An optional float in the range of 0 - 1 or integer. If
            passed, the array will be binarized, with ROI values above the
            threshold assigned to True and values below the threshold
            assigned to False. (E.g., if threshold = 0.05, only ROIs in
            which more than 5% of voxels are active will be considered
            active.) If threshold is integer, studies will only be
            considered active if they activate more than that number of
            voxels in the ROI.
        remove_zero: An optional boolean; when True, assume that voxels
            with value of 0 should not be considered as a separate ROI, and
            will be ignored.

    Returns:
        A 2D numpy array with ROIs in rows and mappables in columns.
Aggregates over all voxels within each ROI in the input image .