Function compute_selected_rois

pyxrf/core/map_processing.py:984–1137

Compute XRF map based on ROIs for XRF dataset. Parameters ---------- data: da.core.Array, np.ndarray or RawHDF5Dataset (this is a custom type) Raw XRF map represented as Dask array, numpy array or reference to a dataset in HDF5 file. The XRF map must have dimensions

(
    data,
    data_sel_indices,
    roi_dict,
    snip_param=None,
    use_snip=True,
    chunk_pixels=5000,
    n_chunks_min=4,
    progress_bar=None,
    client=None,
)

def compute_selected_rois(
    data,
    data_sel_indices,
    roi_dict,
    snip_param=None,
    use_snip=True,
    chunk_pixels=5000,
    n_chunks_min=4,
    progress_bar=None,
    client=None,
):
    """
    Compute XRF map based on ROIs for XRF dataset.

    Parameters
    ----------
    data: da.core.Array, np.ndarray or RawHDF5Dataset (this is a custom type)
        Raw XRF map represented as Dask array, numpy array or reference to a dataset in
        HDF5 file. The XRF map must have dimensions `(ny, nx, ne)`, where `ny` and `nx`
        define image size and `ne` is the number of spectrum points
    data_sel_indices: tuple
        tuple `(n_start, n_end)` which defines the indices along axis 2 of `data` array
        that are used for fitting. Note that `ne` (in `data`). Indexes
        `n_start .. n_end - 1` will be selected from each pixel.
    roi_dict: dict
        Dictionary that specifies ROIs for the selected emission lines:
        key - emission line, value - tuple (left_val, right_val).
        Energy values are in keV.
    snip_param: dict
        Dictionary of parameters forwarded to 'snip' method for background removal.
        Keys: `e_offset`, `e_linear`, `e_quadratic` (parameters of the energy axis approximation),
        `b_width` (width of the window that defines resolution of the snip algorithm).
        The values of `e_offset` and `e_linear` are used to compute indices for ROIs, so they
        need to be always provided.
    use_snip: bool, optional
        enable/disable background removal using snip algorithm
    chunk_pixels: int
        The number of pixels in a single chunk. The XRF map will be rechunked so that
        each block contains approximately `chunk_pixels` pixels and contain all `ne`
        spectrum points for each pixel.
    n_chunks_min: int
        Minimum number of chunks. The algorithm will try to split the map into the number
        of chunks equal or greater than `n_chunks_min`.
    progress_bar: callable or None
        reference to the callable object that implements progress bar. The example of
        such a class for progress bar object is `TerminalProgressBar`.
    client: dask.distributed.Client or None
        Dask client. If None, then local client will be created

    Returns
    -------
    roi_dict_computed: dict
        Dictionary with XRF maps computed for ROIs specified by `roi_dict`.
        Key: emission line. Value: numpy array with shape `(ny, nx)`.
        XRF map values represent area under of the experimental spectrum computed
        over ROI.
    """