smooth_los_threshold#
- smart.processing.smooth_los_threshold(im_map: <sunpy.map.map_factory.MapFactory object at 0x7ff1d3e39a80>, thresh: ~astropy.units.quantity.Annotated[~astropy.units.quantity.Quantity, Unit("G")] = <Quantity 100. G>, dilation_radius: ~astropy.units.quantity.Annotated[~astropy.units.quantity.Quantity, Unit("arcsec")] = <Quantity 5. arcsec>, sigma: ~astropy.units.quantity.Annotated[~astropy.units.quantity.Quantity, Unit("arcsec")] = <Quantity 10. arcsec>, min_size: ~astropy.units.quantity.Annotated[~astropy.units.quantity.Quantity, Unit("arcsec")] = <Quantity 2250. arcsec>)[source]#
We apply Smoothing, a noise Threshold, and an LOS correction, respectively, to the data.
- Parameters:
im_map (
~sunpy.map.Map) – Processed SunPy magnetogram map.thresh (
int, optional) – Threshold value to identify regions of interest (default is 100 Gauss).dilation_radius (
int, optional) – Radius of the disk for binary dilation (default is 2 arcsecs).sigma (
int, optional) – Standard deviation for Gaussian smoothing (default is 10 arcsecs).min_size (
int, optional) – Minimum size of regions to keep in final mask (default is 2250 arcsecs**2).
- Returns:
smooth_map (
Map) – Map after applying Gaussian smoothing.filtered_labels (
numpy.ndarray) – 2D array with each pixel labelled.mask_sizes (
ndarray) – Boolean array indicating the sizes of each labeled region.