lsst.meas.algorithms.gp_interpolation Namespace Reference

Classes
class	GaussianProcessTreegp
class	InterpolateOverDefectGaussianProcess

Functions
	updateMaskFromArray (mask, bad_pixel, interpBit)
	median_with_mad_clipping (data, mad_multiplier=2.0)

Function Documentation

median_with_mad_clipping()

lsst.meas.algorithms.gp_interpolation.median_with_mad_clipping	(		data,
			mad_multiplier = 2.0 )

Calculate the median of the input data after applying Median Absolute Deviation (MAD) clipping.

The MAD clipping method is used to remove outliers from the data. The median of the data is calculated,
and then the MAD is calculated as the median absolute deviation from the median. The data is then clipped
by removing values that are outside the range of median +/- mad_multiplier * MAD. Finally, the median of
the clipped data is returned.

Parameters:
-----------
data : `np.array`
    Input data array.
mad_multiplier : `float`, optional
    Multiplier for the MAD value used for clipping. Default is 2.0.

Returns:
--------
median_clipped : `float`
    Median value of the clipped data.

Examples:
---------
>>> data = [1, 2, 3, 4, 5, 100]
>>> median_with_mad_clipping(data)
3.5

Definition at line 60 of file gp_interpolation.py.

def median_with_mad_clipping(data, mad_multiplier=2.0):
    """
    Calculate the median of the input data after applying Median Absolute Deviation (MAD) clipping.
 
    The MAD clipping method is used to remove outliers from the data. The median of the data is calculated,
    and then the MAD is calculated as the median absolute deviation from the median. The data is then clipped
    by removing values that are outside the range of median +/- mad_multiplier * MAD. Finally, the median of
    the clipped data is returned.
 
    Parameters:
    -----------
    data : `np.array`
        Input data array.
    mad_multiplier : `float`, optional
        Multiplier for the MAD value used for clipping. Default is 2.0.
 
    Returns:
    --------
    median_clipped : `float`
        Median value of the clipped data.
 
    Examples:
    ---------
    >>> data = [1, 2, 3, 4, 5, 100]
    >>> median_with_mad_clipping(data)
    3.5
    """
    median = np.median(data)
    mad = np.median(np.abs(data - median))
    clipping_range = mad_multiplier * mad
    clipped_data = np.clip(data, median - clipping_range, median + clipping_range)
    median_clipped = np.median(clipped_data)
    return median_clipped
 
 

updateMaskFromArray()

lsst.meas.algorithms.gp_interpolation.updateMaskFromArray	(		mask,
			bad_pixel,
			interpBit )

Update the mask array with the given bad pixels.

Parameters
----------
mask : `lsst.afw.image.MaskedImage`
    The mask image to update.
bad_pixel : `np.array`
    An array-like object containing the coordinates of the bad pixels.
    Each row should contain the x and y coordinates of a bad pixel.
interpBit : `int`
    The bit value to set for the bad pixels in the mask.

Definition at line 37 of file gp_interpolation.py.

def updateMaskFromArray(mask, bad_pixel, interpBit):
    """
    Update the mask array with the given bad pixels.
 
    Parameters
    ----------
    mask : `lsst.afw.image.MaskedImage`
        The mask image to update.
    bad_pixel : `np.array`
        An array-like object containing the coordinates of the bad pixels.
        Each row should contain the x and y coordinates of a bad pixel.
    interpBit : `int`
        The bit value to set for the bad pixels in the mask.
    """
    x0 = mask.getX0()
    y0 = mask.getY0()
    for row in bad_pixel:
        x = int(row[0] - x0)
        y = int(row[1] - y0)
        mask.array[y, x] |= interpBit
    # TO DO --> might be better: mask.array[int(bad_pixel[:,1]-y0), int(bad_pixel[:,0]-x)] |= interpBit