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LSST Data Management Base Package
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Classes | Functions
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.

60def median_with_mad_clipping(data, mad_multiplier=2.0):
61 """
62 Calculate the median of the input data after applying Median Absolute Deviation (MAD) clipping.
63
64 The MAD clipping method is used to remove outliers from the data. The median of the data is calculated,
65 and then the MAD is calculated as the median absolute deviation from the median. The data is then clipped
66 by removing values that are outside the range of median +/- mad_multiplier * MAD. Finally, the median of
67 the clipped data is returned.
68
69 Parameters:
70 -----------
71 data : `np.array`
72 Input data array.
73 mad_multiplier : `float`, optional
74 Multiplier for the MAD value used for clipping. Default is 2.0.
75
76 Returns:
77 --------
78 median_clipped : `float`
79 Median value of the clipped data.
80
81 Examples:
82 ---------
83 >>> data = [1, 2, 3, 4, 5, 100]
84 >>> median_with_mad_clipping(data)
85 3.5
86 """
87 median = np.median(data)
88 mad = np.median(np.abs(data - median))
89 clipping_range = mad_multiplier * mad
90 clipped_data = np.clip(data, median - clipping_range, median + clipping_range)
91 median_clipped = np.median(clipped_data)
92 return median_clipped
93
94

◆ 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.

37def updateMaskFromArray(mask, bad_pixel, interpBit):
38 """
39 Update the mask array with the given bad pixels.
40
41 Parameters
42 ----------
43 mask : `lsst.afw.image.MaskedImage`
44 The mask image to update.
45 bad_pixel : `np.array`
46 An array-like object containing the coordinates of the bad pixels.
47 Each row should contain the x and y coordinates of a bad pixel.
48 interpBit : `int`
49 The bit value to set for the bad pixels in the mask.
50 """
51 x0 = mask.getX0()
52 y0 = mask.getY0()
53 for row in bad_pixel:
54 x = int(row[0] - x0)
55 y = int(row[1] - y0)
56 mask.array[y, x] |= interpBit
57 # TO DO --> might be better: mask.array[int(bad_pixel[:,1]-y0), int(bad_pixel[:,0]-x)] |= interpBit
58
59