Adding masks¶
Masks can be used to restrict an analysis within a given region of interest
(ROI). Masks are boolean arrays where pixels labelled as true are masked and
those labelled false are unmasked. Masks can be loaded into an instance of
nptfit.NPTF
(see Initializing a scan) as:
>>> nptf.load_mask(mask)
Warning
The mask must be the same length as the data.
Creating masks¶
For the case where the data is a
HEALPix
array, masks can be created by the create_mask
module. For example:
>>> from NPTFit import create_mask as cm
>>> mask = cm.make_mask_total(nside, mask_ring = True, outer = 2, ring_b = -45)
where nside is the HEALPix resolution parameter. The following mask options are available. When set to True
, the associated
arguments determine the parameters of the mask. If multiple options are set to
True
then the total mask is the combination of each option.
Argument | Default | Purpose |
---|---|---|
band_mask |
False |
If True ,
masks within
|b|
<
band_mask_
range |
l_mask |
False |
If
|
b_mask |
False |
If
|
mask_ring |
False |
If of a ring
centred at
( |
custom_mask |
None |
Optional user-provided mask |
Note
By convention, True
pixels are masked and False
unmasked.
Note
Creation of masks by create_mask
is only supported for HEALPix
formatted maps. For other inputs, masks otherwise made can be used as
usual with nptf.load_mask()
.
Tip
See the Example 2: Creating Masks for an exposition of the options described here.