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)


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 True, masks longitude outside

l_deg_min < l < l_deg_max

b_mask False

If True, masks latitude outside

b_deg_min < b < b_deg_max

mask_ring False

If True, masks outside inner < r < outer,

of a ring centred at (ring_b, ring_l )

custom_mask None Optional user-provided mask


By convention, True pixels are masked and False unmasked.


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().


See the Example 2: Creating Masks for an exposition of the options described here.