Loading data and exposure¶
All maps are passed as 1-d numpy
arrays. The data
and exposure maps can be loaded into an instance of nptfit.NPTF
(see Initializing a scan) as
follows:
>>> nptf.load_data(data, exposure)
Adding templates¶
Spatial templates can be added as follows:
>>> nptf.add_template(template, key, units)
where the first argument is the template numpy
array and the second argument
is the template key, used to identify the template in later calls.
The argument units specifies the template units (counts or flux) or type (to be used in either Poissonian or PS models). The following values are allowed:
'counts'
: template in counts/pixel, to be used in a Poissonian model. Exposure and PSF corrected.'flux'
: template in counts/cm^2/s/pixel, to be used in a Poissonian model. Not exposure corrected.'PS'
: template for the underlying PS distribution, to be used in a non-Poissonian model. This shouldn’t account for exposure effects.
For example, a template specifying an underlying isotropic PS distribution for a non-Poissonian model would be added with the keyword 'PS'
as a truly isotropic array, e.g. [1,1,1,...,1]
.
Warning
The data, exposure and template maps must be of the same length.
Note
While the data, exposure and template maps need not be in HEALPix format, creation of masks is only supported for HEALPix formatted arrays. Conversion between Cartesian and HEALPix maps can be performed using grid2healpix