Specifying new priors¶

You can specify new priors by subclassing blendz.model.ModelBase, redefining the four functions that return the priors and instantiating your new model for blendz.photoz.

Creating a new model class¶

Your new class should have the following basic layout:

from blendz.model import ModelBase

class MyNewModel(ModelBase):

    #Optional:
    def __init__(self, new_prior_params, **kwargs):
        #Run the setup defined in ModelBase
        super(MyNewModel, self).__init__(**kwargs)
        #Do some other setup with your new_prior_parameters

    #Mandatory:
    def lnPrior(self, redshift, magnitude):
        #Definition of P(z_a, t_a, m_0a) for all t_a
        return 0.

    #Optional:
    def correlationFunction(self, redshifts):
        #Definition of xi({z})
        return 0.

    #Optional:
    def calibrate(self, photometry, cached_likelihood, **kwargs):
        return 0.

A few things to note:

The __init__ function is optional but allows you to define additional setup tasks that are done when your model is instantiated. It is important you call the superclass __init__ if you define this.
The correlationFunction function is also optional. The function self.comovingSeparation(z_lo, z_hi) defined in ModelBase may be helpful.
While __init__ is optional, you must redefine lnPrior. This function takes a float for both the redshift and magnitude, and returns a numpy.array of the natural log of the prior for each template type (not each template). The self.possible_types attribute is a list of the possible types, where each element is a string with the name of that type. These are automatically read from the template set file supplied at runtime.
The **kwargs get passed by ModelBase to Configuration, allowing you to edit the configuration like other blendz classes using keyword arguments.
The redshift, magnitude and component_ref_mag arguments passed to natural-log prior functions are floats, while the redshifts argument in correlationFunction is a 1D numpy array.
The calibrate function is also optional. This takes as arguments a blendz.photometry.Photometry object and a numpy.array of shape (num_galaxies, num_templates) filled with the likelihood. This function is called by the blendz.Photoz.calibrate(**kwargs) function, with any keyword arguments passed to the function here. There is no return value for this function; the default model writes the resulting parameters to a configuration file that can be read by blendz.Photoz.

Using the new model¶

The new model can simply be instantiated and passed to blendz.Photoz as a keyword argument.

new_model = MyNewModel(new_prior_params=42, template_set='BPZ6')

pz = blendz.Photoz(model=new_model)