Custom prior distribution

Questions
1

Hello

I am solving a problem with definition my own joint prior distribution. This distribution is similar to Uniform but with some “bricks” inside this distributions.

class LogPrior(tt.Op):
    itypes = [tt.dvector] # expects a vector of parameter values when called
    otypes = [tt.dscalar] # outputs a single scalar value (the log likelihood)

    def __init__(self, lim):

        # add inputs as class attributes
        self.lim = lim
    
        self.init_x = 38.45;
        self.init_y = 20;
    
        self.size_x = 2.42;
        self.size_y = 12.2;
        self.size_z = 2.54;
        
        self.delta_y = 7.9 + self.size_y;
        self.delta_x = 12.9 + self.size_x;
        
        self.N_x = 12
        self.N_y = 10

    def perform(self, node, inputs, outputs):
        # the method that is used when calling the Op
        coords, = inputs  # this will contain my variables
        coord = np.array(coords)
        xs=coord[0];
        ys=coord[1];
        zs=coord[2];
        # call the log-likelihood function
        logp = None
        # Loop over data points; create box from errors at each point
        if tt.gt(xs,self.lim['x_max']) or tt.lt(xs,self.lim['x_min']) or  \
            tt.gt(ys,self.lim['y_max']) or tt.lt(ys,self.lim['y_min']) or \
            tt.gt(zs,self.lim['z_max']) or tt.lt(zs,self.lim['z_min']):
                is_coord_inside_domain = False;
                logp = -np.inf;
        container_vol = 0;
    
        if logp !=None:
            x = self.init_x - self.delta_x
            for i in np.arange(self.N_x):
                x = x + self.delta_x;
                y = self.init_y - self.delta_y
                for j in np.arange(self.N_y):
                    y = y + self.delta_y
                    if xs>x and xs<x+self.size_x and ys>y and ys<y+self.size_y and zs<self.size_z:
                        is_coord_inside_domain = False;
                        logp = -np.inf;
        
        if logp !=None:
            container_vol = self.N_x*self.size_x*self.N_y*self.size_y*self.size_z;
            domain_vol = (self.lim['x_max']- self.lim['x_min'])* \
                (self.lim['y_max']- self.lim['y_min'])* \
                    (self.lim['z_max']- self.lim['z_min']);
            allowed_vol = domain_vol - container_vol    
            logp = -tt.log(allowed_vol)
        outputs[0][0] = np.array(logp)

But this code do not sample. It return the same value all the time. Could you help me with that?