quarantine.py

""" THIS IS PISSING ME OFF!!! """

def static_wedge_vis(sim_dict, fs):
    raise NotImplementedError("I have only updated" + \
    " the dynamic wedge routine at this moment.")
    
    """
    Read from the wedge data structure @sim_dict
        (specifically, one using the format
        established in load_wedge_sim)
    and generate 3D points appropriate for a wedge plot.
    i.e., return a list of triples:
        (k_perpendicular, k_parallel, power*)
    
    * currently, the implementation uses values that
    should be proportional to power. The final constants
    have not yet been considered.
    
    This function is distinct from
        dynamic_wedge_vis
    in assuming that the simulation corresponding to
        @sim_dict
    runs over only one value of LST.
    """
    wedge_data = []
    
    etas = f2etas(fs)    

    center_f = np.average(fs)
    z = fq2z(center_f)
    lambda_ = C / center_f

    k_par = k_parallel(etas, z)
    k_starter = k_perp(z) / lambda_ # this will need to be
    # multiplied on a per-baseline basis
    
    ### aliasing ###
    nu_idxs = sim_dict['fs']

    sim = sim_dict['sim']

    for ant1 in sim.keys():
        for ant2 in sim[ant1].keys():
            """
            Since we only used one LST,
            we do not need to do any averaging
                (averaging is supposed to happen over LSTs,
                    not frequency)
            """
            k_orth = k_starter * sf.ant.baselength(ant1, ant2)
            for nu_idx in nu_idxs:
                # this is a proportionality.
                # The real deal uses the power equation 6
                    # from Nunhokee et al.
                    
                brightness = sim[ant1][ant2][nu_idx]
                    
                power_prop = np.log10(np.vdot(
                    brightness,
                    brightness
                ))
                
                wedge_datum = np.array([
                    k_orth,
                    k_par[nu_idx],
                    float(power_prop)
                ])

                wedge_data.append(wedge_datum)
                
    return np.array(wedge_data)