run_adv_and_diffusion_OSNAP_EG(fieldset, num_of_particles, seed=0, dt=12,
                                   out_dt=24, adv_time=2, run_dir=1, prefix="",
                                   kernels=(AdvectionRK4, ),fcode = '',
                                   fvar = ['a','b']):

    # default is forward plus = forward / - = backward 

    if run_dir == 1:
        rstr = 'forward'
    else:
        rstr = 'backward'  
    
    # falls partikel aus der Domaine laufen
    
    def DeleteParticle(particle, fieldset, time):
        particle.delete()
        
    # legt anfangszustand von Zufallsgenerator fest    
    np.random.seed(seed)
    
    # lon_argo / lat_argo ranges
    # release mask anlegen - land 0 ozean 1 - darin zufällig startpunkte wählen
  
    # OSNAP East Greenland    
    lo = np.random.uniform(-41.95, -37.798262, num_of_particles)
    la = -0.09913834*lo + 55.82196759

    pset = ParticleSet(fieldset=fieldset,
                       pclass=JITParticle,
                       lon=lo,
                       lat=la,
                       depth=np.array([0 for n in range(num_of_particles)]))

    output_file = ParticleFile(
                     name=(prefix + "_OSNAP_EG_" + rstr),
                     particleset=pset,
                     outputdt=timedelta(hours=out_dt))
    
    #kernels = pset.Kernel(AdvectionRK4) + Roach_Diff
    kernel = pset.Kernel(kernels[0])
    for k in kernels[1:]:
        ke = Kernel(fieldset=fieldset, ptype=JITParticle.getPType(), funcname=k.__name__, pyfunc=k,
                   funccode=fcode, funcvars=fvar)
        kernel += ke
    
    # läuft 5 tage mit 5 min time delta und output zeiten alle 12 stunden
    pset.execute(kernel,
                 runtime=timedelta(days=365*adv_time),
                 dt=timedelta(hours=dt*run_dir),
                 output_file=output_file,
                 recovery={
                     ErrorCode.ErrorOutOfBounds: DeleteParticle})
    
    output_file.dataset.close()


%%file _Diff_hom_200.py  

def _Diff_hom_200(particle, fieldset, time):
    # Kernel for Roach effective Diffusion with 25th persent of diffusivity
    
    alpha=1
    # Diffusivities are defined above
    Ky = 200 * fieldset.Ky[time, particle.depth, particle.lat, particle.lon]
    Kx = 200 * fieldset.Kx[time, particle.depth, particle.lat, particle.lon]
   
    particle.lat += random.normalvariate(0, 1) * alpha * math.sqrt(2*Ky*math.fabs(particle.dt))
    particle.lon += random.normalvariate(0, 1)* alpha * math.sqrt(2*Kx*math.fabs(particle.dt))



from _Diff_hom_200 import _Diff_hom_200
_funccode_Diff_hom_200 = inspect.getsource(_Diff_hom_200.__code__) 
_funcvars_Diff_hom_200 = list(_Diff_hom_200.__code__.co_varnames)


seeds = db.from_sequence(range(120), npartitions=30)
ff = path

runs = seeds.map(lambda s: 
                         run_adv_and_diffusion_OSNAP_EG(
                             fieldset=fieldset_mean, num_of_particles = int(100), seed=s, dt=12,
                             out_dt=24, adv_time=4, run_dir=run_dir,
                             prefix= f"{ff}" + "/{:03d}_Diff_hom_200_Adv".format(s),
                             kernels=(AdvectionRK4, _Diff_hom_200),fcode =_funccode_Diff_hom_200,
                             fvar =_funcvars_Diff_hom_200)
                        )