def do_step(k,i):
print i
ds = timeseries[i]
#Get the surface and scalars from the first one in the series
surf_poly = ttf.read_step(linesurf[i])
normals = ttf.get_data(surf_poly, 'perp')
parallels = ttf.get_data(surf_poly, 'par')
torsionals = ttf.get_data(surf_poly, 'phi')
#Extract the wave flux mayavi field from the yt dataset
fwfield = util.get_mlab_field_yt(ds, 'wave_flux_x', 'wave_flux_y', 'wave_flux_z', cube_slice=cube_slice)
#Interpolate the Wave flux to the velocity surface
surface_fwave_filter, surface_fwave = ttf.interpolate_vectors(fwfield.outputs[0],
surf_poly)
Fwperp, Fwpar, Fwphi = ttf.get_surface_velocity_comp(surface_fwave,
normals, torsionals,
parallels)
ttf.write_wave_flux(path_join("WaveFlux_%s_%s_%s_%05i.vtp"%(cfg.driver, cfg.str_period, cfg.amp, i+1)),
surf_poly, parallels, normals, torsionals, Fwpar, Fwperp, Fwphi)
Fpar_line[k] = Fwpar[save_index[i]]
Fperp_line[k] = Fwperp[save_index[i]]
Fphi_line[k] = Fwphi[save_index[i]]
def path_join(filename):
return os.path.join(os.path.join(cfg.data_dir,'%s/'%tube_r),filename)
for tube_r in cfg.tube_radii:
print tube_r
wave = glob_files(tube_r, 'Wave*')
Fpar = np.zeros([len(wave)])
Fperp = np.zeros([len(wave)])
Fphi = np.zeros([len(wave)])
for i, awave in enumerate(wave):
surf_poly = ttf.read_step(awave)
Fwperp = ttf.get_data(surf_poly, 'Fwperp')
Fwpar = ttf.get_data(surf_poly, 'Fwpar')
Fwphi = ttf.get_data(surf_poly, 'Fwphi')
Fperp[i] = np.mean(Fwperp)
Fpar[i] = np.mean(Fwpar)
Fphi[i] = np.mean(Fwphi)
Fwpar[np.abs(Fwpar)<1e-5], Fwperp[np.abs(Fwperp)<1e-5], Fwphi[np.abs(Fwphi)<1e-5] = 0., 0., 0.
Fwtot = np.sqrt(Fwpar**2 + Fwperp**2 + Fwphi**2)
Fwpar, Fwperp, Fwphi = (Fwpar/Fwtot)*100, (Fwperp/Fwtot)*100, (Fwphi/Fwtot)*100
Fwpar, Fwperp, Fwphi = map(np.abs, [Fwpar, Fwperp, Fwphi])
Fwpar = np.mean(np.ma.masked_array(Fwpar,np.isnan(Fwpar)))
