spectral_cube brainstorm

brainstorm.py

from spectral_cube import read

mycube = read("ant_COline_5kms_evenbeam.fits")


my_cube.get_data(views, fill='min')  # nan replacement  fill='min'/'max'
my_cube.flatten(views)  # flat, reject nans
my_cube.spectral_slab(30 * u.km / u.s, 50 * u.km / u.s)
my_cube[:5, :10, :10]  # Not implemented, but add. Returns Cube
# [:5, :10, :10] -> (slice(None, 5), slice(None, 10), slice(None, 10))
my_cube.subcube(center=[deg, deg, spectral_coord], size=[deg, deg, spectral_width])  #boundary handling
my_cube.subcube_corners(xlo, xhi, ylo, yhi, vlo, vhi)  #boundary handling

noise = Noise(my_cube)
mad = noise.calculate_mad()
signal = my_cube > (3 * mad)  # returns a functional mask, supports broadcasting

def island_masks(mask):
    lbl = ndimage.label(mask.included())
    return lbl.find_objects()

masks = signal.island_masks()  # long list of masks
my_cube.save_islands(signal, prefix='signal_', format='.fits', padding=4)  # signal_0.fits, signal_1.fits... with WCS


# Plot the mask over the cube
#cube.set_mask(signal)

def contour_on_peak(cube, noise, signal, axis):
    squashed = cube.max(axis=axis)  # numpy array, ignore non-valid pixels
    signal = signal.max(axis=axis) # to be implemented
    plt.imshow(squashed, vmin=0, vmax=noise.mad() * 3)
    plt.contour(signal > 0.5)

def pare_on_volume(signal):
    ndimage.morphology stuff on a boolean mask
    signal.included()  # bools
    #pass to scipy
    # build new mask from a boolean array
    return SpectralCubeMask(mask_of_bools)

def cprops(noise, **kwargs):
    std = noise.snr_cube()
    #...
    return SpectralCubeMask()

# Plot the mask again
mymask.contour_on_peak(scale=10)

# Link the mask back in to the cube
mycube.set_signal(mymask)


with cube.use_mask(~signal, stack=False):
    cube.sum()

cube_with_signal = cube.add_mask(signal)
cube.reset_mask()  # punt for now