Adding of Noise

Gaussian noise can be added in order to account for random background.

With

you can add random noise to a synthetic measurement val with

  • 3D (x, y, wav)
  • 2D (x, y)

dimesion for the different FC_objects, respectively.

Note

In case of a 3D SyntheticCube different noise is added to every slice.

In any case add to your script:

# add noise
noise = FC_object.add_noise(mu_noise=0, sigma_noise=1e-13, seed=2, diagnostics=None)

The mu_noise and sigma_noise are given in the same units as the enteries of the image(s). If diagnostics=True an output of the noise will be created with the name test_cube_process-output_SC-noise.fits. Here you can see an example.

Warning

add_noise will not work for objects of SyntheticSED or SyntheticFlux.

Example: Plot

If the FC_object is a SyntheticCube, you can produce an image output by following the instruction Image Plots.

The essentials are given here; add to your script:

# plot noise.val at 60 microns
noise.plot_image(name='noise', wav_interest=60., set_cut=(1e-14, 9.4e-13),
                 single_cut=None, multi_cut=None, dpi=300)

In this case you will find the file test_cube_image_noise_set_cut_1.00e-14_9.40e-13_46.42_68.13.png in the same directory as example.py. If you extend the example described in SyntheticCube, the image will be similar the following.

../_images/test_cube_image_noise_set_cut_1.00e-14_9.40e-13_46.42_68.13.png

Note

The plot is not exactly the same at the previous cases, since we used random noise. Only if you fixed it with the seed option.

If the FC_objects is a SyntheticImage, because it was already convolved with a filter before, you plot with the following:

# plot noise.val (2D) at noise.wav
noise.plot_image(name='noise', set_cut=(1e-14, 9.4e-13), single_cut=None,
                 multi_cut=None, dpi=300)

In this case you will find the file test_cube_image_noise_set_cut_1.00e-14_9.40e-13_*.png in the same directory as example.py, where * stands for the filter limits.