Context

class phise.Context(

interferometer: Interferometer,

target: Target,

h: astropy.units.Quantity,

Δh: astropy.units.Quantity,

Γ: astropy.units.Quantity,

monochromatic=False,

name: str = 'Unnamed Context',

)

Bases: object

__init__(

interferometer: Interferometer,

target: Target,

h: astropy.units.Quantity,

Δh: astropy.units.Quantity,

Γ: astropy.units.Quantity,

monochromatic=False,

name: str = 'Unnamed Context',

)

Créer un contexte d’observation.

Paramètres

interferometerInterferometer

Objet décrivant l’instrument et sa géométrie.

targetTarget

Objet décrivant la cible (coordonnées, flux, compagnons).

hastropy.units.Quantity

Heure locale/angle horaire central de l’observation.

Δhastropy.units.Quantity

Durée / intervalle en angle horaire observé.

Γastropy.units.Quantity

Erreur de cophasage RMS (quantité en longueur).

monochromaticbool, optionnel

Si True, utiliser l’approximation monochromatique.

namestr, optionnel

Nom du contexte.

property pf: astropy.units.Quantity

Photon flux in the context

update_photon_flux()

Calculer et stocker le flux photonique reçu par chaque télescope.

Hypothèses

• Le flux spectral de la cible est supposé constant sur la bande Δλ.

• Si Δλ == 0 (cas monochromatique), la valeur est normalisée par 1 nm.

property p: astropy.units.Quantity

Projected position of the telescopes in a plane perpendicular to the line of sight.

project_telescopes_position()

Projeter les positions des télescopes dans un plan perpendiculaire.

Met en place la propriété p (positions projetées en mètres) à partir des positions locales des télescopes et de l’angle horaire h.

plot_projected_positions(

N: int = 11,

return_image=False,

)

Plot the telescope positions over the time.

Parameters:

• N (-)

• return_image (-)

Return type:

• None | Image buffer if return_image is True

get_transmission_maps(

N: int,

) → ndarray[float]

Generate all the kernel-nuller transmission maps for a given resolution

Parameters:

N (-)

Returns:

• - Null outputs map (3 x resolution x resolution)

• - Dark outputs map (6 x resolution x resolution)

• - Kernel outputs map (3 x resolution x resolution)

get_transmission_map_gradient_norm(

N: int,

) → ndarray[float]

Get the norm of the gradient of the transmission maps.

Parameters:

N (-)

Returns:

• - Gradient norm of the null outputs map (3 x resolution x resolution)

• - Gradient norm of the dark outputs map (6 x resolution x resolution)

• - Gradient norm of the kernel outputs map (3 x resolution x resolution)

plot_transmission_map_gradient_norm(

N: int,

return_plot: bool = False,

) → None

Plot the norm of the gradient of the transmission maps.

Parameters:

• N (-)

• return_plot (-)

Return type:

• None | Image buffer if return_plot is True

get_input_fields() → ndarray[complex]

Get the complexe amplitude of the signals acquired by the telescopes.

Return type:

• (nb_companions + 1, nb_telescope) array of acquired signals (complex amplitudes)

get_h_range() → ndarray[float]

Get the hour angle range of the observation.

Return type:

• Hour angle range (in radian)

observe_monochromatic()

Observe the target in this context with monochromatic approximations.

Returns:

• - Dark data (6,) - # of photons events

• - Kernel data (3,) - # of photons events

• - Bright data (1,) - # of photons events

observe(spectral_samples=5)

Observe the target in this context.

Parameters:

spectral_samples (-)

Returns:

• - Dark data (6,) - # of photons events

• - Kernel data (3,) - # of photons events

• - Bright data (1,) - # of photons events

observation_serie(n: int = 1) → ndarray[int]

Generate a series of observations in this context.

Parameters:

n (-)

Returns:

• - Dark data (n, n_h, 6) - # of photons events

• - Kernel data (n, n_h, 3) - # of photons events

• - Bright data (n, n_h) - # of photons events

calibrate_gen(

β: float,

verbose: bool = False,

plot: bool = False,

figsize: tuple = (10, 10),

) → dict

Optimize the phase shifters offsets to maximize the nulling performance.

Parameters:

• β (-)

• verbose (-)

• plot (-)

• figsize (-)

Returns:

- Dict

Return type:

Dictionary with the optimization history (optional)

calibrate_obs(

n: int = 1000,

plot: bool = False,

figsize: tuple[int] = (30, 20),

)

Optimize the phase shifters offsets to maximize the nulling performance.

Parameters:

• n (-)

• plot (-)

• figsize (-)

Returns:

- Context

Return type:

New context with the optimized kernel nuller

get_VLTI() → Context

Get the default context for the analysis. This context uses:

• The VLTI with 4 UTs.

• The first generation of active kernel nuller.

• Vega as target star and an hypothetical companion at 2 mas with a contrast of 1e-6.

get_LIFE() → Context

Get the default context for the analysis. This context uses:

• The 4 telescopes of LIFE

• The first generation of active kernel nuller.

• Vega as target star and an hypothetical companion at 2 mas with a contrast of 1e-6.