beetroots.simulations.astro.forward_map package

Submodules

beetroots.simulations.astro.forward_map.abstract_forward_map module

class beetroots.simulations.astro.forward_map.abstract_forward_map.SimulationForwardMap(simu_name: str, cloud_name: str, max_workers: int, params_names: Dict[str, str], list_lines_fit: List[str], yaml_file: str, path_data: str, path_outputs: str, path_models: str, forward_model_fixed_params: Dict[str, float | None], pixels_of_interest: Dict[int, str] = {}, small_size: int = 16, medium_size: int = 20, bigger_size: int = 24)

Bases: AstroSimulation, ABC

abstract class for to set up the forward map for an inversion of astrophysical data

Parameters:

• simu_name (str) – name of the full inversion procedure, used to name the outputs folder

• cloud_name (str) – name of the observed cloud

• max_workers (int, optional) – maximum number of workers to run the program

• params_names (Dict[str, str]) – pairs of names for each parameter, with first the standard name (to be found as title of column in DataFrames) and second a latex name (to be displayed in figures). For instance, for the thermal pressure “P”: r”$P_{th}$)”

• list_lines_fit (List[str]) – names of the observables used for the inversion

• max_workers – maximum number of workers that can be used for inversion or results extraction, by default 10

• small_size (int, optional) – size for basic text, axes titles, xticks and yticks, by default 16

• medium_size (int, optional) – size of the axis labels, by default 20

• bigger_size (int, optional) – size of the figure title, by default 24

• path_outputs (str) – path to the output folder to be created

scale_dict_fixed_params(scaler: Scaler, dict_fixed_params: Dict[str, float | None]) → Dict[str, float | None]

transforms the fixed parameters from their value in their natural spae to their value in the space in which they are to be sampled.

Note

since the scaler does not accept None or np.NaN, this transformation really needed a dedicated (though simple) method

Parameters:

• scaler (Scaler) – contains the transformation of the physical parameters values from their natural space to their scaled space (in which the sampling happens) and its inverse

• dict_fixed_params (Dict[str, Optional[float]]) – contains the value of the fixed parameters in the physical parameter natural space. For example, {“kappa”:None, “Pth”:None, “G0”:None, “AV”:None, “angle”:0.} (the values with None indicate that the parameter is not fixed, so here, only the angle is set, to 0 degree).

Returns:

contains the value of the fixed parameters in the space in which the physical parameter are to be sampled

Return type:

Dict[str, Optional[float]]

abstract setup_forward_map(**kwargs) → Tuple[Scaler, ForwardMap]

sets up the forward map and the scaler

Returns:

• Scaler – contains the transformation of the Theta values from their natural space to their scaled space (used by the forward map and in which the sampling happens) and its inverse

• ForwardMap – forward map to be used in the inversion

beetroots.simulations.astro.forward_map.abstract_nn module

class beetroots.simulations.astro.forward_map.abstract_nn.SimulationNN(simu_name: str, cloud_name: str, max_workers: int, params_names: Dict[str, str], list_lines_fit: List[str], yaml_file: str, path_data: str, path_outputs: str, path_models: str, forward_model_fixed_params: Dict[str, float | None], pixels_of_interest: Dict[int, str] = {}, small_size: int = 16, medium_size: int = 20, bigger_size: int = 24)

Bases: SimulationForwardMap

abstract class for to set up the forward map to an already defined neural network for an inversion of astrophysical data

Parameters:

• simu_name (str) – name of the full inversion procedure, used to name the outputs folder

• cloud_name (str) – name of the observed cloud

• max_workers (int, optional) – maximum number of workers to run the program

• params_names (Dict[str, str]) – pairs of names for each parameter, with first the standard name (to be found as title of column in DataFrames) and second a latex name (to be displayed in figures). For instance, for the thermal pressure “P”: r”$P_{th}$)”

• list_lines_fit (List[str]) – names of the observables used for the inversion

• max_workers – maximum number of workers that can be used for inversion or results extraction, by default 10

• small_size (int, optional) – size for basic text, axes titles, xticks and yticks, by default 16

• medium_size (int, optional) – size of the axis labels, by default 20

• bigger_size (int, optional) – size of the figure title, by default 24

• path_outputs (str) – path to the output folder to be created

setup_forward_map(forward_model_name: str, force_use_cpu: bool, dict_fixed_params: Dict[str, float | None], dict_is_log_scale_params: Dict[str, bool]) → Tuple[MyScaler, NeuralNetworkApprox]

sets up the forward map and the scaler

Returns:

• Scaler – contains the transformation of the Theta values from their natural space to their scaled space (used by the forward map and in which the sampling happens) and its inverse

• ForwardMap – forward map to be used in the inversion

beetroots.simulations.astro.forward_map.abstract_poly_reg module

class beetroots.simulations.astro.forward_map.abstract_poly_reg.SimulationPolynomialReg(simu_name: str, cloud_name: str, max_workers: int, params_names: Dict[str, str], list_lines_fit: List[str], yaml_file: str, path_data: str, path_outputs: str, path_models: str, forward_model_fixed_params: Dict[str, float | None], pixels_of_interest: Dict[int, str] = {}, small_size: int = 16, medium_size: int = 20, bigger_size: int = 24)

Bases: SimulationForwardMap

abstract class for to set up the forward map to an already defined polynomial for an inversion of astrophysical data

Parameters:

• simu_name (str) – name of the full inversion procedure, used to name the outputs folder

• cloud_name (str) – name of the observed cloud

• max_workers (int, optional) – maximum number of workers to run the program

• params_names (Dict[str, str]) – pairs of names for each parameter, with first the standard name (to be found as title of column in DataFrames) and second a latex name (to be displayed in figures). For instance, for the thermal pressure “P”: r”$P_{th}$)”

• list_lines_fit (List[str]) – names of the observables used for the inversion

• max_workers – maximum number of workers that can be used for inversion or results extraction, by default 10

• small_size (int, optional) – size for basic text, axes titles, xticks and yticks, by default 16

• medium_size (int, optional) – size of the axis labels, by default 20

• bigger_size (int, optional) – size of the figure title, by default 24

• path_outputs (str) – path to the output folder to be created

setup_forward_map(forward_model_name: str, dict_fixed_params: Dict[str, float | None], dict_is_log_scale_params: Dict[str, bool]) → Tuple[MyScaler, PolynomialApprox]

sets up the forward map and the scaler

Returns:

• Scaler – contains the transformation of the Theta values from their natural space to their scaled space (used by the forward map and in which the sampling happens) and its inverse

• ForwardMap – forward map to be used in the inversion

Module contents