Source code for pyretlife.retrieval.config_validation
[docs]
def validate_config(config_file: dict):
raise NotImplementedError
### OLD FUNCTION TO BE SPLIT UP
#
# def check_temperature_parameters(config: dict) -> None:
# """
# This function checks if all temperature variables necessary
# for the given parametrization are provided by the user. If not,
# it stops the run.
# """
#
# # TODO: I would recommend to restructure the configuration of the
# # temperature parameters in a more general and structured way,
# # for example something like this:
# # ```
# # TEMPERATURE PARAMETERS:
# # parametrization: polynomial
# # parameters:
# # a_4 = U 2 5 T 3.67756393
# # a_3 = U 0 100 T 40.08733884
# # a_2 = U 0 300 T 136.42147966
# # a_1 = U 0 500 T 182.6557084
# # a_0 = U 0 600 T 292.92802205
# # extra_parameters:
# # dim_z: dimensionality of the latent space
# # file_path: /path/to/learned/PT/model
# # ...
# # (other parameters that are specific to the parametrization)
# # ```
# # This would make it easier to check if all the necessary parameters
# # for the given parametrization are provided, and it would also make
# # it easier to add new parametrizations in the future.
#
# input_pt = list(config["TEMPERATURE PARAMETERS"].keys())
#
# # check if all parameters are there:
# if (
# config["TEMPERATURE PARAMETERS"]["settings_parametrization"]
# == "polynomial"
# ):
# required_params = ["a_" + str(i) for i in range(len(input_pt) - 1)]
# elif "vae_pt" in config["TEMPERATURE PARAMETERS"]["parametrization"]:
# required_params = [
# "z_" + str(i + 1)
# for i in range(
# len(
# [
# input_pt[i]
# for i in range(len(input_pt))
# if "settings" not in input_pt[i]
# ]
# )
# - 2
# )
# ]
# elif config["TEMPERATURE PARAMETERS"]["parametrization"] == "guillot":
# required_params = [
# "log_delta",
# "log_gamma",
# "t_int",
# "t_equ",
# "log_p_trans",
# "alpha",
# ]
# elif config["TEMPERATURE PARAMETERS"]["parametrization"] == "madhuseager":
# required_params = [
# "T0",
# "log_P1",
# "log_P2",
# "log_P3",
# "alpha1",
# "alpha2",
# ]
# elif (
# config["TEMPERATURE PARAMETERS"]["parametrization"]
# == "mod_madhuseager"
# ):
# required_params = ["T0", "log_P1", "log_P2", "alpha1", "alpha2"]
# elif config["TEMPERATURE PARAMETERS"]["parametrization"] == "isothermal":
# required_params = ["T_eq"]
# elif config["TEMPERATURE PARAMETERS"]["parametrization"] == "input":
# required_params = ["input_path"]
# else:
# raise RuntimeError("Unknown PT parametrization.")
#
# if not all(elem in input_pt for elem in required_params):
# missing_params = [_ for _ in required_params if _ not in input_pt]
# raise RuntimeError(
# "Missing one or more PT parameters/knowns. "
# "Make sure these exist:" + str(missing_params)
# )
# def length_scales_test(self):
# """
# Function to scale lengths check form pc
# and earth radii to cm and m
# """
#
# # Check if distance to system is provided
# if "d_syst" not in self.phys_vars:
# raise RuntimeError("ERROR! Distance from the star is missing!")
#
# # Check if radius of the planet is provided
# if "R_pl" not in self.phys_vars:
# raise RuntimeError("ERROR! Planetary radius is missing!")
#
# # Check if radius of the moon is provided
# if self.settings["moon"] == "True":
# if "R_m" not in self.phys_vars:
# raise RuntimeError("ERROR! Moon radius is missing!")
#def ground_pressure_test(self, ind=None):
# # TODO check function
# """
# Function to check if the surface pressure is provided or can
# be calculated from the provided parameters and brings it to
# the correct format for petit radtrans
# """
#
# # Case dependant setting of the surface pressure
# print(self.config)
# if self.settings["clouds"] == "opaque":
# # Choose a surface pressure below the lower cloud deck
# if not (("log_P0" in self.phys_vars) or ("P0" in self.phys_vars)):
# self.phys_vars["log_P0"] = 4
# else:
# if ("log_P0" in self.knowns) or ("P0" in self.knowns):
# if ind is not None:
# if ind == 0:
# if "P0" in self.knowns:
# self.phys_vars["log_P0"] = np.log10(
# self.knowns["P0"]["value"]
# )
# else:
# self.phys_vars["log_P0"] = self.knowns["log_P0"][
# "value"
# ]
# else:
# self.phys_vars["log_P0"] = 4
# else:
# self.phys_vars["log_P0"] = 4
# else:
# raise RuntimeError(
# "ERROR! For opaque cloud models, the surface pressure "
# "P0 is not retrievable!"
# )
# else:
# if "log_P0" not in self.phys_vars:
# if "P0" in self.phys_vars:
# self.phys_vars["log_P0"] = np.log10(self.phys_vars["P0"])
# else:
# print("ERROR! Either log_P0 or P0 is needed!")
# sys.exit()
