def fittingLatex(self):
from taurex.util.util import decode_string_array
if not self.is_retrieval:
raise Exception(
'HDF5 was not generated from retrieval, no fitting latex found'
)
return decode_string_array(self.fd['Optimizer']['fit_parameter_latex'])
def load_generic_profile_from_hdf5(loc,
module,
identifier,
profile_type=None,
premade_dict=None,
replacement_dict=None):
if profile_type is None:
profile_type = loc[identifier][()]
temp_keys = list(loc.keys())
klass = class_for_name(module, profile_type)
klass_kwargs = get_klass_args(klass)
args_dict = premade_dict or {}
repl_dict = replacement_dict or {}
for kw in klass_kwargs:
if kw in temp_keys:
v = loc[kw][()]
if isinstance(v, np.ndarray) and v.dtype.type is np.string_:
from taurex.util.util import decode_string_array
v = decode_string_array(v)
if kw in repl_dict:
args_dict[kw] = repl_dict[kw]
else:
args_dict[kw] = v
return klass(**args_dict)
def load_chemistry_from_hdf5(loc, replacement_dict=None):
from taurex.data.profiles.chemistry import TaurexChemistry
from taurex.util.util import decode_string_array
chemistry = load_generic_profile_from_hdf5(
loc['Chemistry'],
'taurex.data.profiles.chemistry',
'chemistry_type',
replacement_dict=replacement_dict)
if isinstance(chemistry, TaurexChemistry):
for mol in decode_string_array(loc['Chemistry']['active_gases'][()]):
if mol not in chemistry._fill_gases:
chemistry.addGas(load_gas_from_hdf5(loc['Chemistry'], mol))
for mol in decode_string_array(loc['Chemistry']['inactive_gases']):
if mol not in chemistry._fill_gases:
chemistry.addGas(load_gas_from_hdf5(loc['Chemistry'], mol))
return chemistry
def derivedLatex(self):
from taurex.util.util import decode_string_array
if not self.is_retrieval:
raise Exception(
'HDF5 was not generated from retrieval, no fitting latex found'
)
try:
array = decode_string_array(
self.fd['Optimizer']['derived_parameter_latex'])
return [f'{c} (derived)' for c in array]
except KeyError:
return ['$\mu$ (derived)']
def inactiveGases(self):
return decode_string_array(
self.fd['ModelParameters']['Chemistry']['inactive_gases'])
def condensates(self):
return decode_string_array(
self.fd['ModelParameters']['Chemistry']['condensates'])