"""
Module with reading functionalities for data from individual objects.
"""
import os
from configparser import ConfigParser
from typing import List, Optional, Union, Tuple
import h5py
import numpy as np
from typeguard import typechecked
from species.util.convert_util import apparent_to_absolute
[docs]
class ReadObject:
"""
Class for reading data from an individual object from the database.
"""
@typechecked
def __init__(self, object_name: str) -> None:
"""
Parameters
----------
object_name : str
Object name as stored in the database (e.g. 'beta Pic b',
'PZ Tel B').
Returns
-------
NoneType
None
"""
self.object_name = object_name
config_file = os.path.join(os.getcwd(), "species_config.ini")
config = ConfigParser()
config.read(config_file)
self.database = config["species"]["database"]
with h5py.File(self.database, "r") as h5_file:
if f"objects/{self.object_name}" not in h5_file:
raise ValueError(
f"The object '{self.object_name}' is not "
f"present in the database."
)
[docs]
@typechecked
def list_filters(self, verbose=True) -> List[str]:
"""
Function for listing and returning the filter profile names for
which there is photometric data stored in the database.
Parameters
----------
verbose : bool
Print the filter names if set to ``True``.
Returns
-------
list(str)
List with names of the filter profiles.
"""
filter_list = []
if verbose:
print(f"Available photometric data for {self.object_name}:")
with h5py.File(self.database, "r") as h5_file:
for tel_item in h5_file[f"objects/{self.object_name}"]:
if tel_item not in ["parallax", "distance", "spectrum"]:
for filt_item in h5_file[f"objects/{self.object_name}/{tel_item}"]:
filter_list.append(f"{tel_item}/{filt_item}")
if verbose:
print(f" - {tel_item}/{filt_item}")
return filter_list
[docs]
@typechecked
def get_photometry(self, filter_name: str) -> np.ndarray:
"""
Function for reading photometric data of the object
for a specified filter name.
Parameters
----------
filter_name : str
Filter name as stored in the database. The
:func:`~species.read.read_object.ReadObject.list_filters`
method can be used for listing the filter names for
which photometric data of the object is available.
Returns
-------
np.ndarray
Apparent magnitude, magnitude error (error),
flux (W m-2 um-1), flux error (W m-2 um-1).
"""
with h5py.File(self.database, "r") as h5_file:
if filter_name in h5_file[f"objects/{self.object_name}"]:
obj_phot = np.asarray(
h5_file[f"objects/{self.object_name}/{filter_name}"]
)
else:
raise ValueError(
f"There is no photometric data of {self.object_name} "
f"available with the {filter_name} filter."
)
return obj_phot
[docs]
@typechecked
def get_spectrum(self) -> dict:
"""
Function for reading the spectra and covariance matrices of the
object.
Returns
-------
dict
Dictionary with spectra and covariance matrices.
"""
with h5py.File(self.database, "r") as h5_file:
if f"objects/{self.object_name}/spectrum" in h5_file:
spectrum = {}
for item in h5_file[f"objects/{self.object_name}/spectrum"]:
data_group = f"objects/{self.object_name}/spectrum/{item}"
if f"{data_group}/covariance" not in h5_file:
spectrum[item] = (
np.asarray(h5_file[f"{data_group}/spectrum"]),
None,
None,
h5_file[f"{data_group}"].attrs["specres"],
)
else:
spectrum[item] = (
np.asarray(h5_file[f"{data_group}/spectrum"]),
np.asarray(h5_file[f"{data_group}/covariance"]),
np.asarray(h5_file[f"{data_group}/inv_covariance"]),
h5_file[f"{data_group}"].attrs["specres"],
)
else:
spectrum = None
return spectrum
[docs]
@typechecked
def get_distance(self) -> Tuple[float, float]:
"""
Function for reading the distance to the object.
Returns
-------
float
Distance (pc).
float
Uncertainty (pc).
"""
with h5py.File(self.database, "r") as h5_file:
if f"objects/{self.object_name}/parallax" in h5_file:
parallax = np.asarray(h5_file[f"objects/{self.object_name}/parallax"])
calc_dist = 1.0 / (parallax[0] * 1e-3) # (pc)
dist_plus = 1.0 / ((parallax[0] - parallax[1]) * 1e-3) - calc_dist
dist_minus = calc_dist - 1.0 / ((parallax[0] + parallax[1]) * 1e-3)
distance = (calc_dist, (dist_plus + dist_minus) / 2.0)
elif f"objects/{self.object_name}/distance" in h5_file:
distance = np.asarray(h5_file[f"objects/{self.object_name}/distance"])
else:
raise RuntimeError(
f"Could not read the distance of "
f"{self.object_name}. Please add "
f"the parallax with the add_object "
f"method of the Database class."
)
return distance[0], distance[1]
[docs]
@typechecked
def get_parallax(self) -> Tuple[float, float]:
"""
Function for reading the parallax of the object.
Returns
-------
float
Parallax (mas).
float
Uncertainty (mas).
"""
with h5py.File(self.database, "r") as h5_file:
if f"objects/{self.object_name}/parallax" in h5_file:
obj_parallax = np.asarray(
h5_file[f"objects/{self.object_name}/parallax"]
)
else:
raise RuntimeError(
f"Could not read the parallax of "
f"{self.object_name}. Please add "
f"the parallax with the add_object "
f"method of the Database class."
)
return obj_parallax[0], obj_parallax[1]
[docs]
@typechecked
def get_absmag(
self, filter_name: str
) -> Union[Tuple[float, Optional[float]], Tuple[np.ndarray, Optional[np.ndarray]]]:
"""
Function for calculating the absolute magnitudes of the object
from the apparent magnitudes and distance. The errors on the
apparent magnitude and distance are propagated into an error
on the absolute magnitude.
Parameters
----------
filter_name : str
Filter name as stored in the database.
Returns
-------
float, np.ndarray
Absolute magnitude.
float, np.ndarray
Error on the absolute magnitude.
"""
with h5py.File(self.database, "r") as h5_file:
obj_distance = self.get_distance()
if filter_name in h5_file[f"objects/{self.object_name}"]:
obj_phot = np.asarray(
h5_file[f"objects/{self.object_name}/{filter_name}"]
)
else:
raise ValueError(
f"There is no photometric data of '{self.object_name}' "
f"available with the {filter_name}."
)
if obj_phot.ndim == 1:
abs_mag = apparent_to_absolute(
(obj_phot[0], obj_phot[1]), (obj_distance[0], obj_distance[1])
)
elif obj_phot.ndim == 2:
abs_mag = apparent_to_absolute(
(obj_phot[0, :], obj_phot[1, :]), (obj_distance[0], obj_distance[1])
)
return abs_mag
