"""
Module for the photometric data of L, T, Y dwarfs from Leggett et al.
"""
from pathlib import Path
import h5py
import numpy as np
import pandas as pd
import pooch
from typeguard import typechecked
from species.util.data_util import update_sptype
[docs]
@typechecked
def add_leggett(input_path: str, database: h5py._hl.files.File) -> None:
"""
Function for adding photometric data of L, T, and Y dwarfs from
Leggett et al. to the database.
Parameters
----------
input_path : str
Path of the data folder.
database : h5py._hl.files.File
The HDF5 database that has been opened.
Returns
-------
NoneType
None
"""
input_file_1 = "2010_phot.xls"
data_file_1 = Path(input_path) / input_file_1
url_1 = "http://staff.gemini.edu/~sleggett/2010_phot.xls"
if not data_file_1.exists():
print()
pooch.retrieve(
url=url_1,
known_hash=None,
fname=input_file_1,
path=input_path,
progressbar=True,
)
input_file_2 = "datafile8.txt"
data_file_2 = Path(input_path) / input_file_2
url_2 = "http://staff.gemini.edu/~sleggett/datafile8.txt"
if not data_file_2.exists():
print()
pooch.retrieve(
url=url_2,
known_hash=None,
fname=input_file_2,
path=input_path,
progressbar=True,
)
print("\nAdding Leggett L and T Dwarf Data...", end="", flush=True)
group = "photometry/leggett"
database.create_group(group)
dataframe = pd.pandas.read_excel(data_file_1)
dataframe.columns = dataframe.columns.str.replace("'", "")
modulus = np.asarray(dataframe["M-m"]) # M-m (mag)
modulus_error = np.asarray(dataframe["Mmerr"]) # M-m (mag)
distance = 10.0 ** (-modulus / 5.0 + 1.0) # (pc)
distance_lower = distance - 10.0 ** (-(modulus + modulus_error) / 5.0 + 1.0) # (pc)
distance_upper = 10.0 ** (-(modulus - modulus_error) / 5.0 + 1.0) - distance # (pc)
distance_error = (distance_lower + distance_upper) / 2.0
name = np.asarray(dataframe["Name"])
# Near-infrared spectral type
sptype = np.asarray(dataframe["Type"])
sptype = update_sptype(sptype)
sptype = np.asarray(sptype)
mag_y = np.asarray(dataframe["Y"])
mag_j = np.asarray(dataframe["J"])
mag_h = np.asarray(dataframe["H"])
mag_k = np.asarray(dataframe["K"])
mag_lp = np.asarray(dataframe["L"])
mag_mp = np.asarray(dataframe["M"])
mag_ch1 = np.asarray(dataframe["Ch1"])
mag_ch2 = np.asarray(dataframe["Ch2"])
mag_ch3 = np.asarray(dataframe["Ch3"])
mag_ch4 = np.asarray(dataframe["Ch4"])
mag_w1 = np.repeat(np.nan, np.size(name))
mag_w2 = np.repeat(np.nan, np.size(name))
mag_w3 = np.repeat(np.nan, np.size(name))
mag_w4 = np.repeat(np.nan, np.size(name))
print(" [DONE]")
print("Adding Leggett T6+ and Y Dwarf Data...", end="", flush=True)
with open(data_file_2, "r", encoding="utf-8") as open_file:
lines = open_file.readlines()[69:]
for item in lines:
name = np.append(name, item[0:16])
spt_tmp = item[62:66]
if spt_tmp[0] == "2":
spt_tmp = "T" + spt_tmp[1]
elif spt_tmp[0] == "3":
spt_tmp = "Y" + spt_tmp[1]
sptype = np.append(sptype, spt_tmp)
modulus = float(item[67:73]) # M-m (mag)
if modulus == 999.0:
modulus = np.nan
distance = np.append(distance, 10.0 ** (-modulus / 5.0 + 1.0)) # (pc)
mag = np.zeros(14)
mag[0] = float(item[95:101]) # MKO Y
mag[1] = float(item[102:107]) # MKO J
mag[2] = float(item[108:114]) # MKO H
mag[3] = float(item[115:121]) # MKO K
mag[4] = float(item[122:128]) # MKO L'
mag[5] = float(item[129:135]) # MKO M'
mag[6] = float(item[136:142]) # Spitzer/IRAC 3.6 um
mag[7] = float(item[143:149]) # Spitzer/IRAC 4.5 um
mag[8] = float(item[150:156]) # Spitzer/IRAC 5.8 um
mag[9] = float(item[157:163]) # Spitzer/IRAC 8.0 um
mag[10] = float(item[164:170]) # WISE W1
mag[11] = float(item[171:176]) # WISE W2
mag[12] = float(item[177:183]) # WISE W3
mag[13] = float(item[184:190]) # WISE W4
for j, mag_item in enumerate(mag):
if mag_item == 999.0:
mag[j] = np.nan
mag_y = np.append(mag_y, mag[0])
mag_j = np.append(mag_j, mag[1])
mag_h = np.append(mag_h, mag[2])
mag_k = np.append(mag_k, mag[3])
mag_lp = np.append(mag_lp, mag[4])
mag_mp = np.append(mag_mp, mag[5])
mag_ch1 = np.append(mag_ch1, mag[6])
mag_ch2 = np.append(mag_ch2, mag[7])
mag_ch3 = np.append(mag_ch3, mag[8])
mag_ch4 = np.append(mag_ch4, mag[9])
mag_w1 = np.append(mag_w1, mag[10])
mag_w2 = np.append(mag_w2, mag[11])
mag_w3 = np.append(mag_w3, mag[12])
mag_w4 = np.append(mag_w4, mag[13])
dtype = h5py.special_dtype(vlen=str)
dset = database.create_dataset(group + "/name", (np.size(name),), dtype=dtype)
dset[...] = name
dset = database.create_dataset(group + "/sptype", (np.size(sptype),), dtype=dtype)
dset[...] = sptype
flag = np.repeat("null", np.size(name))
dset = database.create_dataset(group + "/flag", (np.size(flag),), dtype=dtype)
dset[...] = flag
database.create_dataset(group + "/distance", data=distance)
database.create_dataset(group + "/distance_error", data=distance_error)
database.create_dataset(group + "/MKO/NSFCam.Y", data=mag_y)
database.create_dataset(group + "/MKO/NSFCam.J", data=mag_j)
database.create_dataset(group + "/MKO/NSFCam.H", data=mag_h)
database.create_dataset(group + "/MKO/NSFCam.K", data=mag_k)
database.create_dataset(group + "/MKO/NSFCam.Lp", data=mag_lp)
database.create_dataset(group + "/MKO/NSFCam.Mp", data=mag_mp)
database.create_dataset(group + "/Spitzer/IRAC.I1", data=mag_ch1)
database.create_dataset(group + "/Spitzer/IRAC.I2", data=mag_ch2)
database.create_dataset(group + "/Spitzer/IRAC.I3", data=mag_ch3)
database.create_dataset(group + "/Spitzer/IRAC.I4", data=mag_ch4)
database.create_dataset(group + "/WISE/WISE.W1", data=mag_w1)
database.create_dataset(group + "/WISE/WISE.W2", data=mag_w2)
database.create_dataset(group + "/WISE/WISE.W3", data=mag_w3)
database.create_dataset(group + "/WISE/WISE.W4", data=mag_w4)
print(" [DONE]")
database.close()
