"""
Text
"""
import os
import urllib.request
import h5py
import numpy as np
import pandas as pd
from species.util import data_util
[docs]def add_leggett(input_path,
database):
"""
Function for adding the Database of Ultracool Parallaxes to the database.
Parameters
----------
input_path : str
Path of the data folder.
database : h5py._hl.files.File
Database.
Returns
-------
NoneType
None
"""
data_file1 = os.path.join(input_path, '2010_phot.xls')
url1 = 'http://staff.gemini.edu/~sleggett/2010_phot.xls'
data_file2 = os.path.join(input_path, 'datafile8.txt')
url2 = 'http://staff.gemini.edu/~sleggett/datafile8.txt'
if not os.path.isfile(data_file1):
print('Downloading Leggett L and T Dwarf Data (88 kB)...', end='', flush=True)
urllib.request.urlretrieve(url1, data_file1)
print(' [DONE]')
if not os.path.isfile(data_file2):
print('Downloading Leggett T6+ and Y Dwarf Data (44 kB)...', end='', flush=True)
urllib.request.urlretrieve(url2, data_file2)
print(' [DONE]')
print('Adding Leggett L and T Dwarf Data...', end='', flush=True)
group = 'photometry/leggett'
database.create_group(group)
dataframe = pd.pandas.read_excel(data_file1)
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.**(-modulus/5.+1.) # (pc)
distance_lower = distance - 10.**(-(modulus+modulus_error)/5.+1.) # (pc)
distance_upper = 10.**(-(modulus-modulus_error)/5.+1.) - distance # (pc)
distance_error = (distance_lower+distance_upper)/2.
name = np.asarray(dataframe['Name'])
sptype = np.asarray(dataframe['Type'])
sptype = data_util.update_sptype(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)
file_io = open(data_file2, 'r')
lines = file_io.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.:
modulus = np.nan
distance = np.append(distance, 10.**(-modulus/5.+1.)) # (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.:
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])
file_io.close()
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()