Source code for species.data.leggett

"""
Text
"""

import os

import wget
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='') wget.download(url1, out=data_file1, bar=None) print(' [DONE]') if not os.path.isfile(data_file2): print('Downloading Leggett T6+ and Y Dwarf Data (44 kB)...', end='') wget.download(url2, out=data_file2, bar=None) print(' [DONE]') print('Adding Leggett L and T Dwarf Data...', end='') 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] distance = 10.**(-modulus/5.+1.) # [pc] 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='') 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 micron mag[7] = float(item[143:149]) # Spitzer/IRAC 4.5 micron mag[8] = float(item[150:156]) # Spitzer/IRAC 5.8 micron mag[9] = float(item[157:163]) # Spitzer/IRAC 8.0 micron 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=bytes) 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, dtype='f') database.create_dataset(group+'/MKO/NSFCam.Y', data=mag_y, dtype='f') database.create_dataset(group+'/MKO/NSFCam.J', data=mag_j, dtype='f') database.create_dataset(group+'/MKO/NSFCam.H', data=mag_h, dtype='f') database.create_dataset(group+'/MKO/NSFCam.K', data=mag_k, dtype='f') database.create_dataset(group+'/MKO/NSFCam.Lp', data=mag_lp, dtype='f') database.create_dataset(group+'/MKO/NSFCam.Mp', data=mag_mp, dtype='f') database.create_dataset(group+'/Spitzer/IRAC.I1', data=mag_ch1, dtype='f') database.create_dataset(group+'/Spitzer/IRAC.I2', data=mag_ch2, dtype='f') database.create_dataset(group+'/Spitzer/IRAC.I3', data=mag_ch3, dtype='f') database.create_dataset(group+'/Spitzer/IRAC.I4', data=mag_ch4, dtype='f') database.create_dataset(group+'/WISE/WISE.W1', data=mag_w1, dtype='f') database.create_dataset(group+'/WISE/WISE.W2', data=mag_w2, dtype='f') database.create_dataset(group+'/WISE/WISE.W3', data=mag_w3, dtype='f') database.create_dataset(group+'/WISE/WISE.W4', data=mag_w4, dtype='f') print(' [DONE]') database.close()