def filter_by_tile():
"""
Splits the input data tables into 16 tiles, avoiding overlap regions.
Uses the spatial extent of the `maxvars` spreadsheet to figure out
where the bounds of the tiles are.
Only returns data pertaining to our 1202 variables.
Returns
-------
tile_tables : list of atpy.Table
A list of the variable star photometry tables
corresponding to each tile.
tile_spreadsheets : list of atpy.Table
A list of the variable star variability spreadsheets
corresponding to each table.
"""
vp = variables_photometry
max_ra = maxvars.RA.max()
min_ra = maxvars.RA.min()
max_dec = maxvars.DEC.max()
min_dec = maxvars.DEC.min()
tile_size_ra = (max_ra - min_ra) / 4
tile_size_dec = (max_dec - min_dec) / 4
tile_tables = []
tile_spreadsheets = []
for ra_i in range(4):
for dec_j in range(4):
tile_min_ra = min_ra + tile_size_ra*ra_i + tile_size_ra/10
tile_max_ra = min_ra + tile_size_ra*(ra_i+1) - tile_size_ra/10
tile_min_dec = min_dec + tile_size_dec*dec_j + tile_size_dec/10
tile_max_dec = min_dec + tile_size_dec*(dec_j+1) - tile_size_dec/10
tile_photometry = vp.where(
(vp.RA > tile_min_ra) & (vp.RA < tile_max_ra) &
(vp.DEC > tile_min_dec) & (vp.DEC < tile_max_dec) )
tile_spreadsheet = ukvar_spread.where(
(ukvar_spread.RA > tile_min_ra) &
(ukvar_spread.RA < tile_max_ra) &
(ukvar_spread.DEC > tile_min_dec) &
(ukvar_spread.DEC < tile_max_dec) )
tile_tables.append(tile_photometry)
tile_spreadsheets.append(tile_spreadsheet)
return tile_tables, tile_spreadsheets
def t_table3_variability_nonperiodics_bymegeathclass(write=False):
"""
Generates Table 3, which also comes in three pieces.
Only non-periodic stars; has many of the same columns as Table 2.
Current columns:
N_j, N_h, N_k : three integers
Delta-J, H, K: three floats
Delta-J-H, J-K, H-K: three floats
Color slopes (JH, HK, JHK): three floats
Stetson : one float
Stetson_choice : one string
Data quality : int (0, 1, 2) corresponding to subjective/auto/strict
Parameters
----------
write : bool, optional (default False)
Write to disk? Either way, this function returns an ATpy table.
"""
table = astropy.table.Table()
table.table_name = "Table 3"
nonperiodics = ukvar_spread.where(ukvar_spread.periodic == 0)
megeath_class_by_nonperiodics = make_megeath_class_column()[ukvar_spread.periodic == 0]
# Do some stuff where we blank out color slopes that are no good
jhk_slope_column = nonperiodics.jhk_slope
jhk_slope_error = nonperiodics.jhk_slope_err
jhk_slope_column[~np.in1d(nonperiodics.SOURCEID, jhk_slope_reference.SOURCEID)] = np.nan
jhk_slope_error[~np.in1d(nonperiodics.SOURCEID, jhk_slope_reference.SOURCEID)] = np.nan
jjh_slope_column = nonperiodics.jjh_slope
jjh_slope_error = nonperiodics.jjh_slope_err
jjh_slope_column[~np.in1d(nonperiodics.SOURCEID, jh_slope_reference.SOURCEID)] = np.nan
jjh_slope_error[~np.in1d(nonperiodics.SOURCEID, jh_slope_reference.SOURCEID)] = np.nan
khk_slope_column = nonperiodics.khk_slope
khk_slope_error = nonperiodics.khk_slope_err
khk_slope_column[~np.in1d(nonperiodics.SOURCEID, hk_slope_reference.SOURCEID)] = np.nan
khk_slope_error[~np.in1d(nonperiodics.SOURCEID, hk_slope_reference.SOURCEID)] = np.nan
columns_data_and_formats = [
('ONCvar ID', nonperiodics.UKvar_ID, '%i'),
('N_J', nonperiodics.N_j, '%i'),
('N_H', nonperiodics.N_h, '%i'),
('N_K', nonperiodics.N_k, '%i'),
('J mag range (robust)', nonperiodics.j_ranger, '%.3f'),
('H mag range (robust)', nonperiodics.h_ranger, '%.3f'),
('K mag range (robust)', nonperiodics.k_ranger, '%.3f'),
('J-H range (robust)', nonperiodics.jmh_ranger, '%.3f'),
('H-K range (robust)', nonperiodics.hmk_ranger, '%.3f'),
('(J-H), (H-K) color slope', jhk_slope_column, '%.3f'),
('jhk_slope_error', jhk_slope_error, '%.3f'),
('J, (J-H) color slope', jjh_slope_column, '%.3f'),
('jjh_slope_error', jjh_slope_error, '%.3f'),
('K, (H-K) color slope', khk_slope_column, '%.3f'),
('khk_slope_error', khk_slope_error, '%.3f'),
('Stetson Variability Index', nonperiodics.Stetson, '%.3f'),
('Bands used to compute Stetson', nonperiodics.Stetson_choice, '%s'),
('Data quality flag', nonperiodics.autovar + nonperiodics.strict, '%i'),
('Class', megeath_class_by_nonperiodics, '%s')
]
# Now split it into three pieces and compute medians!
column_to_format = {}
for column, data, format in columns_data_and_formats:
table[column] = data
column_to_format[column] = format
if write:
table.write(output_directory+"AJ_table_5.txt", format='ascii.basic', delimiter="&", formats=column_to_format)
return table
def t_table2_variability_periods_periodics_bymegeathclass(write=False):
"""
Generates Table 2, which comes in three pieces.
Note that we're only using periodic stars here!
TODO: add in flags for whether a given star has valid
J, H, or K data, respectively
Current columns:
N_j, N_h, N_k : three integers
Delta-J, H, K: three floats
Delta-J-H, J-K, H-K: three floats
Color slopes (JH, HK, JHK): three floats
Stetson : one float
Stetson_choice : one string
Period : one float
Data quality : int (0, 1, 2) corresponding to subjective/auto/strict
Parameters
----------
write : bool, optional (default False)
Write to disk? Either way, this function returns an ATpy table.
"""
table = astropy.table.Table()
table.table_name = "Table 2"
periodics = ukvar_spread.where(ukvar_spread.periodic != 0)
periodic_periods = ukvar_periods[ukvar_spread.periodic != 0]
megeath_class_by_periodics = make_megeath_class_column()[ukvar_spread.periodic != 0]
# Do some stuff where we blank out color slopes that are no good
jhk_slope_column = periodics.jhk_slope
jhk_slope_error = periodics.jhk_slope_err
jhk_slope_column[~np.in1d(periodics.SOURCEID,
jhk_slope_reference.SOURCEID)] = np.nan
jhk_slope_error[~np.in1d(periodics.SOURCEID,
jhk_slope_reference.SOURCEID)] = np.nan
jjh_slope_column = periodics.jjh_slope
jjh_slope_error = periodics.jjh_slope_err
jjh_slope_column[~np.in1d(periodics.SOURCEID,
jh_slope_reference.SOURCEID)] = np.nan
jjh_slope_error[~np.in1d(periodics.SOURCEID,
jh_slope_reference.SOURCEID)] = np.nan
khk_slope_column = periodics.khk_slope
khk_slope_error = periodics.khk_slope_err
khk_slope_column[~np.in1d(periodics.SOURCEID,
hk_slope_reference.SOURCEID)] = np.nan
khk_slope_error[~np.in1d(periodics.SOURCEID,
hk_slope_reference.SOURCEID)] = np.nan
columns_data_and_formats = [
('ONCvar ID', periodics.UKvar_ID, '%i'),
('N_J', periodics.N_j, '%i'),
('N_H', periodics.N_h, '%i'),
('N_K', periodics.N_k, '%i'),
('J mag range (robust)', periodics.j_ranger, '%.3f'),
('H mag range (robust)', periodics.h_ranger, '%.3f'),
('K mag range (robust)', periodics.k_ranger, '%.3f'),
('J-H range (robust)', periodics.jmh_ranger, '%.3f'),
('H-K range (robust)', periodics.hmk_ranger, '%.3f'),
('(J-H), (H-K) color slope', jhk_slope_column, '%.3f'),
('jhk_slope_error', jhk_slope_error, '%.3f'),
('J, (J-H) color slope', jjh_slope_column, '%.3f'),
('jjh_slope_error', jjh_slope_error, '%.3f'),
('K, (H-K) color slope', khk_slope_column, '%.3f'),
('khk_slope_error', khk_slope_error, '%.3f'),
('Stetson Variability Index', periodics.Stetson, '%.3f'),
('Bands used to compute Stetson', periodics.Stetson_choice, '%s'),
('Best-fit period', periodic_periods, '%.4f'),
('Data quality flag', periodics.autovar + periodics.strict, '%i'),
('Class', megeath_class_by_periodics, '%s')
]
column_to_format = {}
for column, data, format in columns_data_and_formats:
table[column] = data
column_to_format[column] = format
if write:
table.write(output_directory+"AJ_table_4.txt", format='ascii.basic', delimiter="&", formats=column_to_format)
return table
import matplotlib.pyplot as plt
import astropy.table
import plot3
from variables_data_filterer import source_photometry
from tablemate_comparisons import ukvar_spread, ukvar_periods
from table_maker import make_megeath_class_column, megeath2012_by_ukvar
megeath_class_column = make_megeath_class_column()
path = os.path.expanduser("~/Dropbox/Bo_Tom/aux_catalogs/dipper_counting/")
aatau_ids = np.sort(np.loadtxt(path+'periodic_sub20days_AATau_analogs.txt'))
# this only works if ukvar_spread['UKvar_ID'] is monotonically increasing
aatau_spread = ukvar_spread.where(np.in1d(ukvar_spread['UKvar_ID'], aatau_ids))
aatau_periods = ukvar_periods[np.in1d(ukvar_spread['UKvar_ID'], aatau_ids)]
aatau_classes = megeath_class_column[np.in1d(ukvar_spread['UKvar_ID'], aatau_ids)]
aatau_megeath = megeath2012_by_ukvar.where(np.in1d(ukvar_spread['UKvar_ID'], aatau_ids))
# things to compute:
# 1. number of AA Taus, versus total number of periodic variables
num_aataus = len(aatau_spread)
num_variables = len(ukvar_spread)
num_periodics = np.sum(~np.isnan(ukvar_periods))
print "There are {0} total variables, and {1} total periodic variables.".format(num_variables, num_periodics)
print "Of these, {0} are AA Taus (periodic 'dippers').".format(num_aataus)
def f_magnitude_hists_by_class(threepanels=True, onepanels=False):
"""
Makes a series of multipanel histograms of variability.
Uses "strict" sources only for these.
"""
megeath_class_column = make_megeath_class_column()
strict_protostars = ukvar_spread.where(
(ukvar_spread.strict == 1) & (megeath_class_column == 'P'))
strict_disks = ukvar_spread.where(
(ukvar_spread.strict == 1) & (megeath_class_column == 'D'))
strict_nondisks = ukvar_spread.where(
(ukvar_spread.strict == 1) & (megeath_class_column == 'ND'))
print "Protostars: %d, Disks: %d, Nondisks: %d" % (
len(strict_protostars), len(strict_disks), len(strict_nondisks) )
# Let's test the J mag aspect of this, and then define some dicts or forloops to iterate through all "5" bands.
names = ['J mag', 'H mag', 'K mag', '(J-H) color', '(H-K) color']
bands = ['j', 'h', 'k', 'jmh', 'hmk']
text_xposition = [0.375, 0.375, 0.375, 0.275, 0.275]
figs = []
hist_kwargs = {'range':(0,2), 'bins':20}
if threepanels:
for b, n, x in zip(bands, names, text_xposition):
j_fig = plt.figure(figsize=(5,6))
figs.append(j_fig)
jsub1 = plt.subplot(3,1,1)
jsub1.hist(strict_protostars['%s_ranger' % b], color=color_dict['protostar'],
**hist_kwargs)
jsub1.text(x, 0.65, "protostars \n"
r"median $\Delta %s: $%.2f \pm %.2f$" % (
n.replace(' ', '$ '),
np.median(strict_protostars['%s_ranger' % b]),
rb.mad(strict_protostars['%s_ranger' % b])),
transform = jsub1.transAxes)
jsub2 = plt.subplot(3,1,2, sharex=jsub1)
jsub2.hist(strict_disks['%s_ranger' % b], color=color_dict['disk'], **hist_kwargs)
jsub2.text(x, 0.65, "disks \n"
r"median $\Delta %s: $%.2f \pm %.2f$" % (
n.replace(' ', '$ '),
np.median(strict_disks['%s_ranger' % b]),
rb.mad(strict_disks['%s_ranger' % b])),
transform = jsub2.transAxes)
jsub3 = plt.subplot(3,1,3, sharex=jsub1)
jsub3.hist(strict_nondisks['%s_ranger' % b], color=color_dict['nondisk'],
**hist_kwargs)
jsub3.text(x, 0.65, "non-disks \n"
r"median $\Delta %s: $%.2f \pm %.2f$" % (
n.replace(' ', '$ '),
np.median(strict_nondisks['%s_ranger' % b]),
rb.mad(strict_nondisks['%s_ranger' % b])),
transform = jsub3.transAxes)
jsub1.set_title("%s range for $Q=2$ variables"%n)
jsub3.set_xlabel(r"$\Delta %s (outlier-proof)" %
n.replace(' ', '$ '))
if onepanels:
fig = plt.figure()
figs.append(fig)
plt.hist(strict_nondisks['k_ranger'],
color=color_dict['nondisk'], hatch='/', label='Megeath Non-disks',
**hist_kwargs)
plt.hist(strict_disks['k_ranger'],
color=color_dict['disk'], alpha=0.5, hatch='\\', label='Megeath Disks',
**hist_kwargs)
plt.hist(strict_protostars['k_ranger'],
color=color_dict['protostar'], hatch='--', label='Megeath Protostars',
**hist_kwargs)
plt.title("K magnitude range (robust) for pristine-data variables")
plt.xlabel(r"$\Delta K$ magnitude (outlier-proof)")
plt.legend()
plt.show()
return figs
def f_color_slopes_and_periods(title1="K, H-K color slope distributions: "
"Periodic vs Non-Periodic",
title2="Color slopes versus periods, "
"for periodic stars"):
"""
Generates two figures relating K, H-K color slopes with periods.
a) two-panel histogram (purple/yellow) of color slopes for periodic
vs nonperiodic stars.
b) Scatter plot of color slope versus period, for stars with
well-defined color-slopes and periods.
"""
selection = np.in1d(ukvar_spread.SOURCEID, hk_filled.SOURCEID)
color_periodics = ukvar_spread.where(selection &
(ukvar_spread.periodic > 0))
color_periods = ukvar_periods[selection & (ukvar_spread.periodic > 0)]
color_nonperiodics = ukvar_spread.where(selection &
(ukvar_spread.periodic == 0))
# Fig 1 is the double histogram.
fig1 = plt.figure()
sub1 = plt.subplot(2,1,1)
sub1.hist(np.degrees(np.arctan(color_periodics.khk_slope)),
range=[-90, 90], bins=36, color='m')
plt.text(-45,4, "K, H-K color slopes for Periodic stars")
sub1.set_title(title1)
sub2 = plt.subplot(2,1,2, sharex=sub1)
sub2.hist(np.degrees(np.arctan(color_nonperiodics.khk_slope)),
range=[-90, 90], bins=36, color='y')
plt.text(-45,4, "K, H-K color slopes for Non-Periodic stars")
sub2.set_xlabel("K, H-K color slope (degrees)")
sub2.set_xlim(-90,90)
sub2.set_xticks([-90,-45,0,45,90])
# Fig 2 is the scatterplot.
fig2 = plt.figure()
plt.plot(color_periods,
np.degrees(np.arctan(color_periodics.khk_slope)), 'ko')
plt.xlabel("Period, days")
plt.ylabel("K, H-K color slope (degrees)")
plt.ylim(-90, 90)
plt.yticks([-90, -45, 0, 45, 90])
plt.gca().set_yticks(np.arange(-90, 90, 15), minor=True)
plt.text(30,-60, "Disk activity")
plt.text(30,60, "Dust (A_V)")
plt.title(title2)
plt.show()
return fig1, fig2
plt.legend()
plt.xlabel(r"Spectral index $\alpha_{IRAC}$", fontsize=18)
plt.ylabel("Stetson Index", fontsize=18)
plt.title(r"Stetson vs $\alpha$ for Spitzer-selected sources")
plt.semilogy()
plt.show()
return mated_spitzer, mated_P, mated_D, fig, fig2
# crude copy
ukvar_spread = ukvar_s.where(ukvar_s.SOURCEID > 0)
ukvar_spread.add_column('Period', ukvar_periods)
# Let's do question two first.
# so... disk selection works like this:
# if
# (J-H) > 1.714*(H-K)
# then it's a "photosphere"
# if
# (J-H) < 1.714*(H-K) and (J-H) > 1.714*(H-K)-0.614
# then it's a "disk"
# if
# (J-H) > 1.714*(H-K)-0.614
# then it's an "extreme disk".
