def test_make_megeath_class_column():
# Let's make sure that all the D's stay D's, all the P's stay P's,
# and that all the na's transform into either na's or NDs.
new_class_column = make_megeath_class_column()
old_class_column = megeath2012_by_ukvar.Class
assert (len(old_class_column[old_class_column == 'D']) ==
len(new_class_column[new_class_column == 'D']))
assert (len(old_class_column[old_class_column == 'P']) ==
len(new_class_column[new_class_column == 'P']))
assert (len(old_class_column[old_class_column == 'na']) ==
len(new_class_column[new_class_column == 'ND']) +
len(new_class_column[new_class_column == 'na']))
# make sure you DID change SOMETHING
assert (len(old_class_column[old_class_column == 'na']) !=
len(new_class_column[new_class_column == 'na']))
# and finally make sure you didn't swap the order of any D's or P's
assert (old_class_column[(old_class_column == 'D') |
(old_class_column == 'P')] ==
new_class_column[(new_class_column == 'D') |
(new_class_column == 'P')] ).all()
def f_periods_by_megeath_class(title="Periodic ONCvar stars, with class from Megeath et al. 2012"):
"""
A histogram of periods for stars that have Megeath classes.
Returns
-------
fig : plt.figure
three np.ndarrays
These arrays contain the distribution of periods for
(a) protostars,
(b) disked stars, and
(c) non-disked stars
(on the basis of Megeath2012 membership/Class). Use these
for K-S tests if you gotta (see example)
Example:
In [15]: foo = f_periods_by_megeath_class()
In [16]: red = foo[2]
In [17]: blue = foo[3]
In [18]: scipy.stats.ks_2samp(red, blue)
Out[18]: (0.32200088003520139, 1.2771000514405625e-22)
"""
megeath_class_column = make_megeath_class_column()
fig = plt.figure()
sub1 = plt.subplot(3,1,1)
plt.hist(ukvar_periods[megeath_class_column == 'P'],
range=[0,20], bins=40, color=color_dict['protostar'])
sub2 = plt.subplot(3,1,2, sharex=sub1)
plt.hist(ukvar_periods[megeath_class_column == 'D'],
range=[0,20], bins=40, color=color_dict['disk'])
sub3 = plt.subplot(3,1,3, sharex=sub1)
plt.hist(ukvar_periods[megeath_class_column == 'ND'],
range=[0,20], bins=40, color=color_dict['nondisk'])
sub3.set_xlabel("Period (days)")
sub1.text(0.5, 0.5, "Periodic Protostar sample", transform=sub1.transAxes)
sub2.text(0.5, 0.5, "Periodic Disk sample", transform=sub2.transAxes)
sub3.text(0.5, 0.5, "Periodic Non-disk sample", transform=sub3.transAxes)
if title:
sub1.set_title(title)
plt.show()
print "scipy.stats.ks_2samp(disks, no_disks):"
print scipy.stats.ks_2samp(ukvar_periods[megeath_class_column == 'D'],
ukvar_periods[megeath_class_column == 'ND'])
return (fig,
ukvar_periods[megeath_class_column == 'P'],
ukvar_periods[megeath_class_column == 'D'],
ukvar_periods[megeath_class_column == 'ND'])
import matplotlib.pyplot as plt
from periodic_selector import long_periodic_selector, best_long_period
from orion_plot import OrionStarData
from tablemate_comparisons import ukvar_spread
from official_star_counter import maxvars_spread_per
from variables_data_filterer import variables_photometry
from lightcurve_maker import lc_and_phase_and_colors
from table_maker import make_megeath_class_column
# These can be broken into two groups:
# 1. Anyone with a period longer than 20 days from the official periodics (shorter than 20 days)
# 2. Long periods identified by hand greater than 50 days.
megeath_class_column = make_megeath_class_column()
path = os.path.expanduser("~/Dropbox/Bo_Tom/lightcurve_book/long_periods/")
path2 = os.path.expanduser("~/Dropbox/Bo_Tom/lightcurve_book/short_periods/")
def find_20_50_day_periodics_and_save_to_file():
blp = best_long_period(long_periodic_selector(maxvars_spread_per, max_period=100), max_period=100)
ukvar_blp = blp.where(np.in1d(blp.SOURCEID, ukvar_spread.SOURCEID))
print "There are {0} stars with nominal periods between 50 and 100.".format(len(ukvar_blp))
for sid, period in zip(ukvar_blp.SOURCEID, ukvar_blp.best_period):
sd = OrionStarData(variables_photometry, sid, ukvar_spread.UKvar_ID[ukvar_spread.SOURCEID==sid][0])
fig = lc_and_phase_and_colors(sd, period)
fig.ax_j_phase.set_title("ONCvar {}".format(sd.name))
fig.ax_j_lc.set_title(sid)
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_cc_cmd_and_map_by_megeath_class(sample='all', title=True):
"""
A color-color diagram where stars are colored by Megeath class.
Also a CMD and a map.
"""
megeath_class_column = make_megeath_class_column()
# Nonperiodic stars only
if 'non' in sample.lower():
sample_boolean_criterion = np.isnan(ukvar_periods)
sample_name = "Non-periodic"
elif 'per' in sample.lower():
sample_boolean_criterion = ~np.isnan(ukvar_periods)
sample_name = "Periodic"
elif 'strict' in sample.lower():
sample_boolean_criterion = (ukvar_spread.strict == 1)
sample_name = "Q=2"
else:
sample_boolean_criterion = (np.zeros_like(ukvar_periods) == 0) #all true
sample_name = "All"
# Now we want the rows in ukvar_spread that correspond to certain
# Megeath subsamples.
disk_indices = (
# its Class is 'D'
(megeath_class_column == 'D') &
# and it matches our 'periodic/nonperiodic/all' cut.
sample_boolean_criterion)
protostar_indices = (
(megeath_class_column == 'P') & sample_boolean_criterion)
nondisk_indices = (
(megeath_class_column == 'ND') & sample_boolean_criterion)
unknown_indices = (
(megeath_class_column == 'na') & sample_boolean_criterion)
fig = plt.figure()
ax = plt.gca()
plot_trajectory_vanilla(ax, a_k=3)
plt.plot(ukvar_spread.hmk_median[nondisk_indices],
ukvar_spread.jmh_median[nondisk_indices],
'o', color=color_dict['nondisk'], ms=4, label="Non-disks")
plt.plot(ukvar_spread.hmk_median[disk_indices],
ukvar_spread.jmh_median[disk_indices],
'o', color=color_dict['disk'], ms=4, label="Disks")
plt.plot(ukvar_spread.hmk_median[protostar_indices],
ukvar_spread.jmh_median[protostar_indices],
'*', color=color_dict['protostar'], ms=10, label="Protostars")
plt.xlabel(r"median $H-K$")
plt.ylabel(r"median $J-H$")
plt.xlim(-0.1,3.1)
plt.ylim(-0.2, 4.4)
if title:
plt.title(sample_name + " variables, colored by Megeath+2012 class")
plt.legend(loc='lower right')
fig3 = plt.figure()
ax3 = plt.gca()
plot_trajectory_vanilla(ax, a_k=3)
plt.plot(ukvar_spread.hmk_median[nondisk_indices],
ukvar_spread.k_median[nondisk_indices],
'o', color=color_dict['nondisk'], ms=4, label="Non-disks")
plt.plot(ukvar_spread.hmk_median[disk_indices],
ukvar_spread.k_median[disk_indices],
'o', color=color_dict['disk'], ms=4, label="Disks")
plt.plot(ukvar_spread.hmk_median[protostar_indices],
ukvar_spread.k_median[protostar_indices],
'*', color=color_dict['protostar'], ms=10, label="Protostars")
plt.xlabel(r"median $H-K$")
plt.ylabel(r"median $K$ mag")
plt.xlim(-0.1,3.1)
plt.ylim(17, 8)
plt.title(sample_name + " variables, colored by Megeath 2012 class")
plt.legend(loc='upper right')
fig2 = plt.figure()
plt.plot(np.degrees(ukvar_spread.RA)[nondisk_indices],
np.degrees(ukvar_spread.DEC)[nondisk_indices],
'o', color=color_dict['nondisk'], ms=4, label="Non-disks")
plt.plot(np.degrees(ukvar_spread.RA)[disk_indices],
np.degrees(ukvar_spread.DEC)[disk_indices],
'o', color=color_dict['disk'], ms=4, label="Disks")
plt.plot(np.degrees(ukvar_spread.RA)[protostar_indices],
np.degrees(ukvar_spread.DEC)[protostar_indices],
'*', color=color_dict['protostar'], ms=10, label="Protostars")
plt.legend()
plt.gca().invert_xaxis()
plt.show()
return (fig, fig2, fig3)
