def star_catalog_cmd(df):
f, ax = plt.subplots(figsize=(4, 3))
ax.scatter(df['phot_bp_mean_mag'] - df['phot_rp_mean_mag'],
df['phot_g_mean_mag'],
rasterized=True,
s=0.2,
alpha=1,
linewidths=0,
zorder=5)
ax.yaxis.set_ticks_position('both')
ax.xaxis.set_ticks_position('both')
ax.get_yaxis().set_tick_params(which='both', direction='in')
ax.get_xaxis().set_tick_params(which='both', direction='in')
for tick in ax.xaxis.get_major_ticks():
tick.label.set_fontsize('small')
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize('small')
ax.set_xlabel('$G_{\mathrm{BP}} - G_{\mathrm{RP}}$')
ax.set_ylabel('$G$')
ylim = ax.get_ylim()
ax.set_ylim((max(ylim), min(ylim)))
f.tight_layout(pad=0.2)
savefig(f, '../../results/star_catalog_cmd.png')
def star_catalog_mag_histogram(df, magstr, savpath=None):
f, ax = plt.subplots(figsize=(4, 3))
bins = np.arange(np.floor(np.min(df[magstr])),
np.ceil(np.max(df[magstr])) + 0.5, 1)
ax.hist(df[magstr],
bins=bins,
cumulative=True,
color='black',
fill=False,
linewidth=0.5)
ax.yaxis.set_ticks_position('both')
ax.xaxis.set_ticks_position('both')
ax.get_yaxis().set_tick_params(which='both', direction='in')
ax.get_xaxis().set_tick_params(which='both', direction='in')
for tick in ax.xaxis.get_major_ticks():
tick.label.set_fontsize('small')
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize('small')
if magstr == 'phot_rp_mean_mag':
ax.set_xlabel('Apparent mag [$G_\mathrm{{RP}}$]')
if magstr == 'phot_bp_mean_mag':
ax.set_xlabel('Apparent mag [$G_\mathrm{{BP}}$]')
ax.set_ylabel('Cumulative number')
ax.set_yscale('log')
ax.set_xlim([2, 16])
f.tight_layout(pad=0.2)
if not isinstance(savpath, str):
savpath = '../../results/star_catalog_mag_histogram_{}.png'.format(
magstr)
savefig(f, savpath)
def plot_scatter_x_y_sep(df, outpath):
f, ax = plt.subplots(figsize=(4, 4))
norm = mpl.colors.Normalize(vmin=-2., vmax=1.)
cax = ax.scatter(df['x_meas'],
df['y_meas'],
c=np.log10(df['sep']),
cmap='viridis',
s=1,
rasterized=True,
linewidths=0,
zorder=1,
norm=norm)
ax.set_xlabel('Column index [px]')
ax.set_ylabel('Row index [px]')
cbar = f.colorbar(cax, extend='both')
cbar.set_label(
r'$|\vec{{x}}_{{\mathrm{{measured}}}} - \vec{{x}}_{{\mathrm{{projected}}}}|$ [pixels]'
)
savefig(f, outpath)
def plot_zorro_speckle():
datapath = (
'../data/WASP4_zorro_speckle/WASP-4_20190928_832_companionbounds.csv')
df = pd.read_csv(datapath)
fig, ax = plt.subplots(figsize=(4, 3))
ax.plot(df.ang_sep, df.delta_mag, color='k')
img = mpimg.imread(
'../data/WASP4_zorro_speckle/WASP-4_20190928_832_img.png')
# [left, bottom, width, height]
inset = fig.add_axes([0.55, 0.23, .4, .4])
inset.imshow(img)
inset.axis('off')
plt.setp(inset, xticks=[], yticks=[])
ax.set_ylabel('$\Delta$mag (832 nm)')
ax.set_xlabel('Angular separation [arcsec]')
ax.get_yaxis().set_tick_params(which='both', direction='in')
ax.get_xaxis().set_tick_params(which='both', direction='in')
ax.tick_params(right=True, which='both', direction='in')
ax.tick_params(top=True, which='both', direction='in')
fig.tight_layout(h_pad=0, w_pad=0)
figpath = ('../results/zorro_speckle.png')
savefig(fig, figpath)
def plot_sep_hist(df, outpath):
f, ax = plt.subplots(figsize=(4, 3))
weights = np.ones_like(nparr(df['sep'])) / float(len(df))
ax.hist(df['sep'],
bins=np.logspace(-2, 1, 19),
weights=weights,
color='black',
fill=False,
linewidth=0.5)
ax.text(
0.98,
0.98,
'mean={:.3f}px\nstdev={:.3f}px\nmedian={:.3f}px\n90$^{{\mathrm{{th}}}}$pctile={:.3f}px'
.format(df['sep'].mean(), df['sep'].std(), df['sep'].median(),
df['sep'].quantile(q=0.9)),
va='top',
ha='right',
transform=ax.transAxes)
ax.set_xscale('log')
ax.set_xlabel(
r'$|\vec{{x}}_{{\mathrm{{measured}}}} - \vec{{x}}_{{\mathrm{{projected}}}}|$ [pixels]'
)
#ax.set_xlabel('Measured - projected centroid separation [pixels]')
ax.set_ylabel('Relative fraction')
ax.yaxis.set_ticks_position('both')
ax.xaxis.set_ticks_position('both')
ax.get_yaxis().set_tick_params(which='both', direction='in')
ax.get_xaxis().set_tick_params(which='both', direction='in')
savefig(f, outpath)
def plot_quiver_meas_proj_sep(df, outpath):
f, ax = plt.subplots(figsize=(4, 4))
norm = mpl.colors.Normalize(vmin=-2., vmax=1.)
cax = ax.quiver(
df['x_meas'],
df['y_meas'],
df['x_proj'] - df['x_meas'],
df['y_proj'] - df['y_meas'],
np.log10(df['sep']),
angles='xy',
scale_units='xy',
scale=0.02, #1,
cmap='viridis',
norm=norm)
divider = make_axes_locatable(ax)
cax1 = divider.append_axes('right', size='5%', pad=0.05)
cbar = f.colorbar(cax, cax=cax1, extend='both')
cbar.set_label(
r'$|\vec{{x}}_{{\mathrm{{measured}}}} - \vec{{x}}_{{\mathrm{{projected}}}}|$ [pixels]'
)
ax.set_xlabel('Column index [px]')
ax.set_ylabel('Row index [px]')
ax.yaxis.set_ticks_position('both')
ax.xaxis.set_ticks_position('both')
ax.get_yaxis().set_tick_params(which='both', direction='in')
ax.get_xaxis().set_tick_params(which='both', direction='in')
savefig(f, outpath)
def plot_catalog_to_gaia_match_statistics(bigdf, outpath, isD14=False):
if not isD14:
dist_arcsec = (np.array(bigdf['dist_deg'])*u.deg).to(u.arcsec)
else:
dist_arcsec = (np.array(bigdf['dist'])*u.deg).to(u.arcsec)
f,axs = plt.subplots(nrows=1, ncols=3, figsize=(12,4))
axs[0].hist(dist_arcsec.value, bins=np.arange(0,5.5,0.5), color='black',
fill=False, linewidth=0.5)
axs[0].set_xlabel('Distance [arcsec]', fontsize='x-large')
axs[0].set_ylabel('Number per bin', fontsize='x-large')
axs[0].set_yscale('log')
if isD14:
axs[1].hist(bigdf['gaia_gmag']-bigdf['gmag_estimate'], color='black',
bins=np.arange(-2,2+0.5,0.5),
fill=False, linewidth=0.5)
else:
axs[1].hist(bigdf['gmag_match_minus_estimate'], color='black',
bins=np.arange(-2,2+0.5,0.5),
fill=False, linewidth=0.5)
axs[1].set_xlabel('$G_{\mathrm{true}}$ - '
'$G_{\mathrm{pred}}$', fontsize='x-large')
axs[1].set_ylabel('Number per bin', fontsize='x-large')
axs[1].set_xlim([-2.1,2.1])
axs[1].set_yscale('log')
if isD14:
axs[2].scatter(bigdf['gaia_gmag'],
bigdf['gaia_gmag']-bigdf['gmag_estimate'], s=5,
alpha=0.1, rasterized=True, linewidths=0, color='black')
else:
axs[2].scatter(bigdf['gmag_match'], bigdf['gmag_match_minus_estimate'],
s=5, alpha=0.1, rasterized=True, linewidths=0,
color='black')
axs[2].set_xlabel('$G_{\mathrm{true}}$', fontsize='x-large')
axs[2].set_ylabel('$G_{\mathrm{true}}$ - '
'$G_{\mathrm{pred}}$', fontsize='x-large')
axs[2].set_xlim([4,18])
axs[2].set_ylim([-2.1,2.1])
for ax in axs:
ax.yaxis.set_ticks_position('both')
ax.xaxis.set_ticks_position('both')
ax.get_yaxis().set_tick_params(which='both', direction='in')
ax.get_xaxis().set_tick_params(which='both', direction='in')
for tick in ax.xaxis.get_major_ticks():
tick.label.set_fontsize('large')
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize('large')
f.tight_layout(pad=0.2, w_pad=0.5)
savefig(f, outpath)
def star_catalog_skymap(df, pfdf, closesubset=False):
if closesubset:
df = df[df['parallax'] > 0]
plx_as = df['parallax'] / 1000
df = df[1 / plx_as < 1000]
ra = coord.Angle(nparr(df['ra']) * u.deg)
ra = ra.wrap_at(180 * u.deg)
dec = coord.Angle(nparr(df['dec']) * u.deg)
f = plt.figure(figsize=(4, 3))
ax = f.add_subplot(111, projection='mollweide')
ax.scatter(ra.radian,
dec.radian,
rasterized=True,
s=0.5,
alpha=0.5,
linewidths=0,
zorder=5,
c='C0',
label='CDIPS stars')
if isinstance(pfdf, pd.DataFrame):
ra = coord.Angle(nparr(pfdf['ra_x']) * u.deg)
ra = ra.wrap_at(180 * u.deg)
dec = coord.Angle(nparr(pfdf['dec_x']) * u.deg)
ax.scatter(ra.radian,
dec.radian,
rasterized=True,
s=0.5,
alpha=0.5,
linewidths=0,
zorder=5,
c='C1',
label='Sector 6')
ax.set_xticklabels([
'14h', '16h', '18h', '20h', '22h', '0h', '2h', '4h', '6h', '8h', '10h'
])
ax.tick_params(axis='both', which='major', labelsize='x-small')
ax.tick_params(axis='both', which='minor', labelsize='x-small')
ax.grid(True, linestyle='--', linewidth=0.5, alpha=0.3, zorder=-1)
f.tight_layout()
f.subplots_adjust(left=0.1, right=0.9, top=0.8, bottom=0.2)
csstr = '_closesubset' if closesubset else ''
opstr = '_overplotlcs' if isinstance(pfdf, pd.DataFrame) else ''
outpath = '../../results/star_catalog_skymap{csstr}{opstr}.png'.format(
csstr=csstr, opstr=opstr)
savefig(f, outpath)
def star_catalog_hrd_scat(df):
# 2d SCATTER CASE
df = df[df['parallax'] > 0]
plx_as = df['parallax'] / 1000
df = df[1 / plx_as < 1000]
f, ax = plt.subplots(figsize=(4, 3))
color = df['phot_bp_mean_mag'] - df['phot_rp_mean_mag']
M_omega = df['phot_g_mean_mag'] + 5 * np.log10(df['parallax'] / 1000) + 5
ax.scatter(color,
M_omega,
rasterized=True,
s=0.1,
alpha=1,
linewidths=0,
zorder=5)
ax.yaxis.set_ticks_position('both')
ax.xaxis.set_ticks_position('both')
ax.get_yaxis().set_tick_params(which='both', direction='in')
ax.get_xaxis().set_tick_params(which='both', direction='in')
for tick in ax.xaxis.get_major_ticks():
tick.label.set_fontsize('small')
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize('small')
ax.set_xlabel('$G_{\mathrm{BP}} - G_{\mathrm{RP}}$')
ax.set_ylabel('$M_\omega = G + 5\log_{10}(\omega_{\mathrm{as}}) + 5$')
ylim = ax.get_ylim()
ax.set_ylim((max(ylim), min(ylim)))
ax.set_xlim((-0.7, 4.3))
txtstr = ('$\omega>0$, $1/\omega_{\mathrm{as}} < 1000$, ' +
'{} stars'.format(len(df)))
ax.text(0.97,
0.97,
txtstr,
ha='right',
va='top',
fontsize='x-small',
transform=ax.transAxes)
f.tight_layout(pad=0.2)
savefig(f, '../../results/star_catalog_hrd_scat.png')
def plot_highresimg_mass_semimaj_constraints(df, instrument=None, df2=None):
assert instrument in ['Zorro', 'NIRC2', 'both']
n_grid_edges = 51
mass_grid = (np.logspace(np.log10(1), np.log10(300), num=n_grid_edges) *
u.Mjup)
sma_grid = (np.logspace(np.log10(3), np.log10(500), num=n_grid_edges) *
u.AU)
fig, ax = plt.subplots(figsize=(4, 3))
dist_pc = 1 / (3.7145e-3) # Bouma+2019, Table 1
if df2 is None:
df['sma_AU'] = df.ang_sep * dist_pc
ax.plot(
nparr(df['sma_AU']) * u.AU,
(nparr(df['m_comp/m_sun']) * u.Msun).to(u.Mjup))
if not df2 is None:
labels = ['Zorro', 'NIRC2']
for l, d in zip(labels, [df, df2]):
d['sma_AU'] = d.ang_sep * dist_pc
ax.plot(nparr(d['sma_AU']) * u.AU,
(nparr(d['m_comp/m_sun']) * u.Msun).to(u.Mjup),
label=l)
ax.legend()
ax.set_xscale('log')
ax.set_yscale('log')
ax.set_xlabel('Semi-major axis [AU]')
ax.set_ylabel('Companion mass [M$_\mathrm{{jup}}$]')
ax.set_xlim([sma_grid.value.min(), sma_grid.value.max()])
ax.set_ylim([mass_grid.value.min(), 1000])
ax.get_yaxis().set_tick_params(which='both', direction='in')
ax.get_xaxis().set_tick_params(which='both', direction='in')
fig.tight_layout(h_pad=0, w_pad=0)
figpath = '../results/{}_mass_semimaj_constraints.png'.format(instrument)
savefig(fig, figpath)
def plot_quilt_PCs(overwrite=1, paper_aspect_ratio=1):
"""
paper_aspect_ratio: if true, uses figsize set for paper. if false, uses
figsize set for poster.
"""
aspectstr = '' if paper_aspect_ratio else '_poster'
outpath = os.path.join(OUTDIR, 'quilt_PCs{}.png'.format(aspectstr))
if os.path.exists(outpath) and not overwrite:
print('found {} and not overwrite; skip'.format(outpath))
return
# looked through by eye in geeqie. copied from geeqie paths, if they looked
# good enough to show off in a plot like this. PEP8 forgive me.
fpaths = [
'/home/lbouma/proj/cdips/results/fit_gold/sector-6/fitresults/hlsp_cdips_tess_ffi_gaiatwo0003007171311355035136-0006_tess_v01_llc/hlsp_cdips_tess_ffi_gaiatwo0003007171311355035136-0006_tess_v01_llc_fitparameters_phased_mandelagol_fit_empiricalerrs.png',
'/home/lbouma/proj/cdips/results/fit_gold/sector-6/fitresults/hlsp_cdips_tess_ffi_gaiatwo0003125263468681400320-0006_tess_v01_llc/hlsp_cdips_tess_ffi_gaiatwo0003125263468681400320-0006_tess_v01_llc_fitparameters_phased_mandelagol_fit_empiricalerrs.png',
'/home/lbouma/proj/cdips/results/fit_gold/sector-6/fitresults/hlsp_cdips_tess_ffi_gaiatwo0003220266049321724416-0006_tess_v01_llc/hlsp_cdips_tess_ffi_gaiatwo0003220266049321724416-0006_tess_v01_llc_fitparameters_phased_mandelagol_fit_empiricalerrs.png',
'/home/lbouma/proj/cdips/results/fit_gold/sector-7/fitresults/hlsp_cdips_tess_ffi_gaiatwo0003027361888196408832-0007_tess_v01_llc/hlsp_cdips_tess_ffi_gaiatwo0003027361888196408832-0007_tess_v01_llc_fitparameters_phased_mandelagol_fit_empiricalerrs.png',
'/home/lbouma/proj/cdips/results/fit_gold/sector-7/fitresults/hlsp_cdips_tess_ffi_gaiatwo0003114869682184835584-0007_tess_v01_llc/hlsp_cdips_tess_ffi_gaiatwo0003114869682184835584-0007_tess_v01_llc_fitparameters_phased_mandelagol_fit_empiricalerrs.png',
'/home/lbouma/proj/cdips/results/fit_gold/sector-7/fitresults/hlsp_cdips_tess_ffi_gaiatwo0005510676828723793920-0007_tess_v01_llc/hlsp_cdips_tess_ffi_gaiatwo0005510676828723793920-0007_tess_v01_llc_fitparameters_phased_mandelagol_fit_empiricalerrs.png',
'/home/lbouma/proj/cdips/results/fit_gold/sector-7/fitresults/hlsp_cdips_tess_ffi_gaiatwo0005546259498914769280-0007_tess_v01_llc/hlsp_cdips_tess_ffi_gaiatwo0005546259498914769280-0007_tess_v01_llc_fitparameters_phased_mandelagol_fit_empiricalerrs.png',
'/home/lbouma/proj/cdips/results/fit_gold/sector-7/fitresults/hlsp_cdips_tess_ffi_gaiatwo0005605128927705695232-0007_tess_v01_llc/hlsp_cdips_tess_ffi_gaiatwo0005605128927705695232-0007_tess_v01_llc_fitparameters_phased_mandelagol_fit_empiricalerrs.png',
'/home/lbouma/proj/cdips/results/fit_gold/sector-7/fitresults/hlsp_cdips_tess_ffi_gaiatwo0005617126180115568256-0007_tess_v01_llc/hlsp_cdips_tess_ffi_gaiatwo0005617126180115568256-0007_tess_v01_llc_fitparameters_phased_mandelagol_fit_empiricalerrs.png'
]
np.random.seed(42)
spaths = np.random.choice(fpaths, size=3 * 2, replace=False)
ylims = [ # for seed 42
(0.957, 1.015), (0.97, 1.017), (0.995, 1.003), (0.983, 1.008),
(0.983, 1.008), (0.972, 1.008)
]
yticks = [[0.975, 1.000], [0.98, 1.00], [0.996, 1.000], [0.99, 1.00],
[0.990, 1.000], [0.98, 1.00]]
alphas = np.ones_like(spaths)
#alphas = [
# 0.45, 0.6, 0.5, 0.45, 0.5, 0.45
#]
inds = [''] * 6 # ['a)','b)','c)','d)','e)','f)']
gaiaids = list(
map(
lambda x: int(
os.path.basename(x).split('gaiatwo')[1].split('-')[0].lstrip(
'0')), spaths))
if paper_aspect_ratio:
f, axs = plt.subplots(nrows=3, ncols=2, figsize=(6, 4.5))
else:
f, axs = plt.subplots(nrows=3, ncols=2, figsize=(6, 3))
axs = axs.flatten()
ix = 0
for fpath, ax, a, ind in zip(spaths, axs, alphas, inds):
plot_phase_PC(fpath, ax, ind, alpha=a)
print('{}: {}'.format(ind, gaiaids[ix]))
ix += 1
for ix, ax in enumerate(axs):
ax.set_ylim(ylims[ix])
if yticks[ix] is not None:
ax.set_yticks(yticks[ix])
ax.yaxis.set_ticks_position('both')
ax.xaxis.set_ticks_position('both')
ax.get_yaxis().set_tick_params(which='both', direction='in')
ax.get_xaxis().set_tick_params(which='both', direction='in')
#f.text(0.5,0, 'Phase', ha='center')
f.text(0.5,
-0.02,
'Time from transit center [hours]',
ha='center',
fontsize='medium')
f.text(-0.01,
0.5,
'Relative flux',
va='center',
rotation=90,
fontsize='medium')
f.tight_layout(h_pad=0.35, w_pad=0.85, pad=0.8)
savefig(f, outpath)
def plot_mass_semimaj_constraints(prob_arr=None,
mass_grid=None,
sma_grid=None,
with_contrast=False,
discrete_color=False,
linear_z=False,
figpath=None,
rvsonly=False):
if prob_arr is None:
# n_mass_grid_edges = 31 # a 4x4 grid has 5 edges. want: 64+1, 128+1...
# n_sma_grid_edges = 31 # a 4x4 grid has 5 edges.
# n_injections_per_cell = 16 # 500 # want: 500
n_mass_grid_edges = 129 # a 4x4 grid has 5 edges. want: 51
n_sma_grid_edges = 129 # a 4x4 grid has 5 edges. want: 51
n_injections_per_cell = 512 # 500 # want: 500
mass_grid = (
np.logspace(np.log10(1), np.log10(900), num=n_mass_grid_edges) *
u.Mjup)
sma_grid = (
np.logspace(np.log10(3), np.log10(500), num=n_sma_grid_edges) *
u.AU)
sizestr = '{}x{}x{}'.format(n_mass_grid_edges - 1,
n_sma_grid_edges - 1,
n_injections_per_cell)
pklpath = ('../data/rv_simulations/mass_semimaj_loglikearr_{}.pickle'.
format(sizestr))
log_like_arr = pickle.load(open(pklpath, 'rb'))
ao_detected_arr = pickle.load(
open(pklpath.replace('loglikearr', 'aodetectedarr'), 'rb'))
#
# Convert log-likelihood values to relative probability by taking the exp.
# Then average out the "sample" dimension (mass, sma, eccentricity, etc).
#
rv_log_like = np.log(np.exp(log_like_arr).mean(axis=2))
rv_and_ao_log_like = np.log(
nparr(np.exp(log_like_arr) * (1 - ao_detected_arr)).mean(axis=2))
# -2*logprob == chi^2
# Convert likelihood values to a normalized probability via
# P ~ -exp(-chi^2/2)
rv_prob_arr = np.exp(rv_log_like) / np.exp(rv_log_like).sum().sum()
rv_and_ao_prob_arr = np.exp(rv_and_ao_log_like) / np.exp(
rv_and_ao_log_like).sum().sum()
prob_arr = rv_and_ao_prob_arr
if rvsonly:
prob_arr = rv_prob_arr
#################
# make the plot #
#################
fig, ax = plt.subplots(figsize=(4, 3))
X, Y = np.meshgrid(sma_grid[:-1].value, mass_grid[:-1].value)
cmap = plt.cm.gray_r
if not linear_z:
cutoff = -6.3
if discrete_color:
bounds = np.round(np.linspace(cutoff,
np.log10(prob_arr).max(), 5), 1)
norm = mpl.colors.BoundaryNorm(bounds, cmap.N)
else:
norm = mpl.colors.Normalize(vmin=np.log10(prob_arr).max(),
vmax=cutoff)
im = ax.pcolormesh(X,
Y,
np.log10(prob_arr),
cmap=cmap,
norm=norm,
shading='flat') # vs 'gouraud'
else:
if discrete_color:
bounds = np.linspace(10**(cutoff), prob_arr.max(), 5)
norm = mpl.colors.BoundaryNorm(bounds, cmap.N)
else:
norm = mpl.colors.Normalize(vmin=prob_arr.max(), vmax=10**(cutoff))
im = ax.pcolormesh(X, Y, prob_arr, cmap=cmap, norm=norm)
if with_contrast:
from contrast_to_masslimit import get_companion_bounds
zdf = get_companion_bounds('Zorro')
dist_pc = 1 / (3.7145e-3) # Bouma+2019, Table 1
zdf['sma_AU'] = zdf.ang_sep * dist_pc
ax.plot(nparr(zdf['sma_AU']) * u.AU,
(nparr(zdf['m_comp/m_sun']) * u.Msun).to(u.Mjup),
color='C0',
lw=1,
zorder=4)
# NOTE: not really ruled out!
# t = ax.text(
# 100, 450, 'Ruled out by\nspeckle imaging',
# fontsize=7.5, ha='center', va='center',
# # path_effects=[pe.withStroke(linewidth=0.5, foreground="white")],
# color='C0', zorder=3
# )
# ax.fill_between(
# nparr(zdf['sma_AU'])*u.AU,
# (nparr(zdf['m_comp/m_sun'])*u.Msun).to(u.Mjup),
# 1000,
# color='white',
# alpha=0.8,
# zorder=2
# )
_sma = np.logspace(np.log10(90), np.log10(180))
_mass_min = 2 # rescale
k = _mass_min / min(_sma**2)
_mass = k * _sma**2
ax.plot(_sma, _mass, color='k', lw=1)
ax.text(np.percentile(_sma, 60),
np.percentile(_mass, 40),
'$M\propto a^2$',
fontsize=7.5,
ha='left',
va='center',
path_effects=[pe.withStroke(linewidth=0.5, foreground="white")],
color='black')
ax.set_xscale('log')
ax.set_yscale('log')
ax.set_xlabel('Semi-major axis [AU]')
ax.set_ylabel('Companion mass [M$_\mathrm{{Jup}}$]')
cbar = fig.colorbar(im,
orientation='vertical',
extend='min',
label='$\log_{{10}}$(probability)')
cbar.ax.tick_params(labelsize=6, direction='out')
ax.set_ylim([1, 1e3])
ax.set_xlim([3, 310])
ax.get_yaxis().set_tick_params(which='both', direction='in')
ax.get_xaxis().set_tick_params(which='both', direction='in')
ax.tick_params(right=True, which='both', direction='in')
ax.tick_params(top=True, which='both', direction='in')
fig.tight_layout(h_pad=0, w_pad=0)
dstr = '_discretecolor' if discrete_color else ''
wstr = '_with_contrast' if with_contrast else ''
lzstr = '_linearz' if linear_z else ''
rvstr = '_rvonly' if rvsonly else ''
if figpath is None:
figpath = ('../results/mass_semimaj_constraints{}{}{}{}.png'.format(
wstr, dstr, lzstr, rvstr))
savefig(fig, figpath)
def plot_quilt_s6_s7(overwrite=1):
outpath = os.path.join(OUTDIR, 'quilt_s6_s7.png')
if os.path.exists(outpath) and not overwrite:
print('found {} and not overwrite; skip'.format(outpath))
return
gaiaids = [
3064530810048196352, # TOI 496 -- V-shaped giant PC, NGC 2548
3080104185367102592, # TOI 625, a UMS HJ. T=9.6, so nice.
3027361888196408832, # eccentric EB
3125738423345810048, # 80Myr member semidetached EB with sick OOT
3326715714242517248, # V684 Mon, 10 Myr old detached binary.
3064487241899832832, # semidetached EB, NGC 2548
3024952755135530496, # two-spotted pulsator, NGC 2184
3209428644243836928, # M42 Dias, rapid rotating m dwarf, V468 Ori
3214130293403123712 # zhuchang-class weirdo.
#2949587413510194688, # double-hump rotator / pulsator??
#5274902193830445312 # sick PMS star. pulsating? spots??
]
lctypes = ['PCA2'] * 9
pgtypes = ['tls'] * 3 + ['gls'] + ['tls'] * 2 + ['gls', 'gls', 'gls']
peakindices = [0, 0, 0, 0, 1, 0, 0, 0, 0]
lcpaths = []
for g in gaiaids:
globpaths = glob(
'/home/lbouma/cdips_lcs/sector-?/cam?_ccd?/*{}*.fits'.format(g))
if len(globpaths) >= 1:
# weird edge case
if g == 2949587413510194688:
lcpaths.append(globpaths[1])
continue
lcpaths.append(globpaths[0])
else:
raise AssertionError('error! got no lc matches')
ylims = [
None, (0.989, 1.005), None, None, None, None, None, (0.96, 1.04), None
]
yticks = [[0.98, 1.00], [0.99, 1.00], None, [0.90, 1.00], [0.93, 1.00],
[0.98, 1.01], [0.98, 1.01], [0.97, 1.03], [0.85, 1.15]]
alphas = np.ones_like(lcpaths).astype(int) * 0.7
inds = [''] * len(lcpaths)
f, axs = plt.subplots(nrows=3, ncols=3, figsize=(7.5, 4.5))
axs = axs.flatten()
ix = 0
overwritecsv = 0 # option to save time
for fpath, ax, a, ind, lctype, pgtype, peakindex in zip(
lcpaths, axs, alphas, inds, lctypes, pgtypes, peakindices):
plot_phase(fpath,
ax,
ind,
alpha=a,
lctype=lctype,
periodogramtype=pgtype,
peakindex=peakindex,
overwritecsv=overwritecsv)
print('{}: {}'.format(ind, gaiaids[ix]))
ix += 1
if ix < 7:
ax.set_xticklabels('')
for ix, ax in enumerate(axs):
if ylims[ix] is not None:
ax.set_ylim(ylims[ix])
if yticks[ix] is not None:
ax.set_yticks(yticks[ix])
ax.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))
ax.yaxis.set_ticks_position('both')
ax.xaxis.set_ticks_position('both')
ax.get_yaxis().set_tick_params(which='both', direction='in')
ax.get_xaxis().set_tick_params(which='both', direction='in')
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize('small')
for tick in ax.xaxis.get_major_ticks():
tick.label.set_fontsize('small')
f.text(0.5, -0.035, 'Phase', ha='center', fontsize='medium')
f.text(-0.03,
0.5,
'Relative flux',
va='center',
rotation=90,
fontsize='medium')
f.tight_layout(h_pad=0.15, w_pad=0.5, pad=0.1)
savefig(f, outpath)