def plot_nondetection_limits(ax, sn, band, x_col, yerr_col, ymax=1e42):
"""Plot UV luminosity limits for GALEX non-detections."""
lc = LightCurve(sn, band)
# Effective filter wavelength
wl_eff = effective_wavelength(band).value
# Remove detections, inc. spurious
detections = lc.detect_csm(DET_SIGMA, count=DET_COUNT)
nondetections = lc(tmin=XLIM[0], tmax=XLIM[1]).drop(detections.index)
# Plot nondetections below SN 2015cp luminosity
below_max = nondetections[PLOT_SIGMA * nondetections[yerr_col] <= ymax]
ax.scatter(below_max[x_col],
wl_eff * PLOT_SIGMA * below_max[yerr_col],
marker='v',
s=MS_GALEX**2,
color=COLORS[band],
edgecolors='none',
alpha=ALPHA_NON,
zorder=3,
rasterized=True)
return ax
def from_name(self,
sn_name,
band,
tstart,
scale,
sn_info=[],
model='Chev94',
**kwargs):
"""Generate Injection instance from a supernova name and GALEX band,
also creating a Supernova and LightCurve object in the process."""
sn = Supernova(sn_name, sn_info=sn_info)
lc = LightCurve(sn, band, sed=model)
return Injection(sn, lc, tstart, scale, **kwargs)
def run_all(supernovae,
iterations,
tstart_lims,
scale_lims,
sn_info=[],
overwrite=False,
model='Chev94',
save_dir=SAVE_DIR,
**kwargs):
"""Run injection recovery trials on all supernovae in given list.
Inputs:
supernovae: list of supernova names
iterations: iterations of injection & recovery for each SN
tstart_lims: tuple or list of bounds on start time
scale_lims: tuple or list of bounds on scale factor
sn_info: supernova info reference DataFrame
overwrite: bool, whether to overwrite previous save files
kwargs: keyword arguments for run_trials
"""
# Remove SNe with previous save files from list
if not overwrite:
supernovae = [
s for s in supernovae
if not check_save(s, iterations, save_dir=save_dir)
]
for i, sn_name in enumerate(supernovae):
print('\n%s [%s/%s]' % (sn_name, i + 1, len(supernovae)))
# Initialize SN object
sn = Supernova(sn_name, sn_info=sn_info)
# Import light curves
lcs = []
for band in ['FUV', 'NUV']:
try:
lc = LightCurve(sn, band, data_dir=DATA_DIR, sed=model)
except:
# No data for this channel
continue
# Skip if no data after minimum recovery time
if np.max(lc.data['t_delta_rest']) < RECOV_MIN:
print(
'\tno %s data after minimum %s days past discovery, skipping'
% (band, RECOV_MIN))
continue
lcs.append(lc)
# If light curve import was unsuccessful
if len(lcs) == 0:
print('\tno data available!')
continue
run_trials(sn,
lcs,
iterations,
tstart_lims,
scale_lims,
model=model,
save_dir=save_dir,
**kwargs)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from light_curve import LightCurve
# sn_name = 'SNLS-04D3df'
# sn_name = 'SDSS-II SN 18647'
sn_name = 'PS1-10k'
lc = LightCurve.from_name(sn_name, 'NUV')
fig, axs = plt.subplots(2, 2, figsize=(8, 5))
fig.suptitle(sn_name)
axs[0, 0].scatter(lc('detxs') / 600, lc('detys') / 600, s=10)
axs[0, 0].set_xlabel('detxs [deg]')
axs[0, 0].set_ylabel('detys [deg]')
axs[0, 1].scatter(lc('detrad') / 600, lc('detxs') / 600, s=10)
axs[0, 1].axvline(0.55, c='r')
axs[0, 1].set_xlabel('detrad [deg]')
axs[0, 1].set_ylabel('detxs [deg]')
axs[1, 0].scatter(lc('detrad') / 600, lc('exptime'), s=10)
axs[1, 0].axvline(0.55, c='r')
axs[1, 0].set_xlabel('detrad [deg]')
axs[1, 0].set_ylabel('exptime')
axs[1, 1].scatter(lc('detrad') / 600, lc('responses'), s=10)
axs[1, 1].axvline(0.55, c='r')
def main():
sn_info = pd.read_csv(Path('ref/sn_info.csv'), index_col='name')
det_sne = ['SN2007on', 'SN2008hv', 'SN2009gf', 'SN2010ai']
x_col = 't_delta_rest'
y_col = 'luminosity_hostsub'
yerr_col = 'luminosity_hostsub_err'
fig, ax = plt.subplots(figsize=(6.5, 5), tight_layout=True)
# Plot Swift SN2011fe from Brown+ 2012
band = 'UVM2'
disc_date = Time('2011-08-24', format='iso').mjd
dist = 6.4 * u.Mpc #from Shappee & Stanek 2011
dist_err = 0. * u.Mpc
z = 0 # too close to need correction
z_err = 0
a_v = 0 # won't worry about it right now
a_band = 'NUV' # close enough
wl_eff = effective_wavelength(band).value # UVM2 effective wavelength
lc = import_swift('SN2011fe', disc_date, z)
lc = lc[lc['Filter'] == band]
lc['luminosity'], lc['luminosity_err'] = flux2luminosity(
lc['flux'], lc['flux_err'], dist, dist_err, z, z_err, a_v, a_band)
ax.plot(lc['t_delta_rest'],
wl_eff * lc['luminosity'],
color='brown',
label='SN2011fe (%s)' % band,
zorder=1,
lw=2,
rasterized=True)
# Import and plot HST non-detections
print('Importing HST non-detections...')
hst_data = pd.read_csv(Path('ref/Graham_observations.csv'), index_col=0)
wl_eff = effective_wavelength('F275W').value
nondetections = hst_data[hst_data['Detection'] == False]
for i, sn_name in enumerate(nondetections.index):
obs = GrahamObservation(sn_name, hst_data)
ax.scatter(obs.rest_phase,
wl_eff * obs.luminosity_limit,
marker='v',
s=MS_HST**2,
color='w',
edgecolors=COLORS['F275W'],
alpha=ALPHA_NON,
zorder=3,
rasterized=True)
# Plot near-peak SNe Ia
for sn_name in det_sne:
sn = Supernova(sn_name, sn_info)
band = 'NUV'
lc = LightCurve(sn, band)
lc.to_hz() # Convert to erg/s/Hz units
# Effective filter frequency
# nu_eff = (const.c / effective_wavelength(band)).to('Hz').value
wl_eff = effective_wavelength(band).value
# Plot near-peak detections
detections = lc.detect_csm(DET_SIGMA, count=DET_COUNT)
# ax.errorbar(detections[x_col], nu_eff * detections[y_col],
# yerr=nu_eff * detections[yerr_col], label='%s (%s)' % (sn.name, band),
# linestyle='none', ms=MS_DET, marker=MARKERS[sn.name],
# color=COLORS[sn.name], mec='k', ecolor='k', elinewidth=1,
# zorder=9, rasterized=True)
ax.errorbar(detections[x_col],
wl_eff * detections[y_col],
yerr=wl_eff * detections[yerr_col],
label='%s (%s)' % (sn.name, band),
linestyle='none',
ms=MS_DET,
marker=MARKERS[sn.name],
color=COLORS[sn.name],
mec='k',
ecolor='k',
elinewidth=1,
zorder=9,
rasterized=True)
# Plot non-detection limits
print('Importing GALEX detections and limits...')
for sn_name in tqdm(sn_info.index):
sn = Supernova(sn_name, sn_info)
for band in ['FUV', 'NUV']:
try:
plot_nondetection_limits(ax,
sn,
band,
x_col,
yerr_col,
ymax=YLIM[1])
except (FileNotFoundError, pd.errors.EmptyDataError):
continue
# Import and plot HST detections
print('Importing HST detections...')
wl_eff = effective_wavelength('F275W').value
detections = hst_data[hst_data['Detection']]
markers = ['X', '*']
colors = ['y', 'r']
sizes = [64, 81]
for i, sn_name in enumerate(detections.index):
obs = GrahamObservation(sn_name, hst_data)
ax.scatter(obs.rest_phase,
wl_eff * obs.luminosity,
marker=markers[i],
color=colors[i],
edgecolors='k',
label='%s (F275W)' % sn_name,
zorder=10,
s=sizes[i])
print('Plotting...')
# Format axes
ax.set_xlabel('Time since discovery [rest-frame days]', size=12)
ax.set_ylabel('$\\lambda L_\\lambda$ [erg s$^{-1}$]', size=12)
ax.set_xlim(XLIM)
ax.set_yscale('log')
ax.set_ylim(YLIM)
# Legend
handles, labels = ax.get_legend_handles_labels()
legend_elements = [
Line2D([0], [0],
marker='v',
markerfacecolor='w',
markeredgecolor=COLORS['F275W'],
markersize=MS_HST,
alpha=ALPHA_NON,
label='detection limit (F275W)',
lw=0),
Line2D([0], [0],
marker='v',
markerfacecolor=COLORS['FUV'],
markeredgecolor='none',
markersize=MS_GALEX,
alpha=ALPHA_NON,
label='detection limit (FUV)',
lw=0),
Line2D([0], [0],
marker='v',
markerfacecolor=COLORS['NUV'],
markeredgecolor='none',
markersize=MS_GALEX,
alpha=ALPHA_NON,
label='detection limit (NUV)',
lw=0)
]
plt.legend(handles=handles + legend_elements,
loc='lower center',
ncol=3,
handletextpad=0.5,
handlelength=1.0,
bbox_to_anchor=(0.5, 1.01))
plt.savefig(Path('out/limits.pdf'), dpi=300)
plt.savefig(Path('out/limits.png'), dpi=300)
plt.show()
sn_info = pd.read_csv(Path('ref/sn_info.csv'), index_col='name')
detections = []
total_epochs = 0
bg_epochs = 0
post_epochs = 0
below_15cp = 0
for sn_name in tqdm(sn_info.index):
sn = Supernova(sn_name, sn_info)
below_15cp_val = 0
for band in ['FUV', 'NUV']:
try:
lc = LightCurve(sn, band)
except (FileNotFoundError, pd.errors.EmptyDataError):
continue
# Count observation epochs
total_epochs += lc.data.shape[0]
bg_epochs += lc.bg_data.shape[0]
post_epochs += lc.data[lc.data['t_delta_rest'] > 0].shape[0]
det = lc.detect_csm(SIGMA, SIGCOUNT)
if len(det.index) > 0:
det_info = {
'name':
sn_name,
'band':
if sn_name in ['SN2007on', 'SN2008hv', 'SN2009gf']:
lc = import_swift(sn.name, sn.disc_date.mjd, sn.z)
plot_external(ax, sn, lc, ['UVW1', 'UVM2', 'UVW2'])
# Plot CfA data
if sn_name == 'SN2010ai':
lc = import_cfa(sn)
plot_external(ax, sn, lc, ['B', 'V', "r'", "i'"], marker='s')
# Import and plot GALEX data
ymin = []
bg_max = 0
pre_obs = 0
for band in ['FUV', 'NUV']:
try:
lc = LightCurve(sn, band)
# Pre-SN obs.
before = lc.data[lc('t_delta_rest') <= DT_MIN]
pre_obs += len(before.index)
ymin.append(effective_wavelength(band).value * lc.bg / 1.5)
bg_max = max(bg_max, lc.bg)
plot_lc(ax, lc, TMAX)
except FileNotFoundError:
# No data for this channel
continue
# In-plot labels
ax.set_title(sn_name.replace('SN', 'SN '), ha='right', va='top', x=0.9, y=0.85, size=14)
xlim = np.array(ax.get_xlim())