def get_ffilc(name=None, tic=None, coords=None, sectors='all'):
"""
"""
if name is None and tic is None and coords is None:
raise ValueError('Please specify a name, TIC ID, or coordinates!')
if name is None and tic is not None:
name = 'TIC ' + str(tic)
if coords is not None:
if isinstance(coords, tuple) or isinstance(coords, list):
coords = SkyCoord(coords[0], coords[1], unit='deg')
if sectors == 'all' and coords is not None:
sector_table = Tesscut.get_sectors(coordinates=coord)
sectors = list(
map(int, [row[6:10] for row in sector_table['sectorName']]))
#tesscut add more here to parse sectors?
elif sectors == 'all' and name is not None:
sector_table = Tesscut.get_sectors(objectname=name)
sectors = list(
map(int, [row[6:10] for row in sector_table['sectorName']]))
if name is None and tic is not None:
name = "TIC " + str(tic)
search_result = lk.search_tesscut(name, sector=sectors)
else:
print('hi')
def showStar(self):
plt.close('all')
if (len(self.RA) == 0):
self.changeCounter(0)
self.errorWindow("ALL DONE.")
self.clearValues()
self.changeRADEC()
coord = SkyCoord(self.RA[len(self.RA) - 1],
self.DE[len(self.DE) - 1],
unit="deg")
self.tpf = search_tesscut(coord).download_all(cutout_size=(10, 10))
print(self.tpf)
if (hasattr(self.tpf, "__len__")):
self.target_mask0 = self.tpf[0].create_threshold_mask(threshold=3)
n_target_pixels = self.target_mask0.sum()
raw_lc = self.tpf[0].to_lightcurve(aperture_mask=self.target_mask0)
time_mask = (
(raw_lc.time < 1856) | (raw_lc.time > 1858)) # sector 20
raw_lc = raw_lc[time_mask]
self.bkgr_mask0 = ~self.tpf[0].create_threshold_mask(
threshold=0.001, reference_pixel=None)
n_background_pixels = self.bkgr_mask0.sum()
bkgr_lc_per_pixel = self.tpf[0].to_lightcurve(
aperture_mask=self.bkgr_mask0) / n_background_pixels
bkgr_lc_per_pixel = bkgr_lc_per_pixel[time_mask]
bkgr_estimate_lc = bkgr_lc_per_pixel * n_target_pixels
self.star_lc = (raw_lc - bkgr_estimate_lc.flux).flatten()
self.star_lc = self.star_lc.remove_nans().remove_outliers(sigma=6)
for itpf in self.tpf[1:]:
target_mask = itpf.create_threshold_mask(threshold=3)
n_target_pixels = target_mask.sum()
raw_lc = itpf.to_lightcurve(aperture_mask=target_mask)
raw_lc = raw_lc[time_mask]
bkgr_mask = ~itpf.create_threshold_mask(threshold=0.001,
reference_pixel=None)
n_background_pixels = bkgr_mask.sum()
bkgr_lc_per_pixel = itpf.to_lightcurve(
aperture_mask=bkgr_mask) / n_background_pixels
bkgr_lc_per_pixel = bkgr_lc_per_pixel[time_mask]
bkgr_estimate_lc = bkgr_lc_per_pixel * n_target_pixels
temp_lc = (raw_lc - bkgr_estimate_lc.flux).flatten()
temp_lc = temp_lc.remove_nans().remove_outliers(sigma=6)
self.star_lc = self.star_lc.append(temp_lc)
self.lspg = self.star_lc.to_periodogram(maximum_frequency=12.0,
oversample_factor=10)
else:
self.errorWindow("NOT IN SECTOR")
self.saveStar()
return
# Put TPF in window
self.addTPF()
# Make plots
self.updatePlots()
def download_target_px_file(tic_id):
"""
"""
sr = lk.search_tesscut("TIC {}".format(tic_id))
lcfs = sr.download_all()
tpf = lcfs[0]
aperture_mask = tpf.create_threshold_mask(threshold=7)
lc = tpf.to_lightcurve(aperture_mask=aperture_mask)
return lc
def Get_TESS(RA, DEC, Size, Sector=None):
'''
Use the lightkurve mast query to get FFI cutouts of a given position
'''
c = SkyCoord(ra=float(RA) * u.degree,
dec=float(DEC) * u.degree,
frame='icrs')
tess = lk.search_tesscut(c, sector=Sector)
tpf = tess.download(cutout_size=Size)
return tpf
def plot_gaia_overlay(self, tic=None, tpf=None, magnitude_limit=18):
"""Check if the source is contaminated."""
if tic is None:
tic = self.obj.source_info.tic
if tpf is None:
tpf = lk.search_tesscut(f'TIC {tic}')[0].download(cutout_size=(self.obj.tpf.shape[1],
self.obj.tpf.shape[2]))
fig = tpf.plot(show_colorbar=False, title='TIC {0}'.format(tic))
fig = self._add_gaia_figure_elements(tpf, fig, magnitude_limit=magnitude_limit)
return fig
def xmkpy3_plot_add_compass_rose_v5():
import matplotlib.pyplot as plt
import lightkurve as lk
#
obj = 3 # USER CUSTOMIZE
#
tpf = None
if (obj == 1):
north_arm_arcsec = 8
tpf = lk.search_targetpixelfile(
target='Kepler-138b', mission='Kepler', quarter=10)\
.download(quality_bitmask=0)
# ^--- Exoplanet Kelper-138b is "KIC 7603200"
elif (obj == 2):
north_arm_arcsec = 42
search_results = lk.search_tesscut(target='CD Ind', sector=1)
tpf = search_results[0].download(cutout_size=(11, 11),
quality_bitmask=0)
elif (obj == 3):
north_arm_arcsec = 42
tpf = lk.search_targetpixelfile(target='CD Ind',
mission='TESS',
sector=1).download(quality_bitmask=0)
else:
print('***ERROR*** BAD OBJECT NUMBER:', obj)
# pass:if
assert (tpf is not None)
#
# Plot the 2nd frame of the TPF
ax = tpf.plot(frame=1, cmap='gray_r')
#
# add a compass rose using the WCS from the TargetPixelFile
mkpy3_plot_add_compass_rose_v5(ax=ax,
wcs=tpf.wcs,
north_arm_arcsec=north_arm_arcsec,
verbose=True)
#
print(tpf.path, '\n^--- tpf.path\n')
print(tpf.ra, '=tpf.ra')
print(tpf.dec, '=tpf.dec')
print(obj, '=obj')
#
plot_file = 'mkpy3_plot1.png'
plt.savefig(plot_file, bbox_inches="tight")
plt.close()
print('\n', plot_file, ' <--- new PNG file written')
def test_tpf_cutout():
"""
"""
t = FFI_cutout(
ticid=TICID,
sector=SECTOR,
quality_bitmask="default",
apply_data_quality_mask=False,
cutout_size=CUTOUT_SIZE,
)
tpf1 = t.get_tpf_tesscut()
assert tpf1.targetid == TICID
assert tpf1.sector == SECTOR
assert tpf1.flux.shape[1:] == CUTOUT_SIZE
res = lk.search_tesscut(f"TIC {t.ticid}", sector=SECTOR)
# assert tpf2.flux.shape[1:] == CUTOUT_SIZE
tpf2 = res.download()
assert f"TIC {tpf1.targetid}" == tpf2.targetid
assert tpf1.sector == tpf2.sector
assert tpf1.quality_bitmask == tpf2.quality_bitmask
def extract_sectors(object_info, cache_dir):
mission, mission_prefix, id_int = LcBuilder().parse_object_info(
object_info.mission_id())
object_sectors = None
if mission == "Kepler":
lcf_search_results = lightkurve.search_targetpixelfile(
object_info.mission_id(),
mission=object_info.mission_id(),
cadence="long")
object_sectors = lcf_search_results.download_all(
download_dir=cache_dir).quarter
elif mission == "K2":
lcf_search_results = lightkurve.search_targetpixelfile(
object_info.mission_id(),
mission=object_info.mission_id(),
cadence="long")
object_sectors = lcf_search_results.download_all(
download_dir=cache_dir).campaign
elif mission == "TESS":
lcf_search_results = lightkurve.search_tesscut(
object_info.mission_id())
object_sectors = lcf_search_results.download_all(
download_dir=cache_dir).sector
return object_sectors
if load_handpicked:
# Get hand picked group key
gmin = targets[handpicked_key].min()
# Get original apertures
group_apts = getOrigAps(targets)
# Download each TESS cuts for each target
for ticid, Ggroup in targets[[ticid_col, handpicked_key]]:
# Get aperture and convert to Keppler/Tess format
apt = group_apts[Ggroup - gmin] + 1
apt[apt == 2] += 1
print(f'Downloading TIC ID={ticid}')
tpf = search_tesscut(ticid,
sector=sector).download(cutout_size=cutout_size)
# Write TIC ID and pipeline aperture to HDUs
tpf.hdu[0].header['TIC ID'] = ticid
tpf.hdu[0].header['TICID'] = ticid
tpf.hdu[2].data = apt
tpf_fn = template_fn.format(ticid=ticid,
sec=sector,
cutsize=cutout_size)
print(f'Saving to {tpf_fn}')
tpf.to_fits(output_fn=tpf_fn, overwrite=True)
else:
# Download each TESS cuts for each target
for ticid in targets[ticid_col]:
print('\n\nDATA ==========================================================')
obj = 6 # <--- USER CUSTOMIZE
if (obj == 1):
target = 'CD Ind'
mission = 'TESS'
sector = 1
width_height_arcmin = 6 # use with TESS <--- USER CUSTOMIZE
north_arm_arcsec = 42 # USER CUSTOMIZE
frame = 1 # USER CUSTOMIZE
percentile = 99.9 # USER CUSTOMIZE
title_ = target + ' : TESS : Sector ' + str(sector) # USER CUSTOMIZE
title2_ = title_ + " : Frame " + str(frame)
print('DOWNLOAD START:', str(datetime.datetime.now()))
search_results = lk.search_tesscut(target=target, sector=sector)[0]
tpf = search_results[0].download(cutout_size=(11, 11), quality_bitmask=0)
print('DOWNLOAD STOP:', str(datetime.datetime.now()))
# pass:if
if (obj == 2):
target = 'XZ Cyg'
mission = 'TESS'
sector = 14
width_height_arcmin = 6 # use with TESS <--- USER CUSTOMIZE
north_arm_arcsec = 42 # USER CUSTOMIZE
frame = 1 # USER CUSTOMIZE
percentile = 99.9 # USER CUSTOMIZE
title_ = target + ' : TESS : Sector ' + str(sector) # USER CUSTOMIZE
title2_ = title_ + " : Frame " + str(frame)
print('DOWNLOAD START:', str(datetime.datetime.now()))
def mkpy3_tess_tpf_overlay_v6(
tpf=None,
frame=0,
survey='2MASS-J',
rotationAngle_deg=None, # None or '123.456' (float) or 'tpf'
width_height_arcmin=6.0,
shrink=1.0,
show_plot=True,
plot_file='mkpy3_plot.png',
overwrite=False,
figsize_str='[9,9]',
title=None,
percentile=99.5,
cmap='gray_r',
colors_str="[None,'dodgerblue','red']",
lws_str='[0,3,4]',
zorders_str='[0,2,4]',
marker_kwargs_str="{'edgecolor':'yellow', 's':600, 'facecolor':'None', "
"'lw':3, 'zorder':10}", # or 'None'
print_gaia_dr2=True,
gaia_dr2_kwargs_str="{'edgecolor':'cyan', 's':150, 'facecolor':'None', "
"'lw':3, 'zorder':20}", # or 'None'
print_vsx=True,
vsx_kwargs_str="{'s':900, 'color':'lawngreen', 'marker':'x', 'lw':5, "
"'zorder':30}", # or 'None'
sexagesimal=False,
verbose=False
):
"""
Function: mkpy3_tess_tpf_overlay_v6()
Purpose: Plot a TESS TargetPixelFile (TPF) overlay on a sky survey image.
Parameters
----------
tpf : (lightkurve TargetPixelFile object) (optional)
A lightkurve TargetPixelFile (TPF) object.
[default: None]
frame : (int) (optional)
Frame number to use.
[range: 0 to number of cadences in the TPF minus 1]
[default: 0]
survey : (str) (optional)
A sky survey name.
[default: '2MASS-J'] [verified: '2MASS-J', 'DSS2 Red']
rotationAngle_deg : None, (float), or 'tpf' (3-char str) (optional)
Angle in degrees to rotate the sky survey image.
[default: None ---> 'tpf']
[example values: 'None' or 12.345 (float) or 'tpf']
width_height_arcmin : (float) (optional)
Width and height of the survey image [arcmin].
[default: 6.0]
shrink : (float) (optional)
Survey search radius shrink factor.
[range: 0.0 to 1.0]
[default: 1.0]
show_plot : (bool) (optional)
If True, show the plot.
[default=True]
plot_file : (str) (optional)
Filename of the output plot.
[default: 'mkpy3_plot.png']
overwrite : (bool) (optional)
If True, overwrite ("clobber") an existing output file.
If False, do *not* create output file when plot_file != 'mkpy3_plot.png'.
[default: False]
figsize_str : (str) (optional)
A string of a 2-time list of figure widht and height [Matplotlib].
[default: '[9,9]']
title : (str) (optional)
Title of the plot.
If None, a title will be created.
An empty string ('') will produce a blank title.
[default: None]
percentile : (float) (optional)
Percentile [percentage of pixels to keep] used to set the colorbar.
[range: 0.0 to 100.0]
[default: 99.5]
cmap : (str) (optional)
Colormap name [Matplotlib].
[default: 'gray_r']
colors_str : (str) (optional)
A string of a 3-item list of overlay color names [Matplotlib].
[default: "['None','dodgerblue','red']"]
lws_str : (str) (optional)
A string of a 3-item list of overlay line widths [Matplotlib].
[default: '[0,3,4]']
zorders_str : (str) (optional)
A string of a 3-item list of overlay zorder values [Matplotlib].
[default: '[0,2,4]']
marker_kwargs_str : (str) (optional)
A string of a dictionary of arguments for ax.scatter() [Matplotlib].
The target is marked according to the kwarg values.
If set to None, the target is *not* marked.
[default: "{'edgecolor':'yellow', 's':600, 'facecolor':'None', 'lw':3,
'zorder':10}"]
print_gaia_dr2 : (bool) (optional)
If True, print the GAIA DR2 catalog results.
[default=True]
gaia_dr2_kwargs_str : (str) (optional)
A string of a dictionary of arguments for ax.scatter() [Matplotlib].
GAIA DR2 stars are marked accordinbg to the kwarg values.
If set to None, no GAIA DR2 data are shown and plotted.
[default: "{'edgecolor':'cyan', 's':150, 'facecolor':'None', 'lw':3,
'zorder':20}"]
print_vsx : (bool) (optional)
If True, print the VSX catalog results.
[default=True]
vsx_kwargs_str : (str) (optional)
A string of a dictionary of arguments for ax.scatter() [Matplotlib].
VSX varaible stars are marked accordinbg to the kwarg values.
If set to None, no VSX data are shown and plotted.
[default: "{'s':900, 'color':'lawngreen', 'marker':'x', 'lw':5,
'zorder':30}"]
sexagesimal : (bool) (optional)
If True, print catalog positions as sexagesimal [hms dms].
[default=False]
verbose : (bool) (optional)
If True, print extra information.
[default: False]
Returns
-------
ax : (matplotlib axes object) or (None)
A matplotlib axes object *if* show_plot is False *else* None .
# Kenneth Mighell
# Kepler Support Scientist
# NASA Ames Research Center / SETI Institute
"""
import ntpath
import os
import sys
import lightkurve as lk
import mkpy3
if (tpf is None):
target = 'V1460 Her'
sector = 24
# alternative:
# target = 'XZ Cyg'
# sector = 14
title = target + ' : TESS : Sector ' + str(sector)
search_result = lk.search_tesscut(target=target, sector=sector)[0]
tpf = search_result.download(cutout_size=(11, 13), quality_bitmask=0)
# pass:if
assert(tpf is not None)
tpf_filename = ntpath.basename(tpf.path)
tpf_dirname = os.path.dirname(tpf.path)
try:
print()
print('TPF filename:', tpf_filename)
print('TPF dirname: ', tpf_dirname)
assert(tpf.mission == 'TESS')
print()
except Exception:
print(tpf_filename, '=tpf_filename')
print('^--- *** ERROR *** This file does not appear to be a TESS '
'TargetPixelFile')
print()
print('Bye...\n', flush=True)
sys.exit(1)
# pass:try
ax = mkpy3.mkpy3_tpf_overlay_v6(
tpf=tpf,
frame=frame,
survey=survey,
rotationAngle_deg=rotationAngle_deg,
width_height_arcmin=width_height_arcmin,
shrink=shrink,
show_plot=show_plot,
plot_file=plot_file,
overwrite=overwrite,
figsize_str=figsize_str,
title=title,
percentile=percentile,
cmap=cmap,
colors_str=colors_str,
lws_str=lws_str,
zorders_str=zorders_str,
marker_kwargs_str=marker_kwargs_str,
print_gaia_dr2=print_gaia_dr2,
gaia_dr2_kwargs_str=gaia_dr2_kwargs_str,
print_vsx=print_vsx,
vsx_kwargs_str=vsx_kwargs_str,
sexagesimal=sexagesimal,
verbose=verbose)
return ax
if args.gid != None:
gaia_id, mag = args.gid, np.float(args.gmag)
else:
if args.COORD is not False:
gaia_id, mag = get_gaia_data(ra, dec)
else:
gaia_id, mag = get_gaia_data_from_tic(tic)
if np.isnan(mag):
gaia_id, mag = get_gaia_data(ra, dec)
# By coordinates -----------------------------------------------------------------
if args.COORD is not False:
#
if args.sector != None:
tpf = search_tesscut(ra + " " + dec,
sector=int(args.sector)).download(
cutout_size=(12, 12)) #
else:
tpf = search_tesscut(ra + " " + dec).download(
cutout_size=(12, 12)) #
pipeline = "False"
print(' --> Using TESScut to get the TPF')
# By TIC name --------------------------------------------------------------------
else:
# If the target is in the CTL (short-cadance targets)...
try:
if args.sector != None:
tpf = search_targetpixelfile("TIC " + tic,
sector=int(args.sector),
def xmkpy3_tpf_overlay_v6():
"""
Unit test
"""
import matplotlib.pyplot as plt
import os
import ntpath
import datetime
import lightkurve as lk
from astropy.visualization import ImageNormalize, PercentileInterval,\
SqrtStretch
#
import mkpy3
cmap = 'gray_r'
verbose = False
mission = None
target = None
quarter = None
campaign = None
sector = None
print(
'\n\nDATA ==========================================================')
obj = 6 # <--- USER CUSTOMIZE
if (obj == 1):
target = 'CD Ind'
mission = 'TESS'
sector = 1
width_height_arcmin = 6 # use with TESS <--- USER CUSTOMIZE
north_arm_arcsec = 42 # USER CUSTOMIZE
frame = 1 # USER CUSTOMIZE
percentile = 99.9 # USER CUSTOMIZE
title_ = target + ' : TESS : Sector ' + str(sector) # USER CUSTOMIZE
print('DOWNLOAD START:', str(datetime.datetime.now()))
search_results = lk.search_tesscut(target=target, sector=sector)[0]
tpf = search_results[0].download(cutout_size=(11, 11),
quality_bitmask=0)
print('DOWNLOAD STOP:', str(datetime.datetime.now()))
# pass:if
if (obj == 2):
target = 'XZ Cyg'
mission = 'TESS'
sector = 14
width_height_arcmin = 6 # use with TESS <--- USER CUSTOMIZE
north_arm_arcsec = 42 # USER CUSTOMIZE
frame = 1 # USER CUSTOMIZE
percentile = 99.9 # USER CUSTOMIZE
title_ = target + ' : TESS : Sector ' + str(sector) # USER CUSTOMIZE
print('DOWNLOAD START:', str(datetime.datetime.now()))
search_results = lk.search_tesscut(target=target, sector=sector)[0]
tpf = search_results[0].download(cutout_size=(11, 11),
quality_bitmask=0)
print('DOWNLOAD STOP:', str(datetime.datetime.now()))
# pass:if
if (obj == 3):
target = 'V1460 Her'
mission = 'TESS'
sector = 24
width_height_arcmin = 6 # use with TESS <--- USER CUSTOMIZE
north_arm_arcsec = 42 # USER CUSTOMIZE
frame = 1 # USER CUSTOMIZE
percentile = 99.9 # USER CUSTOMIZE
title_ = target + ' : TESS : Sector ' + str(sector) # USER CUSTOMIZE
print('DOWNLOAD START:', str(datetime.datetime.now()))
search_results = lk.search_tesscut(target=target, sector=sector)[0]
tpf = search_results[0].download(cutout_size=(11, 11),
quality_bitmask=0)
print('DOWNLOAD STOP:', str(datetime.datetime.now()))
# pass:if
if (obj == 4):
target = 'Kepler-138b'
mission = 'Kepler'
quarter = 10
width_height_arcmin = 1.8 # use with Kepler/K2 <--- USER CUSTOMIZE
north_arm_arcsec = 6 # USER CUSTOMIZE
frame = 1 # USER CUSTOMIZE
percentile = 99.0 # USER CUSTOMIZE
title_ = target + ' : Kepler : Quarter ' + str(
quarter) # USER CUSTOMIZE
print('DOWNLOAD START:', str(datetime.datetime.now()))
tpf = lk.search_targetpixelfile(
target=target, mission=mission,
quarter=quarter).download(quality_bitmask=0)
# ^--- exoplanet Kelper-138b is "KIC 7603200"
print('DOWNLOAD STOP:', str(datetime.datetime.now()))
# pass:if
if (obj == 5):
target = 'K2-34b'
mission = 'k2'
campaign = 18
width_height_arcmin = 1.8 # use with Kepler/K2 <--- USER CUSTOMIZE
north_arm_arcsec = 6 # USER CUSTOMIZE
frame = 1 # USER CUSTOMIZE
percentile = 99.0 # USER CUSTOMIZE
title_ = target + ' : K2 : Campaign ' + str(campaign) # USER CUSTOMIZE
print('DOWNLOAD START:', str(datetime.datetime.now()))
tpf = lk.search_targetpixelfile(
target=target, mission=mission,
campaign=campaign).download(quality_bitmask=0)
# ^--- exoplanet K2-34b is "EPIC 212110888"
print('DOWNLOAD STOP:', str(datetime.datetime.now()))
# pass:if
if (obj == 6):
target = 'CD Ind'
sector = 1
mission = 'TESS'
frame = 1
width_height_arcmin = 6 # USER CUSTOMIZE
north_arm_arcsec = 42 # USER CUSTOMIZE
percentile = 99.5 # USER CUSTOMIZE
title_ = target + ' : TESS : Sector ' + str(sector) # USER CUSTOMIZE
#
radius = 120 # arcsec
print('DOWNLOAD START:', str(datetime.datetime.now()))
search_results = lk.search_targetpixelfile(target,
radius=radius,
mission=mission,
sector=sector)
tpf = search_results[0].download(quality_bitmask=0)
print('DOWNLOAD STOP:', str(datetime.datetime.now()))
print(search_results)
print(tpf)
print(tpf.wcs)
print(':-)')
# pass:if
print()
print(mission, '=mission')
print(target, '=target')
if (mission == 'kepler'):
print(quarter, '=quarter')
# pass:if
if (mission == 'k2'):
print(campaign, '=campaign')
# pass:if
if (mission == 'tess'):
print(sector, '=sector')
# pass:if
tpf_dir = os.path.dirname(tpf.path)
tpf_file = ntpath.basename(tpf.path)
cwd = os.getcwd()
print('\n CWD:', cwd)
print(' TPF dirname:', tpf_dir)
print('TPF filename:', tpf_file)
print()
print(tpf)
print('^--- tpf')
if (verbose):
print()
print(tpf.wcs)
print('^--- tpf.wcs')
# pass:if
print('\n\nPLOT#1 =======================================================')
ax = tpf.plot(frame=frame, cmap=cmap)
ax.set_title(title_)
mkpy3.mkpy3_plot_add_compass_rose_v5(ax=ax,
north_arm_arcsec=north_arm_arcsec,
wcs=tpf.wcs,
verbose=verbose)
# mark the target with a yellow circle:
target_wx0, target_wy0 = tpf.wcs.wcs_world2pix(tpf.ra, tpf.dec, 0)
marker_kwargs = \
{'edgecolor': 'yellow', 's': 600, 'facecolor': 'None', 'lw': 3, 'zorder': 10}
ax.scatter(target_wx0 + tpf.column, target_wy0 + tpf.row, **marker_kwargs)
oplot1 = 'mkpy3_plot1.png'
plt.savefig(oplot1, bbox_inches="tight")
print('\n', oplot1, ' <--- new PNG file written')
plt.close()
print('\n\nPLOT#2 =======================================================')
fig = plt.figure(figsize=(7, 7))
ax = plt.subplot(projection=tpf.wcs)
image_data = tpf.hdu[1].data['flux'][frame]
norm = ImageNormalize(image_data,
interval=PercentileInterval(percentile),
stretch=SqrtStretch())
ax.imshow(image_data, norm=norm, cmap=cmap)
ax.tick_params(axis='x',
labelsize=16,
length=5,
width=2,
labeltop=True,
labelbottom=True)
ax.tick_params(axis='y',
labelsize=16,
length=5,
width=2,
labelright=True,
labelleft=True)
ax.coords[0].set_major_formatter('d.dd')
ax.coords[1].set_major_formatter('d.dd')
ax.set_xlabel('Right Ascension (J2000)', size=24)
ax.set_ylabel('Declination (J2000)', size=24)
fig.suptitle(title_, size=24)
ax.grid(True, color='palegreen', lw=2, zorder=1)
mkpy3.mkpy3_plot_add_compass_rose_v5(ax=ax,
north_arm_arcsec=north_arm_arcsec,
wcs=tpf.wcs,
verbose=verbose)
marker_kwargs =\
{'edgecolor': 'yellow', 's': 600, 'facecolor': 'None', 'lw': 3, 'zorder': 10}
ax.scatter(tpf.ra,
tpf.dec,
transform=ax.get_transform('icrs'),
**marker_kwargs)
oplot2 = 'mkpy3_plot2.png'
plt.savefig(oplot2, dpi=150, bbox_inches="tight")
print('\n', oplot2, ' <--- plot file written')
plt.close()
# =========================================================================
shrink = 0.75
print_gaia_dr2 = False
print_vsx = False
print('\n\nPLOT#4 =======================================================')
rotationAngle_deg = 0.0 # no rotation
ax = mkpy3_tpf_overlay_v6(tpf=tpf,
rotationAngle_deg=rotationAngle_deg,
width_height_arcmin=width_height_arcmin,
percentile=percentile,
shrink=shrink,
show_plot=False,
plot_file='',
title=title_,
print_gaia_dr2=print_gaia_dr2,
print_vsx=print_vsx,
verbose=verbose)
ax.coords[0].set_major_formatter('d.dd')
ax.coords[1].set_major_formatter('d.dd')
ax.tick_params(axis='x',
labelsize=16,
length=5,
width=2,
labeltop=True,
labelbottom=True)
ax.tick_params(axis='y',
labelsize=16,
length=5,
width=2,
labelright=True,
labelleft=True)
ax.grid(True, color='palegreen', lw=2, zorder=1)
mkpy3.mkpy3_plot_add_compass_rose_v5(ax=ax,
north_arm_arcsec=2 * north_arm_arcsec)
oplot4 = 'mkpy3_plot4.png'
plt.savefig(oplot4, bbox_inches="tight")
print('\n', oplot4, ' <--- new PNG file written')
plt.close()
print('\n\nPLOT#3 =======================================================')
#
# compute rotation based on the WCS of the TPF:
rotationAngle_deg = 'tpf'
#
ax = mkpy3_tpf_overlay_v6(tpf=tpf,
rotationAngle_deg=rotationAngle_deg,
width_height_arcmin=width_height_arcmin,
shrink=shrink,
show_plot=False,
plot_file='',
title=title_,
percentile=percentile,
print_gaia_dr2=print_gaia_dr2,
print_vsx=print_vsx,
verbose=verbose)
ax.coords[0].set_major_formatter('d.dd')
ax.coords[1].set_major_formatter('d.dd')
ax.tick_params(axis='x',
labelsize=16,
length=5,
width=2,
labeltop=True,
labelbottom=True)
ax.tick_params(axis='y',
labelsize=16,
length=5,
width=2,
labelright=True,
labelleft=True)
ax.grid(True, color='palegreen', lw=2, zorder=1)
mkpy3.mkpy3_plot_add_compass_rose_v5(ax=ax,
north_arm_arcsec=2 * north_arm_arcsec)
oplot3 = 'mkpy3_plot3.png'
plt.savefig(oplot3, bbox_inches="tight")
print('\n', oplot3, ' <--- new PNG file written')
plt.close()
print()
print('==================================================================')
print()
print('[*]', ax.tpf_positionAngle_deg, '=ax.tpf_positionAngle_deg')
print('[*]', ax.tpf_n_pa_deg, '=ax.tpf_n_pa_deg')
print('[*]', ax.tpf_mirrored, '=ax.tpf_mirrored')
print('[*]', ax.tpf_north_top_half, '=ax.tpf_north_top_half')
print('[*]', ax.tpf_east_left_half, '=ax.tpf_east_left_half')
print('[*]')
print('[*] <---', ax.survey_rotationAngle_deg,
'=ax.survey_rotationAngle_deg')
print('[*] --->', ax.survey_rotate_deg, '=ax.survey_rotate_deg')
print('[*]')
print('[*]', ax.survey_positionAngle_deg, '=ax.survey_positionAngle_deg')
print('[*]', ax.survey_n_pa_deg, '=ax.survey_n_pa_deg')
print('[*]', ax.survey_mirrored, '=ax.survey_mirrored')
print('[*]', ax.survey_north_top_half, '=ax.survey_north_top_half')
print('[*]', ax.survey_east_left_half, '=ax.survey_east_left_half')
print('[*]')
print('[*]', ax.xaxis_inverted, '=ax.xaxis_inverted')
print('[*]', ax.yaxis_inverted, '=ax.yaxis_inverted')
print()
print('[-]', target, '=target')
print('[-]', mission, '=mission')
if (mission == 'TESS'):
print('[-]', sector, '=sector')
elif (mission == 'kepler'):
print('[-]', quarter, '=quarter')
else:
print('[-]', campaign, '=campaign')
# pass:if
print('[-]', tpf.ra, '=tpf.ra [deg]')
print('[-]', tpf.dec, '=tpf.dec [deg]')
print('[-]', obj, '=obj')
print()
print()
print('PLOTS DONE =======================================================')
print()
print('plot files written:\n')
print(' ', oplot1, oplot2, oplot3, oplot4)
print()
print('FILE:', __file__)
print('TIME:', str(datetime.datetime.now()))
print('DONE:', __name__)
continue
# Assuming we got at least one sample tpf, extract their aperture masks
apertures = []
for sample in group_samples:
print('Gathering aperture masks from sample TPF', sample)
lcf = TessLightCurveFile(sample)
current_aperture = lcf.hdu[2].data
apertures.append(current_aperture)
# Generate boolean master aperture mask
# I want to keep the mask pixels that are shared between at least two images.
# That means that if I sum the aperture masks, I will keep the mask pixels that are above 6.
boolean_apt = np.sum(apertures, axis=0) > max(6, 3 * n_samples // 3)
# Now use the boolean aperture mask to export light curves of current group
for target in group_targets:
# Skip lightcurves files that already exist
if isfile(lc_filename.format(target, sector)):
continue
print('Exporting light curve file for TIC =', target)
# Download cutouts to create light curve
tpf = search_tesscut(target,
sector=sector).download(cutout_size=cutoutsize)
# Generate light curve using the aperture mask `new_apt`
lc = tpf.to_lightcurve(aperture_mask=boolean_apt)
# Export light curve into FITS file
lc.to_fits(lc_filename.format(target, sector), overwrite=True)
# scatter plot light curve
# lc.scatter()
def get_cutout(ticid, cutout_size=9):
""" """
tpf = lk.search_tesscut(ticid)[0].download(cutout_size=cutout_size)
return tpf
Complete_Clusters=Table.read(data_dir+'Cluster_Catalog_Kharchenko_updated.fits')
Complete_Clusters=Complete_Clusters.to_pandas()
Complete_Clusters['CLUSTER_RADIUS']=Complete_Clusters['CLUSTER_RADIUS']
for i in range(len(Complete_Clusters)):
Complete_Clusters['NAME'][i] = Complete_Clusters['NAME'][i].decode("utf-8").strip()
## Download target pixel file ##
# input name of the interested cluster
CLUSTERS = ["NGC 2422"]
# initialize the target pixel file and cutout size
tpfs = [0]
cutout_size = 99
search = lk.search_tesscut(CLUSTERS[0])#search for the cluster in TESS using lightkurve
char = ""
if len(search) != 1: char = "s"
print("{0} has {1} result{2}.".format(CLUSTERS, len(search), char))
tpfs = search.download_all(cutout_size=cutout_size)# download target pixel file for corresponding cluster
sectors = [this_tpfs.sector for this_tpfs in tpfs] #sectors cluster was observed in
orbit_times = pd.read_csv(data_dir+'orbit_times_20201013_1338.csv', comment = '#')# read the file containing epoch info of the sectors
start_times = orbit_times[orbit_times['Sector'].isin(sectors)]['Start TJD'].values # start times for the sectors that this cluster was observed in
end_times = orbit_times[orbit_times['Sector'].isin(sectors)]['End TJD'].values # end times for the sectors
#get the frequency and Lomb-Scargle periodogram power for the ensemble photometry of the cluster
omega, P_LS = get_LCs(CLUSTERS,'ensemble')
plt.plot(omega,P_LS)
plt.xscale('log') # quick plot of the periodogram
def make_ica_plot(tic, tpf=None):
"""
"""
if tpf is None:
tpf = lk.search_tesscut(f'TIC {tic}').download(cutout_size=11)
raw_lc = tpf.to_lightcurve(aperture_mask='all')
##Perform ICA
n_components = 20
X = np.ascontiguousarray(np.nan_to_num(tpf.flux), np.float64)
X_flat = X.reshape(len(tpf.flux), -1) #turns three dimensional into two dimensional
f1 = np.reshape(X_flat, (len(X), -1))
X_pix = f1 / np.nansum(X_flat, axis=-1)[:, None]
ica = FastICA(n_components=n_components) #define n_components
S_ = ica.fit_transform(X_pix)
A_ = ica.mixing_ #combine x_flat to get x
a = np.dot(S_.T, S_)
a[np.diag_indices_from(a)] += 1e-5
b = np.dot(S_.T, raw_lc.flux)
w = np.linalg.solve(a, b)
comp_lcs = []
blss = []
max_powers = []
for i,s in enumerate(S_.T):
component_lc = s * w[i]
comp_lcs.append(component_lc)
# plt.plot(component_lc + i*1e5)
model = BoxLeastSquares(tpf.time, component_lc)
results = model.autopower(0.16, minimum_period=.5, maximum_period=24.)
# model = transitleastsquares(tpf.time, component_lc)
# results = model.power()
period, power = results.period, results.power
blss.append([period, power])
# print(results.depth_snr[np.argmax(power)])
if (np.std(component_lc) > 1e4) or (np.abs(period[np.argmax(power)] - 14) < 2) or (results.depth[np.argmax(power)]/np.median(component_lc) < 0):
power = [0]
max_powers.append(np.max(power))
# plt.ylim(-1e5, 10e5)
best_pers = blss[np.argmax(max_powers)][0]
best_powers = blss[np.argmax(max_powers)][1]
period = best_pers[np.argmax(best_powers)]
transit_lc = lk.LightCurve(time=tpf.time, flux=comp_lcs[np.argmax(max_powers)])
fig, ax = plt.subplots(2, 3, figsize=(10, 7))
fig.suptitle(f'TIC {tic}')
for i,c in enumerate(comp_lcs):
ax[0,0].plot(tpf.time, c + i*1e5)
ax[0,0].set_ylim(-1e5, n_components*1e5)
ax[0,0].set_xlim(tpf.time[0], tpf.time[-1])
ax[0,0].set_xlabel('Time')
ax[0,0].set_ylabel('Flux')
ax[0,0].yaxis.set_major_formatter(mtick.FormatStrFormatter('%.e'))
ax[0,0].set_title('ICA Components')
transit_lc.plot(ax=ax[0,1])
ax[0,1].set_xlim(tpf.time[0], tpf.time[-1])
ax[0,1].yaxis.set_major_formatter(mtick.FormatStrFormatter('%.e'))
ax[0,1].set_title('ICA comp with max BLS power')
transit_lc.remove_outliers(9).fold(period).scatter(ax=ax[0,2], c='k', label=f'Period={period:.2f}')
transit_lc.remove_outliers(9).fold(period).bin(7).plot(ax=ax[0,2], c='r', lw=2, C='C1', label='Binned')
ax[0,2].set_ylim(-5*np.std(transit_lc.flux), 2*np.std(transit_lc.flux))
ax[0,2].set_xlim(-.5,.5)
ax[0,2].set_title('Folded ICA Transit Component')
A_useful = A_.reshape(11,11,n_components).T #reshape from 2d to 3d
weighted_comp = A_useful[np.argmax(max_powers)].T * w[np.argmax(max_powers)]
ax[1,0].imshow(weighted_comp, origin='lower')
ax[1,1].imshow(tpf.flux[200], origin='lower')
im = ax[1,2].imshow(weighted_comp / tpf.flux[200], origin='lower')
ax[1,0].set_title('Weighted Transit Component')
ax[1,1].set_title('TPF')
ax[1,2].set_title('Model / Flux')
plt.colorbar(im)
fig.tight_layout(rect=[0, 0.03, 1, 0.95])
fig.patch.set_facecolor('white')
fig.set_size_inches(10, 7)
return fig
from lightkurve import search_tesscut
from lightkurve import DesignMatrix
from lightkurve import RegressionCorrector
import numpy as np
import astropy.units as u
import matplotlib.pyplot as plt
# ADD:
# quality_bitmask='hard'
# to download_all for harder cut on QUALITY flag
tpf = search_tesscut("BV Aqr").download_all()
print(tpf)
if (hasattr(tpf, "__len__")):
N = len(tpf)
target_mask1 = tpf[0].create_threshold_mask(threshold=3)
raw_lc = tpf[0].to_lightcurve(aperture_mask=target_mask1)
comb_mask = ((raw_lc.time < 1347) |
(raw_lc.time > 1350)) & (raw_lc.flux_err > 0)
raw_lc = raw_lc[comb_mask]
bkgr = tpf[0].flux[:, ~target_mask1]
dm = DesignMatrix(bkgr[comb_mask],
name='regressors').pca(5).append_constant()
rc = RegressionCorrector(raw_lc)
rc.correct(dm)
corrected_lc = (raw_lc - rc.model_lc +
np.percentile(rc.model_lc.flux, 5)).flatten()
for itpf in tpf[1:]:
target_mask = itpf.create_threshold_mask(threshold=3)
raw_lc = itpf.to_lightcurve(aperture_mask=target_mask)
raw_lc = raw_lc[raw_lc.flux_err > 0]
bkgr = itpf.flux[:, ~target_mask]
def vetting_field_of_view(self, indir, tic, ra, dec, sectors):
maglim = 6
sectors_search = None if sectors is not None and len(
sectors) == 0 else sectors
tpf_source = lightkurve.search_targetpixelfile("TIC " + str(tic),
sector=sectors,
mission='TESS')
if tpf_source is None or len(tpf_source) == 0:
ra_str = str(ra)
dec_str = "+" + str(dec) if dec >= 0 else str(dec)
tpf_source = lightkurve.search_tesscut(ra_str + " " + dec_str,
sector=sectors_search)
for i in range(0, len(tpf_source)):
tpf = tpf_source[i].download(cutout_size=(12, 12))
pipeline = True
fig = plt.figure(figsize=(6.93, 5.5))
gs = gridspec.GridSpec(1,
3,
height_ratios=[1],
width_ratios=[1, 0.05, 0.01])
gs.update(left=0.05,
right=0.95,
bottom=0.12,
top=0.95,
wspace=0.01,
hspace=0.03)
ax1 = plt.subplot(gs[0, 0])
# TPF plot
mean_tpf = np.mean(tpf.flux.value, axis=0)
nx, ny = np.shape(mean_tpf)
norm = ImageNormalize(stretch=stretching.LogStretch())
division = np.int(np.log10(np.nanmax(tpf.flux.value)))
splot = plt.imshow(np.nanmean(tpf.flux, axis=0) / 10 ** division, norm=norm, \
extent=[tpf.column, tpf.column + ny, tpf.row, tpf.row + nx], origin='lower', zorder=0)
# Pipeline aperture
if pipeline: #
aperture_mask = tpf.pipeline_mask
aperture = tpf._parse_aperture_mask(aperture_mask)
maskcolor = 'lightgray'
print(" --> Using pipeline aperture...")
else:
aperture_mask = tpf.create_threshold_mask(
threshold=10, reference_pixel='center')
aperture = tpf._parse_aperture_mask(aperture_mask)
maskcolor = 'lightgray'
print(" --> Using threshold aperture...")
for i in range(aperture.shape[0]):
for j in range(aperture.shape[1]):
if aperture_mask[i, j]:
ax1.add_patch(
patches.Rectangle((j + tpf.column, i + tpf.row),
1,
1,
color=maskcolor,
fill=True,
alpha=0.4))
ax1.add_patch(
patches.Rectangle((j + tpf.column, i + tpf.row),
1,
1,
color=maskcolor,
fill=False,
alpha=1,
lw=2))
# Gaia sources
gaia_id, mag = tpfplotter.get_gaia_data_from_tic(tic)
r, res = tpfplotter.add_gaia_figure_elements(tpf,
magnitude_limit=mag +
np.float(maglim),
targ_mag=mag)
x, y, gaiamags = r
x, y, gaiamags = np.array(x) + 0.5, np.array(y) + 0.5, np.array(
gaiamags)
size = 128.0 / 2**((gaiamags - mag))
plt.scatter(x,
y,
s=size,
c='red',
alpha=0.6,
edgecolor=None,
zorder=10)
# Gaia source for the target
this = np.where(np.array(res['Source']) == int(gaia_id))[0]
plt.scatter(x[this],
y[this],
marker='x',
c='white',
s=32,
zorder=11)
# Legend
add = 0
if np.int(maglim) % 2 != 0:
add = 1
maxmag = np.int(maglim) + add
legend_mags = np.linspace(-2, maxmag, np.int((maxmag + 2) / 2 + 1))
fake_sizes = mag + legend_mags # np.array([mag-2,mag,mag+2,mag+5, mag+8])
for f in fake_sizes:
size = 128.0 / 2**((f - mag))
plt.scatter(0,
0,
s=size,
c='red',
alpha=0.6,
edgecolor=None,
zorder=10,
label=r'$\Delta m=$ ' + str(np.int(f - mag)))
ax1.legend(fancybox=True, framealpha=0.7)
# Source labels
dist = np.sqrt((x - x[this])**2 + (y - y[this])**2)
dsort = np.argsort(dist)
for d, elem in enumerate(dsort):
if dist[elem] < 6:
plt.text(x[elem] + 0.1,
y[elem] + 0.1,
str(d + 1),
color='white',
zorder=100)
# Orientation arrows
tpfplotter.plot_orientation(tpf)
# Labels and titles
plt.xlim(tpf.column, tpf.column + ny)
plt.ylim(tpf.row, tpf.row + nx)
plt.xlabel('Pixel Column Number', fontsize=16)
plt.ylabel('Pixel Row Number', fontsize=16)
plt.title('Coordinates ' + tic + ' - Sector ' + str(tpf.sector),
fontsize=16) # + ' - Camera '+str(tpf.camera)) #
# Colorbar
cbax = plt.subplot(gs[0, 1]) # Place it where it should be.
pos1 = cbax.get_position() # get the original position
pos2 = [pos1.x0 - 0.05, pos1.y0, pos1.width, pos1.height]
cbax.set_position(pos2) # set a new position
cb = Colorbar(ax=cbax,
mappable=splot,
orientation='vertical',
ticklocation='right')
plt.xticks(fontsize=14)
exponent = r'$\times 10^' + str(division) + '$'
cb.set_label(r'Flux ' + exponent + r' (e$^-$)',
labelpad=10,
fontsize=16)
save_dir = indir + "/tpfplot"
if not os.path.exists(save_dir):
os.makedirs(save_dir)
plt.savefig(save_dir + '/TPF_Gaia_TIC' + tic + '_S' +
str(tpf.sector) + '.pdf')
# Save Gaia sources info
dist = np.sqrt((x - x[this])**2 + (y - y[this])**2)
GaiaID = np.array(res['Source'])
srt = np.argsort(dist)
x, y, gaiamags, dist, GaiaID = x[srt], y[srt], gaiamags[srt], dist[
srt], GaiaID[srt]
IDs = np.arange(len(x)) + 1
inside = np.zeros(len(x))
for i in range(aperture.shape[0]):
for j in range(aperture.shape[1]):
if aperture_mask[i, j]:
xtpf, ytpf = j + tpf.column, i + tpf.row
_inside = np.where((x > xtpf) & (x < xtpf + 1)
& (y > ytpf) & (y < ytpf + 1))[0]
inside[_inside] = 1
data = Table([
IDs, GaiaID, x, y, dist, dist * 21., gaiamags,
inside.astype('int')
],
names=[
'# ID', 'GaiaID', 'x', 'y', 'Dist_pix',
'Dist_arcsec', 'Gmag', 'InAper'
])
ascii.write(data,
save_dir + '/Gaia_TIC' + tic + '_S' + str(tpf.sector) +
'.dat',
overwrite=True)
return save_dir
if (__name__ == '__main__'):
import matplotlib.pyplot as plt
import lightkurve as lk
#
obj = 3 # USER CUSTOMIZE
#
tpf = None
if (obj == 1):
north_arm_arcsec = 8
tpf = lk.search_targetpixelfile(
target='Kepler-138b', mission='Kepler', quarter=10)\
.download(quality_bitmask=0)
# ^--- Exoplanet Kelper-138b is "KIC 7603200"
elif (obj == 2):
north_arm_arcsec = 42
search_results = lk.search_tesscut(target='CD Ind', sector=1)
tpf = search_results[0].download(cutout_size=(11, 11),
quality_bitmask=0)
elif (obj == 3):
north_arm_arcsec = 42
tpf = lk.search_targetpixelfile(target='CD Ind',
mission='TESS',
sector=1).download(quality_bitmask=0)
else:
print('***ERROR*** BAD OBJECT NUMBER:', obj)
# pass:if
assert (tpf is not None)
#
# Plot the 2nd frame of the TPF
ax = tpf.plot(frame=1, cmap='gray_r')
#
def showStar(self, updateOnly=False):
plt.close('all')
## Check to see if we've reached the end of the list
if (len(self.RA) == 0):
self.changeCounter(0)
self.errorWindow("ALL DONE.")
self.updateObjectList()
return
self.apthresh = float(self.apselectEntry.get())
## Change RA/dec if this is a new star
if (not updateOnly):
## clear marked values and update RA/DEC
self.clearValues()
self.changeRADEC()
self.changeCounter()
## get the coordinates of the next star
coord = SkyCoord(self.RA[-1], self.DE[-1], unit="deg")
## get the name of this star from Simbad and update GUI
try:
self.starName = Simbad.query_region(
coord, radius='0d0m2s')['MAIN_ID'][0]
except:
self.starName = "Unknown"
self.changeTIC()
## download the TPFs for every possible sector
self.tpf = search_tesscut(coord).download_all(cutout_size=(10, 10))
print(self.tpf)
## make sure the 1st sector is OK
ntrgtpix = 0
while (ntrgtpix == 0 and hasattr(self.tpf, "__len__")
and len(self.tpf) > 0):
tmsk = self.tpf[0].create_threshold_mask(threshold=self.apthresh)
ntrgtpix = tmsk.sum()
if (ntrgtpix == 0):
print('sector %d...SKIPPED.' % (self.tpf[0].sector),
flush=True)
self.tpf = self.tpf[1:]
if (hasattr(self.tpf, "__len__") and len(self.tpf) > 0):
self.NSECS[self.NDONE] = len(self.tpf)
print('sector %d...' % (self.tpf[0].sector), end='', flush=True)
## create an aperture mask for the target star
self.target_mask0 = self.tpf[0].create_threshold_mask(
threshold=self.apthresh)
n_target_pixels = self.target_mask0.sum()
## create the lightcurve for the first sector using aperture
raw_lc = self.tpf[0].to_lightcurve(aperture_mask=self.target_mask0)
## create a mask for the background signal
self.bkgr_mask0 = ~self.tpf[0].create_threshold_mask(
threshold=0.001, reference_pixel=None)
n_background_pixels = self.bkgr_mask0.sum()
## create a lightcurve for the background and scale it to the target aperture
bkgr_lc = self.tpf[0].to_lightcurve(
aperture_mask=self.bkgr_mask0
) / n_background_pixels * n_target_pixels
## subtract the background from the signal
self.star_lc = (raw_lc - bkgr_lc.flux)
## normalize the flux and remove problem points
forFeatures = self.star_lc.remove_nans().remove_outliers(sigma=6)
self.star_lc = forFeatures.normalize()
#self.star_lc = self.star_lc.remove_nans().remove_outliers(sigma=6).normalize()
#self.star_lc = self.star_lc[(self.star_lc.flux>0.6) & (self.star_lc.flux<1.6)]
print('DONE.', flush=True)
for itpf in self.tpf[1:]:
print('sector %d...' % (itpf.sector), end='', flush=True)
## create an aperture mask for the target star
target_mask = itpf.create_threshold_mask(
threshold=self.apthresh)
n_target_pixels = target_mask.sum()
## check to make sure the mask is OK
if (n_target_pixels == 0):
print('SKIPPED.', flush=True)
continue
## create the lightcurve for the first sector using aperture
raw_lc = itpf.to_lightcurve(aperture_mask=target_mask)
## create a mask for the background signal
bkgr_mask = ~itpf.create_threshold_mask(threshold=0.001,
reference_pixel=None)
n_background_pixels = bkgr_mask.sum()
## create a lightcurve for the background and scale it to the target aperture
bkgr_lc = itpf.to_lightcurve(
aperture_mask=bkgr_mask
) / n_background_pixels * n_target_pixels
## subtract the background from the signal
temp_lc = (raw_lc - bkgr_lc.flux)
## normalize the flux and remove problem points
temp_lc = temp_lc.remove_nans().remove_outliers(sigma=6)
forFeatures = forFeatures.append(temp_lc)
#temp_lc = temp_lc.remove_nans().remove_outliers(sigma=6).normalize()
#temp_lc = temp_lc[(temp_lc.flux>0.6) & (temp_lc.flux<1.6)]
## add this sector to the previous
self.star_lc = self.star_lc.append(temp_lc.normalize())
#self.star_lc = self.star_lc.append(temp_lc)
print('DONE.', flush=True)
# mask out bad data selection(s)
self.star_lc_all = self.star_lc.copy()
bddt_msk = np.ones(len(self.star_lc), dtype=bool)
for item in self.lc_spans:
bddt_msk[int(item[0]):int(item[1]) + 1] = False
self.star_lc = self.star_lc[bddt_msk]
# get peaks
self.peaks = find_peaks(self.star_lc.flux, prominence=0.25)[0]
# do a LombScargle analysis of the lightcurve
self.lspg = self.star_lc.to_periodogram(maximum_frequency=12.0,
oversample_factor=50)
# determine Fourier features of the lightcurve
forFeatures = forFeatures[bddt_msk]
self.lc_features = getFeatures(forFeatures,
self.lspg.period_at_max_power.value)
#print(self.lc_features)
else:
self.errorWindow("NOT IN SECTOR")
self.saveStar()
return
## Make plots
self.updatePlots()
def lk_tesscut(tic,
ra=None,
dec=None,
size=50,
bkg_subtract=True,
bkg_n=300,
k=100,
n=100,
l2_reg=[0.1],
exclusion_size=5,
apt_size=1,
pred_pix_method="similar_brightness",
add_poly=False,
poly_scale=2,
poly_num_terms=4):
#search light curve for given TIC ID
search_res = lk.search_tesscut('TIC ' + str(tic))
#initialize SPOC found,first found
tesscut_found = False
tesscut_first = False
try:
authors = search_res.table['author']
except:
tc_avail = False
tesscut_lc = median_im = im_header = pd.DataFrame()
return (tesscut_lc, median_im, im_header, tc_avail)
if 'TESScut' in authors:
tc_avail = True
else:
tc_avail = False
tesscut_lc = median_im = im_header = pd.DataFrame()
return (tesscut_lc, median_im, im_header, tc_avail)
lc_holder = []
for i in range(len(search_res)):
#select search result object
search_i = search_res[i]
#skip if not TESScut
if (search_i.author[0] == 'TESScut'
): #& (search_i.exptime.data[0] == 120):
print("Found " + str(search_i.mission[0]) +
" TESScut data for TIC " + str(tic) + "!")
tesscut_found = True
if (tesscut_first == False) & (tesscut_first is not None):
tesscut_first = True
else:
continue
#download this sector's tesscut
lk_tesscut_obj = search_res[i].download(cutout_size=size)
# #instantiate cpm_obj
# cpm_obj = cpm_int(tic = tic,ra = ra,dec = dec)
# #get cpm_lc for this sector by passing lk_tess_obj to cpm_obj
# if i == 0:
# med_im_header = True
# lc_df,median_im,im_header = cpm_obj.lk_cpm_lc(lk_tesscut_obj = lk_tesscut_obj,
# med_im_header = med_im_header)
# else:
# med_im_header = False
# lc_df = cpm_obj.lk_cpm_lc(lk_tesscut_obj = lk_tesscut_obj,
# med_im_header = med_im_header)
#get cpm_lc for this sector by passing lk_tess_obj to lk_cpm_lc function
if i == 0:
med_im_header = True
lc_df, median_im, im_header = lk_cpm_lc(
lk_tesscut_obj=lk_tesscut_obj,
med_im_header=med_im_header,
bkg_subtract=bkg_subtract,
bkg_n=bkg_n,
k=k,
n=n,
l2_reg=l2_reg,
exclusion_size=exclusion_size,
apt_size=apt_size,
pred_pix_method=pred_pix_method,
add_poly=add_poly,
poly_scale=poly_scale,
poly_num_terms=poly_num_terms)
else:
med_im_header = False
lc_df = lk_cpm_lc(lk_tesscut_obj=lk_tesscut_obj,
med_im_header=med_im_header,
bkg_subtract=bkg_subtract,
bkg_n=bkg_n,
k=k,
n=n,
l2_reg=l2_reg,
exclusion_size=exclusion_size,
apt_size=apt_size,
pred_pix_method=pred_pix_method,
add_poly=add_poly,
poly_scale=poly_scale,
poly_num_terms=poly_num_terms)
#append to lc_holder for later concatenation
lc_holder.append(lc_df) #store in lc_holder
# #save median_im and im_header if i == 0
# if i == 0:
# median_im = cpm_obj.median_im
# im_header = cpm_obj.im_header
#delete stuff
path = lk_tesscut_obj.path
#del cpm_obj
del lk_tesscut_obj
os.remove(path=path)
if tesscut_found == False:
print("No TESScut data found for TIC " + str(tic) + ".")
tesscut_lc = median_im = im_header = pd.DataFrame()
else:
tesscut_lc = pd.concat(lc_holder) #combine lc into 1 pandas dataframe
return (tesscut_lc, median_im, im_header, tc_avail)
def download_tesscuts_single(TIC,
outputdir=Path.cwd(),
imsize=20,
overwrite=False,
max_tries_download=10,
max_tries_save=2,
max_tries_query=3,
name_pattern='tess{TIC}_sec{SECTOR}.fits',
onlysectors=None):
'''
Purpose:
Downoad the TESS cut for all the available sectors given the TIC number
Args:
- TIC: string
TIC number of the target star.
- outputdir: pathlib.Path
Directory where to store the images to be downloaded.
If the directory does not exist, it will be created
- imsize: int
Size in pixels of the square images to be downloaded.
- overwrite: bool
If True, then overwrite the FITS images.
Note that the code actually skips the files that havealready been
downloaded and so, ovewrite=True should not be needed.
- max_tries_download: int
Maximum number of attempts to download a same TESS sector.
- max_tries_save: int
Maximum number of attempts to save a same FITS image.
- max_tries_query: int
Maximum number of attempts to querry MAST for a particular TIC.
- name_pattern: str
Pattern use to name the files to be saved. The five charachters
{TIC} and the eight characters {SECTOR} are mandatory in the pattern
and will replaced for the TIC number and sector number,
respectively.
'''
# Ensure TIC is a string (and not a number)
if not isinstance(TIC, str):
raise TypeError('TIC must be a string instance. Ex: TIC="349092922"')
# Ensure outputdir is a Path instance
if not isinstance(outputdir, Path):
raise TypeError(
'outputdir must be a Path instance. Ex: outputdir=pathlib.Path.cwd()'
)
# Ensure imsize is an integer instance
if not isinstance(imsize, int):
raise TypeError('imsize must be an int instance. Ex: imsize=20')
# Ensure name_pattern is a string instance
if not isinstance(name_pattern, str):
raise TypeError(
'name_pattern must be a string instance containing the characters {TIC} and {SECTOR}. Ex: "tess{TIC}_sec{SECTOR}.fits"'
)
# Ensure name_pattern contains characters {TIC} and {SECTOR} and ends with .fits
else:
if (not '{TIC}' in name_pattern) \
or (not '{SECTOR}' in name_pattern) \
or (not name_pattern.endswith('.fits')):
raise TypeError(
'name_pattern must be a string instance containing the characters {TIC} and {SECTOR}. Ex: "tess{TIC}_sec{SECTOR}.fits"'
)
# Create the output directory if needed
if outputdir.exists():
if not outputdir.is_dir():
raise ValueError(
'The outputdir exist but is not a directory. It must be a directory'
)
else:
outputdir.mkdir()
# Search MAST for Full Frame Images availables for TIC in question
tries_query = 1
while True:
if tries_query > max_tries_query:
print(
f'Skipped TIC = {TIC}: Maximum number of MAST query retries ({max_tries_query}) exceeded'
)
return
try:
tesscuts = lk.search_tesscut(f'TIC {TIC}')
break
except Exception as e:
# If exception rised
ename = e.__class__.__name__
print(
f'MAST query attempt {tries_query}, TIC = {TIC}. Excepion {ename}: {e}'
)
# Count it as one attempt
tries_query += 1
if len(tesscuts) == 0:
print(f'No images found for TIC={TIC}')
return
# Check that the returned ids match the TIC number
ids = np.unique(tesscuts.table['targetid'].data)
if not ids.size == 1:
print(f'The MAST query returned multiple ids: {ids}')
print('No FITS files saved')
return
_TIC = re.match('TIC (\d+)', ids.item()).group(1)
if TIC != _TIC:
print(f'The MAST query returned a different id: {ids}')
print('No FITS files saved')
return
# Get the sector numbers
sectors = np.array([
re.match('TESS Sector (\d+)', text).group(1)
for text in tesscuts.table['observation']
])
# Filter only requested sectors
if not onlysectors is None:
ind = [
True if sec in onlysectors else False
for sec in sectors.astype('int32')
]
tesscuts = tesscuts[ind]
# Get the sector numbers
sectors = np.array([
re.match('TESS Sector (\d+)', text).group(1)
for text in tesscuts.table['observation']
])
# Generate the output names
outputnames = np.array([
outputdir / Path(name_pattern.format(TIC=TIC, SECTOR=s))
for s in sectors
])
# Skip already downloaded files
files = np.array([file.exists() for file in outputnames])
ind = np.argwhere(files == True).flatten()
if len(ind) > 0:
print(
f'Skipped already downloaded sectors for TIC {TIC} = {sectors[ind]}'
)
ind = np.argwhere(files == False).flatten().tolist()
tesscuts = tesscuts[ind]
if len(tesscuts) == 0:
print(f'Skipped: No new images to download for TIC={TIC}')
return
# Download the cut target pixel files
tries = 1
while True:
if tries > max_tries_download:
print(
f'Skipped TIC = {TIC}: Maximum number of retries ({max_tries_download}) exceeded'
)
return
try:
tpfs = tesscuts.download_all(cutout_size=imsize)
break
except TypeError as e:
ename = e.__class__.__name__
print(
f'Skipped TIC = {TIC}: There seems to be a problem with the requested image: Excepion {ename}: {e}.'
)
return
except Exception as e:
# If exception rised
ename = e.__class__.__name__
if ename == 'SearchError':
print(
f'Skipped TIC = {TIC}: There seems to be a problem with the requested image: Excepion {ename}: {e}.'
)
return
print(f'Attempt {tries}, TIC = {TIC}. Excepion {ename}: {e}')
# Count it as one attempt
tries += 1
# Save as FITS files
for tpf in tpfs:
# Store TIC number in the header
tpf.header.set('TICID', value=TIC)
sector = tpf.sector
outputname = outputdir / Path(
name_pattern.format(TIC=TIC, SECTOR=sector))
counter = 1
# Attempt to write FITS file
while True:
if counter > max_tries_save:
print(
f'Skipped TIC = {TIC}: Maximum number of retries ({max_tries_save}) exceeded'
)
return
try:
tpf.to_fits(outputname.as_posix(), overwrite=overwrite)
break
except Exception as e:
# If exception rised
ename = e.__class__.__name__
print(
f'Attempt {counter} when saving FITS file, TIC = {TIC}. Excepion {ename}: {e}'
)
time.sleep(0.5)
# Count it as one attempt
counter += 1
print(f'Saved: {outputname.as_posix()}')
def cpm_multi_lk(tic,
sectors=None,
size=[32],
bkg_subtract=[False],
bkg_n=[40],
k=[5],
n=[35],
exclusion_size=[5],
apt_size=[1],
l2_reg=[[0.1]],
pred_pix_method=["cosine_similarity"],
add_poly=False,
poly_scale=2,
poly_num_terms=4,
med_im_header=False,
obj_num=None):
#search light curve for given TIC ID
search_res = lk.search_tesscut('TIC ' + str(tic))
#initialize SPOC found,first found
tesscut_found = False
tesscut_first = False
try:
authors = search_res.table['author']
except:
tc_avail = False
tesscut_lc = median_im = im_header = pd.DataFrame()
return (multi_lc_df, tc_avail)
if 'TESScut' in authors:
tc_avail = True
else:
tc_avail = False
tesscut_lc = median_im = im_header = pd.DataFrame()
return (multi_lc_df, tc_avail)
lc_holder = []
for i in range(len(search_res)):
#select search result object
search_i = search_res[i]
#skip if not TESScut
if (search_i.author[0] == 'TESScut'
): #& (search_i.exptime.data[0] == 120):
if sectors is None:
print("Found " + str(search_i.mission[0]) +
" TESScut data for TIC " + str(tic) + "!")
tesscut_found = True
if (tesscut_first == False) & (tesscut_first is not None):
tesscut_first = True
else:
table_sector = search_i.table['sequence_number'][0]
if (table_sector in np.array(sectors, dtype='int')) == False:
print("Found TESScut, but not in desired sector.")
continue
else:
print("Found " + str(search_i.mission[0]) +
" TESScut data for TIC " + str(tic) + "!")
tesscut_found = True
if (tesscut_first == False) & (tesscut_first is not None):
tesscut_first = True
else:
continue
#download this sector's tesscut, run multi extract
j = 0
for sz in size:
lk_tesscut_obj = search_res[i].download(cutout_size=sz)
for bkg_sub in bkg_subtract:
for bkg_N in bkg_n:
for N in n:
for exclusion in exclusion_size:
for apt_s in apt_size:
for choose_pix in pred_pix_method:
for K in k:
for reg in l2_reg:
flux_type = choose_pix[
0] + '_bkg=' + str(bkg_sub)[0]
if bkg_sub:
flux_type = flux_type + '_bkgN' + str(
bkg_N)
else:
flux_type = flux_type + '_bkgNa'
flux_type = flux_type + '_s' + str(
sz
) + '_n' + str(N) + '_ex' + str(
exclusion) + '_apt' + str(
apt_s) + '_k' + str(
K) + '_l2-' + str(
reg[0])
print("Flux type: " +
str(flux_type))
if obj_num is not None:
print("Object number " +
str(obj_num) + ".")
if med_im_header == False:
temp_lc = lk_cpm_lc(
lk_tesscut_obj=
lk_tesscut_obj,
med_im_header=med_im_header,
bkg_subtract=bkg_sub,
bkg_n=bkg_N,
k=K,
n=N,
exclusion_size=exclusion,
apt_size=apt_s,
l2_reg=reg,
pred_pix_method=choose_pix)
if med_im_header == True:
temp_lc, _, _ = lk_cpm_lc(
lk_tesscut_obj=
lk_tesscut_obj,
med_im_header=med_im_header,
bkg_subtract=bkg_sub,
bkg_n=bkg_N,
k=K,
n=N,
exclusion_size=exclusion,
apt_size=apt_s,
l2_reg=reg,
pred_pix_method=choose_pix)
## add cutout size to flux title!
if j == 0:
multi_sector_df = temp_lc.rename(
columns={'cpm': flux_type})
if j > 0:
multi_sector_df[
flux_type] = temp_lc['cpm']
j = j + 1
#delete stuff
path = lk_tesscut_obj.path
#del cpm_obj
del lk_tesscut_obj
time.sleep(5)
os.remove(path=path)
#add multi sector df to lc holder at end of each sector extraction
lc_holder.append(multi_sector_df)
if tesscut_found == False:
print("No TESScut data found for TIC " + str(tic) +
"in desired sectors.")
multi_lc_df = pd.DataFrame()
else:
multi_lc_df = pd.concat(lc_holder) #combine lc into 1 pandas dataframe
return (multi_lc_df, tc_avail)
def test_tpf_with_zero_flux_cadence():
"""Regression test for #873."""
tpf = search_tesscut("TIC 123835353", sector=6).download(cutout_size=5)
tpf.to_corrector("pld").correct()
