def test_plot_image_data_change():
"""Test that the plotting function does not change input data"""
# Construct random image:
img = np.random.randn(15, 10)
img[0, 0] = -1.0 # Make 100% sure there is a negative point
# Save the original image for comparison:
img_before = np.copy(img)
# Make a couple of plots trying out the different settings:
fig = plt.figure()
ax1 = fig.add_subplot(131)
plot_image(img, ax=ax1, scale='linear')
np.testing.assert_allclose(img, img_before)
ax2 = fig.add_subplot(132)
plot_image(img, ax=ax2, scale='sqrt')
np.testing.assert_allclose(img, img_before)
ax3 = fig.add_subplot(133)
plot_image(img, ax=ax3, scale='log')
np.testing.assert_allclose(img, img_before)
fig = plt.figure()
plot_image_fit_residuals(fig, img, img, img)
np.testing.assert_allclose(img, img_before)
def test_aperturephotometry():
INPUT_DIR = os.path.join(os.path.dirname(__file__), 'input')
OUTPUT_DIR = tempfile.mkdtemp(prefix='tessphot_tests_aperture')
for datasource in ('tpf', 'ffi'):
with AperturePhotometry(182092046, INPUT_DIR, OUTPUT_DIR, plot=True, datasource=datasource, camera=1, ccd=1) as pho:
pho.photometry()
pho.save_lightcurve()
print( pho.lightcurve )
# It should set the status to one of these:
assert(pho.status in (STATUS.OK, STATUS.WARNING))
plt.figure()
plot_image(pho.sumimage, title=datasource)
plt.show()
# They shouldn't be exactly zero:
assert( ~np.all(pho.lightcurve['flux'] == 0) )
assert( ~np.all(pho.lightcurve['pos_centroid'][:,0] == 0) )
assert( ~np.all(pho.lightcurve['pos_centroid'][:,1] == 0) )
# They shouldn't be NaN (in this case!):
assert( ~np.all(np.isnan(pho.lightcurve['flux'])) )
assert( ~np.all(np.isnan(pho.lightcurve['pos_centroid'][:,0])) )
assert( ~np.all(np.isnan(pho.lightcurve['pos_centroid'][:,1])) )
def test_aperturephotometry():
with TemporaryDirectory() as OUTPUT_DIR:
for datasource in ('tpf', 'ffi'):
with AperturePhotometry(DUMMY_TARGET,
INPUT_DIR,
OUTPUT_DIR,
plot=True,
datasource=datasource,
**DUMMY_KWARG) as pho:
pho.photometry()
pho.save_lightcurve()
print(pho.lightcurve)
# It should set the status to one of these:
assert (pho.status in (STATUS.OK, STATUS.WARNING))
plt.figure()
plot_image(pho.sumimage, title=datasource)
plt.show()
# They shouldn't be exactly zero:
assert (~np.all(pho.lightcurve['flux'] == 0))
assert (~np.all(pho.lightcurve['pos_centroid'][:, 0] == 0))
assert (~np.all(pho.lightcurve['pos_centroid'][:, 1] == 0))
# They shouldn't be NaN (in this case!):
assert (~np.all(np.isnan(pho.lightcurve['flux'])))
assert (
~np.all(np.isnan(pho.lightcurve['pos_centroid'][:, 0])))
assert (
~np.all(np.isnan(pho.lightcurve['pos_centroid'][:, 1])))
def test_aperturephotometry(SHARED_INPUT_DIR, datasource):
with TemporaryDirectory() as OUTPUT_DIR:
with AperturePhotometry(DUMMY_TARGET,
SHARED_INPUT_DIR,
OUTPUT_DIR,
plot=True,
datasource=datasource,
**DUMMY_KWARG) as pho:
pho.photometry()
filepath = pho.save_lightcurve()
print(pho.lightcurve)
# It should set the status to one of these:
assert (pho.status in (STATUS.OK, STATUS.WARNING))
# Check the sumimage:
plt.figure()
plot_image(pho.sumimage, title=datasource)
assert not anynan(pho.sumimage), "There are NaNs in the SUMIMAGE"
# They shouldn't be exactly zero:
assert not np.all(pho.lightcurve['flux'] == 0)
assert not np.all(pho.lightcurve['flux_err'] == 0)
assert not np.all(pho.lightcurve['pos_centroid'][:, 0] == 0)
assert not np.all(pho.lightcurve['pos_centroid'][:, 1] == 0)
# They shouldn't be NaN (in this case!):
assert not allnan(pho.lightcurve['flux'])
assert not allnan(pho.lightcurve['flux_err'])
assert not allnan(pho.lightcurve['pos_centroid'][:, 0])
assert not allnan(pho.lightcurve['pos_centroid'][:, 1])
assert not np.any(~np.isfinite(pho.lightcurve['time']))
assert not np.any(pho.lightcurve['time'] == 0)
# Test the outputted FITS file:
with fits.open(filepath, mode='readonly') as hdu:
# Should be the same vectors in FITS as returned in Table:
np.testing.assert_allclose(pho.lightcurve['time'],
hdu[1].data['TIME'])
np.testing.assert_allclose(pho.lightcurve['timecorr'],
hdu[1].data['TIMECORR'])
np.testing.assert_allclose(pho.lightcurve['flux'],
hdu[1].data['FLUX_RAW'])
np.testing.assert_allclose(pho.lightcurve['flux_err'],
hdu[1].data['FLUX_RAW_ERR'])
np.testing.assert_allclose(pho.lightcurve['cadenceno'],
hdu[1].data['CADENCENO'])
# Test FITS aperture image:
ap = hdu['APERTURE'].data
print(ap)
assert np.all(pho.aperture == ap), "Aperture image mismatch"
assert not anynan(ap), "NaN in aperture image"
assert np.all(ap >= 0), "Negative values in aperture image"
assert np.any(ap & 2 != 0), "No photometric mask set"
assert np.any(ap & 8 != 0), "No position mask set"
def test_position_velocity(keep_figures=False):
with TESS_SPICE() as knl:
# We should be able to load and close without affecting the results of the following:
with TESS_SPICE():
pass
time_nocorr = np.array(
[1325.32351727, 1325.34435059, 1325.36518392, 1325.38601724])
# Get the location of TESS as a function of time relative to Earth in kilometers:
pos = knl.position(time_nocorr + 2457000)
assert pos.shape == (len(time_nocorr), 3)
# Get the location of TESS as a function of time relative to Earth in kilometers:
vel = knl.velocity(time_nocorr + 2457000)
assert vel.shape == (len(time_nocorr), 3)
# Plot TESS orbits in 3D:
time_inter = np.linspace(time_nocorr[0] - 3, time_nocorr[-1] + 3, 2000)
pos_inter = knl.position(time_inter + 2457000)
vel_inter = knl.velocity(time_inter + 2457000)
fig1 = plt.figure()
ax = fig1.add_subplot(111, projection='3d')
ax.plot(pos_inter[:, 0], pos_inter[:, 1], pos_inter[:, 2], 'r-')
ax.scatter(pos[:, 0], pos[:, 1], pos[:, 2], alpha=0.5)
ax.scatter(0, 0, 0, alpha=0.5, c='b')
fig2 = plt.figure()
ax1 = fig2.add_subplot(211)
ax1.plot(time_inter, np.linalg.norm(pos_inter, axis=1), 'r-')
ax1.scatter(time_nocorr, np.linalg.norm(pos, axis=1), alpha=0.5)
ax1.set_ylabel('Distance (km)')
ax1.set_xticks([])
ax2 = fig2.add_subplot(212)
ax2.plot(time_inter, np.linalg.norm(vel_inter, axis=1), 'r-')
ax2.scatter(time_nocorr, np.linalg.norm(vel, axis=1), alpha=0.5)
ax2.set_ylabel('Velocity (km/s)')
ax2.set_xlabel('Time')
plt.tight_layout()
if not keep_figures:
plt.close(fig1)
plt.close(fig2)
def test_plot_image_grid_offset():
img = np.zeros((5, 7))
img[:, 0] = 1
img[0, :] = 1
img[:, -1] = 1
img[-1, :] = 1
fig = plt.figure()
ax = fig.add_subplot(111)
plot_image(img, ax=ax, scale='linear', offset_axes=(3, 2))
ax.plot(3.5, 2.5, 'r+')
ax.plot(8.5, 5.5, 'g+')
ax.grid(True)
return fig
def test_plot_image_grid():
img = np.zeros((5, 7))
img[:, 0] = 1
img[0, :] = 1
img[:, -1] = 1
img[-1, :] = 1
fig = plt.figure()
ax = fig.add_subplot(111)
plot_image(img, ax=ax, scale='linear')
ax.plot(0.5, 0.5, 'r+')
ax.plot(5.5, 3.5, 'g+')
ax.grid(True)
return fig
def test_plot_image():
mu = [3.5, 3]
x, y = np.mgrid[0:10, 0:10]
pos = np.dstack((x, y))
var = scipy.stats.multivariate_normal(mean=mu, cov=[[1, 0], [0, 1]])
gauss = var.pdf(pos)
fig = plt.figure(figsize=(12, 6))
ax1 = fig.add_subplot(131)
plot_image(gauss, ax=ax1, scale='linear', title='Linear')
ax1.plot(mu[1], mu[0], 'r+')
ax2 = fig.add_subplot(132)
plot_image(gauss, ax=ax2, scale='sqrt', title='Sqrt')
ax2.plot(mu[1], mu[0], 'r+')
ax3 = fig.add_subplot(133)
plot_image(gauss, ax=ax3, scale='log', title='Log')
ax3.plot(mu[1], mu[0], 'r+')
return fig
def test_spice(SHARED_INPUT_DIR, starid):
# Initialize our home-made TESS Kernel object:
with TESS_SPICE() as knl:
print("="*72)
print("TIC %d" % starid)
tpf_file = find_tpf_files(SHARED_INPUT_DIR, starid=starid)[0]
with fits.open(tpf_file, mode='readonly', memmap=True) as hdu:
time_tpf = hdu[1].data['TIME']
timecorr_tpf = hdu[1].data['TIMECORR']
camera = hdu[0].header['CAMERA']
ccd = hdu[0].header['CCD']
# Coordinates of the target as astropy SkyCoord object:
star_coord = coord.SkyCoord(
ra=hdu[0].header['RA_OBJ'],
dec=hdu[0].header['DEC_OBJ'],
unit=u.deg,
frame='icrs',
obstime=Time('J2000'),
pm_ra_cosdec=hdu[0].header['PMRA']*u.mas/u.yr,
pm_dec=hdu[0].header['PMDEC']*u.mas/u.yr,
radial_velocity=0*u.km/u.s
)
# Load the original timestamps from FFIs:
hdf_file = find_hdf5_files(SHARED_INPUT_DIR, camera=camera, ccd=ccd)[0]
with h5py.File(hdf_file, 'r') as hdf:
ffi_time = np.asarray(hdf['time'])
ffi_timecorr = np.asarray(hdf['timecorr'])
f = interp1d(time_tpf-timecorr_tpf, timecorr_tpf, kind='linear')
# Change the timestamps bach to JD:
time_nocorr = ffi_time - ffi_timecorr
times = Time(time_nocorr, 2457000, format='jd', scale='tdb')
ras, decs = add_proper_motion(
star_coord.ra.value,
star_coord.dec.value,
star_coord.pm_ra_cosdec.value,
star_coord.pm_dec.value,
times.jd
)
star_coord = coord.SkyCoord(
ra=ras[0],
dec=decs[0],
unit=u.deg,
frame='icrs'
)
print(star_coord)
# Use Greenwich as location instead of TESS (similar to what is done in Elenor):
greenwich = coord.EarthLocation.of_site('greenwich')
times_greenwich = Time(time_nocorr+2457000, format='jd', scale='utc', location=greenwich)
timecorr_greenwich = times_greenwich.light_travel_time(star_coord, kind='barycentric', ephemeris='builtin').value
#time_greenwich = time_nocorr + timecorr_greenwich
# Calculate barycentric correction using our method:
time_astropy, timecorr_astropy = knl.barycorr(time_nocorr + 2457000, star_coord)
# Calculate barycentric correction using second method:
timecorr_knl = knl.barycorr2(time_nocorr + 2457000, star_coord)
print(timecorr_knl)
# Plot the new barycentric time correction and the old one:
fig1 = plt.figure()
ax = fig1.add_subplot(111)
ax.plot(time_tpf - timecorr_tpf, timecorr_tpf*86400, 'g.-', label='TPF timecorr')
ax.plot(time_nocorr, ffi_timecorr*86400, 'b.-', label='FFI centre')
ax.scatter(time_nocorr, timecorr_greenwich*86400, marker='x', label='Elenor timecorr')
ax.scatter(time_nocorr, timecorr_astropy*86400, marker='x', c='r', label='Our timecorr')
ax.set_xlabel('Uncorrected Time [TJD]')
ax.set_ylabel('Barycentric Time Correction [s]')
ax.set_title('TIC %d' % starid)
ax.set_yscale('log')
plt.legend()
# Plot the new barycentric time correction and the old one:
fig2 = plt.figure(figsize=(8,10))
ax3 = fig2.add_subplot(111)
ax3.axhline(1.0, color='k', ls='--', lw=1)
ax3.plot(time_nocorr, np.abs(ffi_timecorr - f(time_nocorr))*86400, '.-', label='FFI centre')
ax3.plot(time_nocorr, np.abs(timecorr_greenwich - f(time_nocorr))*86400*1000, '.-', label='Elenor')
ax3.plot(time_nocorr, np.abs(timecorr_astropy - f(time_nocorr))*86400*1000, '.-', label='Astropy')
ax3.plot(time_nocorr, np.abs(timecorr_knl - f(time_nocorr))*86400*1000, '.-', label='Kernel')
ax3.set_ylabel('abs(TimeCorr - TPF TimeCorr) [ms]')
ax3.set_xlabel('Uncorrected Time [TJD]')
ax3.set_yscale('log')
ax3.set_ylim(bottom=0.1)
ax3.legend()
ax3.grid(axis='y', which='both', color='0.7', ls=':', lw=0.5)
ax3.set_title('TIC %d' % starid)
plt.tight_layout()
assert np.all(86400000*np.abs(timecorr_astropy - f(time_nocorr)) < 1), "AstroPy method: More than 1 ms away"
assert np.all(86400000*np.abs(timecorr_knl - f(time_nocorr)) < 1), "HomeMade method: More than 1 ms away"
print("="*72)
def test_spice(keep_figures=False):
# Initialize our home-made TESS Kernel object:
with TESS_SPICE() as knl:
for starid in (260795451, 267211065):
print("=" * 72)
print("TIC %d" % starid)
tpf_file = find_tpf_files(INPUT_DIR, starid=starid)[0]
with fits.open(tpf_file, mode='readonly', memmap=True) as hdu:
time_tpf = hdu[1].data['TIME']
timecorr_tpf = hdu[1].data['TIMECORR']
camera = hdu[0].header['CAMERA']
ccd = hdu[0].header['CCD']
# Coordinates of the target as astropy SkyCoord object:
star_coord = coord.SkyCoord(
ra=hdu[0].header['RA_OBJ'],
dec=hdu[0].header['DEC_OBJ'],
unit=u.deg,
frame='icrs',
obstime=Time('J2000'),
pm_ra_cosdec=hdu[0].header['PMRA'] * u.mas / u.yr,
pm_dec=hdu[0].header['PMDEC'] * u.mas / u.yr,
radial_velocity=0 * u.km / u.s)
# Load the original timestamps from FFIs:
hdf_file = find_hdf5_files(INPUT_DIR, camera=camera, ccd=ccd)[0]
with h5py.File(hdf_file, 'r') as hdf:
ffi_time = np.asarray(hdf['time'])
ffi_timecorr = np.asarray(hdf['timecorr'])
f = interp1d(time_tpf - timecorr_tpf, timecorr_tpf, kind='linear')
# Change the timestamps bach to JD:
time_nocorr = ffi_time - ffi_timecorr
times = Time(time_nocorr, 2457000, format='jd', scale='tdb')
ras, decs = add_proper_motion(star_coord.ra.value,
star_coord.dec.value,
star_coord.pm_ra_cosdec.value,
star_coord.pm_dec.value, times.jd)
star_coord = coord.SkyCoord(ra=ras[0],
dec=decs[0],
unit=u.deg,
frame='icrs')
print(star_coord)
# Use Greenwich as location instead of TESS (similar to what is done in Elenor):
greenwich = coord.EarthLocation.of_site('greenwich')
times_greenwich = Time(time_nocorr + 2457000,
format='jd',
scale='utc',
location=greenwich)
timecorr_greenwich = times_greenwich.light_travel_time(
star_coord, kind='barycentric', ephemeris='builtin').value
#time_greenwich = time_nocorr + timecorr_greenwich
# Calculate barycentric correction using our method:
time_astropy, timecorr_astropy = knl.barycorr(
time_nocorr + 2457000, star_coord)
# Caluclate barycentric correction uning second method:
timecorr_knl = knl.barycorr2(time_nocorr + 2457000, star_coord)
print(timecorr_knl)
# Plot the new barycentric time correction and the old one:
fig1 = plt.figure()
ax = fig1.add_subplot(111)
ax.scatter(time_nocorr,
ffi_timecorr * 86400,
alpha=0.3,
s=4,
label='FFI timecorr')
ax.scatter(time_tpf - timecorr_tpf,
timecorr_tpf * 86400,
alpha=0.3,
s=4,
label='TPF timecorr')
ax.scatter(time_nocorr,
timecorr_greenwich * 86400,
alpha=0.3,
s=4,
label='Elenor timecorr')
ax.scatter(time_nocorr,
timecorr_astropy * 86400,
alpha=0.3,
s=4,
label='Our timecorr')
ax.set_xlabel('Uncorrected Time (JD - 2457000)')
ax.set_ylabel('Barycentric Time Correction (s)')
ax.set_title('TIC %d' % starid)
plt.legend()
# Plot the new barycentric time correction and the old one:
fig2 = plt.figure(figsize=(8, 10))
ax1 = fig2.add_subplot(311)
ax1.axhline(0, color='k', ls=':', lw=0.5)
ax1.plot(time_nocorr, (ffi_timecorr - f(time_nocorr)) * 86400, '.')
ax1.set_ylabel('FFI - TPF (seconds)')
ax1.set_title('TIC %d' % starid)
ax2 = fig2.add_subplot(312)
ax2.axhline(0, color='k', ls=':', lw=0.5)
ax2.plot(time_nocorr,
(timecorr_greenwich - f(time_nocorr)) * 86400 * 1000, '.')
ax2.set_ylabel('Elenor - TPF (ms)')
ax3 = plt.subplot(313)
ax3.axhline(0, color='k', ls=':', lw=0.5)
ax3.plot(time_nocorr,
(timecorr_astropy - f(time_nocorr)) * 86400 * 1000,
'.',
label='Astropy')
ax3.plot(time_nocorr,
(timecorr_knl - f(time_nocorr)) * 86400 * 1000,
'.',
label='Kernel')
ax3.set_ylabel('Our - TPF (ms)')
ax3.set_xlabel('Uncorrected Time (TJD)')
ax3.legend()
ax1.set_xticks([])
ax2.set_xticks([])
plt.tight_layout()
if not keep_figures:
plt.close(fig1)
plt.close(fig2)
print("=" * 72)
from photometry.plots import plt, plot_image, matplotlib
from photometry.spice import TESS_SPICE
from photometry.utilities import find_tpf_files, find_hdf5_files, add_proper_motion
matplotlib.rcParams['font.family'] = 'serif'
matplotlib.rcParams['font.size'] = '14'
matplotlib.rcParams['axes.titlesize'] = '18'
matplotlib.rcParams['axes.labelsize'] = '16'
plt.rc('text', usetex=True)
if __name__ == '__main__':
plt.switch_backend('Qt5Agg')
plt.close('all')
INPUT_DIR = r'F:\tess_data\S06_DR08'
fig4 = plt.figure()
ax = fig4.add_subplot(111)
ax.axhline(0, color='k', ls='--')
for starid in (59545062, 25155664, 14003429):
# Initialize our home-made TESS Kernel object:
with TESS_SPICE() as knl:
print("=" * 72)
print("TIC %d" % starid)
tpf_file = find_tpf_files(INPUT_DIR, starid=starid)[0]
with fits.open(tpf_file, mode='readonly', memmap=True) as hdu:
time_tpf = hdu[1].data['TIME']
timecorr_tpf = hdu[1].data['TIMECORR']
camera = hdu[0].header['CAMERA']