def test_lightcurve_to_time_series():
from gammapy.catalog import SourceCatalog4FGL
catalog_4fgl = SourceCatalog4FGL(
"$GAMMAPY_DATA/catalogs/fermi/gll_psc_v20.fit.gz")
lightcurve = catalog_4fgl["FGES J1553.8-5325"].lightcurve()
table = lightcurve.to_table(sed_type="flux", format="binned-time-series")
timeseries = BinnedTimeSeries(data=table)
assert_allclose(timeseries.time_bin_center.mjd[0], 54863.97885336907)
assert_allclose(timeseries.time_bin_end.mjd[0], 55045.301668796295)
time_axis = lightcurve.geom.axes["time"]
assert_allclose(timeseries.time_bin_end.mjd[0], time_axis.time_max.mjd[0])
# assert that it interfaces with periodograms
p = BoxLeastSquares.from_timeseries(timeseries=timeseries,
signal_column_name="flux",
uncertainty="flux_errp")
result = p.power(1 * u.year, 0.5 * u.year)
assert_allclose(result.duration, 182.625 * u.d)
def main():
root = tk.Tk()
root.withdraw()
filepath = filedialog.askopenfilename()
hdulist = fits.open(filepath)
telescope = hdulist[0].header['telescop'].lower()
if telescope == 'tess':
hdu = hdulist['LIGHTCURVE']
print("TESS file recognized")
tessID = str(hdulist[1].header['TICID'])
print(tessID)
ts = TimeSeries.read(filepath, format='tess.fits')
elif telescope == 'kepler':
hdu = hdulist[1]
print("Kepler file recognized")
keplerID = str(hdulist[0].header['KEPLERID'])
print(keplerID)
ts = TimeSeries.read(filepath, format='kepler.fits')
else:
raise NotImplementedError(
"{} is not implemented, only KEPLER or TESS are "
"supported through this reader".format(
hdulist[0].header['telescop']))
fig, axs = plt.subplots(2, 2)
if telescope == 'tess':
fig.suptitle('TESS Id:' + tessID, fontsize=15)
if telescope == 'kepler':
fig.suptitle('Kepler Id:' + keplerID, fontsize=15)
fig.set_size_inches(18.5, 10.5)
#full data plot
axs[0, 0].plot(ts.time.jd,
ts['sap_flux'],
'k.',
markersize=1,
label='whole')
axs[0, 0].set_xlabel('Julian Date')
axs[0, 0].set_ylabel('SAP Flux (e-/s)')
axs[0, 0].set_title('full data')
#folded time series
periodogram = BoxLeastSquares.from_timeseries(ts, 'sap_flux')
results = periodogram.autopower(0.1 * u.day)
best = np.argmax(results.power)
period = results.period[best]
transit_time = results.transit_time[best]
ts_folded = ts.fold(period=period, midpoint_epoch=transit_time)
axs[0, 1].plot(ts_folded.time.jd,
ts_folded['sap_flux'],
'k.',
markersize=1)
axs[0, 1].set_xlabel('Time (days)')
axs[0, 1].set_ylabel('SAP FLUX (e-/s)')
axs[0, 1].set_title('folded time series')
#binned time series
mean, median, stddev = sigma_clipped_stats(ts_folded['sap_flux'])
ts_folded['sap_flux_norm'] = ts_folded['sap_flux'] / median
ts_binned = aggregate_downsample(ts_folded, time_bin_size=0.003 * u.day)
axs[1, 0].plot(ts_folded.time.jd,
ts_folded['sap_flux_norm'],
'k.',
markersize=1)
axs[1, 0].plot(ts_binned.time_bin_start.jd,
ts_binned['sap_flux_norm'],
'r-',
drawstyle='default')
axs[1, 0].set_xlabel('Time (days)')
axs[1, 0].set_ylabel('Normalized Flux')
axs[1, 0].set_title('binned time series')
#final transit model
axs[1, 1].plot(ts_folded.time.jd,
ts_folded['sap_flux_norm'],
'k.',
markersize=1)
axs[1, 1].plot(ts_binned.time_bin_start.jd,
ts_binned['sap_flux_norm'],
'r-',
drawstyle='default')
axs[1, 1].set_xlabel('Time (days)')
axs[1, 1].set_ylabel('Normalized Flux')
axs[1, 1].set_title('transit model')
plt.show()
