def main():
print("Starting tests for wotan...")
numpy.testing.assert_almost_equal(t14(R_s=1, M_s=1, P=365), 0.6490025258902046)
numpy.testing.assert_almost_equal(
t14(R_s=1, M_s=1, P=365, small_planet=True), 0.5403690143737738
)
print("Transit duration correct.")
numpy.random.seed(seed=0) # reproducibility
print("Slide clipper...")
points = 1000
time = numpy.linspace(0, 30, points)
flux = 1 + numpy.sin(time) / points
noise = numpy.random.normal(0, 0.0001, points)
flux += noise
for i in range(points):
if i % 75 == 0:
flux[i : i + 5] -= 0.0004 # Add some transits
flux[i + 50 : i + 52] += 0.0002 # and flares
clipped = slide_clip(
time, flux, window_length=0.5, low=3, high=2, method="mad", center="median"
)
numpy.testing.assert_almost_equal(numpy.nansum(clipped), 948.9926368754939)
"""
import matplotlib.pyplot as plt
plt.scatter(time, flux, s=3, color='black')
plt.scatter(time, clipped, s=3, color='orange')
plt.show()
"""
# TESS test
print("Loading TESS data from archive.stsci.edu...")
path = "https://archive.stsci.edu/hlsps/tess-data-alerts/"
# path = 'P:/P/Dok/tess_alarm/'
filename = "hlsp_tess-data-alerts_tess_phot_00062483237-s01_tess_v1_lc.fits"
time, flux = load_file(path + filename)
window_length = 0.5
print("Detrending 1 (biweight)...")
flatten_lc, trend_lc = flatten(
time,
flux,
window_length,
edge_cutoff=1,
break_tolerance=0.1,
return_trend=True,
cval=5.0,
)
numpy.testing.assert_equal(len(trend_lc), 20076)
numpy.testing.assert_almost_equal(
numpy.nanmax(trend_lc), 28755.03811866676, decimal=2
)
numpy.testing.assert_almost_equal(
numpy.nanmin(trend_lc), 28615.110229935075, decimal=2
)
numpy.testing.assert_almost_equal(trend_lc[500], 28671.650565730513, decimal=2)
numpy.testing.assert_equal(len(flatten_lc), 20076)
numpy.testing.assert_almost_equal(
numpy.nanmax(flatten_lc), 1.0034653549250616, decimal=2
)
numpy.testing.assert_almost_equal(
numpy.nanmin(flatten_lc), 0.996726610702177, decimal=2
)
numpy.testing.assert_almost_equal(flatten_lc[500], 1.000577429565131, decimal=2)
print("Detrending 2 (andrewsinewave)...")
flatten_lc, trend_lc = flatten(
time, flux, window_length, method="andrewsinewave", return_trend=True
)
numpy.testing.assert_almost_equal(
numpy.nansum(flatten_lc), 18123.15456308313, decimal=2
)
print("Detrending 3 (welsch)...")
flatten_lc, trend_lc = flatten(
time, flux, window_length, method="welsch", return_trend=True
)
numpy.testing.assert_almost_equal(
numpy.nansum(flatten_lc), 18123.16770590837, decimal=2
)
print("Detrending 4 (hodges)...")
flatten_lc, trend_lc = flatten(
time[:1000], flux[:1000], window_length, method="hodges", return_trend=True
)
numpy.testing.assert_almost_equal(
numpy.nansum(flatten_lc), 996.0113241694287, decimal=2
)
print("Detrending 5 (median)...")
flatten_lc, trend_lc = flatten(
time, flux, window_length, method="median", return_trend=True
)
numpy.testing.assert_almost_equal(
numpy.nansum(flatten_lc), 18123.12166552401, decimal=2
)
print("Detrending 6 (mean)...")
flatten_lc, trend_lc = flatten(
time, flux, window_length, method="mean", return_trend=True
)
numpy.testing.assert_almost_equal(
numpy.nansum(flatten_lc), 18123.032058753546, decimal=2
)
print("Detrending 7 (trim_mean)...")
flatten_lc, trend_lc = flatten(
time, flux, window_length, method="trim_mean", return_trend=True
)
numpy.testing.assert_almost_equal(
numpy.nansum(flatten_lc), 18123.094751124332, decimal=2
)
print("Detrending 8 (supersmoother)...")
flatten_lc, trend_lc = flatten(
time, flux, window_length, method="supersmoother", return_trend=True
)
numpy.testing.assert_almost_equal(
numpy.nansum(flatten_lc), 18123.00632204841, decimal=2
)
print("Detrending 9 (hspline)...")
flatten_lc, trend_lc = flatten(
time, flux, window_length, method="hspline", return_trend=True
)
numpy.testing.assert_almost_equal(
numpy.nansum(flatten_lc), 18123.082601463717, decimal=1
)
print("Detrending 10 (cofiam)...")
flatten_lc, trend_lc = flatten(
time[:2000], flux[:2000], window_length, method="cofiam", return_trend=True
)
numpy.testing.assert_almost_equal(
numpy.nansum(flatten_lc), 1948.9999999987976, decimal=1
)
print("Detrending 11 (savgol)...")
flatten_lc, trend_lc = flatten(
time, flux, window_length=301, method="savgol", return_trend=True
)
numpy.testing.assert_almost_equal(
numpy.nansum(flatten_lc), 18123.003465539354, decimal=1
)
print("Detrending 12 (medfilt)...")
flatten_lc, trend_lc = flatten(
time, flux, window_length=301, method="medfilt", return_trend=True
)
numpy.testing.assert_almost_equal(
numpy.nansum(flatten_lc), 18123.22609806557, decimal=1
)
print("Detrending 12 (gp squared_exp)...")
flatten_lc, trend_lc1 = flatten(
time[:2000],
flux[:2000],
method="gp",
kernel="squared_exp",
kernel_size=10,
return_trend=True,
)
numpy.testing.assert_almost_equal(
numpy.nansum(flatten_lc), 1948.9672036416687, decimal=2
)
print("Detrending 13 (gp squared_exp robust)...")
flatten_lc, trend_lc1 = flatten(
time[:2000],
flux[:2000],
method="gp",
kernel="squared_exp",
kernel_size=10,
robust=True,
return_trend=True,
)
numpy.testing.assert_almost_equal(
numpy.nansum(flatten_lc), 1948.8820772313468, decimal=2
)
print("Detrending 14 (gp matern)...")
flatten_lc, trend_lc2 = flatten(
time[:2000],
flux[:2000],
method="gp",
kernel="matern",
kernel_size=10,
return_trend=True,
)
numpy.testing.assert_almost_equal(
numpy.nansum(flatten_lc), 1948.9672464898367, decimal=2
)
print("Detrending 15 (gp periodic)...")
flatten_lc, trend_lc2 = flatten(
time[:2000],
flux[:2000],
method="gp",
kernel="periodic",
kernel_size=1,
kernel_period=10,
return_trend=True,
)
numpy.testing.assert_almost_equal(
numpy.nansum(flatten_lc), 1948.9999708985608, decimal=2
)
time_synth = numpy.linspace(0, 30, 200)
flux_synth = numpy.sin(time_synth) + numpy.random.normal(0, 0.1, 200)
flux_synth = 1 + flux_synth / 100
time_synth *= 1.5
print("Detrending 16 (gp periodic_auto)...")
flatten_lc, trend_lc2 = flatten(
time_synth,
flux_synth,
method="gp",
kernel="periodic_auto",
kernel_size=1,
return_trend=True,
)
numpy.testing.assert_almost_equal(numpy.nansum(flatten_lc), 200, decimal=1)
print("Detrending 17 (rspline)...")
flatten_lc, trend_lc2 = flatten(
time, flux, method="rspline", window_length=1, return_trend=True
)
numpy.testing.assert_almost_equal(
numpy.nansum(flatten_lc), 18121.812790732245, decimal=2
)
"""
print("Detrending 18 (huber)...")
flatten_lc, trend_lc = flatten(
time[:1000],
flux[:1000],
method='huber',
window_length=0.5,
edge_cutoff=0,
break_tolerance=0.4,
return_trend=True)
numpy.testing.assert_almost_equal(numpy.nansum(flatten_lc), 996.0112964009066, decimal=2)
"""
print("Detrending 19 (winsorize)...")
flatten_lc, trend_lc2 = flatten(
time,
flux,
method="winsorize",
window_length=0.5,
edge_cutoff=0,
break_tolerance=0.4,
proportiontocut=0.1,
return_trend=True,
)
numpy.testing.assert_almost_equal(
numpy.nansum(flatten_lc), 18123.064149766662, decimal=2
)
print("Detrending 20 (pspline)...")
flatten_lc, trend_lc = flatten(time, flux, method="pspline", return_trend=True)
# import matplotlib.pyplot as plt
# plt.scatter(time, flux, s=3, color='black')
# plt.plot(time, trend_lc)
# plt.show()
numpy.testing.assert_almost_equal(
numpy.nansum(flatten_lc), 18122.740535799767, decimal=2
)
print("Detrending 21 (hampelfilt)...")
flatten_lc, trend_lc5 = flatten(
time, flux, method="hampelfilt", window_length=0.5, cval=3, return_trend=True
)
numpy.testing.assert_almost_equal(
numpy.nansum(flatten_lc), 18123.157973016467, decimal=2
)
print("Detrending 22 (lowess)...")
flatten_lc, trend_lc1 = flatten(
time, flux, method="lowess", window_length=1, return_trend=True
)
numpy.testing.assert_almost_equal(
numpy.nansum(flatten_lc), 18123.039744125545, decimal=2
)
print("Detrending 23 (huber_psi)...")
flatten_lc, trend_lc1 = flatten(
time, flux, method="huber_psi", window_length=0.5, return_trend=True
)
numpy.testing.assert_almost_equal(
numpy.nansum(flatten_lc), 18123.110893063527, decimal=2
)
print("Detrending 24 (tau)...")
flatten_lc, trend_lc2 = flatten(
time, flux, method="tau", window_length=0.5, return_trend=True
)
numpy.testing.assert_almost_equal(
numpy.nansum(flatten_lc), 18123.026005725977, decimal=2
)
print("Detrending 25 (cosine)...")
flatten_lc, trend_lc2 = flatten(
time, flux, method="cosine", window_length=0.5, return_trend=True
)
numpy.testing.assert_almost_equal(
numpy.nansum(flatten_lc), 18122.999999974905, decimal=2
)
print("Detrending 25 (cosine robust)...")
flatten_lc, trend_lc2 = flatten(
time, flux, method="cosine", robust=True, window_length=0.5, return_trend=True
)
numpy.testing.assert_almost_equal(
numpy.nansum(flatten_lc), 18122.227938535038, decimal=2
)
import numpy as np
points = 1000
time = numpy.linspace(0, 30, points)
flux = 1 + numpy.sin(time) / points
noise = numpy.random.normal(0, 0.0001, points)
flux += noise
for i in range(points):
if i % 75 == 0:
flux[i : i + 5] -= 0.0004 # Add some transits
flux[i + 50 : i + 52] += 0.0002 # and flares
print("Detrending 26 (hampel 17A)...")
flatten_lc, trend_lc1 = flatten(
time,
flux,
method="hampel",
cval=(1.7, 3.4, 8.5),
window_length=0.5,
return_trend=True,
)
print("Detrending 27 (hampel 25A)...")
flatten_lc, trend_lc2 = flatten(
time,
flux,
method="hampel",
cval=(2.5, 4.5, 9.5),
window_length=0.5,
return_trend=True,
)
numpy.testing.assert_almost_equal(
numpy.nansum(flatten_lc), 999.9992212031945, decimal=2
)
print("Detrending 28 (ramsay)...")
flatten_lc, trend_lc3 = flatten(
time, flux, method="ramsay", cval=0.3, window_length=0.5, return_trend=True
)
numpy.testing.assert_almost_equal(
numpy.nansum(flatten_lc), 999.9970566765148, decimal=2
)
print("Detrending 29 (ridge)...")
flatten_lc, trend_lc1 = flatten(
time, flux, window_length=0.5, method="ridge", return_trend=True, cval=1
)
numpy.testing.assert_almost_equal(
numpy.nansum(flatten_lc), 999.9999958887022, decimal=1
)
print("Detrending 30 (lasso)...")
flatten_lc, trend_lc2 = flatten(
time, flux, window_length=0.5, method="lasso", return_trend=True, cval=1
)
numpy.testing.assert_almost_equal(
numpy.nansum(flatten_lc), 999.9999894829843, decimal=1
)
print("Detrending 31 (elasticnet)...")
flatten_lc, trend_lc3 = flatten(
time, flux, window_length=0.5, method="elasticnet", return_trend=True, cval=1
)
numpy.testing.assert_almost_equal(
numpy.nansum(flatten_lc), 999.9999945063338, decimal=1
)
# Test of transit mask
print("Testing transit_mask")
filename = "hlsp_tess-data-alerts_tess_phot_00207081058-s01_tess_v1_lc.fits"
time, flux = load_file(path + filename)
from wotan import transit_mask
mask = transit_mask(time=time, period=14.77338, duration=0.21060, T0=1336.141095)
numpy.testing.assert_almost_equal(numpy.sum(mask), 302, decimal=1)
print("Detrending 32 (transit_mask cosine)")
flatten_lc1, trend_lc1 = flatten(
time,
flux,
method="cosine",
window_length=0.4,
return_trend=True,
robust=True,
mask=mask,
)
numpy.testing.assert_almost_equal(
numpy.nansum(flatten_lc1), 18119.281265446625, decimal=1
)
print("Detrending 33 (transit_mask lowess)")
flatten_lc2, trend_lc2 = flatten(
time,
flux,
method="lowess",
window_length=0.8,
return_trend=True,
robust=True,
mask=mask,
)
# print(numpy.nansum(flatten_lc2))
numpy.testing.assert_almost_equal(
numpy.nansum(flatten_lc2), 18119.30865711536, decimal=1
)
print("Detrending 34 (transit_mask GP)")
mask = transit_mask(time=time[:2000], period=100, duration=0.3, T0=1327.4)
flatten_lc2, trend_lc2 = flatten(
time[:2000],
flux[:2000],
method="gp",
kernel="matern",
kernel_size=0.8,
return_trend=True,
robust=True,
mask=mask,
)
# print(numpy.nansum(flatten_lc2))
numpy.testing.assert_almost_equal(
numpy.nansum(flatten_lc2), 1948.9000170463796, decimal=1
)
print("Detrending 35 (pspline full features)")
flatten_lc, trend_lc, nsplines = flatten(
time,
flux,
method="pspline",
max_splines=100,
edge_cutoff=0.5,
return_trend=True,
return_nsplines=True,
verbose=True,
)
# print('lightcurve was split into', len(nsplines), 'segments')
# print('chosen number of splines', nsplines)
"""
import matplotlib.pyplot as plt
plt.scatter(time, flux, s=3, color='black')
plt.plot(time, trend_lc)
plt.show()
plt.scatter(time, flatten_lc, s=3, color='black')
plt.show()
"""
numpy.testing.assert_almost_equal(
numpy.nansum(flatten_lc), 16678.312693036027, decimal=1
)
print("Detrending 36 (pspline variable PSPLINES_STDEV_CUT)")
flatten_lc, trend_lc, nsplines = flatten(
time,
flux,
method="pspline",
max_splines=100,
edge_cutoff=0.5,
stdev_cut=3,
return_trend=True,
return_nsplines=True,
verbose=True,
)
numpy.testing.assert_almost_equal(
numpy.nansum(flatten_lc), 16678.292210380347, decimal=2
)
"""
import matplotlib.pyplot as plt
plt.scatter(time, flux, s=1, color='black')
plt.plot(time, trend_lc, color='red', linewidth=2, linestyle='dashed')
plt.show()
plt.close()
"""
print("All tests completed.")
def explore_object(self,
object_id,
input_lc_file=None,
time_units=1,
auto_detrend_periodic_signals=False,
auto_detrend_ratio=1 / 4,
detrend_method="cosine",
smooth=False,
sectors=None,
cadence=300):
lc_builder = LcBuilder()
object_info = lc_builder.build_object_info(object_id, None, sectors,
input_lc_file, cadence,
None, [{
"P": 0.59925,
"D": 200,
"T0": 1354.57
}], None, None, None)
lc_build = lc_builder.build(object_info, ".")
lc = lc_build.lc
lc = lc.remove_outliers(sigma_lower=float('inf'),
sigma_upper=3) # remove outliers over 3sigma
flux = lc.flux.value
flux_err = lc.flux_err.value
if time_units == 0:
time = lc.astropy_time.jd
else:
time = lc.time.value
if smooth:
flux = savgol_filter(flux, 11, 2)
flux = uniform_filter1d(flux, 11)
lc_df = pd.DataFrame(columns=['#time', 'flux', 'flux_err'])
lc_df['#time'] = lc.time.value
lc_df['flux'] = lc.flux.value
lc_df['flux_err'] = lc.flux_err.value
lc_df = pd.DataFrame(columns=['flux'])
lc_df['flux'] = acf(lc.flux.value, nlags=7200)
test_df = pd.DataFrame(lc_df)
ax = test_df.plot()
ax.set_ylim(lc_df["flux"].min(), lc_df["flux"].max())
#ax.set_xticks(list(range(0, len(ax.get_xticks()))), ax.get_xticks() / 30 / 24)
plt.show()
#periodogram = lc.to_periodogram(oversample_factor=10)
#fig = px.line(x=periodogram.period.astype('<f4'), y=periodogram.power.astype('<f4'), log_x=True)
#fig.show()
fig = px.scatter(x=lc.time.value, y=lc.flux.value)
fig.show()
if auto_detrend_periodic_signals:
detrend_period = self.__calculate_max_significant_period(
lc, periodogram)
if detrend_period is not None:
flatten_lc, trend_lc = self.__detrend_by_period(
lc, detrend_period * auto_detrend_ratio, detrend_method)
fig = go.Figure()
fig.add_trace(
go.Scatter(x=lc.time.value,
y=flux,
mode='markers',
name='Flux'))
fig.add_trace(
go.Scatter(x=lc.time.value,
y=trend_lc,
mode='lines+markers',
name='Main Trend'))
fig.show()
if auto_detrend_periodic_signals:
fig = px.line(x=lc.time.value, y=flatten_lc)
fig.show()
lc.flux = flatten_lc
bin_means, bin_edges, binnumber = stats.binned_statistic(
lc.time.value, lc.flux.value, statistic='mean', bins=len(time) / 5)
bin_stds, _, _ = stats.binned_statistic(time,
flux,
statistic='std',
bins=len(time) / 3)
bin_width = (bin_edges[1] - bin_edges[0])
bin_centers = bin_edges[1:] - bin_width / 2
time_binned = bin_centers
flux_binned = bin_means
lc_binned = lk.LightCurve(time=time_binned, flux=flux_binned)
fig = px.scatter(x=lc.time.value, y=lc.flux.value)
fig.show()
while True:
try:
user_input = input(
"Select the period to fold the light curve: ")
if user_input.startswith("q"):
break
period = float(user_input)
except ValueError:
print("Wrong number.")
continue
try:
user_input = input("Select the t0 to fold the light curve: ")
if user_input.startswith("q"):
break
t0 = float(user_input)
except ValueError:
print("Wrong number.")
continue
try:
user_input = input("Select the duration (minutes): ")
if user_input.startswith("q"):
break
duration = float(user_input)
except ValueError:
print("Wrong number.")
continue
# flux = lc.flux
# args_flux_outer = np.argwhere(flux > 1 + depth * 0.001)
# flux[args_flux_outer] = np.nan
# time[args_flux_outer] = np.nan
# args_flux_outer = np.argwhere(flux < 1 + depth * 0.001)
# flux[args_flux_outer] = np.nan
# time[args_flux_outer] = np.nan
j = 0
fig_transit, axs = plt.subplots(4, 4, figsize=(16, 16))
#fig_transit.suptitle("Transit centered plots for TIC " + str(object_id))
for i in np.arange(t0, t0 + period * 16, period)[0:16]:
args_plot = np.argwhere((i - 0.1 < time)
& (time < i + 0.1)).flatten()
plot_time = time[args_plot]
plot_flux = flux[args_plot]
axs[j // 4][j % 4].scatter(plot_time,
plot_flux,
color='gray',
alpha=1,
rasterized=True,
label="Flux Transit " + str(j))
j = j + 1
folded_lc = lk.LightCurve(time=lc.time,
flux=lc.flux,
flux_err=lc.flux_err)
folded_lc.remove_nans()
# folded_lc.flux_err = np.nan
# folded_lc = folded_lc.bin(bins=500)
folded_lc.fold(period=period).scatter()
plt.title("Phase-folded period: " + format(period, ".2f") +
" days")
plt.show()
fig_transit.show()
fig = px.line(x=folded_lc.time.value, y=folded_lc.flux.value)
fig.show()
mask = wotan.transit_mask(time, period, duration * 2 / 24 / 60, t0)
fig = px.scatter(x=time[~mask], y=flux[~mask])
fig.show()
def flatten(lc,
t0=None,
period=None,
duration=1. / 24.,
window_length=3. / 24.,
**kwargs):
"""
Flatten a light curve using the `wotan` package.
Parameters
----------
lc : `~lightkurve.LightCurve`
A light curve object with the data.
t0 : float or iterable, optional
Mid-transit time of transit signal(s) to mask.
period : float or iterable, optional
Period of transit signal(s) to mask.
duration : float, optional
Duration of transit signal to mask. Defaults to 1 hr.
window_length : float, optional
Length of the filter window for `wotan.flatten()`. Defaults to 3 hr.
kwargs : dict, optional
Any extra keyword arguments to pass to `wotan.flatten()`.
Returns
-------
lc : `~lightkurve.LightCurve`
A light curve object with the flattened light curve.
trend : ndarray
The removed flux trend. Only returned if ``return_trend`` is `True`.
"""
# Mask transits if any ephemerides are given
mask = np.zeros_like(lc.time, dtype=bool)
if t0 is not None:
t0s = np.array(t0).flatten()
periods = np.array(period).flatten()
for t0, period in zip(t0s, periods):
mask += wotan.transit_mask(time=lc.time,
T0=t0,
period=period,
duration=duration)
# Flatten the light curve
flux_flat = wotan.flatten(lc.time,
lc.flux,
window_length=window_length,
mask=mask,
**kwargs)
# Return trend if return_trend=True
return_trend = False
if isinstance(flux_flat, tuple):
return_trend = True
flux_flat, trend = flux_flat
lcflat = lc.copy()
lcflat.flux = flux_flat
if return_trend:
return (lcflat, trend)
else:
return lcflat
def main():
print("Starting tests for wotan...")
numpy.testing.assert_almost_equal(
t14(R_s=1, M_s=1, P=365),
0.6490025258902046)
numpy.testing.assert_almost_equal(
t14(R_s=1, M_s=1, P=365, small_planet=True),
0.5403690143737738)
print("Transit duration correct.")
numpy.random.seed(seed=0) # reproducibility
print("Slide clipper...")
points = 1000
time = numpy.linspace(0, 30, points)
flux = 1 + numpy.sin(time) / points
noise = numpy.random.normal(0, 0.0001, points)
flux += noise
for i in range(points):
if i % 75 == 0:
flux[i:i+5] -= 0.0004 # Add some transits
flux[i+50:i+52] += 0.0002 # and flares
clipped = slide_clip(
time,
flux,
window_length=0.5,
low=3,
high=2,
method='mad',
center='median'
)
numpy.testing.assert_almost_equal(numpy.nansum(clipped), 948.9926368754939)
"""
import matplotlib.pyplot as plt
plt.scatter(time, flux, s=3, color='black')
plt.scatter(time, clipped, s=3, color='orange')
plt.show()
"""
# TESS test
print('Loading TESS data from archive.stsci.edu...')
path = 'https://archive.stsci.edu/hlsps/tess-data-alerts/'
#path = 'P:/P/Dok/tess_alarm/'
filename = "hlsp_tess-data-alerts_tess_phot_00062483237-s01_tess_v1_lc.fits"
time, flux = load_file(path + filename)
window_length = 0.5
print("Detrending 1 (biweight)...")
flatten_lc, trend_lc = flatten(
time,
flux,
window_length,
edge_cutoff=1,
break_tolerance=0.1,
return_trend=True,
cval=5.0)
numpy.testing.assert_equal(len(trend_lc), 20076)
numpy.testing.assert_almost_equal(numpy.nanmax(trend_lc), 28755.03811866676, decimal=2)
numpy.testing.assert_almost_equal(numpy.nanmin(trend_lc), 28615.110229935075, decimal=2)
numpy.testing.assert_almost_equal(trend_lc[500], 28671.650565730513, decimal=2)
numpy.testing.assert_equal(len(flatten_lc), 20076)
numpy.testing.assert_almost_equal(numpy.nanmax(flatten_lc), 1.0034653549250616, decimal=2)
numpy.testing.assert_almost_equal(numpy.nanmin(flatten_lc), 0.996726610702177, decimal=2)
numpy.testing.assert_almost_equal(flatten_lc[500], 1.000577429565131, decimal=2)
print("Detrending 2 (andrewsinewave)...")
flatten_lc, trend_lc = flatten(time, flux, window_length, method='andrewsinewave', return_trend=True)
numpy.testing.assert_almost_equal(numpy.nansum(flatten_lc), 18119.15471987987, decimal=2)
print("Detrending 3 (welsch)...")
flatten_lc, trend_lc = flatten(time, flux, window_length, method='welsch', return_trend=True)
numpy.testing.assert_almost_equal(numpy.nansum(flatten_lc), 18119.16764691235, decimal=2)
print("Detrending 4 (hodges)...")
flatten_lc, trend_lc = flatten(time[:1000], flux[:1000], window_length, method='hodges', return_trend=True)
numpy.testing.assert_almost_equal(numpy.nansum(flatten_lc), 994.0110525909206, decimal=2)
print("Detrending 5 (median)...")
flatten_lc, trend_lc = flatten(time, flux, window_length, method='median', return_trend=True)
numpy.testing.assert_almost_equal(numpy.nansum(flatten_lc), 18119.122065014355, decimal=2)
print("Detrending 6 (mean)...")
flatten_lc, trend_lc = flatten(time, flux, window_length, method='mean', return_trend=True)
numpy.testing.assert_almost_equal(numpy.nansum(flatten_lc), 18119.032473037714, decimal=2)
print("Detrending 7 (trim_mean)...")
flatten_lc, trend_lc = flatten(time, flux, window_length, method='trim_mean', return_trend=True)
numpy.testing.assert_almost_equal(numpy.nansum(flatten_lc), 18119.095164910334, decimal=2)
print("Detrending 8 (supersmoother)...")
flatten_lc, trend_lc = flatten(time, flux, window_length, method='supersmoother', return_trend=True)
numpy.testing.assert_almost_equal(numpy.nansum(flatten_lc), 18123.00632204841, decimal=2)
print("Detrending 9 (hspline)...")
flatten_lc, trend_lc = flatten(time, flux, window_length, method='hspline', return_trend=True)
numpy.testing.assert_almost_equal(numpy.nansum(flatten_lc), 18123.082601463717, decimal=1)
print("Detrending 10 (cofiam)...")
flatten_lc, trend_lc = flatten(time[:2000], flux[:2000], window_length, method='cofiam', return_trend=True)
numpy.testing.assert_almost_equal(numpy.nansum(flatten_lc), 1948.9999999987976, decimal=1)
print("Detrending 11 (savgol)...")
flatten_lc, trend_lc = flatten(time, flux, window_length=301, method='savgol', return_trend=True)
numpy.testing.assert_almost_equal(numpy.nansum(flatten_lc), 18123.003465539354, decimal=1)
print("Detrending 12 (medfilt)...")
flatten_lc, trend_lc = flatten(time, flux, window_length=301, method='medfilt', return_trend=True)
numpy.testing.assert_almost_equal(numpy.nansum(flatten_lc), 18123.22609806557, decimal=1)
print("Detrending 12 (gp squared_exp)...")
flatten_lc, trend_lc1 = flatten(
time[:2000],
flux[:2000],
method='gp',
kernel='squared_exp',
kernel_size=10,
return_trend=True)
numpy.testing.assert_almost_equal(numpy.nansum(flatten_lc), 1948.9672036416687, decimal=2)
print("Detrending 13 (gp squared_exp robust)...")
flatten_lc, trend_lc1 = flatten(
time[:2000],
flux[:2000],
method='gp',
kernel='squared_exp',
kernel_size=10,
robust=True,
return_trend=True)
numpy.testing.assert_almost_equal(numpy.nansum(flatten_lc), 1948.8820772313468, decimal=2)
print("Detrending 14 (gp matern)...")
flatten_lc, trend_lc2 = flatten(
time[:2000],
flux[:2000],
method='gp',
kernel='matern',
kernel_size=10,
return_trend=True)
numpy.testing.assert_almost_equal(numpy.nansum(flatten_lc), 1948.9672464898367, decimal=2)
print("Detrending 15 (gp periodic)...")
flatten_lc, trend_lc2 = flatten(
time[:2000],
flux[:2000],
method='gp',
kernel='periodic',
kernel_size=1,
kernel_period=10,
return_trend=True)
numpy.testing.assert_almost_equal(numpy.nansum(flatten_lc), 1948.9999708985608, decimal=2)
time_synth = numpy.linspace(0, 30, 200)
flux_synth = numpy.sin(time_synth) + numpy.random.normal(0, 0.1, 200)
flux_synth = 1 + flux_synth / 100
time_synth *= 1.5
print("Detrending 16 (gp periodic_auto)...")
flatten_lc, trend_lc2 = flatten(
time_synth,
flux_synth,
method='gp',
kernel='periodic_auto',
kernel_size=1,
return_trend=True)
numpy.testing.assert_almost_equal(numpy.nansum(flatten_lc), 200, decimal=1)
print("Detrending 17 (rspline)...")
flatten_lc, trend_lc2 = flatten(
time,
flux,
method='rspline',
window_length=1,
return_trend=True)
numpy.testing.assert_almost_equal(numpy.nansum(flatten_lc), 18121.812790732245, decimal=2)
print("Detrending 18 (huber)...")
flatten_lc, trend_lc = flatten(
time[:1000],
flux[:1000],
method='huber',
window_length=0.5,
edge_cutoff=0,
break_tolerance=0.4,
return_trend=True)
numpy.testing.assert_almost_equal(numpy.nansum(flatten_lc), 994.01102, decimal=2)
print("Detrending 19 (winsorize)...")
flatten_lc, trend_lc2 = flatten(
time,
flux,
method='winsorize',
window_length=0.5,
edge_cutoff=0,
break_tolerance=0.4,
proportiontocut=0.1,
return_trend=True)
numpy.testing.assert_almost_equal(numpy.nansum(flatten_lc), 18119.064587196448, decimal=2)
print("Detrending 20 (pspline)...")
flatten_lc, trend_lc = flatten(
time,
flux,
method='pspline',
return_trend=True
)
numpy.testing.assert_almost_equal(numpy.nansum(flatten_lc), 18121.832133916843, decimal=2)
print("Detrending 21 (hampelfilt)...")
flatten_lc, trend_lc5 = flatten(
time,
flux,
method='hampelfilt',
window_length=0.5,
cval=3,
return_trend=True
)
numpy.testing.assert_almost_equal(numpy.nansum(flatten_lc), 18119.158072498867, decimal=2)
print("Detrending 22 (lowess)...")
flatten_lc, trend_lc1 = flatten(
time,
flux,
method='lowess',
window_length=1,
return_trend=True
)
numpy.testing.assert_almost_equal(numpy.nansum(flatten_lc), 18123.039744125545, decimal=2)
print("Detrending 23 (huber_psi)...")
flatten_lc, trend_lc1 = flatten(
time,
flux,
method='huber_psi',
window_length=0.5,
return_trend=True
)
numpy.testing.assert_almost_equal(numpy.nansum(flatten_lc), 18119.122065014355, decimal=2)
print("Detrending 24 (tau)...")
flatten_lc, trend_lc2 = flatten(
time,
flux,
method='tau',
window_length=0.5,
return_trend=True
)
numpy.testing.assert_almost_equal(numpy.nansum(flatten_lc), 18119.02772621119, decimal=2)
print("Detrending 25 (cosine)...")
flatten_lc, trend_lc2 = flatten(
time,
flux,
method='cosine',
window_length=0.5,
return_trend=True
)
numpy.testing.assert_almost_equal(numpy.nansum(flatten_lc), 18122.999999974905, decimal=2)
print("Detrending 25 (cosine robust)...")
flatten_lc, trend_lc2 = flatten(
time,
flux,
method='cosine',
robust=True,
window_length=0.5,
return_trend=True
)
numpy.testing.assert_almost_equal(numpy.nansum(flatten_lc), 18122.227938535038, decimal=2)
import numpy as np
points = 1000
time = numpy.linspace(0, 30, points)
flux = 1 + numpy.sin(time) / points
noise = numpy.random.normal(0, 0.0001, points)
flux += noise
for i in range(points):
if i % 75 == 0:
flux[i:i+5] -= 0.0004 # Add some transits
flux[i+50:i+52] += 0.0002 # and flares
print("Detrending 26 (hampel 17A)...")
flatten_lc, trend_lc1 = flatten(
time,
flux,
method='hampel',
cval=(1.7, 3.4, 8.5),
window_length=0.5,
return_trend=True
)
print("Detrending 27 (hampel 25A)...")
flatten_lc, trend_lc2 = flatten(
time,
flux,
method='hampel',
cval=(2.5, 4.5, 9.5),
window_length=0.5,
return_trend=True
)
numpy.testing.assert_almost_equal(numpy.nansum(flatten_lc), 997.9994362858843, decimal=2)
print("Detrending 28 (ramsay)...")
flatten_lc, trend_lc3 = flatten(
time,
flux,
method='ramsay',
cval=0.3,
window_length=0.5,
return_trend=True
)
numpy.testing.assert_almost_equal(numpy.nansum(flatten_lc), 997.9974021484584, decimal=2)
print("Detrending 29 (ridge)...")
flatten_lc, trend_lc1 = flatten(
time,
flux,
window_length=0.5,
method='ridge',
return_trend=True,
cval=1)
numpy.testing.assert_almost_equal(numpy.nansum(flatten_lc), 999.9999958887022, decimal=1)
print("Detrending 30 (lasso)...")
flatten_lc, trend_lc2 = flatten(
time,
flux,
window_length=0.5,
method='lasso',
return_trend=True,
cval=1)
numpy.testing.assert_almost_equal(numpy.nansum(flatten_lc), 999.9999894829843, decimal=1)
print("Detrending 31 (elasticnet)...")
flatten_lc, trend_lc3 = flatten(
time,
flux,
window_length=0.5,
method='elasticnet',
return_trend=True,
cval=1)
numpy.testing.assert_almost_equal(numpy.nansum(flatten_lc), 999.9999945063338, decimal=1)
# Test of transit mask
print('Testing transit_mask')
filename = 'hlsp_tess-data-alerts_tess_phot_00207081058-s01_tess_v1_lc.fits'
time, flux = load_file(path + filename)
from wotan import transit_mask
mask = transit_mask(
time=time,
period=14.77338,
duration=0.21060,
T0=1336.141095
)
numpy.testing.assert_almost_equal(numpy.sum(mask), 302, decimal=1)
print('Detrending 32 (transit_mask cosine)')
flatten_lc1, trend_lc1 = flatten(
time,
flux,
method='cosine',
window_length=0.4,
return_trend=True,
robust=True,
mask=mask
)
numpy.testing.assert_almost_equal(numpy.nansum(flatten_lc1), 18119.281265446625, decimal=1)
print('Detrending 33 (transit_mask lowess)')
flatten_lc2, trend_lc2 = flatten(
time,
flux,
method='lowess',
window_length=0.8,
return_trend=True,
robust=True,
mask=mask
)
#print(numpy.nansum(flatten_lc2))
numpy.testing.assert_almost_equal(numpy.nansum(flatten_lc2), 18119.30865711536, decimal=1)
print('Detrending 34 (transit_mask GP)')
mask = transit_mask(
time=time[:2000],
period=100,
duration=0.3,
T0=1327.4
)
flatten_lc2, trend_lc2 = flatten(
time[:2000],
flux[:2000],
method='gp',
kernel='matern',
kernel_size=0.8,
return_trend=True,
robust=True,
mask=mask
)
#print(numpy.nansum(flatten_lc2))
numpy.testing.assert_almost_equal(numpy.nansum(flatten_lc2), 1948.9000170463796, decimal=1)
"""
import matplotlib.pyplot as plt
plt.scatter(time[:2000], flux[:2000], s=1, color='black')
plt.plot(time[:2000], trend_lc2, color='red', linewidth=2, linestyle='dashed')
plt.show()
plt.close()
"""
print('All tests completed.')