""" This module contains functions to split, join and mask light curve data. """
import numpy as np
from democratic_detrender.helper_functions import find_nearest
[docs]
def split_around_problems(x, y, yerr, mask, mask_fitted_planet, problem_times):
problem_split_x = []
problem_split_y = []
problem_split_yerr = []
problem_split_mask = []
problem_split_mask_fitted_planet = []
split_x = []
split_y = []
split_yerr = []
split_mask = []
split_mask_fitted_planet = []
split_times = []
for time in problem_times:
split_times.append(find_nearest(x, time))
for ii in range(0, len(x)):
time = x[ii]
flux = y[ii]
flux_err = yerr[ii]
a_mask = mask[ii]
a_mask_fitted_planet = mask_fitted_planet[ii]
if time in split_times:
problem_split_x.append(np.array(split_x))
problem_split_y.append(np.array(split_y))
problem_split_yerr.append(np.array(split_yerr))
problem_split_mask.append(np.array(split_mask))
problem_split_mask_fitted_planet.append(np.array(split_mask_fitted_planet))
split_x = []
split_y = []
split_yerr = []
split_mask = []
split_mask_fitted_planet = []
else:
split_x.append(time)
split_y.append(flux)
split_yerr.append(flux_err)
split_mask.append(a_mask)
split_mask_fitted_planet.append(a_mask_fitted_planet)
output = [
np.array(problem_split_x, dtype=object),
np.array(problem_split_y, dtype=object),
np.array(problem_split_yerr, dtype=object),
np.array(problem_split_mask, dtype=object),
np.array(problem_split_mask_fitted_planet, dtype=object),
]
return output
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def add_nans_for_missing_data(
sap_x_local,
sap_detrended_lcs,
sap_yerr_local,
sap_mask_local,
sap_mask_fitted_planet_local,
pdc_x_local,
pdc_detrended_lcs,
pdc_yerr_local,
pdc_mask_local,
pdc_mask_fitted_planet_local,
):
print("")
print("")
print("pdc length in: ", str(len(pdc_x_local)))
print("sap length in: ", str(len(sap_x_local)))
print("---")
for ii in range(0, len(pdc_x_local)):
time = pdc_x_local[ii]
yerr = pdc_yerr_local[ii]
mask = pdc_mask_local[ii]
mask_fitted_planet = pdc_mask_fitted_planet_local[ii]
if time not in sap_x_local:
for kk in range(0, len(sap_x_local)):
if sap_x_local[kk] > time:
sap_x_local = np.insert(sap_x_local, kk, time)
sap_yerr_local = np.insert(sap_yerr_local, kk, yerr)
sap_mask_local = np.insert(sap_mask_local, kk, mask)
sap_mask_fitted_planet_local = np.insert(
sap_mask_fitted_planet_local, kk, mask_fitted_planet
)
for jj in range(0, len(sap_detrended_lcs)):
sap_detrended_lcs[jj] = np.insert(
sap_detrended_lcs[jj], kk, np.nan
)
break
elif kk + 1 == len(sap_x_local):
sap_x_local = np.insert(sap_x_local, kk + 1, time)
sap_yerr_local = np.insert(sap_yerr_local, kk + 1, yerr)
sap_mask_local = np.insert(sap_mask_local, kk + 1, mask)
sap_mask_fitted_planet_local = np.insert(
sap_mask_fitted_planet_local, kk + 1, mask_fitted_planet
)
for jj in range(0, len(sap_detrended_lcs)):
sap_detrended_lcs[jj] = np.insert(
sap_detrended_lcs[jj], kk + 1, np.nan
)
break
for ii in range(0, len(sap_x_local)):
time = sap_x_local[ii]
yerr = sap_yerr_local[ii]
mask = sap_mask_local[ii]
mask_fitted_planet = sap_mask_fitted_planet_local[ii]
if time not in pdc_x_local:
for kk in range(0, len(pdc_x_local)):
if pdc_x_local[kk] > time:
pdc_x_local = np.insert(pdc_x_local, kk, time)
pdc_yerr_local = np.insert(pdc_yerr_local, kk, yerr)
pdc_mask_local = np.insert(pdc_mask_local, kk, mask)
pdc_mask_fitted_planet_local = np.insert(
pdc_mask_fitted_planet_local, kk, mask_fitted_planet
)
for jj in range(0, len(pdc_detrended_lcs)):
pdc_detrended_lcs[jj] = np.insert(
pdc_detrended_lcs[jj], kk, np.nan
)
break
elif kk + 1 == len(pdc_x_local):
pdc_x_local = np.insert(pdc_x_local, kk + 1, time)
pdc_yerr_local = np.insert(pdc_yerr_local, kk + 1, yerr)
pdc_mask_local = np.insert(pdc_mask_local, kk + 1, mask)
pdc_mask_fitted_planet_local = np.insert(
pdc_mask_fitted_planet_local, kk + 1, mask_fitted_planet
)
for jj in range(0, len(pdc_detrended_lcs)):
pdc_detrended_lcs[jj] = np.insert(
pdc_detrended_lcs[jj], kk + 1, np.nan
)
break
print("pdc length out: ", str(len(pdc_x_local)))
print("sap length out: ", str(len(sap_x_local)))
print("")
print("")
print("")
if (pdc_x_local == sap_x_local).all():
x_detrended = pdc_x_local
else:
print("ERROR, pdc and sap x arrays aren't the same")
yerr_detrended = np.nanmean([pdc_yerr_local, sap_yerr_local], axis=0)
if (pdc_mask_local == sap_mask_local).all():
mask_detrended = pdc_mask_local
else:
for ii in range(0, len(pdc_mask_local)):
if pdc_mask_local[ii] != sap_mask_local[ii]:
print(pdc_x_local[ii], pdc_mask_local[ii])
print(sap_x_local[ii], sap_mask_local[ii])
print("")
print("ERROR, pdc and sap mask arrays aren't the same")
if (pdc_mask_fitted_planet_local == sap_mask_fitted_planet_local).all():
mask_fitted_planet_detrended = pdc_mask_fitted_planet_local
else:
print("ERROR, pdc and sap mask for fitted planet arrays aren't the same")
return (
x_detrended,
sap_detrended_lcs,
pdc_detrended_lcs,
yerr_detrended,
mask_detrended,
mask_fitted_planet_detrended,
)
[docs]
def split_around_transits(
x, y, yerr, mask, mask_fitted_planet, t0s, window, period, model="None"
):
# x = time
# y = flux
# yerr = flux error
# mask = mask
# t0s = midtransits in data
# window = what fraction of the period to plot on either side of transit (ie. xlim=1/2 means 1/2 period on either side)
# period = planet period to define plotting limit
xlims = []
x_split = []
y_split = []
yerr_split = []
mask_split = []
mask_fitted_planet_split = []
if type(model) is np.ndarray:
model_split = []
for t0 in t0s:
xlims.append([float(t0 - (period * window)), float(t0 + (period * window))])
x_split.append([])
y_split.append([])
yerr_split.append([])
mask_split.append([])
mask_fitted_planet_split.append([])
if type(model) is np.ndarray:
model_split.append([])
split_index = 0
for xlim in xlims:
for ii in range(0, len(x)):
time = x[ii]
lc = y[ii]
lc_err = yerr[ii]
transit_mask = mask[ii]
fitted_planet_mask = mask_fitted_planet[ii]
if type(model) is np.ndarray:
model_val = model[ii]
if time >= xlim[0] and time <= xlim[1]:
x_split[split_index].append(time)
y_split[split_index].append(lc)
yerr_split[split_index].append(lc_err)
mask_split[split_index].append(transit_mask)
mask_fitted_planet_split[split_index].append(fitted_planet_mask)
if type(model) is np.ndarray:
model_split[split_index].append(model_val)
x_split[split_index] = np.array(x_split[split_index])
y_split[split_index] = np.array(y_split[split_index])
yerr_split[split_index] = np.array(yerr_split[split_index])
mask_split[split_index] = np.array(mask_split[split_index])
mask_fitted_planet_split[split_index] = np.array(
mask_fitted_planet_split[split_index]
)
split_index += 1
x_split = np.array(x_split, dtype=object)
y_split = np.array(y_split, dtype=object)
yerr_split = np.array(yerr_split, dtype=object)
mask_split = np.array(mask_split, dtype=object)
mask_fitted_planet_split = np.array(mask_fitted_planet_split, dtype=object)
if type(model) is np.ndarray:
model_split = np.array(model_split, dtype=object)
return (
x_split,
y_split,
yerr_split,
mask_split,
mask_fitted_planet_split,
model_split,
)
return x_split, y_split, yerr_split, mask_split, mask_fitted_planet_split
[docs]
def find_quarters_with_transits(
x_quarters,
y_quarters,
yerr_quarters,
mask_quarters,
mask_fitted_planet_quarters,
t0s,
):
x_transits = []
y_transits = []
yerr_transits = []
mask_transits = []
mask_fitted_planet_transits = []
quarters_included = []
for ii in range(0, len(x_quarters)):
x_quarter = x_quarters[ii]
y_quarter = y_quarters[ii]
yerr_quarter = yerr_quarters[ii]
mask_quarter = mask_quarters[ii]
mask_fitted_planet_quarter = mask_fitted_planet_quarters[ii]
xmin = np.min(x_quarter)
xmax = np.max(x_quarter)
for t0 in t0s:
if t0 > xmin and t0 < xmax:
# make sure this quarter hasn't already been added to the data
# this ensures there aren't duplicates if multiple transits
# exist in a single quarter or sector
if ii not in quarters_included:
quarters_included.append(ii)
x_transits.append(x_quarter.astype("float64"))
y_transits.append(y_quarter.astype("float64"))
yerr_transits.append(yerr_quarter.astype("float64"))
mask_transits.append(mask_quarter.astype("bool"))
mask_fitted_planet_transits.append(
mask_fitted_planet_quarter.astype("bool")
)
return (
x_transits,
y_transits,
yerr_transits,
mask_transits,
mask_fitted_planet_transits,
)
