Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Correct difference photometry for reference flux #91

Open
wants to merge 1 commit into
base: main
Choose a base branch
from
Open

Correct difference photometry for reference flux #91

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
80 changes: 73 additions & 7 deletions kowalski/alert_broker_ztf.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -33,6 +33,7 @@
from typing import Mapping, Optional, Sequence

from utils import (
ccd_quad_to_rc,
deg2dms,
deg2hms,
great_circle_distance,
Expand Down Expand Up @@ -88,12 +89,15 @@ def __str__(self):
return self.message


def make_photometry(alert: dict, jd_start: float = None):
def make_photometry(
alert: dict, jd_start: float = None, include_reference_flux: bool = True, **kwargs
):
"""
Make a de-duplicated pandas.DataFrame with photometry of alert['objectId']

:param alert: ZTF alert packet/dict
:param jd_start:
:param include_reference_flux: correct for candidate.magnr?
:return:
"""
alert = deepcopy(alert)
Expand Down Expand Up @@ -121,6 +125,12 @@ def make_photometry(alert: dict, jd_start: float = None):
.sort_values(by=["mjd"])
)

match_radius_arcsec = kwargs.get("match_radius_arcsec", 1.5)
star_galaxy_threshold = kwargs.get("star_galaxy_threshold", 0.4)
is_star = (alert["candidate"]["distpsnr1"] < match_radius_arcsec) and (
alert["candidate"]["sgscore1"] > star_galaxy_threshold
)

# filter out bad data:
mask_good_diffmaglim = df_light_curve["diffmaglim"] > 0
df_light_curve = df_light_curve.loc[mask_good_diffmaglim]
Expand All @@ -130,34 +140,90 @@ def make_photometry(alert: dict, jd_start: float = None):
# step 1: calculate the coefficient that determines whether the
# flux should be negative or positive
coeff = df_light_curve["isdiffpos"].apply(
lambda x: 1.0 if x in [True, 1, "y", "Y"] else -1.0
lambda x: 1.0 if x in [True, 1, "y", "Y", "t", "1"] else -1.0
)

# step 2: calculate the flux normalized to an arbitrary AB zeropoint of
# 23.9 (results in flux in uJy)
df_light_curve["flux"] = coeff * 10 ** (-0.4 * (df_light_curve["magpsf"] - 23.9))

# step 3: separate detections from non detections
# step 3: compute corrections if flux is present in reference image
if include_reference_flux and is_star:
# fix very old alerts:
df_light_curve.replace("None", np.nan, inplace=True)

# prior to 2018-11-12, candidate.field and candidate.rcid were not reported for non-detections,
# which makes inferring upper limits more difficult.
# attempt fixing this using pdiffimfilename, e.g. ztf_20210309547431_000690_zr_c01_o_q4_scimrefdiffimg.fits:
mask_null_rcid = df_light_curve.rcid.isnull()
if any(mask_null_rcid):
df_light_curve.loc[mask_null_rcid, "rcid"] = df_light_curve.loc[
mask_null_rcid, "pdiffimfilename"
].apply(
lambda x: ccd_quad_to_rc(
ccd=int(os.path.basename(x).split("_")[4][1:]),
quad=int(os.path.basename(x).split("_")[6][1:]),
)
)
df_light_curve.loc[mask_null_rcid, "field"] = df_light_curve.loc[
mask_null_rcid, "pdiffimfilename"
].apply(lambda x: int(os.path.basename(x).split("_")[2][1:]))

grouped_light_curves = df_light_curve.groupby(["fid", "field", "rcid"])
impute_magnr = grouped_light_curves["magnr"].agg(
lambda x: np.median(x[np.isfinite(x)])
)
impute_sigmagnr = grouped_light_curves["sigmagnr"].agg(
lambda x: np.median(x[np.isfinite(x)])
)

for idx, light_curve_group in grouped_light_curves:
mask_nan_magnr_group = np.isnan(light_curve_group["magnr"])
mask_nan_magnr = light_curve_group[mask_nan_magnr_group].index
df_light_curve.loc[mask_nan_magnr, "magnr"] = impute_magnr[idx]
df_light_curve.loc[mask_nan_magnr, "sigmagnr"] = impute_sigmagnr[idx]

df_light_curve["reference_flux"] = 10 ** (
-0.4 * (df_light_curve["magnr"] - 23.9)
)
df_light_curve["reference_fluxerr"] = np.abs(
df_light_curve["sigmagnr"]
* df_light_curve["reference_flux"]
* np.log(10)
/ 2.5
)

# step 4: separate detections from non-detections
detected = np.isfinite(df_light_curve["magpsf"])
undetected = ~detected

# step 4: calculate the flux error
# step 5: calculate the flux error
df_light_curve["fluxerr"] = None # initialize the column

# step 4a: calculate fluxerr for detections using sigmapsf
# step 5a: calculate fluxerr for detections using sigmapsf
df_light_curve.loc[detected, "fluxerr"] = np.abs(
df_light_curve.loc[detected, "sigmapsf"]
* df_light_curve.loc[detected, "flux"]
* np.log(10)
/ 2.5
)

# step 4b: calculate fluxerr for non detections using diffmaglim
# step 5b: calculate fluxerr for non-detections using diffmaglim
df_light_curve.loc[undetected, "fluxerr"] = (
10 ** (-0.4 * (df_light_curve.loc[undetected, "diffmaglim"] - 23.9)) / 5.0
) # as diffmaglim is the 5-sigma depth

# step 5: set the zeropoint and magnitude system
# step 6: correct for reference flux
if include_reference_flux and is_star:
df_light_curve["flux"] = (
df_light_curve["flux"] + df_light_curve["reference_flux"]
)
df_light_curve.loc[detected, "fluxerr"] = np.sqrt(
df_light_curve.loc[detected, "fluxerr"] ** 2
+ df_light_curve.loc[detected, "reference_flux"] ** 2
)

# step 7: set the zeropoint and magnitude system
df_light_curve["zp"] = 23.9
df_light_curve["zpsys"] = "ab"

Expand Down