__all__ = [
"predicted_zeropoint",
"predicted_zeropoint_hardware",
"predicted_zeropoint_itl",
"predicted_zeropoint_hardware_itl",
"predicted_zeropoint_e2v",
"predicted_zeropoint_hardware_e2v",
]
import numpy as np
[docs]
def predicted_zeropoint(band: str, airmass: float, exptime: float = 1) -> float:
"""General zeropoint values derived from v1.9 throughputs.
Extinction coefficients and zeropoint intercepts calculated in
https://github.com/lsst-pst/syseng_throughputs/blob/main/notebooks/InterpolateZeropoint.ipynb
Parameters
----------
band : `str`
The bandpass name.
airmass : `float`
The airmass at which to evaluate the zeropoint.
exptime : `float`, optional
The exposure time to calculate zeropoint.
Returns
-------
zeropoint : `float`
The zeropoint for the `band` at airmass `airmass` for an exposure
of time `exptime`.
Notes
-----
Useful for comparing to DM pipeline zeropoints or for calculating counts
in an image (`np.power(10, (zp - mag)/2.5)) = counts`).
"""
extinction_coeff = {
"u": -0.45815823969467745,
"g": -0.20789273881603035,
"r": -0.12233514157672552,
"i": -0.07387773563152214,
"z": -0.0573260392897174,
"y": -0.09549137502152871,
}
# Interestingly, because these come from a fit over X, these
# values are not identical to the zp values found for one second,
# without considering the fit. They are within 0.005 magnitudes though.
zeropoint_X1 = {
"u": 26.52229760811932,
"g": 28.50754554409417,
"r": 28.360365503331952,
"i": 28.170373076693625,
"z": 27.781368851776005,
"y": 26.813708013594344,
}
return zeropoint_X1[band] + extinction_coeff[band] * (airmass - 1) + 2.5 * np.log10(exptime)
[docs]
def predicted_zeropoint_hardware(band: str, exptime: float = 1) -> float:
"""Zeropoint values for the hardware throughput curves only,
without atmospheric contributions.
Parameters
----------
band : `str`
The bandpass name.
exptime : `float`, optional
The exposure time to calculate zeropoint.
Returns
-------
zeropoint : `float`
The zeropoint for the hardware only component of `band` for an
exposure of `exptime`.
Notes
-----
This hardware-only zeropoint is primarily useful for converting sky
background magnitudes into counts.
"""
zeropoint = {
"u": 26.99435242519598,
"g": 28.72132437054738,
"r": 28.487206668180864,
"i": 28.267160381353793,
"z": 27.850681356053688,
"y": 26.988827459758397,
}
return zeropoint[band] + 2.5 * np.log10(exptime)
[docs]
def predicted_zeropoint_itl(band: str, airmass: float, exptime: float = 1) -> float:
"""Average ITL zeropoint values derived from v1.9 throughputs.
Parameters
----------
band : `str`
The bandpass name.
airmass : `float`
The airmass at which to evaluate the zeropoint.
exptime : `float`, optional
The exposure time to calculate zeropoint.
Returns
-------
zeropoint : `float`
The zeropoint for the `band` at airmass `airmass` for an exposure
of time `exptime`.
Notes
-----
Useful for comparing to DM pipeline zeropoints or for calculating counts
in an image (`np.power(10, (zp - mag)/2.5)) = counts`).
"""
extinction_coeff = {
"u": -0.45815223255080606,
"g": -0.20789245761381037,
"r": -0.12233512060667238,
"i": -0.07387767800064417,
"z": -0.05739100372986528,
"y": -0.09474605376660676,
}
zeropoint_X1 = {
"u": 26.52231025834397,
"g": 28.507547620761827,
"r": 28.360365812523426,
"i": 28.170380673108845,
"z": 27.796728189989665,
"y": 26.870922441512732,
}
return zeropoint_X1[band] + extinction_coeff[band] * (airmass - 1) + 2.5 * np.log10(exptime)
[docs]
def predicted_zeropoint_hardware_itl(band: str, exptime: float = 1) -> float:
"""Zeropoint values for the ITL hardware throughput curves only,
without atmospheric contributions.
Parameters
----------
band : `str`
The bandpass name.
exptime : `float`, optional
The exposure time to calculate zeropoint.
Returns
-------
zeropoint : `float`
The zeropoint for the hardware only component of `band` for an
exposure of `exptime`.
Notes
-----
This hardware-only zeropoint is primarily useful for converting sky
background magnitudes into counts.
"""
zeropoint = {
"u": 26.994361876151476,
"g": 28.721326283280213,
"r": 28.487206961551088,
"i": 28.26716792192087,
"z": 27.86618994188104,
"y": 27.04446387553851,
}
return zeropoint[band] + 2.5 * np.log10(exptime)
[docs]
def predicted_zeropoint_e2v(band: str, airmass: float, exptime: float = 1) -> float:
"""Average E2V zeropoint values derived from v1.9 throughputs.
Parameters
----------
band : `str`
The bandpass name.
airmass : `float`
The airmass at which to evaluate the zeropoint.
exptime : `float`, optional
The exposure time to calculate zeropoint.
Returns
-------
zeropoint : `float`
The zeropoint for the `band` at airmass `airmass` for an exposure
of time `exptime`.
Notes
-----
Useful for comparing to DM pipeline zeropoints or for calculating counts
in an image (`np.power(10, (zp - mag)/2.5)) = counts`).
"""
extinction_coeff = {
"u": -0.4600735940453953,
"g": -0.20651340321330425,
"r": -0.12276192263131014,
"i": -0.07398443681400534,
"z": -0.057334002964289726,
"y": -0.095496483868828,
}
zeropoint_X1 = {
"u": 26.58989678516041,
"g": 28.567959743207357,
"r": 28.44712188941494,
"i": 28.19470048013101,
"z": 27.7817595301949,
"y": 26.813791858927964,
}
return zeropoint_X1[band] + extinction_coeff[band] * (airmass - 1) + 2.5 * np.log10(exptime)
[docs]
def predicted_zeropoint_hardware_e2v(band: str, exptime: float = 1) -> float:
"""Zeropoint values for the E2V hardware throughput curves only,
without atmospheric contributions.
Parameters
----------
band : `str`
The bandpass name.
exptime : `float`, optional
The exposure time to calculate zeropoint.
Returns
-------
zeropoint : `float`
The zeropoint for the hardware only component of `band` for an
exposure of `exptime`.
Notes
-----
This hardware-only zeropoint is primarily useful for converting sky
background magnitudes into counts.
"""
zeropoint = {
"u": 27.063967445283826,
"g": 28.78030646345493,
"r": 28.574328242939043,
"i": 28.291563456601306,
"z": 27.85108207854988,
"y": 26.988912028019346,
}
return zeropoint[band] + 2.5 * np.log10(exptime)
