|
| | maximum_nearest_psf_distance (image_mask, psf_cat, sampling=8, bad_mask_bits=["BAD", "CR", "INTRP", "SAT", "SUSPECT", "NO_DATA", "EDGE"]) |
| |
| | compute_psf_image_deltas (image_mask, image_psf, sampling=96, ap_radius_pix=3.0, bad_mask_bits=["BAD", "CR", "INTRP", "SAT", "SUSPECT", "NO_DATA", "EDGE"]) |
| |
| | compute_ap_corr_sigma_scaled_delta (image_mask, image_ap_corr_field, psfSigma, sampling=96, bad_mask_bits=["BAD", "CR", "INTRP", "SAT", "SUSPECT", "NO_DATA", "EDGE"]) |
| |
| | compute_magnitude_limit (psfArea, skyBg, zeroPoint, readNoise, gain, snr) |
| |
| | psf_sigma_to_psf_area (psfSigma, pixelScale) |
| |
| | compute_effective_time (magLim, fiducialMagLim, fiducialExpTime) |
| |
| lsst.pipe.tasks.computeExposureSummaryStats.compute_ap_corr_sigma_scaled_delta |
( |
| image_mask, |
|
|
| image_ap_corr_field, |
|
|
| psfSigma, |
|
|
| sampling = 96, |
|
|
| bad_mask_bits = ["BAD", "CR", "INTRP", "SAT", "SUSPECT", "NO_DATA", "EDGE"] ) |
Compute the delta between the maximum and minimum aperture correction
values scaled (divided) by ``psfSigma`` for the given field representation,
``image_ap_corr_field`` evaluated on a grid of points lying in the
unmasked region of the image.
Parameters
----------
image_mask : `lsst.afw.image.Mask`
The mask plane associated with the exposure.
image_ap_corr_field : `lsst.afw.math.ChebyshevBoundedField`
The ChebyshevBoundedField representation of the aperture correction
of interest for the exposure.
psfSigma : `float`
The PSF model second-moments determinant radius (center of chip)
in pixels.
sampling : `int`, optional
Sampling rate in each dimension to create the grid of points at which
to evaluate ``image_psf``s trace radius value. The tradeoff is between
adequate sampling versus speed.
bad_mask_bits : `list` [`str`], optional
Mask bits required to be absent for a pixel to be considered
"unmasked".
Returns
-------
ap_corr_sigma_scaled_delta : `float`
The delta between the maximum and minimum of the (multiplicative)
aperture correction values scaled (divided) by ``psfSigma`` evaluated
on the x,y-grid subsampled on the unmasked detector pixels by a factor
of ``sampling``. If the aperture correction evaluates to NaN on any
of the grid points, this is set to NaN.
Definition at line 933 of file computeExposureSummaryStats.py.
| lsst.pipe.tasks.computeExposureSummaryStats.compute_effective_time |
( |
| magLim, |
|
|
| fiducialMagLim, |
|
|
| fiducialExpTime ) |
Compute the effective exposure time from m5 following the prescription described in SMTN-296.
teff = 10**(0.8 * (magLim - fiducialMagLim) ) * fiducialExpTime
where `magLim` is the magnitude limit, `fiducialMagLim` is the fiducial magnitude limit calculated from
the fiducial values of the ``psfArea``, ``skyBg``, ``zeroPoint``, and ``readNoise``, and
`fiducialExpTime` is the fiducial exposure time (s).
Parameters
----------
magLim : `float`
The measured magnitude limit [mag].
fiducialMagLim : `float`
The fiducial magnitude limit [mag].
fiducialExpTime : `float`
The fiducial exposure time [s].
Returns
-------
effectiveTime : `float`
The effective exposure time.
Definition at line 1076 of file computeExposureSummaryStats.py.
| lsst.pipe.tasks.computeExposureSummaryStats.compute_magnitude_limit |
( |
| psfArea, |
|
|
| skyBg, |
|
|
| zeroPoint, |
|
|
| readNoise, |
|
|
| gain, |
|
|
| snr ) |
Compute the expected point-source magnitude limit at a given
signal-to-noise ratio given the exposure-level metadata. Based on
the signal-to-noise formula provided in SMTN-002 (see LSE-40 for
more details on the calculation).
SNR = C / sqrt( C/g + (B/g + sigma_inst**2) * neff )
where C is the counts from the source, B is counts from the (sky)
background, sigma_inst is the instrumental (read) noise, neff is
the effective size of the PSF, and g is the gain in e-/ADU. Note
that input values of ``skyBg``, ``zeroPoint``, and ``readNoise``
should all consistently be in electrons or ADU.
Parameters
----------
psfArea : `float`
The effective area of the PSF [pix].
skyBg : `float`
The sky background counts for the exposure [ADU or e-].
zeroPoint : `float`
The zeropoint (includes exposure time) [ADU or e-].
readNoise : `float`
The instrumental read noise for the exposure [ADU or e-].
gain : `float`
The instrumental gain for the exposure [e-/ADU]. The gain should
be 1.0 if the skyBg, zeroPoint, and readNoise are in e-.
snr : `float`
Signal-to-noise ratio at which magnitude limit is calculated.
Returns
-------
magnitude_limit : `float`
The expected magnitude limit at the given signal to noise.
Definition at line 990 of file computeExposureSummaryStats.py.
| lsst.pipe.tasks.computeExposureSummaryStats.compute_psf_image_deltas |
( |
| image_mask, |
|
|
| image_psf, |
|
|
| sampling = 96, |
|
|
| ap_radius_pix = 3.0, |
|
|
| bad_mask_bits = ["BAD", "CR", "INTRP", "SAT", "SUSPECT", "NO_DATA", "EDGE"] ) |
Compute the delta between the maximum and minimum model PSF trace radius
values evaluated on a grid of points lying in the unmasked region of the
image.
Parameters
----------
image_mask : `lsst.afw.image.Mask`
The mask plane associated with the exposure.
image_psf : `lsst.afw.detection.Psf`
The PSF model associated with the exposure.
sampling : `int`, optional
Sampling rate in each dimension to create the grid of points at which
to evaluate ``image_psf``s trace radius value. The tradeoff is between
adequate sampling versus speed.
ap_radius_pix : `float`, optional
Radius in pixels of the aperture on which to measure the flux of the
PSF model.
bad_mask_bits : `list` [`str`], optional
Mask bits required to be absent for a pixel to be considered
"unmasked".
Returns
-------
psf_trace_radius_delta, psf_ap_flux_delta : `float`
The delta (in pixels) between the maximum and minimum model PSF trace
radius values and the PSF aperture fluxes (with aperture radius of
max(2, 3*psfSigma)) evaluated on the x,y-grid subsampled on the
unmasked detector pixels by a factor of ``sampling``. If both the
model PSF trace radius value and aperture flux value on the grid
evaluate to NaN, then NaNs are returned immediately.
Definition at line 865 of file computeExposureSummaryStats.py.
1.13.2