utils.py

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# This file is part of ip_diffim.
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# (https://www.lsst.org).
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"""Support utilities for Measuring sources"""
 
 
__all__ = ["evaluateMeanPsfFwhm", "getPsfFwhm", "getKernelCenterDisplacement",
           "computeDifferenceImageMetrics", "checkMask", "setSourceFootprints",
           ]
 
import itertools
import numpy as np
import os
import requests
import lsst.geom as geom
import lsst.afw.detection as afwDetection
import lsst.afw.image as afwImage
import lsst.afw.math as afwMath
from lsst.pex.exceptions import InvalidParameterError, RangeError
import lsst.pipe.base
from lsst.utils.logging import getLogger
 
 
_LOG = getLogger(__name__)
 
 
def getKernelCenterDisplacement(kernel, x, y, image=None):
    """Calculate the PSF matching kernel peak offset from the nominal
    position.
 
    Parameters
    ----------
    kernel : `~lsst.afw.math.LinearCombinationKernel`
        The PSF matching kernel to evaluate.
    x : `float`
        The x position on the detector to evaluate the kernel
    y : `float`
        The y position on the detector to evaluate the kernel
    image : `~lsst.afw.image.ImageD`
        The image to use as base for computing kernel pixel values
 
    Returns
    -------
    kernel_sum : `float`
        The sum of the kernel on the desired location
    dx : `float`
        The displacement of the kernel averaged peak, with respect to the
        center of the extraction of the kernel
    dy : `float`
        The displacement of the kernel averaged peak, with respect to the
        center of the extraction of the kernel
    pos_angle: `float`
        The position angle in detector coordinates of the displacement
    length : `float`
        The displacement module of the kernel centroid in pixel units
    """
 
    if image is None:
        image = afwImage.ImageD(kernel.getDimensions())
 
    # obtain the kernel image
    hsize = kernel.getWidth()//2
    kernel_sum = kernel.computeImage(image, doNormalize=False, x=x, y=y)
 
    data = image.array
    h, w = data.shape
    xx = np.arange(w)
    yy = np.arange(h)
 
    # create sum vectors and estimate weighted average
    vx = data.sum(axis=0)
    vx /= vx.sum()
    dx = np.dot(vx, xx) - hsize
 
    vy = data.sum(axis=1)
    vy /= vy.sum()
    dy = np.dot(vy, yy) - hsize
 
    # obtain position angle and norm of displacement
    pos_angle = np.arctan2(dy, dx)
    length = np.sqrt(dx**2 + dy**2)
 
    return kernel_sum, dx, dy, pos_angle, length
 
 
def getPsfFwhm(psf, average=True, position=None):
    """Directly calculate the horizontal and vertical widths
    of a PSF at half its maximum value.
 
    Parameters
    ----------
    psf : `~lsst.afw.detection.Psf`
        Point spread function (PSF) to evaluate.
    average : `bool`, optional
        Set to return the average width over Y and X axes.
    position : `~lsst.geom.Point2D`, optional
        The position at which to evaluate the PSF. If `None`, then the
        average position is used.
 
    Returns
    -------
    psfSize : `float` | `tuple` [`float`]
        The FWHM of the PSF computed at its average position.
        Returns the widths along the Y and X axes,
        or the average of the two if `average` is set.
 
    See Also
    --------
    evaluateMeanPsfFwhm
    """
    if position is None:
        position = psf.getAveragePosition()
    shape = psf.computeShape(position)
    sigmaToFwhm = 2*np.log(2*np.sqrt(2))
 
    if average:
        return sigmaToFwhm*shape.getTraceRadius()
    else:
        return [sigmaToFwhm*np.sqrt(shape.getIxx()), sigmaToFwhm*np.sqrt(shape.getIyy())]
 
 
def evaluateMeanPsfFwhm(exposure: afwImage.Exposure,
                        fwhmExposureBuffer: float, fwhmExposureGrid: int) -> float:
    """Get the mean PSF FWHM by evaluating it on a grid within an exposure.
 
    Parameters
    ----------
    exposure : `~lsst.afw.image.Exposure`
        The exposure for which the mean FWHM of the PSF is to be computed.
        The exposure must contain a `psf` attribute.
    fwhmExposureBuffer : `float`
        Fractional buffer margin to be left out of all sides of the image
        during the construction of the grid to compute mean PSF FWHM in an
        exposure.
    fwhmExposureGrid : `int`
        Grid size to compute the mean FWHM in an exposure.
 
    Returns
    -------
    meanFwhm : `float`
        The mean PSF FWHM on the exposure.
 
    Raises
    ------
    ValueError
        Raised if the PSF cannot be computed at any of the grid points.
 
    See Also
    --------
    `getPsfFwhm`
    `computeAveragePsf`
    """
 
    psf = exposure.psf
 
    bbox = exposure.getBBox()
    xmax, ymax = bbox.getMax()
    xmin, ymin = bbox.getMin()
 
    xbuffer = fwhmExposureBuffer*(xmax-xmin)
    ybuffer = fwhmExposureBuffer*(ymax-ymin)
 
    width = []
    for (x, y) in itertools.product(np.linspace(xmin+xbuffer, xmax-xbuffer, fwhmExposureGrid),
                                    np.linspace(ymin+ybuffer, ymax-ybuffer, fwhmExposureGrid)
                                    ):
        pos = geom.Point2D(x, y)
        try:
            fwhm = getPsfFwhm(psf, average=True, position=pos)
        except (InvalidParameterError, RangeError):
            _LOG.debug("Unable to compute PSF FWHM at position (%f, %f).", x, y)
            continue
 
        width.append(fwhm)
 
    if not width:
        raise ValueError("Unable to compute PSF FWHM at any position on the exposure.")
 
    return np.nanmean(width)
 
 
def computeAveragePsf(exposure: afwImage.Exposure,
                      psfExposureBuffer: float, psfExposureGrid: int) -> afwImage.ImageD:
    """Get the average PSF by evaluating it on a grid within an exposure.
 
    Parameters
    ----------
    exposure : `~lsst.afw.image.Exposure`
        The exposure for which the average PSF is to be computed.
        The exposure must contain a `psf` attribute.
    psfExposureBuffer : `float`
        Fractional buffer margin to be left out of all sides of the image
        during the construction of the grid to compute average PSF in an
        exposure.
    psfExposureGrid : `int`
        Grid size to compute the average PSF in an exposure.
 
    Returns
    -------
    psfImage : `~lsst.afw.image.Image`
        The average PSF across the exposure.
 
    Raises
    ------
    ValueError
        Raised if the PSF cannot be computed at any of the grid points.
 
    See Also
    --------
    `evaluateMeanPsfFwhm`
    """
 
    psf = exposure.psf
 
    bbox = exposure.getBBox()
    xmax, ymax = bbox.getMax()
    xmin, ymin = bbox.getMin()
 
    xbuffer = psfExposureBuffer*(xmax-xmin)
    ybuffer = psfExposureBuffer*(ymax-ymin)
 
    nImg = 0
    psfArray = None
    for (x, y) in itertools.product(np.linspace(xmin+xbuffer, xmax-xbuffer, psfExposureGrid),
                                    np.linspace(ymin+ybuffer, ymax-ybuffer, psfExposureGrid)
                                    ):
        pos = geom.Point2D(x, y)
        try:
            singleImage = psf.computeKernelImage(pos)
        except InvalidParameterError:
            _LOG.debug("Unable to compute PSF image at position (%f, %f).", x, y)
            continue
 
        if psfArray is None:
            psfArray = singleImage.array
        else:
            psfArray += singleImage.array
        nImg += 1
 
    if psfArray is None:
        raise ValueError("Unable to compute PSF image at any position on the exposure.")
 
    psfImage = afwImage.ImageD(psfArray/nImg)
    return psfImage
 
 
def computeRobustStatistics(image, mask, statsCtrl, statistic=afwMath.MEANCLIP):
    """Calculate a robust mean of the variance plane of an exposure.
 
    Parameters
    ----------
    image : `lsst.afw.image.Image`
        Image or variance plane of an exposure to evaluate.
    mask : `lsst.afw.image.Mask`
        Mask plane to use for excluding pixels.
    statsCtrl : `lsst.afw.math.StatisticsControl`
        Statistics control object for configuring the calculation.
    statistic : `lsst.afw.math.Property`, optional
        The type of statistic to compute. Typical values are
        ``afwMath.MEANCLIP`` or ``afwMath.STDEVCLIP``.
 
    Returns
    -------
    value : `float`
        The result of the statistic calculated from the unflagged pixels.
    """
    statObj = afwMath.makeStatistics(image, mask, statistic, statsCtrl)
    return statObj.getValue(statistic)
 
 
def computePSFNoiseEquivalentArea(psf):
    """Compute the noise equivalent area for an image psf
 
    Parameters
    ----------
    psf : `lsst.afw.detection.Psf`
 
    Returns
    -------
    nea : `float`
    """
    psfImg = psf.computeImage(psf.getAveragePosition())
    nea = 1./np.sum(psfImg.array**2)
    return nea
 
 
def angleMean(angles):
    """Calculate the mean of an array of angles.
 
    Parameters
    ----------
    angles : `ndarray`
        An array of angles, in radians
 
    Returns
    -------
    `lsst.geom.Angle`
        The mean angle
    """
    complexArray = [complex(np.cos(angle), np.sin(angle)) for angle in angles]
    return (geom.Angle(np.angle(np.mean(complexArray))))
 
 
def evaluateMaskFraction(mask, maskPlane):
    """Evaluate the fraction of masked pixels in a mask plane.
 
    Parameters
    ----------
    mask : `lsst.afw.image.Mask`
        The mask to evaluate the fraction on
    maskPlane : `str`
        The particular mask plane to evaluate the fraction
 
    Returns
    -------
    value : `float`
        The calculated fraction of masked pixels
    """
    nMaskSet = np.count_nonzero((mask.array & mask.getPlaneBitMask(maskPlane)))
    return nMaskSet/mask.array.size
 
 
def computeDifferenceImageMetrics(science, difference, stars, sky_sources=None):
    r"""Compute quality metrics (saved to the task metadata) on the
    difference image, at the locations of detected stars on the science
    image. This restricts the metric to locations that should be
    well-subtracted.
 
    Parameters
    ----------
    science : `lsst.afw.image.ExposureF`
        Science exposure that was subtracted.
    difference : `lsst.afw.image.ExposureF`
        Result of subtracting template and science.
    stars : `lsst.afw.table.SourceCatalog`
        Good calibration sources detected on science image; these
        footprints are what the metrics are computed on.
 
    Returns
    -------
    metrics : `lsst.pipe.base.Struct`
 
        ``differenceFootprintRatioMean`` : `float`
            Mean of the ratio of the absolute value of the difference image
            (with the mean absolute value of the sky regions on the difference
            image removed) to the science image, computed in the footprints
            of stars detected on the science image (the sums below are of the
            pixels in each star or sky footprint):
            :math:`\mathrm{mean}_{footprints}((\sum |difference| -
            \mathrm{mean}(\sum |difference_{sky}|)) / \sum science)`
        ``differenceFootprintRatioStdev`` : `float`
            Standard Deviation across footprints of the above ratio.
        ``differenceFootprintSkyRatioMean`` : `float`
            Mean of the ratio of the absolute value of sky source regions on
            the difference image to the science image (the sum below is of the
            pixels in each sky source footprint):
            :math:`\mathrm{mean}_{footprints}(\sum |difference_{sky}| / \sum science_{sky})`
        ``differenceFootprintSkyRatioStdev`` : `float`
            Standard Deivation across footprints of the above sky ratio.
    """
    def footprint_mean(sources, sky=0):
        """Compute ratio of the absolute value of the diffim to the science
        image, within each source footprint, subtracting the sky from the
        diffim values if provided.
        """
        n = len(sources)
        science_footprints = np.zeros(n)
        difference_footprints = np.zeros(n)
        ratio = np.zeros(n)
        for i, record in enumerate(sources):
            footprint = record.getFootprint()
            heavy = afwDetection.makeHeavyFootprint(footprint, science.maskedImage)
            heavy_diff = afwDetection.makeHeavyFootprint(footprint, difference.maskedImage)
            science_footprints[i] = abs(heavy.getImageArray()).mean()
            difference_footprints[i] = abs(heavy_diff.getImageArray()).mean()
            ratio[i] = abs((difference_footprints[i] - sky)/science_footprints[i])
        return science_footprints, difference_footprints, ratio
 
    if "sky_source" in stars.schema:
        sky = stars["sky_source"]
        selectStars = stars[~sky]
        if sky_sources is None:
            sky_sources = stars[sky]
    else:
        selectStars = stars
    # Note that the len() below is only evaluated if sky_sources is not None
    if sky_sources is not None and len(sky_sources) > 0:
        sky_science, sky_difference, sky_ratio = footprint_mean(sky_sources)
        sky_mean = sky_ratio.mean()
        sky_std = sky_ratio.std()
        sky_difference = sky_difference.mean()
    else:
        sky_mean = np.nan
        sky_std = np.nan
        sky_difference = np.nanmedian(np.abs(difference.image.array))
    science_footprints, difference_footprints, ratio = footprint_mean(selectStars, sky_difference)
    return lsst.pipe.base.Struct(differenceFootprintRatioMean=ratio.mean(),
                                 differenceFootprintRatioStdev=ratio.std(),
                                 differenceFootprintSkyRatioMean=sky_mean,
                                 differenceFootprintSkyRatioStdev=sky_std,
                                 )
 
 
def populate_sattle_visit_cache(visit_info, historical=False):
    """Populate a cache of predicted satellite positions in the sattle service.
 
    Parameters
    ----------
    visit_info: `lsst.afw.table.ExposureRecord.visitInfo`
        Visit info for the science exposure being processed.
    historical: `bool`
        Set to True if observations are older than the current day.
 
    Raises
    ------
    requests.HTTPError
        Raised if sattle call does not return success.
    """
 
    visit_mjd = visit_info.getDate().toAstropy().mjd
 
    exposure_time_days = visit_info.getExposureTime() / 86400.0
    exposure_end_mjd = visit_mjd + exposure_time_days / 2.0
    exposure_start_mjd = visit_mjd - exposure_time_days / 2.0
 
    boresight_ra = visit_info.boresightRaDec.getRa().asDegrees()
    boresight_dec = visit_info.boresightRaDec.getDec().asDegrees()
 
    r = requests.put(
        f'{os.getenv("SATTLE_URI_BASE")}/visit_cache',
        json={"visit_id": visit_info.getId(),
              "exposure_start_mjd": exposure_start_mjd,
              "exposure_end_mjd": exposure_end_mjd,
              "boresight_ra": boresight_ra,
              "boresight_dec": boresight_dec,
              "historical": historical})
 
    r.raise_for_status()
 
 
def checkMask(mask, sources, excludeMaskPlanes):
    """Exclude sources that have masked pixels in their footprints.
 
    Parameters
    ----------
    mask : `lsst.afw.image.Mask`
        The image mask plane to use to reject sources
        based on the location of their centroid on the ccd.
    sources : `lsst.afw.table.SourceCatalog`
        The source catalog to evaluate.
    excludeMaskPlanes : `list` of `str`
        List of the names of the mask planes to exclude.
 
    Returns
    -------
    good : `numpy.ndarray` of `bool`
        Array indicating whether each source in the catalog should be
        kept (True) or rejected (False) based on the value of the
        mask plane at its location.
    """
    setExcludeMaskPlanes = [
        maskPlane for maskPlane in excludeMaskPlanes if maskPlane in mask.getMaskPlaneDict()
    ]
 
    excludePixelMask = mask.getPlaneBitMask(setExcludeMaskPlanes)
 
    good = np.ones(len(sources), dtype=bool)
    for i, source in enumerate(sources):
        bbox = source.getFootprint().getBBox()
        if not mask.getBBox().contains(bbox):
            good[i] = False
            continue
 
        # Reject footprints with any bad mask bits set.
        if (mask.subset(bbox).array & excludePixelMask).any():
            good[i] = False
            continue
    return good
 
 
def setSourceFootprints(sources, kernelSize):
    """Add footprints of fixed size to a source catalog
 
    Parameters
    ----------
    sources : `lsst.afw.table.SourceCatalog`
        The source catalog to add footprints to.
    kernelSize : `int`
        The "radius" of the footprint, i.e half the size of the bounding box.
 
    Returns
    -------
    sources : `lsst.afw.table.SourceCatalog`
        The modified source catalog
    """
    size = 2*kernelSize + 1
    for source in sources:
        bbox = lsst.geom.Box2I.makeCenteredBox(source.getCentroid(),
                                               lsst.geom.Extent2I(size, size))
        peak = source.getFootprint().getPeaks()[0]
        boxFootprint = lsst.afw.detection.Footprint(lsst.afw.geom.SpanSet(bbox))
        boxFootprint.addPeak(peak.getFx(), peak.getFy(), peak.getPeakValue())
        source.setFootprint(boxFootprint)
    return sources