objectSizeStarSelector.py

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# This file is part of meas_algorithms.
#
# Developed for the LSST Data Management System.
# This product includes software developed by the LSST Project
# (https://www.lsst.org).
# See the COPYRIGHT file at the top-level directory of this distribution
# for details of code ownership.
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program.  If not, see <https://www.gnu.org/licenses/>.
 
__all__ = ["ObjectSizeStarSelectorConfig", "ObjectSizeStarSelectorTask"]
 
import sys
 
import numpy
import warnings
from functools import reduce
 
from lsst.utils.logging import getLogger
from lsst.pipe.base import Struct
import lsst.geom
from lsst.afw.cameraGeom import PIXELS, TAN_PIXELS
import lsst.afw.geom as afwGeom
import lsst.pex.config as pexConfig
import lsst.afw.display as afwDisplay
from .sourceSelector import BaseSourceSelectorTask, sourceSelectorRegistry
 
afwDisplay.setDefaultMaskTransparency(75)
 
_LOG = getLogger(__name__)
 
 
class ObjectSizeStarSelectorConfig(BaseSourceSelectorTask.ConfigClass):
    doFluxLimit = pexConfig.Field(
        doc="Apply flux limit to Psf Candidate selection?",
        dtype=bool,
        default=False,
    )
    fluxMin = pexConfig.Field(
        doc="Minimum flux value for good Psf Candidates.",
        dtype=float,
        default=12500.0,
        check=lambda x: x >= 0.0,
    )
    fluxMax = pexConfig.Field(
        doc="Maximum flux value for good Psf Candidates (ignored if == 0).",
        dtype=float,
        default=0.0,
        check=lambda x: x >= 0.0,
    )
    doSignalToNoiseLimit = pexConfig.Field(
        doc="Apply signal-to-noise (i.e. flux/fluxErr) limit to Psf Candidate selection?",
        dtype=bool,
        default=True,
    )
    # Note that the current default is conditioned on the detection thresholds
    # set in the characterizeImage setDefaults function for the measurePsf
    # stage.
    signalToNoiseMin = pexConfig.Field(
        doc="Minimum signal-to-noise for good Psf Candidates "
            "(value should take into consideration the detection thresholds "
            "set for the catalog of interest).",
        dtype=float,
        default=50.0,
        check=lambda x: x >= 0.0,
    )
    signalToNoiseMax = pexConfig.Field(
        doc="Maximum signal-to-noise for good Psf Candidates (ignored if == 0).",
        dtype=float,
        default=0.0,
        check=lambda x: x >= 0.0,
    )
    widthMin = pexConfig.Field(
        doc="Minimum width to include in histogram.",
        dtype=float,
        default=0.9,
        check=lambda x: x >= 0.0,
    )
    widthMax = pexConfig.Field(
        doc="Maximum width to include in histogram.",
        dtype=float,
        default=10.0,
        check=lambda x: x >= 0.0,
    )
    sourceFluxField = pexConfig.Field(
        doc="Name of field in Source to use for flux measurement.",
        dtype=str,
        default="base_PsfFlux_instFlux",
    )
    widthStdAllowed = pexConfig.Field(
        doc="Standard deviation of width allowed to be interpreted as good stars.",
        dtype=float,
        default=0.15,
        check=lambda x: x >= 0.0,
    )
    nSigmaClip = pexConfig.Field(
        doc="Keep objects within this many sigma of cluster 0's median.",
        dtype=float,
        default=2.0,
        check=lambda x: x >= 0.0,
    )
    badFlags = pexConfig.ListField(
        doc="List of flags which cause a source to be rejected as bad.",
        dtype=str,
        default=[
            "base_PixelFlags_flag_edge",
            "base_PixelFlags_flag_interpolatedCenter",
            "base_PixelFlags_flag_saturatedCenter",
            "base_PixelFlags_flag_crCenter",
            "base_PixelFlags_flag_bad",
            "base_PixelFlags_flag_interpolated",
        ],
    )
 
    def validate(self):
        BaseSourceSelectorTask.ConfigClass.validate(self)
        if self.widthMin > self.widthMax:
            msg = f"widthMin ({self.widthMin}) > widthMax ({self.widthMax})"
            raise pexConfig.FieldValidationError(ObjectSizeStarSelectorConfig.widthMin, self, msg)
 
 
class EventHandler:
    """A class to handle key strokes with matplotlib displays.
    """
 
    def __init__(self, axes, xs, ys, x, y, frames=[0]):
        self.axes = axes
        self.xs = xs
        self.ys = ys
        self.x = x
        self.y = y
        self.frames = frames
 
        self.cid = self.axes.figure.canvas.mpl_connect('key_press_event', self)
 
    def __call__(self, ev):
        if ev.inaxes != self.axes:
            return
 
        if ev.key and ev.key in ("p"):
            dist = numpy.hypot(self.xs - ev.xdata, self.ys - ev.ydata)
            dist[numpy.where(numpy.isnan(dist))] = 1e30
 
            which = numpy.where(dist == min(dist))
 
            x = self.x[which][0]
            y = self.y[which][0]
            for frame in self.frames:
                disp = afwDisplay.Display(frame=frame)
                disp.pan(x, y)
            disp.flush()
        else:
            pass
 
 
def _assignClusters(yvec, centers):
    """Return a vector of centerIds based on their distance to the centers.
    """
    assert len(centers) > 0
 
    minDist = numpy.nan*numpy.ones_like(yvec)
    clusterId = numpy.empty_like(yvec)
    clusterId.dtype = int               # zeros_like(..., dtype=int) isn't in numpy 1.5
    dbl = _LOG.getChild("_assignClusters")
    dbl.setLevel(dbl.INFO)
 
    # Make sure we are logging aall numpy warnings...
    oldSettings = numpy.seterr(all="warn")
    with warnings.catch_warnings(record=True) as w:
        warnings.simplefilter("always")
        for i, mean in enumerate(centers):
            dist = abs(yvec - mean)
            if i == 0:
                update = dist == dist       # True for all points
            else:
                update = dist < minDist
                if w:  # Only do if w is not empty i.e. contains a warning message
                    dbl.trace(str(w[-1]))
 
            minDist[update] = dist[update]
            clusterId[update] = i
    numpy.seterr(**oldSettings)
 
    return clusterId
 
 
def _kcenters(yvec, nCluster, useMedian=False, widthStdAllowed=0.15):
    """A classic k-means algorithm, clustering yvec into nCluster clusters
 
    Return the set of centres, and the cluster ID for each of the points
 
    If useMedian is true, use the median of the cluster as its centre, rather than
    the traditional mean
 
    Serge Monkewitz points out that there other (maybe smarter) ways of seeding the means:
       "e.g. why not use the Forgy or random partition initialization methods"
    however, the approach adopted here seems to work well for the particular sorts of things
    we're clustering in this application
    """
 
    assert nCluster > 0
 
    mean0 = sorted(yvec)[len(yvec)//10]  # guess
    delta = mean0 * widthStdAllowed * 2.0
    centers = mean0 + delta * numpy.arange(nCluster)
 
    func = numpy.median if useMedian else numpy.mean
 
    clusterId = numpy.zeros_like(yvec) - 1            # which cluster the points are assigned to
    clusterId.dtype = int                             # zeros_like(..., dtype=int) isn't in numpy 1.5
    while True:
        oclusterId = clusterId
        clusterId = _assignClusters(yvec, centers)
 
        if numpy.all(clusterId == oclusterId):
            break
 
        for i in range(nCluster):
            # Only compute func if some points are available; otherwise, default to NaN.
            pointsInCluster = (clusterId == i)
            if numpy.any(pointsInCluster):
                centers[i] = func(yvec[pointsInCluster])
            else:
                centers[i] = numpy.nan
 
    return centers, clusterId
 
 
def _improveCluster(yvec, centers, clusterId, nsigma=2.0, nIteration=10, clusterNum=0, widthStdAllowed=0.15):
    """Improve our estimate of one of the clusters (clusterNum) by sigma-clipping around its median.
    """
 
    nMember = sum(clusterId == clusterNum)
    if nMember < 5:  # can't compute meaningful interquartile range, so no chance of improvement
        return clusterId
    for iter in range(nIteration):
        old_nMember = nMember
 
        inCluster0 = clusterId == clusterNum
        yv = yvec[inCluster0]
 
        centers[clusterNum] = numpy.median(yv)
        stdev = numpy.std(yv)
 
        syv = sorted(yv)
        stdev_iqr = 0.741*(syv[int(0.75*nMember)] - syv[int(0.25*nMember)])
        median = syv[int(0.5*nMember)]
 
        sd = stdev if stdev < stdev_iqr else stdev_iqr
 
        if False:
            print("sigma(iqr) = %.3f, sigma = %.3f" % (stdev_iqr, numpy.std(yv)))
        newCluster0 = abs(yvec - centers[clusterNum]) < nsigma*sd
        clusterId[numpy.logical_and(inCluster0, newCluster0)] = clusterNum
        clusterId[numpy.logical_and(inCluster0, numpy.logical_not(newCluster0))] = -1
 
        nMember = sum(clusterId == clusterNum)
        # 'sd < widthStdAllowed * median' prevents too much rejections
        if nMember == old_nMember or sd < widthStdAllowed * median:
            break
 
    return clusterId
 
 
def plot(mag, width, centers, clusterId, marker="o", markersize=2, markeredgewidth=0, ltype='-',
         magType="model", clear=True):
 
    log = _LOG.getChild("plot")
    try:
        import matplotlib.pyplot as plt
    except ImportError as e:
        log.warning("Unable to import matplotlib: %s", e)
        return
 
    try:
        fig
    except NameError:
        fig = plt.figure()
    else:
        if clear:
            fig.clf()
 
    axes = fig.add_axes((0.1, 0.1, 0.85, 0.80))
 
    xmin = sorted(mag)[int(0.05*len(mag))]
    xmax = sorted(mag)[int(0.95*len(mag))]
 
    axes.set_xlim(-17.5, -13)
    axes.set_xlim(xmin - 0.1*(xmax - xmin), xmax + 0.1*(xmax - xmin))
    axes.set_ylim(0, 10)
 
    colors = ["r", "g", "b", "c", "m", "k", ]
    for k, mean in enumerate(centers):
        if k == 0:
            axes.plot(axes.get_xlim(), (mean, mean,), "k%s" % ltype)
 
        li = (clusterId == k)
        axes.plot(mag[li], width[li], marker, markersize=markersize, markeredgewidth=markeredgewidth,
                  color=colors[k % len(colors)])
 
    li = (clusterId == -1)
    axes.plot(mag[li], width[li], marker, markersize=markersize, markeredgewidth=markeredgewidth,
              color='k')
 
    if clear:
        axes.set_xlabel("Instrumental %s mag" % magType)
        axes.set_ylabel(r"$\sqrt{(I_{xx} + I_{yy})/2}$")
 
    return fig
 
 
@pexConfig.registerConfigurable("objectSize", sourceSelectorRegistry)
class ObjectSizeStarSelectorTask(BaseSourceSelectorTask):
    r"""A star selector that looks for a cluster of small objects in a size-magnitude plot.
    """
    ConfigClass = ObjectSizeStarSelectorConfig
    usesMatches = False  # selectStars does not use its matches argument
 
    def selectSources(self, sourceCat, matches=None, exposure=None):
        """Return a selection of PSF candidates that represent likely stars.
 
        A list of PSF candidates may be used by a PSF fitter to construct a PSF.
 
        Parameters
        ----------
        sourceCat : `lsst.afw.table.SourceCatalog`
            Catalog of sources to select from.
            This catalog must be contiguous in memory.
        matches : `list` of `lsst.afw.table.ReferenceMatch` or None
            Ignored in this SourceSelector.
        exposure : `lsst.afw.image.Exposure` or None
            The exposure the catalog was built from; used to get the detector
            to transform to TanPix, and for debug display.
 
        Returns
        -------
        struct : `lsst.pipe.base.Struct`
            The struct contains the following data:
 
            ``selected``
                Boolean array of sources that were selected, same length as
                sourceCat. (`numpy.ndarray` of `bool`)
        """
        import lsstDebug
        display = lsstDebug.Info(__name__).display
        displayExposure = lsstDebug.Info(__name__).displayExposure     # display the Exposure + spatialCells
        plotMagSize = lsstDebug.Info(__name__).plotMagSize             # display the magnitude-size relation
        dumpData = lsstDebug.Info(__name__).dumpData                   # dump data to pickle file?
 
        detector = None
        pixToTanPix = None
        if exposure:
            detector = exposure.getDetector()
        if detector:
            pixToTanPix = detector.getTransform(PIXELS, TAN_PIXELS)
        #
        # Look at the distribution of stars in the magnitude-size plane
        #
        flux = sourceCat[self.config.sourceFluxField]
        fluxErr = sourceCat[self.config.sourceFluxField + "Err"]
 
        xx = numpy.empty(len(sourceCat))
        xy = numpy.empty_like(xx)
        yy = numpy.empty_like(xx)
        for i, source in enumerate(sourceCat):
            Ixx, Ixy, Iyy = source.getIxx(), source.getIxy(), source.getIyy()
            if pixToTanPix:
                p = lsst.geom.Point2D(source.getX(), source.getY())
                linTransform = afwGeom.linearizeTransform(pixToTanPix, p).getLinear()
                m = afwGeom.Quadrupole(Ixx, Iyy, Ixy)
                m.transform(linTransform)
                Ixx, Iyy, Ixy = m.getIxx(), m.getIyy(), m.getIxy()
 
            xx[i], xy[i], yy[i] = Ixx, Ixy, Iyy
 
        width = numpy.sqrt(0.5*(xx + yy))
        with numpy.errstate(invalid="ignore"):  # suppress NAN warnings
            bad = reduce(lambda x, y: numpy.logical_or(x, sourceCat[y]), self.config.badFlags, False)
            bad = numpy.logical_or(bad, numpy.logical_not(numpy.isfinite(width)))
            bad = numpy.logical_or(bad, numpy.logical_not(numpy.isfinite(flux)))
            if self.config.doFluxLimit:
                bad = numpy.logical_or(bad, flux < self.config.fluxMin)
                if self.config.fluxMax > 0:
                    bad = numpy.logical_or(bad, flux > self.config.fluxMax)
            if self.config.doSignalToNoiseLimit:
                bad = numpy.logical_or(bad, flux/fluxErr < self.config.signalToNoiseMin)
                if self.config.signalToNoiseMax > 0:
                    bad = numpy.logical_or(bad, flux/fluxErr > self.config.signalToNoiseMax)
            bad = numpy.logical_or(bad, width < self.config.widthMin)
            bad = numpy.logical_or(bad, width > self.config.widthMax)
        good = numpy.logical_not(bad)
 
        if not numpy.any(good):
            raise RuntimeError("No objects passed our cuts for consideration as psf stars")
 
        mag = -2.5*numpy.log10(flux[good])
        width = width[good]
        #
        # Look for the maximum in the size histogram, then search upwards for the minimum that separates
        # the initial peak (of, we presume, stars) from the galaxies
        #
        if dumpData:
            import os
            import pickle as pickle
            _ii = 0
            while True:
                pickleFile = os.path.expanduser(os.path.join("~", "widths-%d.pkl" % _ii))
                if not os.path.exists(pickleFile):
                    break
                _ii += 1
 
            with open(pickleFile, "wb") as fd:
                pickle.dump(mag, fd, -1)
                pickle.dump(width, fd, -1)
 
        centers, clusterId = _kcenters(width, nCluster=4, useMedian=True,
                                       widthStdAllowed=self.config.widthStdAllowed)
 
        if display and plotMagSize:
            fig = plot(mag, width, centers, clusterId,
                       magType=self.config.sourceFluxField.split(".")[-1].title(),
                       marker="+", markersize=3, markeredgewidth=None, ltype=':', clear=True)
        else:
            fig = None
 
        clusterId = _improveCluster(width, centers, clusterId,
                                    nsigma=self.config.nSigmaClip,
                                    widthStdAllowed=self.config.widthStdAllowed)
 
        if display and plotMagSize:
            plot(mag, width, centers, clusterId, marker="x", markersize=3, markeredgewidth=None, clear=False)
 
        stellar = (clusterId == 0)
        #
        # We know enough to plot, if so requested
        #
        frame = 0
 
        if fig:
            if display and displayExposure:
                disp = afwDisplay.Display(frame=frame)
                disp.mtv(exposure.getMaskedImage(), title="PSF candidates")
 
                global eventHandler
                eventHandler = EventHandler(fig.get_axes()[0], mag, width,
                                            sourceCat.getX()[good], sourceCat.getY()[good], frames=[frame])
 
            fig.show()
 
            while True:
                try:
                    reply = input("continue? [c h(elp) q(uit) p(db)] ").strip()
                except EOFError:
                    reply = None
                if not reply:
                    reply = "c"
 
                if reply:
                    if reply[0] == "h":
                        print("""\
    We cluster the points; red are the stellar candidates and the other colours are other clusters.
    Points labelled + are rejects from the cluster (only for cluster 0).
 
    At this prompt, you can continue with almost any key; 'p' enters pdb, and 'h' prints this text
 
    If displayExposure is true, you can put the cursor on a point and hit 'p' to see it in the
    image display.
    """)
                    elif reply[0] == "p":
                        import pdb
                        pdb.set_trace()
                    elif reply[0] == 'q':
                        sys.exit(1)
                    else:
                        break
 
        if display and displayExposure:
            mi = exposure.getMaskedImage()
            with disp.Buffering():
                for i, source in enumerate(sourceCat):
                    if good[i]:
                        ctype = afwDisplay.GREEN  # star candidate
                    else:
                        ctype = afwDisplay.RED  # not star
 
                    disp.dot("+", source.getX() - mi.getX0(), source.getY() - mi.getY0(), ctype=ctype)
 
        # stellar only applies to good==True objects
        mask = good == True  # noqa (numpy bool comparison): E712
        good[mask] = stellar
 
        return Struct(selected=good)