subtractBackground.py

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#
# LSST Data Management System
#
# Copyright 2008-2017  AURA/LSST.
#
# This product includes software developed by the
# LSST Project (http://www.lsst.org/).
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# it under the terms of the GNU General Public License as published by
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__all__ = ("SubtractBackgroundConfig", "SubtractBackgroundTask")

import itertools

import numpy

from lsstDebug import getDebugFrame
import lsst.afw.display as afwDisplay
import lsst.afw.image as afwImage
import lsst.afw.math as afwMath
import lsst.pex.config as pexConfig
import lsst.pipe.base as pipeBase
from functools import reduce


class SubtractBackgroundConfig(pexConfig.Config):
    """!Config for SubtractBackgroundTask

    @note Many of these fields match fields in lsst.afw.math.BackgroundControl,
    the control class for lsst.afw.math.makeBackground
    """
    statisticsProperty = pexConfig.ChoiceField(
        doc="type of statistic to use for grid points",
        dtype=str, default="MEANCLIP",
        allowed={
            "MEANCLIP": "clipped mean",
            "MEAN": "unclipped mean",
            "MEDIAN": "median",
        }
    )
    undersampleStyle = pexConfig.ChoiceField(
        doc="behaviour if there are too few points in grid for requested interpolation style",
        dtype=str, default="REDUCE_INTERP_ORDER",
        allowed={
            "THROW_EXCEPTION": "throw an exception if there are too few points",
            "REDUCE_INTERP_ORDER": "use an interpolation style with a lower order.",
            "INCREASE_NXNYSAMPLE": "Increase the number of samples used to make the interpolation grid.",
        },
    )
    binSize = pexConfig.RangeField(
        doc="how large a region of the sky should be used for each background point",
        dtype=int, default=128, min=1,
    )
    binSizeX = pexConfig.RangeField(
        doc=("Sky region size to be used for each background point in X direction. "
             "If 0, the binSize config is used."),
        dtype=int, default=0, min=0,
    )
    binSizeY = pexConfig.RangeField(
        doc=("Sky region size to be used for each background point in Y direction. "
             "If 0, the binSize config is used."),
        dtype=int, default=0, min=0,
    )
    algorithm = pexConfig.ChoiceField(
        doc="how to interpolate the background values. This maps to an enum; see afw::math::Background",
        dtype=str, default="AKIMA_SPLINE", optional=True,
        allowed={
            "CONSTANT": "Use a single constant value",
            "LINEAR": "Use linear interpolation",
            "NATURAL_SPLINE": "cubic spline with zero second derivative at endpoints",
            "AKIMA_SPLINE": "higher-level nonlinear spline that is more robust to outliers",
            "NONE": "No background estimation is to be attempted",
        },
    )
    ignoredPixelMask = pexConfig.ListField(
        doc="Names of mask planes to ignore while estimating the background",
        dtype=str, default=["BAD", "EDGE", "DETECTED", "DETECTED_NEGATIVE", "NO_DATA", ],
        itemCheck=lambda x: x in afwImage.Mask().getMaskPlaneDict().keys(),
    )
    isNanSafe = pexConfig.Field(
        doc="Ignore NaNs when estimating the background",
        dtype=bool, default=False,
    )

    useApprox = pexConfig.Field(
        doc="Use Approximate (Chebyshev) to model background.",
        dtype=bool, default=True,
    )
    approxOrderX = pexConfig.Field(
        doc="Approximation order in X for background Chebyshev (valid only with useApprox=True)",
        dtype=int, default=6,
    )
    # Note: Currently X- and Y-orders must be equal due to a limitation in math::Chebyshev1Function2
    # The following is being added so that the weighting attribute can also be configurable for the
    # call to afwMath.ApproximateControl
    approxOrderY = pexConfig.Field(
        doc="Approximation order in Y for background Chebyshev (valid only with useApprox=True)",
        dtype=int, default=-1,
    )
    weighting = pexConfig.Field(
        doc="Use inverse variance weighting in calculation (valid only with useApprox=True)",
        dtype=bool, default=True,
    )




class SubtractBackgroundTask(pipeBase.Task):
    r"""!Subtract the background from an exposure

    @anchor SubtractBackgroundTask_

    @section meas_algorithms_subtractBackground_Contents  Contents

     - @ref meas_algorithms_subtractBackground_Purpose
     - @ref meas_algorithms_subtractBackground_Initialize
     - @ref meas_algorithms_subtractBackground_IO
     - @ref meas_algorithms_subtractBackground_Config
     - @ref meas_algorithms_subtractBackground_Metadata
     - @ref meas_algorithms_subtractBackground_Debug
     - @ref meas_algorithms_subtractBackground_Example

    @section meas_algorithms_subtractBackground_Purpose  Description

    Fit a model of the background of an exposure and subtract it.

    @section meas_algorithms_subtractBackground_Initialize  Task initialisation

    @copydoc \_\_init\_\_

    @section meas_algorithms_subtractBackground_IO  Invoking the Task

    Call `run` to fit the background and subtract it.

    Call `fitBackground` to fit the background without subtracting it.

    @section meas_algorithms_subtractBackground_Config  Configuration parameters

    See @ref SubtractBackgroundConfig

    @section meas_algorithms_subtractBackground_Metadata  Quantities set in exposure Metadata

    The `run` method will optionally set the following items of exposure metadata;
    the names may be overridden; the defaults are shown:
    <dl>
        <dt>BGMEAN <dd>mean value of background
        <dt>BGVAR  <dd>standard deviation of background
    </dl>

    @section meas_algorithms_subtractBackground_Debug  Debug variables

    The @link lsst.pipe.base.cmdLineTask.CmdLineTask command line task@endlink interface supports a flag
    `--debug` to import `debug.py` from your `$PYTHONPATH`; see @ref baseDebug for more about `debug.py`.

    SubtractBackgroundTask has a debug dictionary containing three integer keys:
    <dl>
    <dt>unsubtracted
    <dd>If >0: `fitBackground` displays the unsubtracted masked image overlaid with the grid of cells
                used to fit the background in the specified frame
    <dt>subtracted
    <dd>If >0: `run` displays the background-subtracted exposure is the specified frame
    <dt>background
    <dd>If >0: `run` displays the background image in the specified frame
    </dl>

    For example, put something like:
    @code{.py}
        import lsstDebug
        def DebugInfo(name):
            di = lsstDebug.getInfo(name)  # N.b. lsstDebug.Info(name) would call us recursively
            if name == "lsst.meas.algorithms.subtractBackground":
                di.display = dict(
                    unsubtracted = 1,
                    subtracted = 2,
                    background = 3,
                )

            return di

        lsstDebug.Info = DebugInfo
    @endcode
    into your `debug.py` file and run your task with the `--debug` flag.

    @section meas_algorithms_subtractBackground_Example   A complete example of using SubtractBackgroundTask

    This code is in @link subtractBackgroundExample.py@endlink in the examples directory, and can be run as:
    @code
    python examples/subtractBackgroundExample.py
    @endcode
    @dontinclude subtractBackgroundExample.py

    Import the task (there are some other standard imports; read the file if you're curious)
    @skipline import SubtractBackgroundTask

    Create the task, run it, and report mean and variance of background.
    @skip create the task
    @until print
    """
    ConfigClass = SubtractBackgroundConfig
    _DefaultName = "subtractBackground"

    def run(self, exposure, background=None, stats=True, statsKeys=None):
        """!Fit and subtract the background of an exposure

        @param[in,out] exposure  exposure whose background is to be subtracted
        @param[in,out] background  initial background model already subtracted from exposure
            (an lsst.afw.math.BackgroundList). May be None if no background has been subtracted.
        @param[in] stats  if True then measure the mean and variance of the full background model
                        and record the results in the exposure's metadata
        @param[in] statsKeys  key names used to store the mean and variance of the background
            in the exposure's metadata (a pair of strings); if None then use ("BGMEAN", "BGVAR");
            ignored if stats is false

        @return an lsst.pipe.base.Struct containing:
        - background  full background model (initial model with changes), an lsst.afw.math.BackgroundList
        """
        if background is None:
            background = afwMath.BackgroundList()

        maskedImage = exposure.getMaskedImage()
        fitBg = self.fitBackground(maskedImage)
        maskedImage -= fitBg.getImageF()
        background.append(fitBg)

        if stats:
            self._addStats(exposure, background, statsKeys=statsKeys)

        subFrame = getDebugFrame(self._display, "subtracted")
        if subFrame:
            subDisp = afwDisplay.getDisplay(frame=subFrame)
            subDisp.mtv(exposure, title="subtracted")

        bgFrame = getDebugFrame(self._display, "background")
        if bgFrame:
            bgDisp = afwDisplay.getDisplay(frame=bgFrame)
            bgImage = background.getImage()
            bgDisp.mtv(bgImage, title="background")

        return pipeBase.Struct(
            background=background,
        )

    def _addStats(self, exposure, background, statsKeys=None):
        """Add statistics about the background to the exposure's metadata

        @param[in,out] exposure  exposure whose background was subtracted
        @param[in,out] background  background model (an lsst.afw.math.BackgroundList)
        @param[in] statsKeys  key names used to store the mean and variance of the background
            in the exposure's metadata (a pair of strings); if None then use ("BGMEAN", "BGVAR");
            ignored if stats is false
        """
        netBgImg = background.getImage()
        if statsKeys is None:
            statsKeys = ("BGMEAN", "BGVAR")
        mnkey, varkey = statsKeys
        meta = exposure.getMetadata()
        s = afwMath.makeStatistics(netBgImg, afwMath.MEAN | afwMath.VARIANCE)
        bgmean = s.getValue(afwMath.MEAN)
        bgvar = s.getValue(afwMath.VARIANCE)
        meta.addDouble(mnkey, bgmean)
        meta.addDouble(varkey, bgvar)

    def fitBackground(self, maskedImage, nx=0, ny=0, algorithm=None):
        """!Estimate the background of a masked image

        @param[in] maskedImage  masked image whose background is to be computed
        @param[in] nx  number of x bands; if 0 compute from width and config.binSizeX
        @param[in] ny  number of y bands; if 0 compute from height and config.binSizeY
        @param[in] algorithm  name of interpolation algorithm; if None use self.config.algorithm

        @return fit background as an lsst.afw.math.Background

        @throw RuntimeError if lsst.afw.math.makeBackground returns None,
            which is apparently one way it indicates failure
        """

        binSizeX = self.config.binSize if self.config.binSizeX == 0 else self.config.binSizeX
        binSizeY = self.config.binSize if self.config.binSizeY == 0 else self.config.binSizeY

        if not nx:
            nx = maskedImage.getWidth()//binSizeX + 1
        if not ny:
            ny = maskedImage.getHeight()//binSizeY + 1

        unsubFrame = getDebugFrame(self._display, "unsubtracted")
        if unsubFrame:
            unsubDisp = afwDisplay.getDisplay(frame=unsubFrame)
            unsubDisp.mtv(maskedImage, title="unsubtracted")
            xPosts = numpy.rint(numpy.linspace(0, maskedImage.getWidth() + 1, num=nx, endpoint=True))
            yPosts = numpy.rint(numpy.linspace(0, maskedImage.getHeight() + 1, num=ny, endpoint=True))
            with unsubDisp.Buffering():
                for (xMin, xMax), (yMin, yMax) in itertools.product(zip(xPosts[:-1], xPosts[1:]),
                                                                    zip(yPosts[:-1], yPosts[1:])):
                    unsubDisp.line([(xMin, yMin), (xMin, yMax), (xMax, yMax), (xMax, yMin), (xMin, yMin)])

        sctrl = afwMath.StatisticsControl()
        sctrl.setAndMask(reduce(lambda x, y: x | maskedImage.getMask().getPlaneBitMask(y),
                                self.config.ignoredPixelMask, 0x0))
        sctrl.setNanSafe(self.config.isNanSafe)

        self.log.debug("Ignoring mask planes: %s" % ", ".join(self.config.ignoredPixelMask))

        if algorithm is None:
            algorithm = self.config.algorithm

        bctrl = afwMath.BackgroundControl(algorithm, nx, ny,
                                          self.config.undersampleStyle, sctrl,
                                          self.config.statisticsProperty)

        # TODO: The following check should really be done within lsst.afw.math.
        #       With the current code structure, it would need to be accounted for in the doGetImage()
        #       function in BackgroundMI.cc (which currently only checks against the interpolation settings,
        #       which is not appropriate when useApprox=True)
        #       and/or the makeApproximate() function in afw/Approximate.cc.
        #       See ticket DM-2920: "Clean up code in afw for Approximate background
        #       estimation" (which includes a note to remove the following and the
        #       similar checks in pipe_tasks/matchBackgrounds.py once implemented)
        #
        # Check that config setting of approxOrder/binSize make sense
        # (i.e. ngrid (= shortDimension/binSize) > approxOrderX) and perform
        # appropriate undersampleStlye behavior.
        if self.config.useApprox:
            if self.config.approxOrderY not in (self.config.approxOrderX, -1):
                raise ValueError("Error: approxOrderY not in (approxOrderX, -1)")
            order = self.config.approxOrderX
            minNumberGridPoints = order + 1
            if min(nx, ny) <= order:
                self.log.warn("Too few points in grid to constrain fit: min(nx, ny) < approxOrder) "
                              "[min(%d, %d) < %d]" % (nx, ny, order))
                if self.config.undersampleStyle == "THROW_EXCEPTION":
                    raise ValueError("Too few points in grid (%d, %d) for order (%d) and binSize (%d, %d)" %
                                     (nx, ny, order, binSizeX, binSizeY))
                elif self.config.undersampleStyle == "REDUCE_INTERP_ORDER":
                    if order < 1:
                        raise ValueError("Cannot reduce approxOrder below 0.  "
                                         "Try using undersampleStyle = \"INCREASE_NXNYSAMPLE\" instead?")
                    order = min(nx, ny) - 1
                    self.log.warn("Reducing approxOrder to %d" % order)
                elif self.config.undersampleStyle == "INCREASE_NXNYSAMPLE":
                    # Reduce bin size to the largest acceptable square bins
                    newBinSize = min(maskedImage.getWidth(), maskedImage.getHeight())//(minNumberGridPoints-1)
                    if newBinSize < 1:
                        raise ValueError("Binsize must be greater than 0")
                    newNx = maskedImage.getWidth()//newBinSize + 1
                    newNy = maskedImage.getHeight()//newBinSize + 1
                    bctrl.setNxSample(newNx)
                    bctrl.setNySample(newNy)
                    self.log.warn("Decreasing binSize from (%d, %d) to %d for a grid of (%d, %d)" %
                                  (binSizeX, binSizeY, newBinSize, newNx, newNy))

            actrl = afwMath.ApproximateControl(afwMath.ApproximateControl.CHEBYSHEV, order, order,
                                               self.config.weighting)
            bctrl.setApproximateControl(actrl)

        bg = afwMath.makeBackground(maskedImage, bctrl)
        if bg is None:
            raise RuntimeError("lsst.afw.math.makeBackground failed to fit a background model")
        return bg