Refactor AEW

aemerywatkins · aemerywatkins · commit f37968200256 · 2024-12-16T11:04:10.000-08:00
diff --git a/python/lsst/pipe/tasks/matchBackgrounds.py b/python/lsst/pipe/tasks/matchBackgrounds.py
@@ -21,16 +21,12 @@
 
 __all__ = ["MatchBackgroundsConnections", "MatchBackgroundsConfig", "MatchBackgroundsTask"]
 
-import lsstDebug
 import numpy as np
-from lsst.afw.image import LOCAL, PARENT, ExposureF, ImageF, Mask, MaskedImageF
+from lsst.afw.image import LOCAL, ImageF, Mask, MaskedImageF
 from lsst.afw.math import (
-    MEAN,
     MEANCLIP,
     MEANSQUARE,
-    MEDIAN,
     NPOINT,
-    STDEV,
     VARIANCE,
     ApproximateControl,
     BackgroundControl,
@@ -43,7 +39,6 @@
     stringToStatisticsProperty,
     stringToUndersampleStyle,
 )
-from lsst.geom import Box2D, Box2I, PointI
 from lsst.pex.config import ChoiceField, Field, ListField, RangeField
 from lsst.pipe.base import PipelineTask, PipelineTaskConfig, PipelineTaskConnections, Struct, TaskError
 from lsst.pipe.base.connectionTypes import Input, Output
@@ -137,7 +132,16 @@ class MatchBackgroundsConfig(PipelineTaskConfig, pipelineConnections=MatchBackgr
     )
     badMaskPlanes = ListField[str](
         doc="Names of mask planes to ignore while estimating the background.",
-        default=["NO_DATA", "DETECTED", "DETECTED_NEGATIVE", "SAT", "BAD", "INTRP", "CR"],
+        default=[
+            "NO_DATA",
+            "DETECTED",
+            "DETECTED_NEGATIVE",
+            "SAT",
+            "BAD",
+            "INTRP",
+            "CR",
+            "NOT_DEBLENDED",
+        ],
         itemCheck=lambda x: x in Mask().getMaskPlaneDict(),
     )
     gridStatistic = ChoiceField(
@@ -237,9 +241,6 @@ def __init__(self, *args, **kwargs):
         super().__init__(**kwargs)
         self.statsFlag = stringToStatisticsProperty(self.config.gridStatistic)
         self.statsCtrl = StatisticsControl()
-        # TODO: Check that setting the mask planes here work - these planes
-        # can vary from exposure to exposure, I think?
-        # Aaron: I think only the bit values vary, not the names, which this is referencing.
         self.statsCtrl.setAndMask(Mask.getPlaneBitMask(self.config.badMaskPlanes))
         self.statsCtrl.setNanSafe(True)
         self.statsCtrl.setNumSigmaClip(self.config.numSigmaClip)
@@ -278,7 +279,7 @@ def run(self, warps):
             raise TaskError("No exposures to match")
 
         # Define a reference warp; 'warps' is modified in-place to exclude it
-        refWarp, refInd = self._defineWarps(warps=warps, refWarpVisit=self.config.refWarpVisit)
+        refWarp, refInd, bkgd = self._defineWarps(warps=warps, refWarpVisit=self.config.refWarpVisit)
 
         # Images must be scaled to a common ZP
         # Converting everything to nJy to accomplish this
@@ -287,29 +288,11 @@ def run(self, warps):
 
         self.log.info("Matching %d Exposures", numExp)
 
-        # Creating a null BackgroundList object by fitting a blank image
-        statsFlag = stringToStatisticsProperty(self.config.gridStatistic)
-        self.statsCtrl.setNumSigmaClip(self.config.numSigmaClip)
-        self.statsCtrl.setNumIter(self.config.numIter)
-
-        # TODO: refactor below to construct blank bg model
-        im = refExposure.getMaskedImage()
-        blankIm = im.clone()
-        blankIm.image.array *= 0
-
-        width = blankIm.getWidth()
-        height = blankIm.getHeight()
-        nx = width // self.config.binSize
-        if width % self.config.binSize != 0:
-            nx += 1
-        ny = height // self.config.binSize
-        if height % self.config.binSize != 0:
-            ny += 1
-
-        bctrl = BackgroundControl(nx, ny, self.statsCtrl, statsFlag)
-        bctrl.setUndersampleStyle(self.config.undersampleStyle)
-
-        bkgd = makeBackground(blankIm, bctrl)
+        # Blank ref warp background as reference background
+        bkgdIm = bkgd.getImageF()
+        bkgdStatsIm = bkgd.getStatsImage()
+        bkgdIm *= 0
+        bkgdStatsIm *= 0
         blank = BackgroundList(
             (
                 bkgd,
@@ -325,7 +308,6 @@ def run(self, warps):
         backgroundInfoList = []
         matchedImageList = []
         for exp in warps:
-            # TODO: simplify what this prints?
             self.log.info(
                 "Matching background of %s to %s",
                 exp.dataId,
@@ -347,7 +329,6 @@ def run(self, warps):
             toMatchExposure.image /= instFluxToNanojansky  # Back to cts
             matchedImageList.append(toMatchExposure)
 
-        # TODO: more elegant solution than inserting blank model at ref ind?
         backgroundInfoList.insert(refInd, blank)
         refExposure.image /= instFluxToNanojanskyRef  # Back to cts
         matchedImageList.insert(refInd, refExposure)
@@ -377,6 +358,8 @@ def _defineWarps(self, warps, refWarpVisit=None):
             Reference warped exposure.
         refWarpIndex : `int`
             Index of the reference removed from the list of warps.
+        warpBg : `~lsst.afw.math.BackgroundMI`
+            Temporary background model, used to make a blank BG for the ref
 
         Notes
         -----
@@ -454,7 +437,7 @@ def _defineWarps(self, warps, refWarpVisit=None):
         ind = np.nanargmin(costFunctionVals)
         refWarp = warps.pop(ind)
         self.log.info("Using best reference visit %d", refWarp.dataId["visit"])
-        return refWarp, ind
+        return refWarp, ind, warpBg
 
     def _makeBackground(self, warp: MaskedImageF, binSize) -> tuple[BackgroundMI, BackgroundControl]:
         """Generate a simple binned background masked image for warped data.
@@ -528,11 +511,6 @@ def matchBackgrounds(self, refExposure, sciExposure):
         model : `~lsst.afw.math.BackgroundMI`
             Background model of difference image, reference - science
         """
-        # TODO: this is deprecated
-        if lsstDebug.Info(__name__).savefits:
-            refExposure.writeFits(lsstDebug.Info(__name__).figpath + "refExposure.fits")
-            sciExposure.writeFits(lsstDebug.Info(__name__).figpath + "sciExposure.fits")
-
         # Check Configs for polynomials:
         if self.config.usePolynomial:
             x, y = sciExposure.getDimensions()
@@ -622,17 +600,6 @@ def matchBackgrounds(self, refExposure, sciExposure):
         resids = bgZ - modelValueArr
         rms = np.sqrt(np.mean(resids[~np.isnan(resids)] ** 2))
 
-        # TODO: also deprecated; _gridImage() maybe can go?
-        if lsstDebug.Info(__name__).savefits:
-            sciExposure.writeFits(lsstDebug.Info(__name__).figpath + "sciMatchedExposure.fits")
-
-        if lsstDebug.Info(__name__).savefig:
-            bbox = Box2D(refExposure.getMaskedImage().getBBox())
-            try:
-                self._debugPlot(bgX, bgY, bgZ, bgdZ, bkgdImage, bbox, modelValueArr, resids)
-            except Exception as e:
-                self.log.warning("Debug plot not generated: %s", e)
-
         meanVar = makeStatistics(diffMI.getVariance(), diffMI.getMask(), MEANCLIP, self.statsCtrl).getValue()
 
         diffIm = diffMI.getImage()
@@ -642,7 +609,6 @@ def matchBackgrounds(self, refExposure, sciExposure):
 
         outBkgd = approx if self.config.usePolynomial else bkgd
         # Convert this back into counts
-        # TODO: is there a one-line way to do this?
         statsIm = outBkgd.getStatsImage()
         statsIm /= instFluxToNanojansky
         bkgdIm = outBkgd.getImageF()
@@ -667,119 +633,3 @@ def matchBackgrounds(self, refExposure, sciExposure):
                 False,
             )
         )
-
-    def _debugPlot(self, X, Y, Z, dZ, modelImage, bbox, model, resids):
-        """
-        Consider deleting this entirely
-        Generate a plot showing the background fit and residuals.
-
-        It is called when lsstDebug.Info(__name__).savefig = True.
-        Saves the fig to lsstDebug.Info(__name__).figpath.
-        Displays on screen if lsstDebug.Info(__name__).display = True.
-
-        Parameters
-        ----------
-        X : `np.ndarray`, (N,)
-            Array of x positions.
-        Y : `np.ndarray`, (N,)
-            Array of y positions.
-        Z : `np.ndarray`
-            Array of the grid values that were interpolated.
-        dZ : `np.ndarray`, (len(Z),)
-            Array of the error on the grid values.
-        modelImage : `Unknown`
-            Image of the model of the fit.
-        model : `np.ndarray`, (len(Z),)
-            Array of len(Z) containing the grid values predicted by the model.
-        resids : `Unknown`
-            Z - model.
-        """
-        import matplotlib.colors
-        import matplotlib.pyplot as plt
-        from mpl_toolkits.axes_grid1 import ImageGrid
-
-        zeroIm = MaskedImageF(Box2I(bbox))
-        zeroIm += modelImage
-        x0, y0 = zeroIm.getXY0()
-        dx, dy = zeroIm.getDimensions()
-        if len(Z) == 0:
-            self.log.warning("No grid. Skipping plot generation.")
-        else:
-            max, min = np.max(Z), np.min(Z)
-            norm = matplotlib.colors.normalize(vmax=max, vmin=min)
-            maxdiff = np.max(np.abs(resids))
-            diffnorm = matplotlib.colors.normalize(vmax=maxdiff, vmin=-maxdiff)
-            rms = np.sqrt(np.mean(resids**2))
-            fig = plt.figure(1, (8, 6))
-            meanDz = np.mean(dZ)
-            grid = ImageGrid(
-                fig,
-                111,
-                nrows_ncols=(1, 2),
-                axes_pad=0.1,
-                share_all=True,
-                label_mode="L",
-                cbar_mode="each",
-                cbar_size="7%",
-                cbar_pad="2%",
-                cbar_location="top",
-            )
-            im = grid[0].imshow(
-                zeroIm.getImage().getArray(), extent=(x0, x0 + dx, y0 + dy, y0), norm=norm, cmap="Spectral"
-            )
-            im = grid[0].scatter(
-                X, Y, c=Z, s=15.0 * meanDz / dZ, edgecolor="none", norm=norm, marker="o", cmap="Spectral"
-            )
-            im2 = grid[1].scatter(X, Y, c=resids, edgecolor="none", norm=diffnorm, marker="s", cmap="seismic")
-            grid.cbar_axes[0].colorbar(im)
-            grid.cbar_axes[1].colorbar(im2)
-            grid[0].axis([x0, x0 + dx, y0 + dy, y0])
-            grid[1].axis([x0, x0 + dx, y0 + dy, y0])
-            grid[0].set_xlabel("model and grid")
-            grid[1].set_xlabel("residuals. rms = %0.3f" % (rms))
-            if lsstDebug.Info(__name__).savefig:
-                fig.savefig(lsstDebug.Info(__name__).figpath + self.debugDataIdString + ".png")
-            if lsstDebug.Info(__name__).display:
-                plt.show()
-            plt.clf()
-
-    def _gridImage(self, maskedImage, binsize, statsFlag):
-        """Private method to grid an image for debugging."""
-        width, height = maskedImage.getDimensions()
-        x0, y0 = maskedImage.getXY0()
-        xedges = np.arange(0, width, binsize)
-        yedges = np.arange(0, height, binsize)
-        xedges = np.hstack((xedges, width))  # add final edge
-        yedges = np.hstack((yedges, height))  # add final edge
-
-        # Use lists/append to protect against the case where
-        # a bin has no valid pixels and should not be included in the fit
-        bgX = []
-        bgY = []
-        bgZ = []
-        bgdZ = []
-
-        for ymin, ymax in zip(yedges[0:-1], yedges[1:]):
-            for xmin, xmax in zip(xedges[0:-1], xedges[1:]):
-                subBBox = Box2I(
-                    PointI(int(x0 + xmin), int(y0 + ymin)),
-                    PointI(int(x0 + xmax - 1), int(y0 + ymax - 1)),
-                )
-                subIm = MaskedImageF(maskedImage, subBBox, PARENT, False)
-                stats = makeStatistics(
-                    subIm,
-                    MEAN | MEANCLIP | MEDIAN | NPOINT | STDEV,
-                    self.statsCtrl,
-                )
-                npoints, _ = stats.getResult(NPOINT)
-                if npoints >= 2:
-                    stdev, _ = stats.getResult(STDEV)
-                    if stdev < self.config.gridStdevEpsilon:
-                        stdev = self.config.gridStdevEpsilon
-                    bgX.append(0.5 * (x0 + xmin + x0 + xmax))
-                    bgY.append(0.5 * (y0 + ymin + y0 + ymax))
-                    bgdZ.append(stdev / np.sqrt(npoints))
-                    est, _ = stats.getResult(statsFlag)
-                    bgZ.append(est)
-
-        return np.array(bgX), np.array(bgY), np.array(bgZ), np.array(bgdZ)
