Add tract postage stamp builder

python/lsst/analysis/tools/tasks/__init__.py

@@ -17,6 +17,7 @@
 from .gatherResourceUsage import *
 from .injectedObjectAnalysis import *
 from .makeMetricTable import *
+from .makeTractPostageStamp import *
 from .metadataAnalysis import *
 from .metadataExposureDetectorAnalysis import *
 from .metricAnalysis import *

python/lsst/analysis/tools/tasks/makeTractPostageStamp.py

@@ -0,0 +1,261 @@
+# This file is part of analysis_tools.
+#
+# 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__ = (
+    "MakeTractPostageStampConfig",
+    "MakeTractPostageStampTask",
+)
+
+import copy
+
+import lsst.pipe.base as pipeBase
+import matplotlib.cm as cm
+import matplotlib.patheffects as pathEffects
+import numpy as np
+from lsst.pipe.base import connectionTypes as ct
+from lsst.skymap import BaseSkyMap
+from lsst.utils.plotting import make_figure
+
+from ..utils import getPatchCorners, getTractCorners
+
+
+class MakeTractPostageStampConnections(
+    pipeBase.PipelineTaskConnections,
+    dimensions=("skymap", "tract", "band"),
+    defaultTemplates={
+        "inputName": "deepCoadd_calexpBin",
+        "outputName": "deepTract_PostageStamp",
+    },
+):
+    data = ct.Input(
+        doc="Binned deepCoadd calibrated exposures to read from the butler.",
+        name="{inputName}",
+        storageClass="ExposureF",
+        deferLoad=True,
+        dimensions=(
+            "skymap",
+            "tract",
+            "patch",
+            "band",
+        ),
+        multiple=True,
+    )
+
+    skymap = ct.Input(
+        doc="The skymap that covers the tract that the data is from.",
+        name=BaseSkyMap.SKYMAP_DATASET_TYPE_NAME,
+        storageClass="SkyMap",
+        dimensions=("skymap",),
+    )
+
+    postageStamp = ct.Output(
+        doc="A postagestamp composite image of the tract.",
+        name="{outputName}",
+        storageClass="Plot",
+        dimensions=("skymap", "tract"),
+    )
+
+
+class MakeTractPostageStampConfig(
+    pipeBase.PipelineTaskConfig, pipelineConnections=MakeTractPostageStampConnections
+):
+    pass
+
+
+class MakeTractPostageStampTask(pipeBase.PipelineTask):
+
+    ConfigClass = MakeTractPostageStampConfig
+    _DefaultName = "makeTractPostageStamp"
+
+    def runQuantum(self, butlerQC, inputRefs, outputRefs):
+        """Takes a set of coadded patch Exposures and displays them
+        in their corresponding positions within a tract.
+        Empty patches - those that do not have any coverage - are shown
+        as hatched squares.
+
+        Parameters
+        ----------
+        butlerQC : `lsst.pipe.base.QuantumContext`
+            A butler which is specialized to operate in the context of a
+            `lsst.daf.butler.Quantum`.
+        inputRefs : `lsst.pipe.base.InputQuantizedConnection`
+            Datastructure containing named attributes 'data and 'skymap'.
+            The values of these attributes are the corresponding
+            `lsst.daf.butler.DatasetRef` objects defined in the corresponding
+            `PipelineTaskConnections` class.
+        outputRefs : `lsst.pipe.base.OutputQuantizedConnection`
+            Datastructure containing named attribute 'postageStamp'.
+            The value of this attribute is the corresponding
+            `lsst.daf.butler.DatasetRef` object defined in the corresponding
+            `PipelineTaskConnections` class.
+        """
+
+        inputs = butlerQC.get(inputRefs)
+        patches = inputs["data"]
+        skymap = inputs["skymap"]
+        band = butlerQC.quantum.dataId["band"]
+        tract = butlerQC.quantum.dataId["tract"]
+
+        fig = self.makeTractPostageStamp(skymap, tract, patches, band)
+
+        butlerQC.put(pipeBase.Struct(postageStamp=fig), outputRefs)
+
+    def makeTractPostageStamp(self, skymap, tract, patches, band):
+        """Takes the coadd patch exposures and displays them on a
+        set of axes. The axes boundaries are those of the tract.
+        Patches are annoted with their patch identification number.
+        Empty patches - those that do not have any coverage - are shown
+        as hatched squares.
+
+        Parameters
+        ----------
+        skymap : `lsst.skymap`
+        tract : `int`
+            Identification number of tract.
+        patches : `list` [`DeferredDatasetHandle`]
+            List of handles for patch coadd exposures to display.
+        band : `str`
+            Filter band. Only used to annotate the plot.
+
+        Returns
+        -------
+        fig : `matplotlib.figure.Figure`
+            Plot displaying the tract with coadd exposures displayed.
+        """
+
+        tractInfo = skymap.generateTract(tract)
+        tractCorners = getTractCorners(skymap, tract)
+        tractRas = [ra for (ra, dec) in tractCorners]
+        RaSpansZero = max(tractRas) > 360.0
+
+        cmap = cm.grey
+        cmap.set_bad(alpha=0)
+        cmapred = cm.Reds
+        cmapred.set_bad(alpha=0)
+
+        fig = make_figure(dpi=300)
+        ax = fig.add_subplot(111)
+
+        emptyPatches = np.arange(tractInfo.getNumPatches()[0] * tractInfo.getNumPatches()[1]).tolist()
+
+        for patch in patches:
+
+            patchId = patch.dataId["patch"]
+            emptyPatches.remove(patchId)
+
+            # Create the patch axes at the appropriate location in tract:
+            patchCorners = getPatchCorners(tractInfo, patchId)
+            ras = [ra for (ra, dec) in patchCorners]
+            decs = [dec for (ra, dec) in patchCorners]
+
+            # Account for the RA wrapping using negative RA values.
+            # This is rectified when the final axes are built.
+            if RaSpansZero:
+                ras = [ra - 360 if ra > 180.0 else ra for ra in ras]
+            Extent = (max(ras), min(ras), max(decs), min(decs))
+            ax.plot(
+                [min(ras), max(ras), max(ras), min(ras), min(ras)],
+                [min(decs), min(decs), max(decs), max(decs), min(decs)],
+                "k",
+                lw=0.5,
+            )
+
+            # Fetch the images and display:
+            im = patch.get(component="image").array
+            mask = patch.get(component="mask").array & 2**8 > 0
+
+            imdata = copy.deepcopy(im)
+            imdata[mask] = np.nan
+            med = np.nanmedian(imdata)
+            mad = np.nanmedian(np.fabs(im - med))
+            vmin = med - 1 * mad
+            vmax = med + 15 * mad
+            ax.imshow(imdata, extent=Extent, vmin=vmin, vmax=vmax, cmap=cmap)
+
+            # Highlight regions of "NODATA".
+            imnodata = copy.deepcopy(im)
+            imnodata[~mask] = np.nan
+            ax.imshow(imnodata, extent=Extent, alpha=0.8, cmap=cmapred)
+
+            ax.annotate(
+                patchId,
+                (np.mean(ras), np.mean(decs)),
+                color="k",
+                ha="center",
+                va="center",
+                path_effects=[pathEffects.withStroke(linewidth=2, foreground="w")],
+            )
+
+        # Indicate blank patches as hatched regions
+        for patchId in emptyPatches:
+
+            patchCorners = getPatchCorners(tractInfo, patchId)
+            ras = [ra for (ra, dec) in patchCorners]
+            decs = [dec for (ra, dec) in patchCorners]
+
+            # Account for the RA wrapping using negative RA values.
+            if RaSpansZero:
+                ras = [ra - 360 if ra > 180.0 else ra for ra in ras]
+
+            Extent = (max(ras), min(ras), max(decs), min(decs))
+            ax.plot(
+                [min(ras), max(ras), max(ras), min(ras), min(ras)],
+                [min(decs), min(decs), max(decs), max(decs), min(decs)],
+                "k",
+                lw=0.5,
+            )
+
+            cs = ax.contourf(np.ones((10, 10)), 1, hatches=["xx"], extent=Extent, colors="none")
+            for c in cs.collections:
+                c.set_edgecolor("red")
+            ax.annotate(
+                patchId,
+                (np.mean(ras), np.mean(decs)),
+                color="k",
+                ha="center",
+                va="center",
+                path_effects=[pathEffects.withStroke(linewidth=2, foreground="w")],
+            )
+
+        # Draw axes around the entire tract:
+        ax.set_xlabel("R.A. (deg)", fontsize=20)
+        ax.set_ylabel("Dec. (deg)", fontsize=20)
+
+        tractRas = [ra for (ra, dec) in tractCorners]
+        # Account for the RA wrapping using negative RA values.
+        if RaSpansZero:
+            tractRas = [ra - 360.0 for ra in tractRas]
+
+        ax.set_xlim(max(tractRas), min(tractRas))
+        ticks = [t for t in ax.get_xticks() if t >= min(tractRas) and t <= max(tractRas)]
+
+        # Rectify potential negative RA values via tick labels
+        tickLabels = [f"{t % 360:.1f}" for t in ticks]
+        ax.set_xticks(ticks, tickLabels)
+
+        tractDecs = [dec for (ra, dec) in tractCorners]
+        ax.set_ylim(min(tractDecs), max(tractDecs))
+
+        ax.tick_params(axis="both", labelsize=10, length=0, pad=1.5)
+        ax.set_title(str(tract) + ": " + band, fontsize=20)
+        fig.canvas.draw()
+
+        return fig