make the pipeline compatible with the HSC configure

examples/anacal_dm/hsc_calibration/skymap.py

@@ -5,7 +5,10 @@
 config.skyMap["discrete"].decList = [41.269]  # degrees
 config.skyMap["discrete"].radiusList = [5.0]  # degrees
 config.skyMap["discrete"].pixelScale = 0.168  # HSC arcsec/pixel
-config.skyMap["discrete"].patchInnerDimensions = [500, 500]  # in pixels # change it to [5000, 5000]
+config.skyMap["discrete"].patchInnerDimensions = [
+    500,
+    500,
+]  # in pixels # change it to [5000, 5000]
 config.skyMap["discrete"].projection = "TAN"
 config.skyMap["discrete"].tractOverlap = 0
 config.name = "hsc_sim"

xlens/process_pipe/__init__.py

@@ -1,5 +1,9 @@
 from . import (
-    fpfs_force, fpfs_joint, fpfs_multiband, systematics_multiband, hsc,
+    fpfs_force,
+    fpfs_joint,
+    fpfs_multiband,
+    systematics_multiband,
+    hsc,
     detect_multiband,
 )
 

xlens/process_pipe/detect_multiband.py

@@ -1,5 +1,8 @@
 from lsst.pipe.base import (
-    Struct, PipelineTask, PipelineTaskConfig, PipelineTaskConnections,
+    Struct,
+    PipelineTask,
+    PipelineTaskConfig,
+    PipelineTaskConnections,
 )
 import lsst.pipe.base.connectionTypes as cT
 from lsst.pex.config import ConfigurableField
@@ -12,7 +15,7 @@
 class DetectCoaddSourcesConnections(
     PipelineTaskConnections,
     dimensions=("tract", "patch", "band", "skymap"),
-    defaultTemplates={"inputCoaddName": "deep", "outputCoaddName": "deep"}
+    defaultTemplates={"inputCoaddName": "deep", "outputCoaddName": "deep"},
 ):
     detectionSchema = cT.InitOutput(
         doc="Schema of the detection catalog",
@@ -23,31 +26,30 @@ class DetectCoaddSourcesConnections(
         doc="Exposure post detection",
         name="{outputCoaddName}Coadd_calexp",
         storageClass="ExposureF",
-        dimensions=("tract", "patch", "band", "skymap")
+        dimensions=("tract", "patch", "band", "skymap"),
     )
     outputBackgrounds = cT.Output(
         doc="Output Backgrounds used in detection",
         name="{outputCoaddName}Coadd_calexp_background",
         storageClass="Background",
-        dimensions=("tract", "patch", "band", "skymap")
+        dimensions=("tract", "patch", "band", "skymap"),
     )
     outputSources = cT.Output(
         doc="Detected sources catalog",
         name="{outputCoaddName}Coadd_det",
         storageClass="SourceCatalog",
-        dimensions=("tract", "patch", "band", "skymap")
+        dimensions=("tract", "patch", "band", "skymap"),
     )
 
 
 class DetectCoaddSourcesConfig(
-    PipelineTaskConfig,
-    pipelineConnections=DetectCoaddSourcesConnections
+    PipelineTaskConfig, pipelineConnections=DetectCoaddSourcesConnections
 ):
-    """Configuration parameters for the DetectCoaddSourcesTask
-    """
+    """Configuration parameters for the DetectCoaddSourcesTask"""
 
     detection = ConfigurableField(
-        target=DynamicDetectionTask, doc="Source detection",
+        target=DynamicDetectionTask,
+        doc="Source detection",
     )
     idGenerator = SkyMapIdGeneratorConfig.make_field()
 
@@ -60,7 +62,7 @@ def setDefaults(self):
         self.detection.reEstimateBackground = False
         self.detection.background.useApprox = False
         self.detection.background.binSize = 4096
-        self.detection.background.undersampleStyle = 'REDUCE_INTERP_ORDER'
+        self.detection.background.undersampleStyle = "REDUCE_INTERP_ORDER"
         # Suppress large footprints that overwhelm the deblender
         self.detection.doTempWideBackground = True
         # Include band in packed data IDs that go into object IDs (None -> "as

xlens/process_pipe/fpfs_force.py

@@ -164,9 +164,9 @@ def run(
         assert isinstance(self.config, FpfsForcePipeConfig)
         det_names = ["y", "x", "fpfs_w", "fpfs_dw_dg1", "fpfs_dw_dg2"]
         det = (
-            joint_catalog[det_names].to_pandas(
-                index=False
-            ).to_records(index=False)
+            joint_catalog[det_names]
+            .to_pandas(index=False)
+            .to_records(index=False)
         )
         catalog = [det]
         for band in exposure_handles_dict.keys():

xlens/process_pipe/hsc/systematics_multiband.py

@@ -222,14 +222,14 @@ def get_noise_corr(self, exposure):
                 "the estimated image noise variance should be positive."
             )
 
-        window_array = (exposure.mask.array == 0).astype(
-            np.float32
-        )[1000: 3000, 1000: 3000]
+        window_array = (exposure.mask.array == 0).astype(np.float32)[
+            1000:3000, 1000:3000
+        ]
         noise_array = (
             np.asarray(
                 exposure.getMaskedImage().image.array,
                 dtype=np.float32,
-            )[1000: 3000, 1000: 3000]
+            )[1000:3000, 1000:3000]
             * window_array
         )
 

xlens/process_pipe/measure_multiband.py

@@ -46,7 +46,11 @@ class MeasureMergedCoaddSourcesConnections(
         "sources",
         name="ref_cat",
         storageClass="SimpleCatalog",
-        dimensions=("tract", "patch", "skymap",),
+        dimensions=(
+            "tract",
+            "patch",
+            "skymap",
+        ),
         deferLoad=True,
         multiple=True,
         minimum=0,
@@ -140,8 +144,10 @@ class MeasureMergedCoaddSourcesConfig(
     doWriteMatchesDenormalized = Field(
         dtype=bool,
         default=False,
-        doc=("Write reference matches in denormalized format? "
-             "This format uses more disk space, but more convenient to read."),
+        doc=(
+            "Write reference matches in denormalized format? "
+            "This format uses more disk space, but more convenient to read."
+        ),
         deprecated="Reference matching will be removed after v29.",
     )
     psfCache = Field(dtype=int, default=100, doc="Size of psfCache")

xlens/processor/fpfs.py

@@ -231,9 +231,7 @@ def rotate90(image):
         else:
             noise_array = None
         if detection is not None:
-            detection = np.array(
-                detection[["y", "x", "is_peak", "mask_value"]]
-            )
+            detection = np.array(detection[["y", "x", "is_peak", "mask_value"]])
 
         if not self.config.use_average_psf:
             psf_object = utils.LsstPsf(psf=lsst_psf, npix=self.config.npix)

xlens/putils/__init__.py

@@ -23,13 +23,13 @@ def chunk_list(lst, num_chunks):
     return chunks
 
 
-butler_setup_script = '''
+butler_setup_script = """
 butler create .
 butler register-skymap ./ --config-file skymap.py
 butler register-instrument ./ lsst.obs.subaru.HyperSuprimeCam
-'''
+"""
 
-sky_map_script = '''
+sky_map_script = """
 config.name = "hsc"
 config.skyMap = "rings"
 
@@ -38,7 +38,7 @@ def chunk_list(lst, num_chunks):
 config.skyMap["rings"].projection = "TAN"
 config.skyMap["rings"].tractOverlap = 1.0 / 60
 config.skyMap["rings"].pixelScale = 0.168  # arcsec/pixel
-'''
+"""
 
 
 def run(
@@ -107,7 +107,7 @@ def run(
                 ["bash", "butler_setup.sh"],
                 capture_output=True,
                 text=True,
-                check=True
+                check=True,
             )
             assert result.returncode == 0
 

xlens/putils/torque/template.py

@@ -1,4 +1,4 @@
-template_string = '''#!/bin/bash
+template_string = """#!/bin/bash
 
 #PBS -S /bin/bash
 #PBS -N ${jobname}
@@ -18,4 +18,4 @@
 
 pipetask run -b ./ -j ${tasks_per_node} -i ${input_collection} -o ${output_collection} -p ${config_file_name} -d "skymap='${skymap_name}' AND tract in (${tract_list}) AND band in ('g', 'r', 'i', 'z', 'y')" --register-dataset-types --skip-existing --clobber-outputs
 
-'''
+"""

xlens/sim_pipe/multibandSim.py

@@ -27,7 +27,8 @@
 from lsst.skymap import BaseSkyMap
 
 from ..simulator.multiband import (
-    MultibandSimShearTask, MultibandSimHaloTask,
+    MultibandSimShearTask,
+    MultibandSimHaloTask,
 )
 
 

xlens/simulator/multiband.py

@@ -94,7 +94,6 @@ def validate(self):
                 self.__class__.irng, self, "We require 0 <= irng < 10"
             )
 
-
     def setDefaults(self):
         super().setDefaults()
         self.survey_name = self.survey_name.lower()
@@ -296,12 +295,10 @@ def run(
             print("No correlation, variance:", variance)
         else:
             noise_corr = noiseCorrImage.getArray()
-            variance = np.amax(
-                noise_corr
-            )
+            variance = np.amax(noise_corr)
             noise_corr = noise_corr / variance
             ny, nx = noise_corr.shape
-            assert noise_corr[ny//2, nx//2] == 1
+            assert noise_corr[ny // 2, nx // 2] == 1
             print("With correlation, variance:", variance)
         noise_std = np.sqrt(variance)