Full focal plane infrastructure

config/imsim-config-instcat.yaml

@@ -25,6 +25,33 @@ input.instance_catalog:
     file_name: default_catalog_file.txt
     sed_dir: $os.environ.get('SIMS_SED_LIBRARY_DIR')
     pupil_area: $pupil_area
+    # A single WCS calculated used when determining which objects are to be drawn
+    # by which detector. We could in principle use any WCS. Here we start from the
+    # same Batoid WCS as in the image. In the code, any SIP components of the
+    # fiducial WCS, once it's been calculated, are removed.
+    fiducial_wcs:
+        type: Batoid
+
+        # These are required:
+        camera: $camera
+        boresight: $boresight
+
+        obstime:
+            type: Eval
+            str: "astropy.time.Time(mjd_val, format='mjd', scale='tai')"
+            fmjd_val: { type: OpsimData, field: mjd }
+
+        # The fiducial WCS will use this detector. We default here to the
+        # central detector, but it can be overrdden if needed.
+        det_name: "R22_S11"
+        wavelength: $(@image.bandpass).effective_wavelength
+
+        # The rest can be omitted, since these are the default values, but shown here
+        # for reference.
+        temperature: 280  # Kelvin
+        pressure: 72.7    # kPa
+        H2O_pressure: 1.0 # kPa
+        order: 3          # Order of the SIP polynomial
 
 input.opsim_data:
     # Read the visit meta data. By default, we use the same file as the above

config/imsim-config-photon-pooling.yaml

@@ -65,4 +65,4 @@ output.photon_pooling_truth:
         nominal_flux: "@nominal_flux"  # The nominal "expectation value" flux.
         phot_flux: "@phot_flux"  # The realized flux for photon shooting.
         fft_flux: "@fft_flux"  # If FFT rendering, then the flux used, including vignetting.
-        incident_flux: "@incident_flux"  # The sum of the fluxes of all photons from the object.
+        incident_flux: "@incident_flux"  # The sum of the fluxes of all photons from the object.

config/imsim-config-skycat.yaml

@@ -26,6 +26,33 @@ input.sky_catalog:
     band: $band
     mjd: { type: OpsimData, field: mjd }
     pupil_area: $pupil_area
+    # A single WCS calculated used when determining which objects are to be drawn
+    # by which detector. We could in principle use any WCS. Here we start from the
+    # same Batoid WCS as in the image. In the code, any SIP components of the
+    # fiducial WCS, once it's been calculated, are removed.
+    fiducial_wcs:
+        type: Batoid
+
+        # These are required:
+        camera: $camera
+        boresight: $boresight
+
+        obstime:
+            type: Eval
+            str: "astropy.time.Time(mjd_val, format='mjd', scale='tai')"
+            fmjd_val: { type: OpsimData, field: mjd }
+
+        # The fiducial WCS will use this detector. We default here to the
+        # central detector, but it can be overrdden if needed.
+        det_name: "R22_S11"
+        wavelength: $(@image.bandpass).effective_wavelength
+
+        # The rest can be omitted, since these are the default values, but shown here
+        # for reference.
+        temperature: 280  # Kelvin
+        pressure: 72.7    # kPa
+        H2O_pressure: 1.0 # kPa
+        order: 3          # Order of the SIP polynomial
 
 input.opsim_data:
     file_name: default_opsim.db

examples/example_instance_catalog_ffp.txt

@@ -0,0 +1,23 @@
+rightascension 60.49045502638662697
+declination -38.16437495898705379
+mjd 60143.42156961110595148
+altitude 53.16185928082865786
+azimuth 114.38574818197552929
+filter 2
+rotskypos 131.23506207988341998
+dist2moon 88.2886136
+moonalt -28.7317781
+moondec 24.6999775
+moonphase 0.5804871
+moonra 127.0562204
+nsnap 1
+obshistid 398414
+rottelpos 40.0386345
+seed 398414
+seeing 0.7501810
+sunalt -19.2243392
+vistime 30.0000000
+seqnum 0
+object 32166149840900 60.5808675 -38.07410111 13.8487927 starSED/phoSimMLT/lte035-4.5-1.0a+0.4.BT-Settl.spec.gz 0 0 0 0 0 0 point none CCM 0.0112685 3.1
+object 32166139536388 60.55213375 -38.04768278 13.2693929 starSED/phoSimMLT/lte035-4.5-1.0a+0.4.BT-Settl.spec.gz 0 0 0 0 0 0 point none CCM 0.01014165 3.1
+object 32166139536388 60.55989542 -38.038889 13.2693929 starSED/phoSimMLT/lte035-4.5-1.0a+0.4.BT-Settl.spec.gz 0 0 0 0 0 0 point none CCM 0.01014165 3.1

examples/imsim-user-instcat-ffp-pass1.yaml

@@ -0,0 +1,56 @@
+# Use imSim custom modules
+modules:
+    - imsim
+
+# Get most of the configuration from the imSim config-template
+# for instance catalogs.
+template: imsim-config-instcat
+
+################################################################
+#  Make your changes below.
+################################################################
+
+# Put your own commands that override the defaults below here.  For example
+# input.instance_catalog.file_name: ./imsim_cat_197356.txt
+# input.instance_catalog.sort_mag: False
+# input.tree_rings.only_dets: [R22_S11]
+# image.nobjects: 5
+
+input.instance_catalog.file_name: $os.environ.get('IMSIM_HOME')+'/imSim/examples/example_instance_catalog_ffp.txt'
+input.instance_catalog.sort_mag: False
+
+# For now, disable tree rings to make the diffraction spikes
+# easier to see in the images.
+input.tree_rings: ""
+image.sensor.treering_center: ""
+image.sensor.treering_func: ""
+
+input.atm_psf: ""
+psf:
+    type: Convolve
+    items:
+        -
+           type: Gaussian
+           fwhm: 0.3
+
+image.nobjects: 10
+
+stamp.fft_sb_thresh: 1.e30
+
+output.det_num.first: 94
+output.nfiles: 2
+output.nproc: 1
+
+output.dir: output
+
+output.off_detector_photons:
+    dir: output
+    file_name:
+        type: FormattedStr
+        format : off_det_%08d-%1d-%s-%s-det%03d.fits
+        items:
+            - { type: OpsimData, field: observationId }
+            - { type: OpsimData, field: snap }
+            - $band
+            - $det_name
+            - "@output.det_num"

examples/imsim-user-instcat-ffp-pass2.yaml

@@ -0,0 +1,81 @@
+# Use imSim custom modules
+modules:
+    - imsim
+
+# Get most of the configuration from the imSim config-template
+# for instance catalogs.
+template: imsim-config-instcat
+
+################################################################
+#  Make your changes below.
+################################################################
+
+input.checkpoint: ""
+# input.instance_catalog: ""
+input.instance_catalog.file_name: $os.environ.get('IMSIM_HOME')+'/imSim/examples/example_instance_catalog_ffp.txt'
+input.instance_catalog.sort_mag: False
+# input.opsim_data: ""
+
+input.tree_rings: ""
+input.atm_psf: ""
+
+# In the second pass, we add off-detector photons to the image generated in the
+# first pass from the the on-detector photons. Give the image here.
+input.initial_image:
+    dir: output
+    file_name:
+        type: FormattedStr
+        format : eimage_%08d-%1d-%s-%s-det%03d.fits
+        items:
+            - { type: OpsimData, field: observationId }
+            - { type: OpsimData, field: snap }
+            - $band
+            - $det_name
+            - "@output.det_num"
+
+# Two methods can be used to read files containing off-detector photons. If a
+# single file only is to be read, then give the file_name. Alternatively, we can
+# give a file_pattern containing the fields DETNAME and DETNUM, which are
+# wildcards for the detector name and number in an expected set of files
+# matching the pattern. The loader will then attempt to read from all files it
+# finds matching this patter, excluding the file with the same name/num as the
+# current detector.
+input.off_detector_photons:
+    dir: output
+    # file_name:
+    #     type: FormattedStr
+    #     format: off_det_%08d-%1d-%s-%s-det%03d.fits
+    #     items:
+    #         - { type: OpsimData, field: observationId }
+    #         - { type: OpsimData, field: snap }
+    #         - $band
+    #         - $det_name
+    #         - "@output.det_num"
+    file_pattern:
+        type: FormattedStr
+        format: off_det_%08d-%1d-%s-DETNAME-detDETNUM.fits
+        items:
+            - { type: OpsimData, field: observationId }
+            - { type: OpsimData, field: snap }
+            - $band
+    camera: $camera
+    det_name: $det_name
+
+psf:
+    type: Convolve
+    items:
+        -
+           type: Gaussian
+           fwhm: 0.3
+
+stamp: ""
+
+image.type: LSST_FocalPlaneImage
+image.sensor.treering_center: ""
+image.sensor.treering_func: ""
+
+output.det_num.first: 95
+output.nfiles: 1
+output.file_name.format: "eimage_full_focal_%08d-%1d-%s-%s-det%03d.fits"
+
+output.dir: output

imsim/__init__.py

@@ -40,3 +40,4 @@
 from .process_info import *
 from .table_row import *
 from .photon_pooling import *
+from .full_focal_plane import *

imsim/camera.py

@@ -1,6 +1,8 @@
 import os
+import re
 import json
 from collections import defaultdict
+import numpy as np
 import galsim
 import lsst.utils
 from .meta_data import data_dir
@@ -214,3 +216,40 @@ def update(self, other):
     def __getattr__(self, attr):
         """Provide access to the attributes of the underlying lsst_camera."""
         return getattr(self.lsst_camera, attr)
+
+    def get_adjacent_detectors(self, det_name):
+        """
+        Given a science detector's name, return a list of the names of science
+        detectors within the 3x3 grid around it, including itself.
+        """
+        if det_name not in self:
+            raise galsim.GalSimValueError("Detector is not in camera", det_name)
+        match = re.match(r"R(\d{2})_S(\d{2})", det_name)
+        if not match:
+            # If the given detector is in the list but couldn't be matched, then
+            # it's a wavefront or guide detector.
+            raise galsim.GalSimValueError(
+                "Arg must be a science detector, not wavefront or guide", det_name)
+        r_det = (int(match.group(1)[0]), int(match.group(1)[1]))
+        s_det = (int(match.group(2)[0]), int(match.group(2)[1]))
+
+        # The following method of manipulating the indices in the detector name
+        # is about 5000 times faster than using lsst.afw.cameraGeom to find the
+        # detector's center and then the names of those shifted away by one
+        # detector width/height.
+
+        # We'll always have a full set of the nine S index pairs, but they need
+        # to be permuted to match the position within the raft.
+        s_rows = np.roll(np.array([0, 1, 2]), 1 - s_det[0])
+        s_cols = np.roll(np.array([0, 1, 2]), 1 - s_det[1])
+
+        # A little integer arithmetic is used to increment or decrement the raft
+        # indices if the detector is positioned on an edge of the raft.
+        r_rows = np.array([r_det[0]-s_rows[0]//2, r_det[0], r_det[0]+(2-s_rows[2])//2])
+        r_cols = np.array([r_det[1]-s_cols[0]//2, r_det[1], r_det[1]+(2-s_cols[2])//2])
+
+        adjacent_detectors = [f"R{ri}{rj}_S{si}{sj}"
+                              for ri, si in zip(r_rows, s_rows)
+                              for rj, sj in zip(r_cols, s_cols)
+                              if f"R{ri}{rj}_S{si}{sj}" in self]
+        return adjacent_detectors