Merge pull request #693 from mperrin/tune_detector_effects

Tune detector effects model parameters to better match measured ePSFs

webbpsf/constants.py

@@ -250,17 +250,22 @@
 # Note, these are parameterized as arcseconds for convenience (and consistency with the jitter paramater)
 # but the underlying physics cares more about detector pixel pitch.
 INSTRUMENT_DETECTOR_CHARGE_DIFFUSION_DEFAULT_PARAMETERS = {
-    'NIRCAM_SW': 0.006,     # Fit by Marcio to WFS TA ePSFs
-    'NIRCAM_LW': 0.012,     # Scaled up by pixel pitch
-    'NIRISS': 0.028,         # Fit by Marcio to MIMF-3 F158M (ePSF)
+    'NIRCAM_SW': 0.0062,    # Fit by Marcio to WFS TA ePSFs, and by Marshall to prelim NIRCam SW ePSFs by J. Anderson
+    'NIRCAM_LW': 0.018,     # Fit by Marshall to prelim LW ePSFs by J. Anderson
+    'NIRISS': 0.0202,       # Fit by Marcio to MIMF-3 F158M (ePSF), and by Marshall to NIRISS ePSFs by Anderson & Libralato
     'FGS': 0.07,            # Fit by Marcio to FGS_ID images
     'NIRSPEC': 0.036,
-    'MIRI': 0.05,          # Fit by Marcio to F560W ePSF and single PSF (MIMF-3)
-                           #  0.070 Based on user reports, see issue #674. However, this is before adding IPC effects
+    'MIRI': 0.001,          # Fit by Marshall + Marcio to ePSFs, after adding IPC
+                            #  0.070 Based on user reports, see issue #674. However, this was before adding IPC effects
 }
 # add Interpixel capacitance (IPC) effects. These are the parameters for each detector kernel
 # For NIRCam we  use CV3/Flight convolution kernels from Jarron Leisenring, see detectors.apply_detector_ipc for details
 # NIRISS has different kernels provided by Kevin Volk (STScI), see detectors.apply_detector_ipc for details
 INSTRUMENT_IPC_DEFAULT_KERNEL_PARAMETERS = {
     'MIRI': (0.033, 0.024, 0.013),          # Based on JWST-STScI-002925 by Mike Engesser
-}
+}
+
+# How many detector pixels to mask out for the inner "hole" in the cruciform?
+# See Gaspar et al. 2021 for illustrative figures.
+# This is a rough approximation of a detector-position-dependent phenomenon
+MIRI_CRUCIFORM_INNER_RADIUS_PIX = 12

webbpsf/detectors.py

@@ -4,7 +4,7 @@
 import numpy as np
 import scipy
 import webbpsf
-from webbpsf import utils
+from webbpsf import utils, constants
 from astropy.convolution.kernels import CustomKernel
 from astropy.convolution import convolve
 from astropy.io import fits
@@ -309,6 +309,9 @@ def _make_miri_scattering_kernel(image, amplitude, nsamples):
     # Create 1d kernel
     kernel_x = amplitude * np.exp(-np.abs(x) / 25)
 
+    # reduce intensity in the inner part, since the cruciform is suppressed at small radii
+    kernel_x[np.abs(x) < constants.MIRI_CRUCIFORM_INNER_RADIUS_PIX] *= 0.5
+
     # Reshape kernel to 2D image for use in convolution
     kernel_x.shape = (1, image.shape[1])
 
@@ -412,6 +415,9 @@ def apply_miri_scattering(hdulist_or_filename=None, kernel_amp=None):
     # and https://github.com/spacetelescope/webbpsf/issues/415
     kernel_amp_corrections = {'F560W': 4.05, 'F770W': 4.1, 'F1000W': 3.8,
                                'F1130W': 2.5, 'F1280W': 2.5, 'F1065C': 2.5, 'F1140C': 2.5}
+    # In-flight correction based on measured cycle 1 ePSFs, coarsely
+    for k in kernel_amp_corrections:
+        kernel_amp_corrections[k] *= 0.5
 
     # Set values if not already set by a keyword argument
     if kernel_amp is None:

webbpsf/gridded_library.py

@@ -24,6 +24,7 @@ class CreatePSFLibrary:
 
     def __init__(self, instrument, filter_name, detectors="all", num_psfs=16, psf_location=None,
                  use_detsampled_psf=False, save=True, outdir=None, filename=None, overwrite=True, verbose=True,
+                 psf_location_list=None,
                  **kwargs):
         """
         Description
@@ -61,6 +62,10 @@ def __init__(self, instrument, filter_name, detectors="all", num_psfs=16, psf_lo
             If num_psfs = 1, then this is used to set the (y,x) location of that PSF.
             Default is the center point for the detector.
 
+        psf_location_list: list of tuples
+            If num_psfs > 1,  then this should be a list of locations for each PSF.
+            This can be used to set irregular sampling, if needed for some reason.
+
         use_detsampled_psf : bool
             If True, the grid of PSFs returned will be detector sampled (made by binning
             down the oversampled PSF). If False, the PSFs will be oversampled by the
@@ -128,7 +133,7 @@ def __init__(self, instrument, filter_name, detectors="all", num_psfs=16, psf_lo
         self.detector_list = self._set_detectors(self.filter, detectors)
 
         # Set the locations on the detector of the fiducial PSFs
-        self.location_list = self._set_psf_locations(num_psfs, psf_location)
+        self.location_list = self._set_psf_locations(num_psfs, psf_location, psf_location_list)
 
         # Set PSF attributes for the 3 kwargs that will be used before the calc_psf() call
         if "add_distortion" in kwargs:
@@ -192,7 +197,7 @@ def _set_detectors(self, filt, detectors):
 
         return det
 
-    def _set_psf_locations(self, num_psfs, psf_location):
+    def _set_psf_locations(self, num_psfs, psf_location, psf_location_list):
         """Set the locations on the detector of the fiducial PSFs"""
 
         self.num_psfs = num_psfs
@@ -207,9 +212,17 @@ def _set_psf_locations(self, num_psfs, psf_location):
             # Want this case to be at the specified location
             location_list = [(psf_location[::-1])]  # tuple of (x,y)
         else:
-            max_size = self.webb._detector_npixels - 1
-            loc_list = [int(round(num * max_size)) for num in np.linspace(0, 1, self.length, endpoint=True)]
-            location_list = list(itertools.product(loc_list, loc_list))  # list of tuples (x,y) (for WebbPSF)
+
+            if psf_location_list is not None:
+                if len(psf_location_list) != num_psfs:
+                    raise ValueErrorf(
+                        "Length of psf_location_list ({len(psf_location_list)})  must equal num_psfs ({num_psfs})")
+                location_list = psf_location_list
+
+            else:
+                max_size = self.webb._detector_npixels - 1
+                loc_list = [int(round(num * max_size)) for num in np.linspace(0, 1, self.length, endpoint=True)]
+                location_list = list(itertools.product(loc_list, loc_list))  # list of tuples (x,y) (for WebbPSF)
 
         return location_list