From faee8663f117ee4f5275eef05869a2ee8601679e Mon Sep 17 00:00:00 2001
From: mlibralato <78165985+mlibralato@users.noreply.github.com>
Date: Thu, 30 Nov 2023 15:16:33 -0500
Subject: [PATCH] JP-3455: Updated MIRI imager photom step (#8096)

Co-authored-by: Howard Bushouse <bushouse@stsci.edu>
---
 CHANGES.rst                      |  5 +++++
 jwst/photom/miri_imager.py       | 35 ++++++++++++++++++++++++++++++++
 jwst/photom/photom.py            | 28 ++++++++++++++++++++++++-
 jwst/photom/tests/test_photom.py | 18 +++++++++++++---
 4 files changed, 82 insertions(+), 4 deletions(-)
 create mode 100644 jwst/photom/miri_imager.py

diff --git a/CHANGES.rst b/CHANGES.rst
index 4596833ef8..67ae78eacb 100644
--- a/CHANGES.rst
+++ b/CHANGES.rst
@@ -61,6 +61,11 @@ imprint
 - Updated the logging to report which imprint image is being subtracted from the
   science image. [#8041]
 
+photom
+--------
+
+- Added time-dependent correction for MIRI Imager data [#8096, spacetelescope/stdatamodels#235]
+
 pixel_replace
 -------------
 
diff --git a/jwst/photom/miri_imager.py b/jwst/photom/miri_imager.py
new file mode 100644
index 0000000000..96eec891ad
--- /dev/null
+++ b/jwst/photom/miri_imager.py
@@ -0,0 +1,35 @@
+import logging
+import numpy as np
+
+log = logging.getLogger(__name__)
+log.setLevel(logging.DEBUG)
+
+# Routine to find a time-dependent correction to the PHOTOM value.
+
+def time_corr_photom(param, t):
+    """
+    Short Summary
+    --------------
+    Time dependent PHOTOM function.
+
+    The model parameters are amplitude, tau, t0. t0 is the reference day 
+    from which the time-dependent parameters were derived. This function will return 
+    a correction to apply to the PHOTOM value at a given MJD.
+
+    Parameters
+    ----------
+    param : numpy array
+        Set of parameters for the PHOTOM value
+    t : int
+        Modified Julian Day (MJD) of the observation
+
+    Returns
+    -------
+    corr: float
+        The time-dependent correction to the photmjsr term.
+    """
+    
+    amplitude, tau, t0 = param["amplitude"], param["tau"], param["t0"]
+    corr = amplitude * np.exp(-(t - t0)/tau)
+
+    return corr
diff --git a/jwst/photom/photom.py b/jwst/photom/photom.py
index a2c61244d9..d5d695cc7d 100644
--- a/jwst/photom/photom.py
+++ b/jwst/photom/photom.py
@@ -10,6 +10,7 @@
 
 from .. lib.wcs_utils import get_wavelengths
 from . import miri_mrs
+from . import miri_imager
 
 log = logging.getLogger(__name__)
 log.setLevel(logging.DEBUG)
@@ -468,7 +469,32 @@ def calc_miri(self, ftab):
                 row = find_row(ftab.phot_table, fields_to_match)
                 if row is None:
                     return
-            self.photom_io(ftab.phot_table[row])
+                self.photom_io(ftab.phot_table[row])
+                
+            # Check if reference file contains the coefficients for the time-dependent correction of the PHOTOM value
+            try:
+                ftab.getarray_noinit("timecoeff")
+                log.info("Applying the time-dependent correction to the PHOTOM value.")
+
+                mid_time = self.input.meta.exposure.mid_time
+                photom_corr = miri_imager.time_corr_photom(ftab.timecoeff[row], mid_time)
+
+                data = np.array(
+                    [(self.filter, self.subarray, ftab.phot_table[row]['photmjsr']+photom_corr, ftab.phot_table[row]['uncertainty'])],
+                    dtype=[
+                        ("filter", "O"),
+                        ("subarray", "O"),
+                        ("photmjsr", "<f4"),
+                        ("uncertainty", "<f4")
+                        ],
+                )
+                fftab = datamodels.MirImgPhotomModel(phot_table=data)
+                self.photom_io(fftab.phot_table[0])
+            except AttributeError:
+                # No time-dependent correction is applied
+                log.info(" Skipping MIRI imager time correction. Extension not found in the reference file.")
+                self.photom_io(ftab.phot_table[row])
+
         # MRS detectors
         elif self.detector == 'MIRIFUSHORT' or self.detector == 'MIRIFULONG':
 
diff --git a/jwst/photom/tests/test_photom.py b/jwst/photom/tests/test_photom.py
index a4a8a46b49..f10105cd88 100644
--- a/jwst/photom/tests/test_photom.py
+++ b/jwst/photom/tests/test_photom.py
@@ -340,6 +340,7 @@ def create_input(instrument, detector, exptype,
             input_model.var_flat = np.ones(shape, dtype=np.float32)
             input_model.meta.subarray.name = 'SUB256'       # matches 'GENERIC'
             input_model.meta.target.source_type = 'POINT'
+            input_model.meta.exposure.mid_time = 60000.0 # Added for new PHOTOM step
             input_model.meta.photometry.pixelarea_arcsecsq = 0.0025
             input_model.meta.photometry.pixelarea_steradians = 0.0025 * A2_TO_SR
     elif instrument == 'FGS':
@@ -780,7 +781,7 @@ def create_photom_miri_image(min_wl=16.5, max_wl=19.5,
     """
 
     filter = ["F1800W", "F2100W", "F2550W"]
-    subarray = ["GENERIC", "GENERIC", "GENERIC"]
+    subarray = ["SUB256", "SUB256", "SUB256"]
 
     nrows = len(filter)
 
@@ -794,7 +795,15 @@ def create_photom_miri_image(min_wl=16.5, max_wl=19.5,
                       ('uncertainty', '<f4')])
     reftab = np.array(list(zip(filter, subarray, photmjsr, uncertainty)),
                       dtype=dtype)
-    ftab = datamodels.MirImgPhotomModel(phot_table=reftab)
+    timecoeff_amp = np.linspace(2.1, 2.1 + (nrows - 1.) * 0.1, nrows)
+    timecoeff_tau = np.asarray([145, 145, 145])
+    timecoeff_t0 = np.asarray([59720, 59720, 59720])
+    dtypec = np.dtype([('amplitude', '<f4'),
+                      ('tau', '<f4'),
+                      ('t0', '<f4')])
+    reftabc = np.array(list(zip(timecoeff_amp, timecoeff_tau, timecoeff_t0)),
+                      dtype=dtypec)
+    ftab = datamodels.MirImgPhotomModel(phot_table=reftab, timecoeff=reftabc)
 
     return ftab
 
@@ -1415,10 +1424,13 @@ def test_miri_image():
     rownum = find_row_in_ftab(save_input, ftab, ['filter'],
                               slitname=None, order=None)
     photmjsr = ftab.phot_table['photmjsr'][rownum]
+    amplitude = ftab.timecoeff['amplitude'][rownum]
+    tau = ftab.timecoeff['tau'][rownum]
+    t0 = ftab.timecoeff['t0'][rownum]
     shape = input.shape
     ix = shape[1] // 2
     iy = shape[0] // 2
-    compare = photmjsr
+    compare = photmjsr + amplitude * np.exp(-(60000 - t0)/tau) # Added for new PHOTOM step
     # Compare the values at the center pixel.
     ratio = output[iy, ix] / input[iy, ix]
     assert np.allclose(ratio, compare, rtol=1.e-7)