initial draft commit to get more eyes on code

jwst/outlier_detection/outlier_detection.py

@@ -568,7 +568,9 @@ def gwcs_blot(median_model, blot_img, interp='poly5', sinscl=1.0):
     log.debug("Pixmap shape: {}".format(pixmap[:, :, 0].shape))
     log.debug("Sci shape: {}".format(blot_img.data.shape))
 
-    pix_ratio = 1
+    pix_ratio = np.sqrt(blot_img.meta.photometry.pixelarea_arcsecsq / \
+                        median_model.meta.photometry.pixelarea_arcsecsq)
+    #log.warning(f"Pixel area ratio: {pix_ratio:.6f}")
     log.info('Blotting {} <-- {}'.format(blot_img.data.shape, median_model.data.shape))
 
     outsci = np.zeros(blot_img.shape, dtype=np.float32)

jwst/outlier_detection/tests/test_outlier_detection.py

@@ -141,6 +141,10 @@ def we_many_sci(
     sci1.var_rnoise = np.zeros(shape) + 1.0
     sci1.meta.filename = "foo1_cal.fits"
 
+    # add pixel areas
+    sci1.meta.photometry.pixelarea_steradians = 1.0
+    sci1.meta.photometry.pixelarea_arcsecsq = 1.0
+
     # Make copies with different noise
     all_sci = [sci1]
     for i in range(numsci - 1):

jwst/regtest/test_nircam_align_to_gaia.py

@@ -13,6 +13,7 @@ def run_image3pipeline(rtdata_module):
     rtdata = rtdata_module
     rtdata.get_asn("nircam/image/level3_F277W_3img_asn.json")
     args = ["calwebb_image3", rtdata.input,
+            "--steps.outlier_detection.save_intermediate_results=True",
             "--steps.tweakreg.abs_refcat=GAIADR2",
             "--steps.tweakreg.save_results=True",
             "--steps.tweakreg.output_use_model=True",
@@ -40,3 +41,36 @@ def test_tweakreg_with_gaia(run_image3pipeline, rtdata_module, root):
 
         assert_allclose(ra, ra_truth)
         assert_allclose(dec, dec_truth)
+
+
+
+
+@pytest.mark.bigdata
+def test_outlier_detection_flux_roundtrip(run_image3pipeline):
+    """
+    Check flux scaling between input and output of OutlierDetectionStep.
+    Covers bugfix for JP-3635. Even after the change, the flux scaling
+    is not perfect, but at least this tolerance fails the test prior 
+    to the fix and passes afterward(?)
+    """
+
+    import os
+    import numpy as np
+    ls = os.listdir(".")
+    for l in ls:
+        print(l)
+
+    stem = "jw01069002004_01101_00013_nrca5"
+
+    calfile = stem+"_cal.fits"
+    blotfile = stem+"_a3001_blot.fits"
+
+    with datamodels.open(calfile) as cal, datamodels.open(blotfile) as blot:
+
+        badcal = (np.isnan(cal.data)) * (np.isinf(cal.data))
+        badblot = (np.isnan(blot.data)) * (np.isinf(blot.data))
+        med_cal = np.nanmedian(cal.data[~badcal])
+        med_blot = np.nanmedian(blot.data[~badblot])
+
+        print(med_cal, med_blot)
+        assert np.isclose(med_cal, med_blot, atol=3e-3)