Fixing non-NaNable ccd images

CHANGES.rst

@@ -5,13 +5,16 @@ New Features
 ^^^^^^^^^^^^
 + Development of new data classes for handling source list, photometry, and catalog data which include data format validation. [#125]
 + Aperture photometry streamlined into ``single_image_photometry``  ``multi_image_photometry`` functions that use the new data classes. [#141]
++ Photometry related notebooks updated to use new data classes. [#151]
 + ``multi_image_photometry`` now is a wrapper for single_image_photometry instead of a completely separate function.
++ Logging has been implemented for photmetry, so all the output can now be logged to a file. [#150]
 
 Other Changes and Additions
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^
 + Major reorganizaiton of code including moving functions to new modules. [#130, #133]
 + Now requires python 3.10 or later. [#147]
 + Use pydantic for aperture settings. [#154]
++ Stomped bug in handling of ``NaN``s in ``single_image_photometry``. [#157]
 
 Bug Fixes
 ^^^^^^^^^

stellarphot/photometry/photometry.py

@@ -249,7 +249,8 @@ def single_image_photometry(ccd_image, sourcelist, camera, observatory_location,
                "SKIPPING THIS IMAGE!")
         return None, None
 
-    # Set high pixels to NaN
+    # Set high pixels to NaN (make sure ccd_image.data is a float array first)
+    ccd_image.data = ccd_image.data.astype(float)
     ccd_image.data[ccd_image.data > max_adu] = np.nan
 
     # Extract necessary values from sourcelist structure

stellarphot/photometry/tests/test_photometry.py

@@ -221,8 +221,11 @@ def test_find_too_close():
 coords2use='pixel'
 
 
-def test_aperture_photometry_no_outlier_rejection():
+# The True case below is a regression test for #157
+@pytest.mark.parametrize('int_data', [True, False])
+def test_aperture_photometry_no_outlier_rejection(int_data):
     fake_CCDimage = FakeCCDImage()
+
     sources = fake_CCDimage.sources
     aperture = sources['aperture'][0]
     inner_annulus = 2 * aperture
@@ -234,6 +237,18 @@ def test_aperture_photometry_no_outlier_rejection():
     found_sources = source_detection(fake_CCDimage,
                                     fwhm=sources['x_stddev'].mean(),
                                     threshold=10)
+
+    # The scale_factor is used to rescale data to integers if needed. It
+    # needs to be set later on when the net counts are "unscaled" in the
+    # asserts that constitute the actual test.
+    scale_factor = 1.0
+    if int_data:
+        scale_factor = 0.75 * max_adu / fake_CCDimage.data.max()
+        # For the moment, ensure the integer data is NOT larger than max_adu
+        # because until #161 is fixed then having NaN in the data will not succeed.
+        data = scale_factor * fake_CCDimage.data
+        fake_CCDimage.data = data.astype(int)
+
     phot, missing_sources = single_image_photometry(fake_CCDimage,
                                                     found_sources,
                                                     fake_camera,
@@ -265,7 +280,9 @@ def test_aperture_photometry_no_outlier_rejection():
 
         # Here we just check whether any difference is consistent with
         # less than the expected one sigma deviation.
-        assert (np.abs(expected_flux - out['aperture_net_cnts'].value) <
+
+        # We need to remove any scaling that has been done of the data values.
+        assert (np.abs(expected_flux - out['aperture_net_cnts'].value / scale_factor) <
                 np.pi * aperture**2 * fake_CCDimage.noise_dev)