adding test coverage

CHANGES.rst

@@ -20,7 +20,7 @@ New Features
 
 - The standalone version of jdaviz now uses solara instead of voila, resulting in faster load times. [#2909]
 
-- New configuration for ramp/Level 1 data products from Roman WFI and JWST [#3120, #3148]
+- New configuration for ramp/Level 1 data products from Roman WFI and JWST [#3120, #3148, #3167]
 
 Cubeviz
 ^^^^^^^

jdaviz/configs/rampviz/plugins/parsers.py

@@ -128,7 +128,9 @@ def parse_data(app, file_obj, data_type=None, data_label=None,
 
     elif isinstance(file_obj, Level1bModel):
         metadata = standardize_metadata({
-            key: value for key, value in file_obj.to_flat_dict().items()
+            key: value for key, value in file_obj.to_flat_dict(
+                include_arrays=False)
+            .items()
             if key.startswith('meta')
         })
 
@@ -271,12 +273,22 @@ def _parse_ramp_cube(app, ramp_cube_data, flux_unit, file_name,
     # Identify NIRSpec IRS2 detector mode, which needs special treatment.
     # jdox: https://jwst-docs.stsci.edu/jwst-near-infrared-spectrograph/nirspec-instrumentation/
     # nirspec-detectors/nirspec-detector-readout-modes-and-patterns/nirspec-irs2-detector-readout-mode
-    header = meta.get('_primary_header', {})
-    from_jwst_nirspec_irs2 = (
-        header.get('TELESCOP') == 'JWST' and
-        header.get('INSTRUME') == 'NIRSPEC' and
-        'IRS2' in header.get('READPATT', '')
-    )
+    if 'meta.model_type' in meta:
+        # this is a Level1bModel, which has metadata in a Node rather
+        # than a dictionary:
+        from_jwst_nirspec_irs2 = (
+            meta.get('meta._primary_header.TELESCOP') == 'JWST' and
+            meta.get('meta._primary_header.INSTRUME') == 'NIRSPEC' and
+            'IRS2' in meta.get('meta._primary_header.READPATT', '')
+        )
+    else:
+        # assume this was parsed from FITS:
+        header = meta.get('_primary_header', {})
+        from_jwst_nirspec_irs2 = (
+            header.get('TELESCOP') == 'JWST' and
+            header.get('INSTRUME') == 'NIRSPEC' and
+            'IRS2' in header.get('READPATT', '')
+        )
 
     # last axis is the group axis, first two are spatial axes:
     diff_data = np.vstack([

jdaviz/configs/rampviz/tests/test_parser.py

@@ -0,0 +1,45 @@
+
+
+def test_load_rectangular_ramp(rampviz_helper, jwst_level_1b_rectangular_ramp):
+    rampviz_helper.load_data(jwst_level_1b_rectangular_ramp)
+
+    # drop the integration axis
+    original_cube_shape = jwst_level_1b_rectangular_ramp.shape[1:]
+
+    # on ramp cube load (1), the parser loads a diff cube (2) and
+    # the ramp extraction plugin produces a default extraction (3):
+    assert len(rampviz_helper.app.data_collection) == 3
+
+    parsed_cube_shape = rampviz_helper.app.data_collection[0].shape
+    assert parsed_cube_shape == (
+        original_cube_shape[2], original_cube_shape[1], original_cube_shape[0]
+    )
+
+
+def test_load_nirspec_irs2(rampviz_helper, jwst_level_1b_rectangular_ramp):
+    # update the Level1bModel to have the header cards that are
+    # expected for an exposure from NIRSpec in IRS2 readout mode
+    jwst_level_1b_rectangular_ramp.update(
+        {
+            'meta': {
+                '_primary_header': {
+                    "TELESCOP": "JWST",
+                    "INSTRUME": "NIRSPEC",
+                    "READPATT": "NRSIRS2"
+                }
+            }
+        }
+    )
+    rampviz_helper.load_data(jwst_level_1b_rectangular_ramp)
+
+    # drop the integration axis
+    original_cube_shape = jwst_level_1b_rectangular_ramp.shape[1:]
+
+    # on ramp cube load (1), the parser loads a diff cube (2) and
+    # the ramp extraction plugin produces a default extraction (3):
+    assert len(rampviz_helper.app.data_collection) == 3
+
+    parsed_cube_shape = rampviz_helper.app.data_collection[0].shape
+    assert parsed_cube_shape == (
+        original_cube_shape[1], original_cube_shape[2], original_cube_shape[0]
+    )

jdaviz/conftest.py

@@ -70,22 +70,30 @@ def roman_level_1_ramp():
     return data_model
 
 
-@pytest.fixture
-def jwst_level_1b_ramp():
+def _make_jwst_ramp(shape=(1, 10, 25, 25)):
     from stdatamodels.jwst.datamodels import Level1bModel
 
     rng = np.random.default_rng(seed=42)
 
     # JWST Level 1b ramp files have an additional preceding dimension
     # compared with Roman. This dimension is the integration number
     # in a sequence (if there's more than one in the visit).
-    shape = (1, 10, 25, 25)
     data_model = Level1bModel(shape)
     data_model.data = 100 + 3 * np.cumsum(rng.uniform(size=shape), axis=0)
 
     return data_model
 
 
+@pytest.fixture
+def jwst_level_1b_ramp():
+    return _make_jwst_ramp()
+
+
+@pytest.fixture
+def jwst_level_1b_rectangular_ramp():
+    return _make_jwst_ramp(shape=(1, 10, 32, 25))
+
+
 @pytest.fixture
 def image_2d_wcs():
     return WCS({'CTYPE1': 'RA---TAN', 'CUNIT1': 'deg', 'CDELT1': -0.0002777777778,