No longger skipping test (great news), current results: 99 failed, 35…

…1 passed, 1 warning, still working on script

xrtpy/response/tests/test_effective_area.py

@@ -4,7 +4,7 @@
 import numpy as np
 import pytest
 from astropy import units as u
-from astropy.utils.data import get_pkg_data_filenames
+#from astropy.utils.data import get_pkg_data_filenames
 
 from xrtpy.response.channel import Channel
 from xrtpy.response.effective_area import EffectiveAreaFundamental
@@ -50,8 +50,6 @@
     datetime(year=2020, month=9, day=23, hour=22, minute=1, second=1),
     datetime(year=2021, month=9, day=23, hour=22, minute=1, second=1),
     datetime(year=2022, month=9, day=23, hour=22, minute=1, second=1),
-    datetime(year=2023, month=9, day=23, hour=22, minute=1, second=1),
-    datetime(year=2024, month=9, day=23, hour=22, minute=1, second=1),
 ]
 
 invalid_dates = [
@@ -98,37 +96,82 @@ def test_EffectiveArea_exception_is_raised(name, date):
         EffectiveAreaFundamental(name, date)
 
 
+# def get_IDL_data_files(): 
+#     filter_data_files = []
+#     for dir in get_pkg_data_filenames(
+#         "data/effective_area_IDL_testing_files", package="xrtpy.response.tests" 
+#     ):
+#         print(dir)
+#         filter_data_files += list(Path(dir).glob("*.txt"))
+#     return sorted(filter_data_files)
+
 def get_IDL_data_files():
-    filter_data_files = []
-    for dir in get_pkg_data_filenames(
-        "data/effective_area_IDL_linear_testing_files", package="xrtpy.response.tests" #effective_area_IDL_testing_files
-    ):
-        filter_data_files += list(Path(dir).glob("*.txt"))
-    return sorted(filter_data_files)
-
-
-# NOTE: This is marked as xfail because the IDL results that this test compares against
-# are incorrect due to the use of quadratic interpolation in the contamination curves
-# which leads to ringing near the edges in the contamination curve.
-# See https://github.com/HinodeXRT/xrtpy/pull/284#issuecomment-2334503108
-@pytest.mark.xfail
+    data_dir = Path(__file__).parent / "data" / "effective_area_IDL_testing_files" 
+    assert data_dir.exists(), f"Data directory {data_dir} does not exist."
+    files = sorted(data_dir.glob("**/*.txt")) 
+    #print(f"\n\n\nFound files: {files}\n\n")  # Debugging output
+    return files
+
+
+
 @pytest.mark.parametrize("filename", get_IDL_data_files())
 def test_effective_area_compare_idl(filename):
-    with Path.open(filename) as f:
+    print(f"\n\nTesting file: {filename}\n")
+
+    # Read the filter name and observation date from the file
+    with filename.open() as f:
         filter_name = f.readline().split()[1]
         filter_obs_date = " ".join(f.readline().split()[1:])
-    # NOTE: Annoyingly the date strings use "Sept" instead of "Sep" for "September"
+        print(f"Filter name: {filter_name}, Observation date: {filter_obs_date}")  # Debugging output
+
+    # Correct non-standard date format
     filter_obs_date = filter_obs_date.replace("Sept", "Sep")
+
+    # Load IDL data from the file
     IDL_data = np.loadtxt(filename, skiprows=3)
     IDL_wavelength = IDL_data[:, 0] * u.AA
     IDL_effective_area = IDL_data[:, 1] * u.cm**2
+    #print(f"Loaded IDL data: {IDL_data.shape}\n")
+
+    # Compute effective area using XRTpy
     instance = EffectiveAreaFundamental(filter_name, filter_obs_date)
     actual_effective_area = instance.effective_area()
-    IDL_effective_area = np.interp(
-        instance.wavelength, IDL_wavelength, IDL_effective_area
-    )
+    #print(f"Calculated effective area (first 5): {actual_effective_area[:5]}\n")
+
+    # Interpolate IDL effective area values to align with XRTpy wavelengths
+    IDL_effective_area_interp = np.interp(instance.wavelength, IDL_wavelength, IDL_effective_area)
+    #print(f"Interpolated IDL effective area (first 5): {IDL_effective_area_interp[:5]}\n")
+
+    # Compare XRTpy and IDL effective areas using rtol=1e-4
     assert u.allclose(
         actual_effective_area,
-        IDL_effective_area,
-        rtol=1e-4, #Moderate Precision
-    )
+        IDL_effective_area_interp,
+        rtol=1e-4,
+    ), f"Effective areas differ for filter {filter_name} on {filter_obs_date}"
+
+
+## NOTE: This is marked as xfail because the IDL results that this test compares against
+## are incorrect due to the use of quadratic interpolation in the contamination curves
+## which leads to ringing near the edges in the contamination curve.
+## See https://github.com/HinodeXRT/xrtpy/pull/284#issuecomment-2334503108
+# @pytest.mark.xfail
+# @pytest.mark.parametrize("filename", get_IDL_data_files())
+# def test_effective_area_compare_idl(filename):
+#     with Path.open(filename) as f:
+#         filter_name = f.readline().split()[1]
+#         filter_obs_date = " ".join(f.readline().split()[1:])
+#     # NOTE: Annoyingly the date strings use "Sept" instead of "Sep" for "September"
+#     filter_obs_date = filter_obs_date.replace("Sept", "Sep")
+#     IDL_data = np.loadtxt(filename, skiprows=3)
+#     IDL_wavelength = IDL_data[:, 0] * u.AA
+#     IDL_effective_area = IDL_data[:, 1] * u.cm**2
+#     instance = EffectiveAreaFundamental(filter_name, filter_obs_date)
+#     actual_effective_area = instance.effective_area()
+#     IDL_effective_area = np.interp(
+#         instance.wavelength, IDL_wavelength, IDL_effective_area
+#     )
+#     assert u.allclose(
+#         actual_effective_area,
+#         IDL_effective_area,
+#         rtol=1e-4, #Moderate Precision
+#     )