Merge branch 'main' into rcal-930-l3wcs

spacetelescope · Jan 23, 2025 · 1596319 · 1596319
2 parents 1133fbd + f8a98f1
commit 1596319
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Showing 12 changed files with 79 additions and 82 deletions.
diff --git a/changes/1474.general.rst b/changes/1474.general.rst
@@ -0,0 +1 @@
+Remove units from reference file datamodels.
diff --git a/pyproject.toml b/pyproject.toml
@@ -23,8 +23,8 @@ dependencies = [
     "photutils >=1.13.0",
     "pyparsing >=2.4.7",
     "requests >=2.26",
-    # "roman_datamodels>=0.22.0,<0.23.0",
-    "roman_datamodels @ git+https://github.com/spacetelescope/roman_datamodels.git",
+    "roman_datamodels>=0.23.0,<0.24.0",
+    #"roman_datamodels @ git+https://github.com/spacetelescope/roman_datamodels.git",
     "scipy >=1.14.1",
     # "stcal>=1.10.0,<1.11.0",
     "stcal @ git+https://github.com/spacetelescope/stcal.git@main",

diff --git a/romancal/dark_current/dark_current_step.py b/romancal/dark_current/dark_current_step.py
@@ -56,7 +56,7 @@ def process(self, input):
             # Do the dark correction
             out_model = input_model
             nresultants = len(input_model.meta.exposure["read_pattern"])
-            out_model.data -= dark_model.data[:nresultants].value
+            out_model.data -= dark_model.data[:nresultants]
             out_model.pixeldq |= dark_model.dq
             out_model.meta.cal_step.dark = "COMPLETE"
 

diff --git a/romancal/dark_current/tests/test_dark.py b/romancal/dark_current/tests/test_dark.py
@@ -60,14 +60,14 @@ def test_dark_step_subtraction(instrument, exptype):
     # populate data array of science cube
     for i in range(0, 20):
         ramp_model.data[0, 0, i] = i
-        darkref_model.data[0, 0, i] = i * 0.1 * darkref_model.data.unit
+        darkref_model.data[0, 0, i] = i * 0.1
     orig_model = ramp_model.copy()
 
     # Perform Dark Current subtraction step
     result = DarkCurrentStep.call(ramp_model, override_dark=darkref_model)
 
     # check that the dark file is subtracted frame by frame from the science data
-    diff = orig_model.data - darkref_model.data.value
+    diff = orig_model.data - darkref_model.data
 
     # test that the output data file is equal to the difference found when subtracting
     # reffile from sci file

diff --git a/romancal/photom/photom.py b/romancal/photom/photom.py
@@ -26,16 +26,14 @@ def photom_io(input_model, photom_metadata):
 
     # Store the conversion factor in the meta data
     log.info(f"photmjsr value: {conversion:.6g}")
-    input_model.meta.photometry.conversion_megajanskys = conversion.value
+    input_model.meta.photometry.conversion_megajanskys = conversion
 
     # Get the scalar conversion uncertainty factor
     uncertainty_conv = photom_metadata["uncertainty"]
 
     # Store the uncertainty conversion factor in the meta data
     log.info(f"uncertainty value: {uncertainty_conv:.6g}")
-    input_model.meta.photometry.conversion_megajanskys_uncertainty = (
-        uncertainty_conv.value
-    )
+    input_model.meta.photometry.conversion_megajanskys_uncertainty = uncertainty_conv
 
     # Return updated input model
     return input_model
@@ -56,9 +54,10 @@ def save_area_info(input_model, photom_parameters):
     """
 
     # Load the average pixel area values from the photom reference file header
-    area_ster = photom_parameters["pixelareasr"].value
+    area_ster = photom_parameters["pixelareasr"]
 
     # Copy the pixel area values to the input model
+    log.debug(f"pixel_area = {area_ster}")
     input_model.meta.photometry.pixel_area = area_ster
 
     # Return updated input model

diff --git a/romancal/photom/tests/test_photom.py b/romancal/photom/tests/test_photom.py
@@ -44,19 +44,13 @@ def create_photom_wfi_image(min_r=3.1, delta=0.1):
     nrows = len(optical_element)
 
     # Create sample photometry keyword values
-    photmjsr = (
-        np.linspace(min_r, min_r + (nrows - 1.0) * delta, nrows)
-        * u.megajansky
-        / u.steradian
-    )
-    uncertainty = (
-        np.linspace(min_r / 20.0, min_r / 20.0 + (nrows - 1.0) * delta / 20.0, nrows)
-        * u.megajansky
-        / u.steradian
+    photmjsr = np.linspace(min_r, min_r + (nrows - 1.0) * delta, nrows)
+    uncertainty = np.linspace(
+        min_r / 20.0, min_r / 20.0 + (nrows - 1.0) * delta / 20.0, nrows
     )
 
     # Create sample area keyword values
-    area_ster = 2.31307642258977e-14 * u.steradian
+    area_ster = 2.31307642258977e-14
     pixelareasr = np.ones(nrows, dtype=np.float64) * area_ster
 
     # Bundle values into a list
@@ -131,32 +125,32 @@ def test_apply_photom1():
     output_model = photom.apply_photom(input_model, photom_model)
 
     # Set reference photometry
-    area_ster = 2.31307642258977e-14 * u.steradian
+    area_ster = 2.31307642258977e-14
 
     # Tests for pixel areas
     assert np.isclose(
         output_model.meta.photometry.pixel_area,
-        area_ster.value,
+        area_ster,
         atol=1.0e-7,
     )
 
     # Set reference photometry
-    phot_ster = 3.5 * u.megajansky / u.steradian
+    phot_ster = 3.5
 
     # Tests for photometry
     assert np.isclose(
         output_model.meta.photometry.conversion_megajanskys,
-        phot_ster.value,
+        phot_ster,
         atol=1.0e-7,
     )
 
     # Set reference photometric uncertainty
-    muphot_ster = 0.175 * u.megajansky / u.steradian
+    muphot_ster = 0.175
 
     # Tests for photometric uncertainty
     assert np.isclose(
         output_model.meta.photometry.conversion_megajanskys_uncertainty,
-        muphot_ster.value,
+        muphot_ster,
         atol=1.0e-7,
     )
 

diff --git a/romancal/ramp_fitting/ramp_fit_step.py b/romancal/ramp_fitting/ramp_fit_step.py
@@ -6,7 +6,6 @@
 from typing import TYPE_CHECKING
 
 import numpy as np
-from astropy import units as u
 from roman_datamodels import datamodels as rdd
 from roman_datamodels import maker_utils
 from roman_datamodels import stnode as rds
@@ -109,8 +108,8 @@ def ols_cas22(self, input_model, readnoise_model, gain_model, dark_model):
 
         resultants = input_model.data
         dq = input_model.groupdq
-        read_noise = readnoise_model.data.value
-        gain = gain_model.data.value
+        read_noise = readnoise_model.data
+        gain = gain_model.data
         read_time = input_model.meta.exposure.frame_time
 
         # Force read pattern to be pure lists not LNodes
@@ -121,9 +120,7 @@ def ols_cas22(self, input_model, readnoise_model, gain_model, dark_model):
         # add dark current back into resultants so that Poisson noise is
         # properly accounted for
         tbar = np.array([np.mean(reads) * read_time for reads in read_pattern])
-        resultants += (
-            dark_model.dark_slope.to(u.DN / u.s).value[None, ...] * tbar[:, None, None]
-        )
+        resultants += dark_model.dark_slope[None, ...] * tbar[:, None, None]
 
         # account for the gain
         resultants *= gain
@@ -148,7 +145,7 @@ def ols_cas22(self, input_model, readnoise_model, gain_model, dark_model):
         dq = output.dq.astype(np.uint32)
 
         # remove dark current contribution to slopes
-        slopes -= dark_model.dark_slope.to(u.DN / u.s).value * gain
+        slopes -= dark_model.dark_slope * gain
 
         # Propagate DQ flags forward.
         ramp_dq = get_pixeldq_flags(dq, input_model.pixeldq, slopes, err, gain)

diff --git a/romancal/ramp_fitting/tests/test_ramp_fit_cas22.py b/romancal/ramp_fitting/tests/test_ramp_fit_cas22.py
@@ -2,7 +2,6 @@
 
 import numpy as np
 import pytest
-from astropy import units as u
 from astropy.time import Time
 from roman_datamodels import maker_utils
 from roman_datamodels.datamodels import (
@@ -272,23 +271,11 @@ def generate_wfi_reffiles(
     gain_ref["meta"]["useafter"] = Time("2022-01-01T11:11:11.111")
 
     if randomize:
-        gain_ref["data"] = u.Quantity(
-            (rng.random(shape) * 0.5).astype(np.float32) * ingain,
-            u.electron / u.DN,
-            dtype=np.float32,
-        )
+        gain_ref["data"] = (rng.random(shape) * 0.5).astype(np.float32) * ingain
     else:
-        gain_ref["data"] = u.Quantity(
-            np.ones(shape).astype(np.float32) * ingain,
-            u.electron / u.DN,
-            dtype=np.float32,
-        )
+        gain_ref["data"] = np.ones(shape).astype(np.float32) * ingain
     gain_ref["dq"] = np.zeros(shape, dtype=np.uint16)
-    gain_ref["err"] = u.Quantity(
-        (rng.random(shape) * 0.05).astype(np.float32),
-        u.electron / u.DN,
-        dtype=np.float32,
-    )
+    gain_ref["err"] = (rng.random(shape) * 0.05).astype(np.float32)
 
     gain_ref_model = GainRefModel(gain_ref)
 
@@ -303,15 +290,9 @@ def generate_wfi_reffiles(
     rn_ref["meta"]["exposure"]["frame_time"] = 666
 
     if randomize:
-        rn_ref["data"] = u.Quantity(
-            (rng.random(shape) * rnoise).astype(np.float32),
-            u.DN,
-            dtype=np.float32,
-        )
+        rn_ref["data"] = ((rng.random(shape) * rnoise).astype(np.float32),)
     else:
-        rn_ref["data"] = u.Quantity(
-            np.ones(shape).astype(np.float32) * rnoise, u.DN, dtype=np.float32
-        )
+        rn_ref["data"] = np.ones(shape).astype(np.float32) * rnoise
 
     rn_ref_model = ReadnoiseRefModel(rn_ref)
 
@@ -327,9 +308,7 @@ def generate_wfi_reffiles(
     dark_ref["meta"]["exposure"]["type"] = "WFI_IMAGE"
     dark_ref["meta"]["exposure"]["frame_time"] = 666
 
-    dark_ref["dark_slope"] = u.Quantity(
-        np.zeros(shape).astype(np.float32) * 0.01, u.DN / u.s, dtype=np.float32
-    )
+    dark_ref["dark_slope"] = np.zeros(shape).astype(np.float32) * 0.01
 
     dark_ref_model = DarkRefModel(dark_ref)
 

diff --git a/romancal/regtest/test_wfi_photom.py b/romancal/regtest/test_wfi_photom.py
@@ -56,18 +56,19 @@ def test_absolute_photometric_calibration(rtdata, ignore_asdf_paths):
     )
     assert photom_out.meta.cal_step.photom == "COMPLETE"
 
+    convval = 0.73678
     step.log.info(
         "DMS140 MSG: Photom megajansky conversion calculated? : "
         + str(
             math.isclose(
                 photom_out.meta.photometry.conversion_megajanskys,
-                0.3324,
+                convval,
                 abs_tol=0.0001,
             )
         )
     )
     assert math.isclose(
-        photom_out.meta.photometry.conversion_megajanskys, 0.3324, abs_tol=0.0001
+        photom_out.meta.photometry.conversion_megajanskys, convval, abs_tol=0.0001
     )
 
     step.log.info(
@@ -86,19 +87,20 @@ def test_absolute_photometric_calibration(rtdata, ignore_asdf_paths):
         abs_tol=1.0e-17,
     )
 
+    uncval = 0.02866405
     step.log.info(
         "DMS140 MSG: Photom megajansky conversion uncertainty calculated? : "
         + str(
             math.isclose(
                 photom_out.meta.photometry.conversion_megajanskys_uncertainty,
-                0.0,
+                uncval,
                 abs_tol=1.0e-6,
             )
         )
     )
     assert math.isclose(
         photom_out.meta.photometry.conversion_megajanskys_uncertainty,
-        0.0,
+        uncval,
         abs_tol=1.0e-6,
     )