remove hyper test

Jammy2211 · Aug 20, 2024 · d95d040 · d95d040
1 parent 9f31531
commit d95d040
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Showing 2 changed files with 1 addition and 86 deletions.
diff --git a/requirements.txt b/requirements.txt
@@ -1 +1 @@
-nautilus-sampler==1.0.2
+nautilus-sampler==1.0.4
diff --git a/test_autocti/charge_injection/model/test_analysis_ci.py b/test_autocti/charge_injection/model/test_analysis_ci.py
@@ -225,88 +225,3 @@ def test__full_and_extracted_fits_from_instance_and_imaging_ci(
     assert fit_full_analysis.log_likelihood == pytest.approx(fit.log_likelihood)
 
 
-def test__extracted_fits_from_instance_and_imaging_ci__include_noise_scaling(
-    imaging_ci_7x7,
-    mask_2d_7x7_unmasked,
-    traps_x1,
-    ccd,
-    parallel_clocker_2d,
-    layout_ci_7x7,
-):
-    model = af.Collection(
-        cti=af.Model(ac.CTI2D, parallel_trap_list=traps_x1, parallel_ccd=ccd),
-        hyper_noise=af.Model(
-            ac.HyperCINoiseCollection, regions_ci=ac.HyperCINoiseScalar
-        ),
-    )
-
-    imaging_ci_7x7.noise_scaling_map_dict = {
-        "regions_ci": ac.Array2D.ones(shape_native=(7, 7), pixel_scales=1.0).native
-    }
-
-    imaging_ci_full = copy.deepcopy(imaging_ci_7x7)
-
-    masked_dataset = imaging_ci_7x7.apply_mask(mask=mask_2d_7x7_unmasked)
-    masked_dataset = masked_dataset.apply_settings(
-        settings=ac.SettingsImagingCI(parallel_pixels=(0, 1))
-    )
-
-    analysis = ac.AnalysisImagingCI(dataset=masked_dataset, clocker=parallel_clocker_2d)
-
-    instance = model.instance_from_prior_medians()
-
-    fit_analysis = analysis.fit_via_instance_from(
-        instance=instance, hyper_noise_scale=True
-    )
-
-    cti = ac.CTI2D(parallel_trap_list=traps_x1, parallel_ccd=ccd)
-
-    post_cti_data = parallel_clocker_2d.add_cti(
-        data=masked_dataset.pre_cti_data, cti=cti
-    )
-
-    fit = ac.FitImagingCI(
-        dataset=masked_dataset,
-        post_cti_data=post_cti_data,
-        hyper_noise_scalar_dict={
-            "regions_ci": instance.hyper_noise.as_dict["regions_ci"]
-        },
-    )
-
-    assert fit.data.shape == (7, 1)
-    assert fit.log_likelihood == pytest.approx(fit_analysis.log_likelihood, 1.0e-4)
-
-    fit = ac.FitImagingCI(
-        dataset=masked_dataset,
-        post_cti_data=post_cti_data,
-        hyper_noise_scalar_dict={"regions_ci": ac.HyperCINoiseScalar(scale_factor=0.0)},
-    )
-
-    assert fit.log_likelihood != pytest.approx(fit_analysis.log_likelihood, 1.0e-4)
-
-    fit_full_analysis = analysis.fit_via_instance_and_dataset_from(
-        instance=instance,
-        dataset=imaging_ci_full,
-        hyper_noise_scale=True,
-    )
-
-    masked_dataset = imaging_ci_7x7.apply_mask(mask=mask_2d_7x7_unmasked)
-
-    fit = ac.FitImagingCI(
-        dataset=masked_dataset,
-        post_cti_data=post_cti_data,
-        hyper_noise_scalar_dict={
-            "regions_ci": instance.hyper_noise.as_dict["regions_ci"]
-        },
-    )
-
-    assert fit.data.shape == (7, 7)
-    assert fit.log_likelihood == pytest.approx(fit_full_analysis.log_likelihood, 1.0e-4)
-
-    fit = ac.FitImagingCI(
-        dataset=masked_dataset,
-        post_cti_data=post_cti_data,
-        hyper_noise_scalar_dict={"regions_ci": ac.HyperCINoiseScalar(scale_factor=0.0)},
-    )
-
-    assert fit.log_likelihood != pytest.approx(fit_full_analysis.log_likelihood, 1.0e-4)