Merge pull request #262 from Jammy2211/feature/keep_the_noise_down

Feature/keep the noise down

autolens/analysis/analysis/dataset.py

@@ -24,6 +24,7 @@
 
 logger.setLevel(level="INFO")
 
+
 class AnalysisDataset(AgAnalysisDataset, AnalysisLens):
     def __init__(
         self,
@@ -192,4 +193,4 @@ def save_results(self, paths: af.DirectoryPaths, result: ResultDataset):
                 object_dict=to_dict(
                     result.max_log_likelihood_fit.tracer_to_inversion.image_plane_mesh_grid_pg_list
                 ),
-            )
+            )

autolens/fixtures.py

@@ -165,6 +165,7 @@ def make_analysis_interferometer_7():
     analysis._adapt_images = make_adapt_images_7x7()
     return analysis
 
+
 def make_analysis_point_x2():
     return al.AnalysisPoint(
         point_dict=make_point_dict(),

autolens/interferometer/model/analysis.py

@@ -365,7 +365,7 @@ def make_result(
             paths=paths,
             samples=samples,
             analysis=self,
-            search_internal=search_internal
+            search_internal=search_internal,
         )
 
     def save_attributes(self, paths: af.DirectoryPaths):

autolens/lens/tracer_util.py

@@ -238,8 +238,8 @@ def grid_2d_at_redshift_from(
         return traced_grid_list[plane_index_with_redshift[0]]
 
     for plane_index, plane_redshift in enumerate(plane_redshifts):
-        if redshift < plane_redshift:
-            plane_index_insert = plane_index
+        if redshift > plane_redshift:
+            plane_index_insert = plane_index + 1
 
     planes.insert(plane_index_insert, [ag.Galaxy(redshift=redshift)])
 

autolens/quantity/model/analysis.py

@@ -164,5 +164,5 @@ def make_result(
             paths=paths,
             samples=samples,
             search_internal=search_internal,
-            analysis=self
+            analysis=self,
         )

test_autolens/aggregator/conftest.py

@@ -66,7 +66,6 @@ def make_model():
 
 @pytest.fixture(name="samples")
 def make_samples(model):
-
     parameters = [model.prior_count * [1.0], model.prior_count * [10.0]]
 
     sample_list = Sample.from_lists(

test_autolens/analysis/analysis/test_analysis_lens.py

@@ -69,4 +69,4 @@ def test__tracer_for_instance__subhalo_redshift_rescale_used(analysis_imaging_7x
     instance = model.instance_from_unit_vector([])
     tracer = analysis_imaging_7x7.tracer_via_instance_from(instance=instance)
 
-    assert tracer.galaxies[1].mass.centre == pytest.approx((-0.19959, -0.39919), 1.0e-4)
+    assert tracer.galaxies[1].mass.centre == pytest.approx((-0.19959, -0.39919), 1.0e-4)

test_autolens/analysis/test_result.py

@@ -133,7 +133,9 @@ def test__source_plane_inversion_centre(analysis_imaging_7x7):
 
     samples_summary = al.m.MockSamplesSummary(max_log_likelihood_instance=tracer)
 
-    result = ResultImaging(samples_summary=samples_summary, analysis=analysis_imaging_7x7)
+    result = ResultImaging(
+        samples_summary=samples_summary, analysis=analysis_imaging_7x7
+    )
 
     assert (
         result.source_plane_inversion_centre.in_list[0]
@@ -151,7 +153,9 @@ def test__source_plane_inversion_centre(analysis_imaging_7x7):
 
     samples_summary = al.m.MockSamplesSummary(max_log_likelihood_instance=tracer)
 
-    result = ResultImaging(samples_summary=samples_summary, analysis=analysis_imaging_7x7)
+    result = ResultImaging(
+        samples_summary=samples_summary, analysis=analysis_imaging_7x7
+    )
 
     assert result.source_plane_inversion_centre == None
 
@@ -162,7 +166,9 @@ def test__source_plane_inversion_centre(analysis_imaging_7x7):
 
     samples_summary = al.m.MockSamplesSummary(max_log_likelihood_instance=tracer)
 
-    result = ResultImaging(samples_summary=samples_summary, analysis=analysis_imaging_7x7)
+    result = ResultImaging(
+        samples_summary=samples_summary, analysis=analysis_imaging_7x7
+    )
 
     assert result.source_plane_inversion_centre == None
 
@@ -185,7 +191,9 @@ def test__source_plane_centre(analysis_imaging_7x7):
 
     samples_summary = al.m.MockSamplesSummary(max_log_likelihood_instance=tracer)
 
-    result = ResultImaging(samples_summary=samples_summary, analysis=analysis_imaging_7x7)
+    result = ResultImaging(
+        samples_summary=samples_summary, analysis=analysis_imaging_7x7
+    )
 
     assert result.source_plane_centre.in_list[0] == pytest.approx(
         (-0.916666, -0.916666), 1.0e-4
@@ -216,7 +224,9 @@ def test__image_plane_multiple_image_positions(analysis_imaging_7x7):
 
     samples_summary = al.m.MockSamplesSummary(max_log_likelihood_instance=tracer)
 
-    result = ResultImaging(samples_summary=samples_summary, analysis=analysis_imaging_7x7)
+    result = ResultImaging(
+        samples_summary=samples_summary, analysis=analysis_imaging_7x7
+    )
 
     multiple_images = result.image_plane_multiple_image_positions
 

test_autolens/config/general.yaml

@@ -36,7 +36,7 @@ profiling:
   perform: true
   repeats: 1
 test:
-  check_likelihood_function: true   # if True, when a search is resumed the likelihood of a previous sample is recalculated to ensure it is consistent with the previous run.
+  check_likelihood_function: false  # if True, when a search is resumed the likelihood of a previous sample is recalculated to ensure it is consistent with the previous run.
   check_preloads: false
   exception_override: false
   preloads_check_threshold: 1.0     # If the figure of merit of a fit with and without preloads is greater than this threshold, the check preload test fails and an exception raised for a model-fit. 

test_autolens/config/output.yaml

@@ -0,0 +1,63 @@
+# Determines whether files saved by the search are output to the hard-disk. This is true both when saving to the
+# directory structure and when saving to database.
+
+# Files can be listed name: bool where the name is the name of the file without a suffix (e.g. model not model.json)
+# and bool is true or false.
+
+# If a given file is not listed then the default value is used.
+
+default: true # If true then files which are not explicitly listed here are output anyway. If false then they are not.
+
+### Samples ###
+
+# The `samples.csv`file contains every sampled value of every free parameter with its log likelihood and weight.
+
+# This file is often large, therefore disabling it can significantly reduce hard-disk space use.
+
+# `samples.csv` is used to perform marginalization, infer model parameter errors and do other analysis of the search
+# chains. Even if output of `samples.csv` is disabled, these tasks are still performed by the fit and output to
+# the `samples_summary.json` file. However, without a `samples.csv` file these types of tasks cannot be performed
+# after the fit is complete, for example via the database.
+
+samples: true
+
+# The `samples.csv` file contains every accepted sampled value of every free parameter with its log likelihood and
+# weight. For certain searches, the majority of samples have a very low weight, which has no numerical impact on the
+# results of the model-fit. However, these samples are still output to the `samples.csv` file, taking up hard-disk space
+# and slowing down analysis of the samples (e.g. via the database).
+
+# The `samples_weight_threshold` below specifies the threshold value of the weight such that samples with a weight
+# below this value are not output to the `samples.csv` file. This can be used to reduce the size of the `samples.csv`
+# file and speed up analysis of the samples.
+
+# Note that for many searches (e.g. MCMC) all samples have equal weight, and thus this threshold has no impact and
+# there is no simple way to save hard-disk space. However, for nested sampling, the majority of samples have a very
+# low weight and this threshold can be used to save hard-disk space.
+
+# Set value to empty (e.g. delete 1.0e-10 below) to disable this feature.
+
+samples_weight_threshold: 1.0e-10
+
+### Search Internal ###
+
+# The search internal folder which contains a saved state of the non-linear search, as a .pickle or .dill file.
+
+# If the entry below is false, the folder is still output during the model-fit, as it is required to resume the fit
+# from where it left off. Therefore, settings `false` below does not impact model-fitting checkpointing and resumption.
+# Instead, the search internal folder is deleted once the fit is completed.
+
+# The search internal folder file is often large, therefore deleting it after a fit is complete can significantly
+# reduce hard-disk space use.
+
+# The search internal representation (e.g. what you can load from the output .pickle file) may have additional
+# quantities specific to the non-linear search that you are interested in inspecting. Deleting the folder means this
+# information is list.
+
+search_internal: false
+
+# Other Files:
+
+covariance: false # `covariance.csv`: The [free parameters x free parameters] covariance matrix.
+data: true # `data.json`: The value of every data point in the data.
+noise_map: true # `noise_map.json`: The value of every RMS noise map value.
+

test_autolens/imaging/model/test_analysis_imaging.py

@@ -19,13 +19,6 @@ def test__make_result__result_imaging_is_returned(masked_imaging_7x7):
 
     analysis = al.AnalysisImaging(dataset=masked_imaging_7x7)
 
-    def modify_after_fit(
-        paths: af.DirectoryPaths, model: af.AbstractPriorModel, result: af.Result
-    ):
-        pass
-
-    analysis.modify_after_fit = modify_after_fit
-
     search = al.m.MockSearch(name="test_search")
 
     result = search.fit(model=model, analysis=analysis)

test_autolens/interferometer/model/test_analysis_interferometer.py

@@ -15,13 +15,6 @@ def test__make_result__result_interferometer_is_returned(interferometer_7):
 
     analysis = al.AnalysisInterferometer(dataset=interferometer_7)
 
-    def modify_after_fit(
-        paths: af.DirectoryPaths, model: af.AbstractPriorModel, result: af.Result
-    ):
-        pass
-
-    analysis.modify_after_fit = modify_after_fit
-
     search = al.m.MockSearch(name="test_search")
 
     result = search.fit(model=model, analysis=analysis)

test_autolens/lens/test_tracer.py

@@ -154,13 +154,38 @@ def test__grid_2d_at_redshift_from(sub_grid_2d_7x7):
 
     tracer = al.Tracer(galaxies=galaxies, cosmology=al.cosmo.Planck15())
 
+    galaxies_plus_extra = [g0, g1, g2, g3, g4, g5, al.Galaxy(redshift=0.5)]
+    tracer_plus_extra = al.Tracer(
+        galaxies=galaxies_plus_extra, cosmology=al.cosmo.Planck15()
+    )
+    grid_2d_list_from = tracer_plus_extra.traced_grid_2d_list_from(grid=grid_simple)
+
     grid_at_redshift = tracer.grid_2d_at_redshift_from(grid=grid_simple, redshift=0.5)
 
-    assert grid_at_redshift[0] == pytest.approx((9.73109691, 19.46219382), 1.0e-4)
+    assert grid_2d_list_from[1] == pytest.approx(grid_at_redshift, 1.0e-4)
+    assert grid_at_redshift[0] == pytest.approx((0.6273814, 1.2547628), 1.0e-4)
+
+    galaxies_plus_extra = [g0, g1, g2, g3, g4, g5, al.Galaxy(redshift=1.75)]
+    tracer_plus_extra = al.Tracer(
+        galaxies=galaxies_plus_extra, cosmology=al.cosmo.Planck15()
+    )
+    grid_2d_list_from = tracer_plus_extra.traced_grid_2d_list_from(grid=grid_simple)
 
     grid_at_redshift = tracer.grid_2d_at_redshift_from(grid=grid_simple, redshift=1.75)
 
-    assert grid_at_redshift[0] == pytest.approx((0.65903649, 1.31807298), 1.0e-4)
+    assert grid_2d_list_from[2] == pytest.approx(grid_at_redshift, 1.0e-4)
+    assert grid_at_redshift[0] == pytest.approx((0.27331481161, 0.5466296232), 1.0e-4)
+
+    galaxies_plus_extra = [g0, g1, g2, g3, g4, g5, al.Galaxy(redshift=2.0)]
+    tracer_plus_extra = al.Tracer(
+        galaxies=galaxies_plus_extra, cosmology=al.cosmo.Planck15()
+    )
+    grid_2d_list_from = tracer_plus_extra.traced_grid_2d_list_from(grid=grid_simple)
+
+    grid_at_redshift = tracer.grid_2d_at_redshift_from(grid=grid_simple, redshift=2.0)
+
+    assert grid_2d_list_from[2] == pytest.approx(grid_at_redshift, 1.0e-4)
+    assert grid_at_redshift[0] == pytest.approx((0.222772465, 0.445544931), 1.0e-4)
 
 
 def test__image_2d_list_from():

test_autolens/quantity/model/test_analysis_quantity.py

@@ -7,9 +7,8 @@
 
 directory = path.dirname(path.realpath(__file__))
 
-def test__make_result__result_quantity_is_returned(
-    dataset_quantity_7x7_array_2d
-):
+
+def test__make_result__result_quantity_is_returned(dataset_quantity_7x7_array_2d):
     model = af.Collection(galaxies=af.Collection(galaxy_0=al.Galaxy(redshift=0.5)))
 
     analysis = al.AnalysisQuantity(
@@ -22,8 +21,9 @@ def test__make_result__result_quantity_is_returned(
 
     assert isinstance(result, ResultQuantity)
 
+
 def test__figure_of_merit__matches_correct_fit_given_galaxy_profiles(
-    dataset_quantity_7x7_array_2d
+    dataset_quantity_7x7_array_2d,
 ):
     galaxy = al.Galaxy(redshift=0.5, light=al.mp.Isothermal(einstein_radius=1.0))