DM-28542: Implement RFC-750 #457

fred3m · 2021-01-29T21:36:15Z

This PR fixes a few things:

It refactors isPrimary to use vectorized columns
instead of iteratinf over sources
It appropriately uses the scarlet outputs to choose
which type of isolated sources are characterized
by isPrimary, and it adds the columns isSimpleLeaf
and isModelLeaf to the catalog to differentiate
between the two different types of isolted sources
Sets isPrimary to isSimpleLeaf, mimicking the behavior
of meas_deblender

python/lsst/pipe/tasks/setPrimaryFlags.py

laurenam · 2021-02-15T04:17:48Z

python/lsst/pipe/tasks/setPrimaryFlags.py

+    isolated parents, and a ModelLeaf, which uses
+    the scarlet models for both isolated and blended sources.
+    In the case of meas_deblender, a ModelLeaf is the same thing
+    as a blended source, and a SimpleModel has the same definition.


SimpleModel?

Good catch, SimpleLeaf.

laurenam · 2021-02-15T04:19:45Z

python/lsst/pipe/tasks/setPrimaryFlags.py

+        nChildKey = "deblend_nChild"
+    nChild = sources[nChildKey]
+    parent = sources["parent"]
+    isModelLeaf = (parent != 0) & (nChild == 0)


Doesn't this explicitly exclude all truly isolated sources (i.e. with parent == 0 & nChild == 0)?

No, because there are no parent==0 & nChild == 0 sources in the scarlet outputs. nChild describes the number of children in the catalog deblended from a parent, and because scarlet will deblend all of the isolated sources, all of the isolated parents have nChild == 1. Maybe that's what you were missing? If you can think of a way for me to make that more clear in the docs let me know.

No, not in scarlet...but what if someone retargets with meas_deblender? Another way to ask the question is "should there be a difference between isSimpleLeaf and isModelLeaf for catalogs deblended with meas_deblender? Taking it a step further, I'm inclined argue that meas_deblender deblended catalogues should not have the isSimpleLeaf and isModelLeaf columns added since they don't really hold any meaning there.

I just ran processCcd on this branch, so only meas_deblender is in play. The source catalog reveals the following:

In [51]: np.all(srcNew["detect_isPrimary"] == srcNew["detect_isSimpleLeaf"]) Out[51]: True In [52]: np.all(srcNew["detect_isPrimary"] == srcNew["detect_isModelLeaf"]) Out[52]: False

So...either the difference between isSimpleLeaf and isModelLeaf is meaningless and those columns should not even be there, or they have meaning and I'm still not "getting it" 😜

python/lsst/pipe/tasks/setPrimaryFlags.py

laurenam · 2021-02-15T04:36:15Z

Commit message: iteratinf --> iterating?

Also, I don't know if it's quite accurate to say anything is "mimicking" the isPrimary setting of meas_deblender. Perhaps state it more loosely, something along the lines "coming as close as possible to..."

fred3m · 2021-02-16T18:01:17Z

Also, I don't know if it's quite accurate to say anything is "mimicking" the isPrimary setting of meas_deblender. Perhaps state it more loosely, something along the lines "coming as close as possible to..."

It think that this was already answered in my previous comments, but just to make sure that this comment isn't dangling, isPrimary returns the exact same set of sources (unblended isolated sources and children from parent blends with more than 1 source) as meas_deblender.

python/lsst/pipe/tasks/setPrimaryFlags.py

laurenam · 2021-02-22T00:14:12Z

python/lsst/pipe/tasks/setPrimaryFlags.py

+        nChildKey = "deblend_nChild"
+    nChild = sources[nChildKey]
+    parent = sources["parent"]
+    isModelLeaf = (parent != 0) & (nChild == 0)


No, not in scarlet...but what if someone retargets with meas_deblender? Another way to ask the question is "should there be a difference between isSimpleLeaf and isModelLeaf for catalogs deblended with meas_deblender? Taking it a step further, I'm inclined argue that meas_deblender deblended catalogues should not have the isSimpleLeaf and isModelLeaf columns added since they don't really hold any meaning there.

I just ran processCcd on this branch, so only meas_deblender is in play. The source catalog reveals the following:

In [51]: np.all(srcNew["detect_isPrimary"] == srcNew["detect_isSimpleLeaf"]) Out[51]: True In [52]: np.all(srcNew["detect_isPrimary"] == srcNew["detect_isModelLeaf"]) Out[52]: False

So...either the difference between isSimpleLeaf and isModelLeaf is meaningless and those columns should not even be there, or they have meaning and I'm still not "getting it" 😜

laurenam · 2021-02-22T00:17:26Z

python/lsst/pipe/tasks/setPrimaryFlags.py

+            isPatchInner = getPatchInner(sources, patchInfo)
+            isTractInner = getTractInner(sources, tractInfo, skyMap)
+            isPseudo = getPseudoSources(sources, self.config.pseudoFilterList, self.schema, self.log)
+            isPrimary = isTractInner & isPatchInner & ~isPseudo


Yeah, so this is not really isPrimary as defined until line 272 is executed...it's just a step on its way to being "for reals" isPrimary.

tests/test_isPrimaryFlag.py

laurenam · 2021-02-22T00:37:18Z

tests/test_isPrimaryFlag.py

        outputCat = calibResults.outputCat
        self.assertTrue("detect_isPrimary" in outputCat.schema.getNames())
        # make sure all sky sources are flagged as not primary
        self.assertEqual(sum((outputCat["detect_isPrimary"]) & (outputCat["sky_source"])), 0)
        # make sure all parent sources are flagged as not primary
        self.assertEqual(sum((outputCat["detect_isPrimary"]) & (outputCat["deblend_nChild"] > 0)), 0)

+    def testIsScarletPrimaryFlag(self):


I think this test has some "issues"...debugging output follows (I wasn't following your skyMap mocking, so had to go digging...)

This should be cleaned up in the new commit. The previous version was using an older version of the mock skymap, tract, etc.

Ok, thanks. Now that you have the functionality, would you mind adding a similar test for meas_deblender runs and, in particular, making sure any scarlet-specific columns (whatever they be named) are NOT present? I.e. something like (warning: not fully tested):

def testIsMeasDeblenderPrimaryFlag(self): """Test detect_isPrimary column for meas_deblender runs. """ coadd = self.exposure # Create a SkyMap with a tract that contains a portion of the image, # subdivided into 3x3 patches. wcs = coadd.getWcs() tractBBox = Box2I(Point2I(100, 100), Extent2I(900, 900)) skyMap = MockSkyMap([tractBBox], wcs, 3) tractInfo = skyMap[0] patchInfo = tractInfo[0, 0] patchBBox = patchInfo.getInnerBBox() schema = SourceCatalog.Table.makeMinimalSchema() # Initialize the detection task detectionTask = SourceDetectionTask(schema=schema) # Initialize the fake source injection task skyConfig = SkyObjectsTask.ConfigClass() skySourcesTask = SkyObjectsTask(name="skySources", config=skyConfig) schema.addField("merge_peak_sky", type="Flag") # Initialize the deblender task deblendTask = SourceDeblendTask(schema=schema) # We'll customize the configuration of measurement to just run the # minimal number of plugins to make setPrimaryFlags work. measureConfig = SingleFrameMeasurementTask.ConfigClass() measureConfig.plugins.names = ["base_SdssCentroid", "base_SkyCoord"] measureConfig.slots.psfFlux = None measureConfig.slots.apFlux = None measureConfig.slots.shape = None measureConfig.slots.modelFlux = None measureConfig.slots.calibFlux = None measureConfig.slots.gaussianFlux = None measureTask = SingleFrameMeasurementTask(config=measureConfig, schema=schema) primaryConfig = SetPrimaryFlagsTask.ConfigClass() setPrimaryTask = SetPrimaryFlagsTask(config=primaryConfig, schema=schema, name="setPrimaryFlags", isSingleFrame=False) table = SourceCatalog.Table.make(schema) # detect sources detectionResult = detectionTask.run(table=table, exposure=coadd) catalog = detectionResult.sources # add fake sources skySources = skySourcesTask.run(mask=coadd.mask, seed=0) for foot in skySources[:5]: src = catalog.addNew() src.setFootprint(foot) src.set("merge_peak_sky", True) # deblend deblendTask.run(exposure=coadd, sources=catalog) catalog = catalog.copy(deep=True) measureTask.run(measCat=catalog, exposure=coadd) outputCat = catalog.copy(deep=True) # Set the primary flags setPrimaryTask.run(outputCat, skyMap=skyMap, tractInfo=tractInfo, patchInfo=patchInfo) # Check that SCARLET-only columns are NOT in the schema self.assertTrue("SCARLET_NAME" not in outputCat.schema.getNames()) # Check that the sources contained in a tract are all marked # appropriately. x = outputCat["slot_Centroid_x"] y = outputCat["slot_Centroid_y"] tractInner = tractBBox.contains(x, y) # tractInner = bboxContainsColumn(tractBBox, x, y) np.testing.assert_array_equal(outputCat["detect_isTractInner"], tractInner) # Check that the sources contained in a patch are all marked # appropriately. patchInner = bboxContainsColumn(patchBBox, x, y) np.testing.assert_array_equal(outputCat["detect_isPatchInner"], patchInner) # make sure all sky sources are flagged as not primary self.assertEqual(sum((outputCat["detect_isPrimary"]) & (outputCat["merge_peak_sky"])), 0)

I don't think that this is necessary, since meas_deblender (SourceDeblendTask) is run in testIsSinglePrimaryFlag. I can add some checks to make sure that the scarlet columns are not contained in the outputs, but I don't think that this test will touch any code that isn't covered by the two existing tests. Please correct me if I missed something.

It may seem like slight overkill, but testIsSinglePrimary above is for a single frame-like catalog (no tract or patch, different sky column name...different code path to setting the flags). It actually wasn’t tested before because no one had gone to the trouble of mocking up a skymap...but you did, so I’m taking full advantage of your efforts (but wrote the above myself in hopes of sparing you too much extra work for your efforts!)

I think that my latest push corrected everything but this. It isn't that it's an inconvenience to implement this test, it would be trivial to implement a deblender generic function and call it once with ScarletDeblendTask and once with SourceDeblendTask. My issue with this request is that it doesn't actually test any new code paths but will add to the overall runtime of the already long pipe_tasks tests. All of the deblend flags are logically separate from the tract/patch code blocks, and looking back through the code I am fairly confident that this module is fully covered without it.

We seem to be at odds on this one. My point is that the code path followed by a meas_deblender multiband run and the flag setting therein is not tested. It has never had a test (no mocked up skymap, yadda, yadda...), but now that you've added the functionality, an explicit test of the flag setting of this code path could easily be added. This long-standing failure-to-test the existing code path is not on you, though, so I will leave this to your discretion if you prefer not to add one.

Yes, either I'm not understanding you or you're not understanding me, but I'll give it one last shot.

You are correct that when meas_deblender is run, the flags set by deblending are not tested with the flags from a skymap. So if the results of the deblender were dependent on those flags in any way, then not testing that would be missing a possible source of failure. Or, if the flags set by the deblender or isPrimary were in any way correlated then this would also be something that needs to be tested.

But... the deblender is completely isolated and independent of those flags. Similarly, the creation of the deblender flags is completely independent of the patch/tract flags. So it doesn't matter which deblender the patch/tract flags are tested against, as long as they are tested against a set of deblender flags, which they are. I'm fairly certain that coverage of this module is 100%, but if you can find a code path that isn't tested then please let me know.

Ok, I'm convinced this can be left as is!

laurenam · 2021-02-22T00:59:34Z

python/lsst/pipe/tasks/setPrimaryFlags.py

+    isSimpleLeaf: `bool` array
+        True for each source that is a `SimpleLeaf` as defined above.
+    isModelLeaf: `bool` array
+        True for each sources that is a `ModelLeaf` as defined above.


Ok, I'll add a comment there. Should I also bring up the question of whether the scarlet-based "leaf" columns should be added to a meas_deblender schema?

laurenam · 2021-02-22T01:00:24Z

python/lsst/pipe/tasks/setPrimaryFlags.py

+                "detect_isModelLeaf", type="Flag",
+                doc=primaryDoc + " and is a deblended child")
+
+    def run(self, sources, skyMap=None, tractInfo=None, patchInfo=None):


That's not quite my point...

fred3m · 2021-02-22T18:02:57Z

Thanks for looking into the tests more. I actually tested them all in a jupyter notebook and verified that they were all working properly, not returning all False. So I was surprised to see your comments but after seeing I checked the tests again it looks like I didn’t update the repo with the correct version of the tests to make them work correctly. :(

So I'll update the tests and push those, as well as try to address some of your other comments.

tests/test_isPrimaryFlag.py

python/lsst/pipe/tasks/setPrimaryFlags.py

laurenam · 2021-03-02T03:15:19Z

python/lsst/pipe/tasks/setPrimaryFlags.py

+
+    Some categories of sources, for example sky objects,
+    are not really detected sources and should not be considered primary
+    sources.


I'm having trouble trying to find a pattern on your choices for line-breaks. You don't seem to ever really use the full 79 chars, and sometimes truncate differently in the same set of lines (as above!).

The problem with having a line limit for docstrings and code comments is that if you write all the way to the end of the line and want to edit it later, you have to reformat the entire paragraph after the edit, even if you're a single character or two over the very small line limit. So I always give myself a large buffer and try to break at logical points where there might be an edit later, for example a comma or period. But at the end of the paragraph, if there are only a few words on the next line, I don't really care as much about leaving space so I tend to go write all the way to the end.

It's why I'm a huge supporter of a suggested line length for docstrings and comments but no strict enforcement, which keeps the diffs down and makes it easier (and thus more likely) that I'll keep docs up to date.

laurenam · 2021-03-02T03:36:21Z

tests/test_isPrimaryFlag.py

        outputCat = calibResults.outputCat
        self.assertTrue("detect_isPrimary" in outputCat.schema.getNames())
        # make sure all sky sources are flagged as not primary
        self.assertEqual(sum((outputCat["detect_isPrimary"]) & (outputCat["sky_source"])), 0)
        # make sure all parent sources are flagged as not primary
        self.assertEqual(sum((outputCat["detect_isPrimary"]) & (outputCat["deblend_nChild"] > 0)), 0)

+    def testIsScarletPrimaryFlag(self):


Ok, thanks. Now that you have the functionality, would you mind adding a similar test for meas_deblender runs and, in particular, making sure any scarlet-specific columns (whatever they be named) are NOT present? I.e. something like (warning: not fully tested):

def testIsMeasDeblenderPrimaryFlag(self): """Test detect_isPrimary column for meas_deblender runs. """ coadd = self.exposure # Create a SkyMap with a tract that contains a portion of the image, # subdivided into 3x3 patches. wcs = coadd.getWcs() tractBBox = Box2I(Point2I(100, 100), Extent2I(900, 900)) skyMap = MockSkyMap([tractBBox], wcs, 3) tractInfo = skyMap[0] patchInfo = tractInfo[0, 0] patchBBox = patchInfo.getInnerBBox() schema = SourceCatalog.Table.makeMinimalSchema() # Initialize the detection task detectionTask = SourceDetectionTask(schema=schema) # Initialize the fake source injection task skyConfig = SkyObjectsTask.ConfigClass() skySourcesTask = SkyObjectsTask(name="skySources", config=skyConfig) schema.addField("merge_peak_sky", type="Flag") # Initialize the deblender task deblendTask = SourceDeblendTask(schema=schema) # We'll customize the configuration of measurement to just run the # minimal number of plugins to make setPrimaryFlags work. measureConfig = SingleFrameMeasurementTask.ConfigClass() measureConfig.plugins.names = ["base_SdssCentroid", "base_SkyCoord"] measureConfig.slots.psfFlux = None measureConfig.slots.apFlux = None measureConfig.slots.shape = None measureConfig.slots.modelFlux = None measureConfig.slots.calibFlux = None measureConfig.slots.gaussianFlux = None measureTask = SingleFrameMeasurementTask(config=measureConfig, schema=schema) primaryConfig = SetPrimaryFlagsTask.ConfigClass() setPrimaryTask = SetPrimaryFlagsTask(config=primaryConfig, schema=schema, name="setPrimaryFlags", isSingleFrame=False) table = SourceCatalog.Table.make(schema) # detect sources detectionResult = detectionTask.run(table=table, exposure=coadd) catalog = detectionResult.sources # add fake sources skySources = skySourcesTask.run(mask=coadd.mask, seed=0) for foot in skySources[:5]: src = catalog.addNew() src.setFootprint(foot) src.set("merge_peak_sky", True) # deblend deblendTask.run(exposure=coadd, sources=catalog) catalog = catalog.copy(deep=True) measureTask.run(measCat=catalog, exposure=coadd) outputCat = catalog.copy(deep=True) # Set the primary flags setPrimaryTask.run(outputCat, skyMap=skyMap, tractInfo=tractInfo, patchInfo=patchInfo) # Check that SCARLET-only columns are NOT in the schema self.assertTrue("SCARLET_NAME" not in outputCat.schema.getNames()) # Check that the sources contained in a tract are all marked # appropriately. x = outputCat["slot_Centroid_x"] y = outputCat["slot_Centroid_y"] tractInner = tractBBox.contains(x, y) # tractInner = bboxContainsColumn(tractBBox, x, y) np.testing.assert_array_equal(outputCat["detect_isTractInner"], tractInner) # Check that the sources contained in a patch are all marked # appropriately. patchInner = bboxContainsColumn(patchBBox, x, y) np.testing.assert_array_equal(outputCat["detect_isPatchInner"], patchInner) # make sure all sky sources are flagged as not primary self.assertEqual(sum((outputCat["detect_isPrimary"]) & (outputCat["merge_peak_sky"])), 0)

python/lsst/pipe/tasks/setPrimaryFlags.py

laurenam · 2021-03-10T02:05:50Z

python/lsst/pipe/tasks/setPrimaryFlags.py

+        their own.
+    isIsolated : array-like of `bool`
+        True for isolated sources, regardless of whether or not they
+        were deblended.


I may be missing something, but aren’t these redundant columns (i.e. doesn’t isIsolated imply ~isBlended?). If there are cases for which they can both both be True (or False), can you describe those here (and perhaps update the names such that they don't read as if they are the NOTs of each other)? I don’t get from the docs what category a parent with multiple children would be in...I believe it has to be isBlended, but a parent is not “deblended from a parent”.

Looking back at the docs I don't know how to be more clear, so perhaps you can help me trim down this long explanation into something that can fit into the docs.

top level parents with multiple peaks: not contained in either isBlended or isIsolated.
top level parents skipped for some reason: not contained in either isBlended or isIsolated.
top level parents with only a single peak: isIsolated
source deblended from a parent with a single peak: isIsolated
source deblended from a parent with multiple peaks: isBlended

So the two are not complementary and we should keep the names, as they describe exactly what they contain. It would be possible to add another flag, isBlend, where isBlend = (parent == 0) & ~isIsolated, so that the entire source catalog would be the combination of the orthogonal flags isBlended | isIolated | isBlend. If you really want me to I can add it, but our catalogs are already large enough and I don't see the point of adding a new flag that will never be used just to clarify isBlended and isIsolated. We should be able to do this in the docs, but obviously I haven't hit on the right explanation yet.

I guess my main sticking point is in part the tense implied by isBlended. Really, this represents a source that wasBlended or isDeblendedChildOfBlend, i.e. the "has been deblended from a parent with more than one child" nature just doesn't come across naturally to me with isBlended. These suggestions might be (ok, are!) terrible for other reasons, so I will leave it to you if you want to change them but I suspect I'm not the only one who will be confused...as I mentioned below, just the first line in the doc jars with the name (isBlended --> "...source that was deblended..."). At the very least, could you add those double-False cases to the docs (prolly for isBlended)...I totally agree we don't want to add yet another flag column!

laurenam · 2021-03-10T02:10:08Z

python/lsst/pipe/tasks/setPrimaryFlags.py

+    isolated parents, and a DeblendedModelPrimary, which uses
+    the scarlet models for both isolated and blended sources.
+    In the case of meas_deblender, DeblendedModelPrimary is
+    `None` because it is not contained in the output catalog.


Wait, was there consensus on using Primary in a name that does not imply any of its usual meanings? I would prefer using (your original) Leaf or something else (Branch, Channel, Fork?) that does not already carry the weight of deeply ingrained implied conditions.

Sorry, this is my mistake. I had an additional comment in JIRA but I apparently never hit Add so while I thought I made a post over a week ago, apparently I did not. I just added (a slightly modified version of) that post now, so let's discuss this there.

laurenam · 2021-03-10T02:14:38Z

python/lsst/pipe/tasks/setPrimaryFlags.py

+                                 "deblended child from a parent with 'deblend_nChild' > 1")
+            self.isBlendedKey = self.schema.addField(
+                "detect_isBlended", type="Flag",
+                doc="This source is deblended from a parent with more than one child."


I find it a bit head spinning for the doc of a column with isBlended in its name to read “This source is deblended...”

Suggestion? The source is blended in the image, the deblender just attempts to make a model for it as if it wasn't. But if you're looking at an output catalog and want to know if a source is isolated or blended in an image, then ones with this flag are blended.

laurenam · 2021-03-11T02:39:10Z

tests/test_isPrimaryFlag.py

        outputCat = calibResults.outputCat
        self.assertTrue("detect_isPrimary" in outputCat.schema.getNames())
        # make sure all sky sources are flagged as not primary
        self.assertEqual(sum((outputCat["detect_isPrimary"]) & (outputCat["sky_source"])), 0)
        # make sure all parent sources are flagged as not primary
        self.assertEqual(sum((outputCat["detect_isPrimary"]) & (outputCat["deblend_nChild"] > 0)), 0)

+    def testIsScarletPrimaryFlag(self):


We seem to be at odds on this one. My point is that the code path followed by a meas_deblender multiband run and the flag setting therein is not tested. It has never had a test (no mocked up skymap, yadda, yadda...), but now that you've added the functionality, an explicit test of the flag setting of this code path could easily be added. This long-standing failure-to-test the existing code path is not on you, though, so I will leave this to your discretion if you prefer not to add one.

tests/test_isPrimaryFlag.py

laurenam · 2021-03-11T02:44:41Z

tests/test_isPrimaryFlag.py

+        # over which model to use for the isolated sources.
+        self.assertEqual(
+            np.sum(outputCat["detect_isDeblendedPrimary"]),
+            np.sum(outputCat["detect_isDeblendedModelPrimary"]))


Again, check that these sums are non-zero (preferably checking against the known number)?

I'll add a check that they are non-zero, but checking against a known number is not advisable. As you know so many things upstream affect the creation of footprints and peaks that the number of peaks and footprints changes with practically every weekly, causing this test to trip frequently when people make changes upstream.

FYI: this is adjusted in DM-27929 because they will be slightly different now that meas_extensions_scarlet skips sky sources, but in a way that can be predicted and tested.

laurenam · 2021-03-11T02:58:43Z

python/lsst/pipe/tasks/setPrimaryFlags.py

+
+    if "deblend_scarletFlux" in sources.schema:
+        # The number of peaks in the sources footprint.
+        # This is different than nChild, the number of sources in


Don't leave me hangin', man!

But it's building suspense! :)

python/lsst/pipe/tasks/setPrimaryFlags.py

laurenam · 2021-03-14T19:21:24Z

python/lsst/pipe/tasks/setPrimaryFlags.py

+        their own.
+    isIsolated : array-like of `bool`
+        True for isolated sources, regardless of whether or not they
+        were deblended.


I guess my main sticking point is in part the tense implied by isBlended. Really, this represents a source that wasBlended or isDeblendedChildOfBlend, i.e. the "has been deblended from a parent with more than one child" nature just doesn't come across naturally to me with isBlended. These suggestions might be (ok, are!) terrible for other reasons, so I will leave it to you if you want to change them but I suspect I'm not the only one who will be confused...as I mentioned below, just the first line in the doc jars with the name (isBlended --> "...source that was deblended..."). At the very least, could you add those double-False cases to the docs (prolly for isBlended)...I totally agree we don't want to add yet another flag column!

laurenam · 2021-03-14T19:33:52Z

tests/test_isPrimaryFlag.py

        outputCat = calibResults.outputCat
        self.assertTrue("detect_isPrimary" in outputCat.schema.getNames())
        # make sure all sky sources are flagged as not primary
        self.assertEqual(sum((outputCat["detect_isPrimary"]) & (outputCat["sky_source"])), 0)
        # make sure all parent sources are flagged as not primary
        self.assertEqual(sum((outputCat["detect_isPrimary"]) & (outputCat["deblend_nChild"] > 0)), 0)

+    def testIsScarletPrimaryFlag(self):


Ok, I'm convinced this can be left as is!

laurenam · 2021-03-14T20:02:18Z

In principle, this PR should be titled (https://developer.lsst.io/work/flow.html#make-a-pull-request):
DM-28542: Implement RFC-750

This commit fixes a few things: 1. It refactors isPrimary to use vectorized columns instead of iteratinf over sources 2. It appropriately uses the scarlet outputs to choose which type of isolated sources are characterized by isPrimary, and it adds new columns to differentiate between the different deblender results.

fred3m force-pushed the tickets/DM-28542 branch 4 times, most recently from b012fd0 to a31d6e7 Compare February 5, 2021 15:46

fred3m force-pushed the tickets/DM-28542 branch from a31d6e7 to dc32f67 Compare February 15, 2021 02:10

laurenam reviewed Feb 15, 2021

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laurenam reviewed Feb 22, 2021

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fred3m force-pushed the tickets/DM-28542 branch from c14dfd6 to b724a40 Compare February 22, 2021 20:17

laurenam reviewed Mar 2, 2021

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laurenam reviewed Mar 11, 2021

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laurenam approved these changes Mar 14, 2021

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fred3m changed the title ~~tickets/DM-28542: Refactor isPrimary to work with scarlet~~ DM-28542: Implement RFC-750 Mar 15, 2021

fred3m force-pushed the tickets/DM-28542 branch 2 times, most recently from 216ab49 to d42e70e Compare March 15, 2021 19:05

fred3m added 2 commits March 16, 2021 20:13

Fix sign error in DeblendCoaddSources config

82028e9

fred3m force-pushed the tickets/DM-28542 branch from d42e70e to 82028e9 Compare March 17, 2021 01:14

fred3m merged commit 0be6b20 into master Mar 17, 2021

fred3m deleted the tickets/DM-28542 branch March 17, 2021 01:16

DM-28542: Implement RFC-750 #457

DM-28542: Implement RFC-750 #457

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fred3m commented Jan 29, 2021

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laurenam commented Feb 15, 2021

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fred3m commented Feb 22, 2021

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laurenam Mar 14, 2021 • edited

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laurenam Mar 14, 2021 • edited

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laurenam commented Mar 14, 2021

laurenam Mar 14, 2021 •

edited

laurenam Mar 14, 2021 •

edited