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Basic outline for new sky catalog search function #1366
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…d time the command is ran indicating that the data is indeed cashed.
This is looking dangerously close, you need to add
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…emoved duplicate fuction in targetpixelfile.py
…re fail test due to empty table
Created a GitHub gist notebook demonstrating how this works. |
The following warning appears when running Looking at https://pyerfa.readthedocs.io/en/latest/api/erfa.pmsafe.html note 6 "6) An extremely small (or zero or negative) parallax is overridden I think that this happens because some stars have zero proper motion - these are listed as "--" in the catalog and as such converted into 0.0 to preserve the original R.A and Dec. |
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Some comments from using and implementing tpf.interact_sky()
.
@@ -2045,7 +2051,60 @@ def plot_pixels( | |||
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return ax | |||
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@property | |||
def skycatalog(self): |
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A style comment: exposing the list of neighboring TICs/KICs/EPICs via a property tpf.skycatalog
could be somewhat misleading from programming point of view.
Conventionally, data exposed via properties tend to be in-memory or easily computed. The tpf.skycatalog
here is much more involved.
An alternative is to provide it as a method tpf.query_skycatalog(...)
, with parameters' default values do what the property currently does.
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@christinahedges what do you think with regards to this?
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For the use cases for supporting contamination calculation / PRF, etc,
- it might make sense to let users easily customize the catalog to
a. include stellar companion identified in follow up, and/or
b. exclude duplicates, artifacts, etc.
One way to do it is through a methodtpf.query_skycatalog(...)
, something like the following:
# add a stellar companion identified in follow up
cat = tpf.query_skycatalog(additions=[
dict(id="stellar companion 1", sep=0.3*u.arcsec, angle=Angle(131.3 * u.deg), delta_mag=1),
])
# TIC 237184773 is split to 1715469662, 1715469667
cat = tpf.query_skycatalog(subtractions=[237184773])
- Are we concerned about the cases a bright star that is just outside of the tpf, but it significantly affects many pixels inside the tpf?
This is probably too much of an edge case. I suppose users can workaround it somewhat easily if they can add additional stars, say, viatpf.query_skycatalog(additions=...)
.
result["Separation"] = sep_pm_correct | ||
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# Calculate the relative flux | ||
result["Relative_Flux"] = np.exp( |
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I believe the relative flux calculation is buggy. np.exp()
should probably be replaced by 10 **
.
E.g., assume the target star is of magnitude 10, the nearby star is of magnitude 9, i.e., the relative flux is about ~2.512.
np.exp((9 - 10) / -2.5) : 1.492
10 ** ((9 - 10) / -2.5) : 2.512
This PR will create a new function called get_skycatalog.
This is very initial code and the function will likely be captured within another file or function like search.py
LOTS OF REFORMATTING
The goal of this function is to provide the user with a list of stars/objects within a defined region around a requested target.
The returned list contains the proper motion corrected R.A and Dec. positions of the objects in addition to several other parameters.
This tool should be applied to the following TESS products
Eventual use case will be:
mytpf.get_skycatalog()
Returning a table of objects and their parameters.
Several adjustments to be made inclide