Preserve mask when assigning a masked Time into an unmasked one - #20178

Thank you for your contribution to Astropy! 🌌 This checklist is meant to remind the package maintainers who will review this pull request of some common things to look for.

• Do the proposed changes actually accomplish desired goals?
• Do the proposed changes follow the Astropy coding guidelines?
• Are tests added/updated as required? If so, do they follow the Astropy testing guidelines?
• Are docs added/updated as required? If so, do they follow the Astropy documentation guidelines?
• Is rebase and/or squash necessary? If so, please provide the author with appropriate instructions. Also see instructions for rebase and squash.
• Did the CI pass? If no, are the failures related? If you need to run daily and weekly cron jobs as part of the PR, please apply the "Extra CI" label. Codestyle issues can be fixed by the bot.
• Is a change log needed? If yes, did the change log check pass? If no, add the "no-changelog-entry-needed" label. If this is a manual backport, use the "skip-changelog-checks" label unless special changelog handling is necessary.
• Is this a big PR that makes a "What's new?" entry worthwhile and if so, is (1) a "what's new" entry included in this PR and (2) the "whatsnew-needed" label applied?
• At the time of adding the milestone, if the milestone set requires a backport to release branch(es), apply the appropriate "backport-X.Y.x" label(s) before merge.

@Mohit-Ak - first, thanks for the contribution. I pushed one commit to refactor some common code into a private method on TimeFormat.

One issue is that now Masked(jd1, copy=False) shares the data buffer but allocates a fresh mask array, so a view's mask never reaches the parent.

import numpy as np
from astropy.time import Time

t = Time(["2001:001", "2001:002", "2001:003", "2001:004"])
s = t[1:3]                                    # view sharing t's jd1/jd2 buffers
print(np.shares_memory(t._time.jd1, s._time.jd1))   # True

masked_value = Time(["2010:001", "2010:002"])
masked_value[1] = np.ma.masked

s[:] = masked_value

print(s.mask)      # [False  True]
print(t.masked)    # False
print(t.value[2])  # 2010:002:00:00:00.000   <-- should be hidden

This seems unavoidable when promoting to Masked, as the parent is unmasked and the view is masked.

@mhvk - do you have any thoughts on the right behavior?

Another issue is that a failed assignment can convert to masked but then not actually do the assignment.

import io, numpy as np
from astropy.table import QTable
from astropy.time import Time

t = Time(["2001:001", "2001:002", "2001:003", "2001:004"])
v = Time(["2010:001", "2010:002"]); v[1] = np.ma.masked

print(t.masked)                # False
try:
    t[:3] = v                  # shape mismatch
except ValueError as exc:
    print(exc)                 # could not broadcast input array from shape (2,) into shape (3,)
print(t.masked, t.mask)        # True [False False False False]

@taldcroft - this is quite tricky, and I don't have a ready answer. FWIW, np.ma.MaskedArray(array) works the same way: if you set an element to something masked, the value in array will change too, but it will of course not get masked.

In principle, in Time, we do have information on whether we are a slice or own our own data, so in that sense we can protect users by, e.g., not allowing a view to become masked (or warning or whatever); see the end of _apply and the use of _id_cache to ensure that caches get cleared if something is written too.

p.s. That Time becomes masked and an assignment then fails is clearly a bug. Thankfully, one that is not too difficult to fix.