Description

Before this PR, if the mask was None as returned by the NDData's .mask attribute, np.logical_or was returning object arrays:

In [2]: np.logical_or(None, np.array([1, 0, 1], dtype=bool))
Out[2]: array([True, False, True], dtype=object)

Object arrays are very memory and CPU inefficient. This PR avoids this by only doing a logical OR if the original mask is not None. This speeds up the initial loading of large cubes into cubeviz by a factor of 3x on my machine (I tested this for a 3.4GB cube).

Change log entry

• Is a change log needed? If yes, is it added to CHANGES.rst? If you want to avoid merge conflicts,
  list the proposed change log here for review and add to CHANGES.rst before merge. If no, maintainer
  should add a no-changelog-entry-needed label.

Checklist for package maintainer(s)

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• Are two approvals required? Branch protection rule does not check for the second approval. If a second approval is not necessary, please apply the trivial label.
• Do the proposed changes actually accomplish desired goals? Also manually run the affected example notebooks, if necessary.
• Do the proposed changes follow the STScI Style Guides?
• Are tests added/updated as required? If so, do they follow the STScI Style Guides?
• Are docs added/updated as required? If so, do they follow the STScI Style Guides?
• If new remote data is added that uses MAST, is the URI added to the cache-download.yml workflow?
• Did the CI pass? If not, are the failures related?
• Is a milestone set? Set this to bugfix milestone if this is a bug fix and needs to be released ASAP; otherwise, set this to the next major release milestone. Bugfix milestone also needs an accompanying backport label.
• After merge, any internal documentations need updating (e.g., JIRA, Innerspace)?