Comments (3)
Ok - but we will want 80%+ coverage if we want to avoid an embarrassing red badge!
I already added a badge on the wiki. I would like to increase coverage of the Version class. The problem is that depends on interaction with a git repository making it hard to test.
Increasing coverage in param/__init__.py
should be easy - we just need to test more parameter types (Callable
, ClassSelector
, NumericTuple
etc).
from param.
You could add a backdoor to Version that overrides the result of the git commands, so that most of the operations can be tested while not being dependent on the specific results returned from git. I.e., when given this special argument it would go through all the motions, potentially causing errors that would then be detected, but then it could throw the results away just for the purposes of testing. Or you could simply discard the specific results in the unit tests, if that can be done with no changes to Version. Seems doable to me to just test whatever can safely be tested...
from param.
Badge moved here from the wiki (where it was a bit confusing).
from param.
Related Issues (20)
- watcher doesn't work with __len__
- Easier subclassing of Parameter HOT 5
- Warnings not displaying the right frame
- Pickle Serialization doesn't work for param.Parameterized with @param.depends function decorators HOT 2
- Cloudpickle compatibility HOT 3
- Subclassing a Parameterized class and changing the class parameters of list / dict does not work
- Callback called before a method that is depended upon
- Depending on the method of a sub parameter object is broken
- Fate of `name`? HOT 2
- Callbacks not triggered when value updated at the class level before being set on an instance HOT 2
- Add API to access watchers HOT 1
- Support async `on_init` methods
- param.pprint breaks if HoloViews is imported HOT 5
- Selector regression in an inheritance context? HOT 3
- Add official support to pathlib.Path objects to param.Path
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- Add `param.watch` function/decorator
- `ListSelector`: regression when `check_on_set=False` HOT 2
- Automatically serialize dataclass / pydantic models into `param.Parameterized`? HOT 1
- Setting a `Parameter` value in `__init__` before calling `super().__init__` HOT 1
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