Comments (4)
Original comment by Jhett Bordwell (Bitbucket: jhbordwell).
- Edited issue description
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Original comment by Keaton Burns (Bitbucket: kburns).
We generally want to avoid explicit type casting and checking, so for a case like this where duck-typing fails in a library with an accurate type error (i.e. a float was passed for an argument that should really be an integer), I think it's probably best for the cause to be fixed in the invoking script. Assuming you're getting float sizes due to some arithmetic calculation, you could either explicitly cast the result to int, or make sure you're using integer arithmetic operations (e.g. integer division with '//') to get integer sizes. How does that sound?
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Original comment by Benjamin Brown (Bitbucket: Benjamin Brown).
Is the error message that's raised by numpy sufficiently informative, or should we throw our own warning on non-integer resolutions?
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Original comment by Keaton Burns (Bitbucket: kburns).
- Edited issue description* changed state from "new" to "wontfix"
One good option might be to add type annotations to Dedalus to allow for recent type-checking tools, etc.
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