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sudeeprp
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sudeeprp
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Good effort @LaserTrajectory 👍
Most of my comments are around readability. Have a look below.
statistics.py
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| if len(numbers) == 0: | ||
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| vals = [math.nan for i in keys] # is_nan test | ||
| # print(vals) |
statistics.py
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| vals = [ round((sum(numbers) / len(numbers)), 3), max(numbers), min(numbers) ] # min_max_report test |
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Hard to read this code and correlate the order of the statistics. Will get harder to read when we add more statistics.
If the intended return is a dictionary, why not initialize it as a dictionary itself, instead of two arrays?
statistics.py
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| if stats["max"] > self.maxThresh: | ||
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| self.alertArrs[0].emailSent = True |
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The meaning of the indexes [0] and [1] are hidden in the implementation, which a caller of this code will need to understand. This means the caller will need to see the implementation of the function.
Find a way to make this explicit by naming the function parameters (instead of an implicit order inside alertArrs)
| self.assertTrue(isnan(computedStats["max"])) | ||
| self.assertTrue(isnan(computedStats["min"])) | ||
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| def test_invalid_arg_returns_None(self): |
statistics.py
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| # below: alerts tests | ||
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| class EmailAlert: |
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Does 'alerting' responsibility belong in statistics.py?
It would be hard for someone to guess that statistics.py contains the alerting stubs as well
statistics.py
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| return # invalid_arg test |
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Took me a while to trace back the else here. This function will tend to grow more complex as more conditions are imposed on the inputs and outputs. You can watch out for this by limiting the cyclomatic complexity. Take a look at that link and we'll talk about tooling related to that quantification later.
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