Adding support for CS output derivatives - #416
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Looks good 🍎, some suggestions for making the implementation easier to read.
| value_refs = np.array([0], dtype=np.uint32, ndmin=1).ravel() | ||
| orders = np.array([order], dtype=np.int32) | ||
| value_refs[0] = self.get_variable_valueref(variables) | ||
| elif isinstance(variables, list) and np.prod([int(isinstance(v, str)) for v in variables]): #prod equals 0 or 1 |
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Could we not just use all(isinstance(v, str)) for v in variables) here?
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| if isinstance(variables, str): | ||
| nref = 1 | ||
| value_refs = np.array([0], dtype=np.uint32, ndmin=1).ravel() | ||
| orders = np.array([order], dtype=np.int32) |
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To have less repetition we can use:
if isinstance(variables, str):
variables = [variables]
to have this case transform to the general
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| else: | ||
| raise FMUException("The variables must either be a string or a list of strings") |
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Consider having the error check first for an early return to have less cognitive load/indentation when reading the actual implementation.
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There are no real tests, since none of the reference FMUs actually support this.