Describe the issue:
I'm trying to add type hints to an existing code base. I currently have a function (simplified):
def array_to_file(
data,
fileobj,
out_dtype,
intercept = 0.0,
divslope = 1.0,
):
if not np.all(np.isfinite((intercept, 1.0 if divslope is None else divslope))):
raise ValueError('divslope and intercept must be finite')
...We have written it to accept numpy or Python types. The general use case is Python types, but if someone wants to avoid doubling their memory by making sure all passed values are float32, they can. I want to make this:
Scalar = np.number | float
def array_to_file(
data: npt.ArrayLike,
fileobj: io.IOBase,
out_dtype: np.dtype | None = None,
intercept: Scalar = 0.0,
divslope: Scalar | None = 1.0,
):
if not np.all(np.isfinite((intercept, 1.0 if divslope is None else divslope))):
raise ValueError('divslope and intercept must be finite')
...But I get the error below.
Reproduce the code example:
import numpy as np
Scalar = np.number | float
def check1(x: Scalar) -> np.bool_:
return np.isfinite(x)
def check2(x: Scalar, y: Scalar) -> np.ndarray:
return np.isfinite((x, y))Error message:
$ mypy example.py
example.py:9: error: Argument 1 to "__call__" of "_UFunc_Nin1_Nout1" has incompatible type "Tuple[Union[number[Any], float]]"; expected "Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]" [arg-type]
Found 1 error in 1 file (checked 1 source file)
Runtime information:
1.23.5
3.10.8 (main, Nov 24 2022, 14:13:03) [GCC 11.2.0]
Context for the issue:
This is not terribly high priority. I can rewrite this in a way that will type check, but it seems like a bug.
If numpy.typing has better ways of writing Scalar that I haven't seen, happy to switch to that.
Describe the issue:
I'm trying to add type hints to an existing code base. I currently have a function (simplified):
We have written it to accept numpy or Python types. The general use case is Python types, but if someone wants to avoid doubling their memory by making sure all passed values are float32, they can. I want to make this:
But I get the error below.
Reproduce the code example:
Error message:
Runtime information:
Context for the issue:
This is not terribly high priority. I can rewrite this in a way that will type check, but it seems like a bug.
If
numpy.typinghas better ways of writingScalarthat I haven't seen, happy to switch to that.