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TYP: numba stub #44233

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Merged
merged 6 commits into from
Oct 31, 2021
Merged

TYP: numba stub #44233

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37bf1b8
TYP: numba stub
simonjayhawkins Oct 29, 2021
a0d7075
Merge branch 'master' into numba-stub
simonjayhawkins Oct 30, 2021
72d666b
remove unused "type: ignore" comments
simonjayhawkins Oct 30, 2021
3ba5975
rename directory stubs -> typings
simonjayhawkins Oct 30, 2021
a3a42ea
include stubs in type checks for mypy
simonjayhawkins Oct 30, 2021
e32e300
Merge remote-tracking branch 'upstream/master' into numba-stub
simonjayhawkins Oct 30, 2021
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2 changes: 1 addition & 1 deletion ci/code_checks.sh
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Expand Up @@ -106,7 +106,7 @@ if [[ -z "$CHECK" || "$CHECK" == "typing" ]]; then
mypy --version

MSG='Performing static analysis using mypy' ; echo $MSG
mypy pandas
mypy
RET=$(($RET + $?)) ; echo $MSG "DONE"

# run pyright, if it is installed
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2 changes: 1 addition & 1 deletion doc/source/development/contributing_codebase.rst
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Expand Up @@ -399,7 +399,7 @@ pandas uses `mypy <http://mypy-lang.org>`_ and `pyright <https://github.com/micr

.. code-block:: shell

mypy pandas
mypy

# let pre-commit setup and run pyright
pre-commit run --hook-stage manual --all-files pyright
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9 changes: 3 additions & 6 deletions pandas/core/_numba/kernels/mean_.py
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Expand Up @@ -14,8 +14,7 @@
from pandas.core._numba.kernels.shared import is_monotonic_increasing


# error: Untyped decorator makes function "add_mean" untyped
@numba.jit(nopython=True, nogil=True, parallel=False) # type: ignore[misc]
@numba.jit(nopython=True, nogil=True, parallel=False)
def add_mean(
val: float, nobs: int, sum_x: float, neg_ct: int, compensation: float
) -> tuple[int, float, int, float]:
Expand All @@ -30,8 +29,7 @@ def add_mean(
return nobs, sum_x, neg_ct, compensation


# error: Untyped decorator makes function "remove_mean" untyped
@numba.jit(nopython=True, nogil=True, parallel=False) # type: ignore[misc]
@numba.jit(nopython=True, nogil=True, parallel=False)
def remove_mean(
val: float, nobs: int, sum_x: float, neg_ct: int, compensation: float
) -> tuple[int, float, int, float]:
Expand All @@ -46,8 +44,7 @@ def remove_mean(
return nobs, sum_x, neg_ct, compensation


# error: Untyped decorator makes function "sliding_mean" untyped
@numba.jit(nopython=True, nogil=True, parallel=False) # type: ignore[misc]
@numba.jit(nopython=True, nogil=True, parallel=False)
def sliding_mean(
values: np.ndarray,
start: np.ndarray,
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9 changes: 6 additions & 3 deletions pandas/core/_numba/kernels/shared.py
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Expand Up @@ -2,9 +2,12 @@
import numpy as np


# error: Untyped decorator makes function "is_monotonic_increasing" untyped
@numba.jit( # type: ignore[misc]
numba.boolean(numba.int64[:]), nopython=True, nogil=True, parallel=False
@numba.jit(
# error: Any? not callable
numba.boolean(numba.int64[:]), # type: ignore[misc]
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partial stub does not include numba types so they resolve to Any (and not callable)

@mroeschke not sure of best practice, is it preferable to use the numba types instead of the string representation for production code?

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Either is recommended in the docs. Strings just have some parsing overhead looking at the code, but probably just fine to use the string representation if it makes typing here easier.

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I think we could evolve the stubs as needed so will leave as-is for now.

nopython=True,
nogil=True,
parallel=False,
)
def is_monotonic_increasing(bounds: np.ndarray) -> bool:
"""Check if int64 values are monotonically increasing."""
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9 changes: 3 additions & 6 deletions pandas/core/_numba/kernels/sum_.py
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Expand Up @@ -14,8 +14,7 @@
from pandas.core._numba.kernels.shared import is_monotonic_increasing


# error: Untyped decorator makes function "add_sum" untyped
@numba.jit(nopython=True, nogil=True, parallel=False) # type: ignore[misc]
@numba.jit(nopython=True, nogil=True, parallel=False)
def add_sum(
val: float, nobs: int, sum_x: float, compensation: float
) -> tuple[int, float, float]:
Expand All @@ -28,8 +27,7 @@ def add_sum(
return nobs, sum_x, compensation


# error: Untyped decorator makes function "remove_sum" untyped
@numba.jit(nopython=True, nogil=True, parallel=False) # type: ignore[misc]
@numba.jit(nopython=True, nogil=True, parallel=False)
def remove_sum(
val: float, nobs: int, sum_x: float, compensation: float
) -> tuple[int, float, float]:
Expand All @@ -42,8 +40,7 @@ def remove_sum(
return nobs, sum_x, compensation


# error: Untyped decorator makes function "sliding_sum" untyped
@numba.jit(nopython=True, nogil=True, parallel=False) # type: ignore[misc]
@numba.jit(nopython=True, nogil=True, parallel=False)
def sliding_sum(
values: np.ndarray,
start: np.ndarray,
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2 changes: 2 additions & 0 deletions pyproject.toml
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Expand Up @@ -60,6 +60,8 @@ markers = [

[tool.mypy]
# Import discovery
mypy_path = "typings"
files = ["pandas", "typings"]
namespace_packages = false
explicit_package_bases = false
ignore_missing_imports = true
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41 changes: 41 additions & 0 deletions typings/numba.pyi
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Original file line number Diff line number Diff line change
@@ -0,0 +1,41 @@
from typing import (
Any,
Callable,
Literal,
overload,
)

import numba

from pandas._typing import F

def __getattr__(name: str) -> Any: ... # incomplete
@overload
def jit(
signature_or_function: F = ...,
) -> F: ...
@overload
def jit(
signature_or_function: str
| list[str]
| numba.core.types.abstract.Type
| list[numba.core.types.abstract.Type] = ...,
locals: dict = ..., # TODO: Mapping of local variable names to Numba types
cache: bool = ...,
pipeline_class: numba.compiler.CompilerBase = ...,
boundscheck: bool | None = ...,
*,
nopython: bool = ...,
forceobj: bool = ...,
looplift: bool = ...,
error_model: Literal["python", "numpy"] = ...,
inline: Literal["never", "always"] | Callable = ...,
# TODO: If a callable is provided it will be called with the call expression
# node that is requesting inlining, the caller's IR and callee's IR as
# arguments, it is expected to return Truthy as to whether to inline.
target: Literal["cpu", "gpu", "npyufunc", "cuda"] = ..., # deprecated
nogil: bool = ...,
parallel: bool = ...,
) -> Callable[[F], F]: ...

njit = jit