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# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file.
"""
.. Checks for 1D and 2D NumPy arrays used in tools (:mod:`sigima.tools.checks`).
"""
from __future__ import annotations
from dataclasses import dataclass
from functools import wraps
from typing import Any, Callable
import numpy as np
@dataclass(frozen=True)
class ArrayValidationRules:
"""Hold 1-D array validation rules."""
#: Label used in error messages (e.g., "x" or "y")
label: str
#: Whether to enforce 1-D.
require_1d: bool = True
#: Check minimum size
min_size: int | None = None
#: Expected dtype (np.issubdtype). Use None to skip.
dtype: type | None = None
#: Whether to enforce finite values only.
finite_only: bool = False
#: Whether to enforce non-decreasing order.
sorted_: bool = False
#: Whether to enforce constant spacing (within rtol).
evenly_spaced: bool = False
#: Relative tolerance for regular spacing.
rtol: float = 1e-5
def _validate_array_1d(arr: np.ndarray, *, rules: ArrayValidationRules) -> None:
"""Validate a single 1D NumPy array according to the provided rules.
Args:
arr: Array to validate.
rules: Validation rules to apply.
Raises:
ValueError: If shape constraint is violated.
ValueError: If size constraint is violated.
ValueError: If finite constraint is violated.
ValueError: If order constraint is violated.
ValueError: If spacing constraint is violated.
TypeError: If dtype does not match.
"""
if rules.require_1d and arr.ndim != 1:
raise ValueError(f"{rules.label} must be 1-D.")
if rules.min_size is not None and arr.size < rules.min_size:
raise ValueError(f"{rules.label} must have at least {rules.min_size} elements.")
if rules.dtype is not None and not np.issubdtype(arr.dtype, rules.dtype):
raise TypeError(
f"{rules.label} must be of type {rules.dtype}, but got {arr.dtype}."
)
if rules.finite_only and not np.all(np.isfinite(arr)):
raise ValueError(f"{rules.label} must contain only finite values.")
if rules.sorted_ and arr.size > 1 and not np.all(np.diff(arr) > 0.0):
raise ValueError(f"{rules.label} must be sorted in ascending order.")
if rules.evenly_spaced and arr.size > 1:
dx = np.diff(arr)
if not np.allclose(dx, np.mean(dx), rtol=rules.rtol):
raise ValueError(f"{rules.label} must be evenly spaced.")
def check_1d_array(
func: Callable[..., Any] | None = None,
*,
require_1d: bool = True,
min_size: int | None = None,
dtype: type | None = np.inexact,
finite_only: bool = False,
sorted_: bool = False,
evenly_spaced: bool = False,
rtol: float = 1e-5,
label: str = "array",
) -> Callable:
"""Decorator to check a single 1D NumPy array.
Can be used with or without parentheses.
Args:
require_1d: Whether to check if the array is 1-D.
min_size: Minimum size of the array.
dtype: Expected dtype of the array (np.issubdtype). Use None to skip.
finite_only: Whether to check if the array contains only finite values.
sorted_: Whether to check if the array is sorted in ascending order.
evenly_spaced: Whether to check if the array is evenly spaced.
rtol: Relative tolerance for regular spacing.
label: Label for error messages (e.g., "x", "y").
Returns:
Decorated function with pre-checks on the single array.
"""
def decorator(inner_func: Callable[..., Any]) -> Callable[..., Any]:
@wraps(inner_func)
def wrapper(arr: np.ndarray, *args: Any, **kwargs: Any) -> Any:
_validate_array_1d(
arr,
rules=ArrayValidationRules(
label=label,
require_1d=require_1d,
min_size=min_size,
dtype=dtype,
finite_only=finite_only,
sorted_=sorted_,
evenly_spaced=evenly_spaced,
rtol=rtol,
),
)
return inner_func(arr, *args, **kwargs)
return wrapper
if func is not None:
return decorator(func)
return decorator
def check_1d_arrays(
func: Callable[..., Any] | None = None,
*,
x_require_1d: bool = True,
x_min_size: int | None = None,
x_dtype: type | None = np.floating,
x_finite_only: bool = False,
x_sorted: bool = False,
x_evenly_spaced: bool = False,
y_require_1d: bool = True,
y_min_size: int | None = None,
y_dtype: type | None = np.inexact,
y_finite_only: bool = False,
same_size: bool = True,
rtol: float = 1e-5,
) -> Callable:
"""Decorator to check paired 1D NumPy arrays (x, y).
Can be used with or without parentheses.
Args:
func: Function to decorate.
x_require_1d: Whether to check if x is 1-D.
x_min_size: Minimum size of x.
x_dtype: Expected dtype of x (np.issubdtype). Use None to skip.
x_finite_only: Whether to check if x contains only finite values.
x_sorted: Whether to check if x is sorted in ascending order.
x_evenly_spaced: Whether to check if x is evenly spaced.
y_require_1d: Whether to check if y is 1-D.
y_min_size: Minimum size of y.
y_dtype: Expected dtype of y (np.issubdtype). Use None to skip.
y_finite_only: Whether to check if y contains only finite values.
same_size: Whether to check that x and y have the same size.
rtol: Relative tolerance for regular spacing (used for x).
Returns:
Decorated function with pre-checks on x/y.
"""
def decorator(inner_func: Callable[..., Any]) -> Callable[..., Any]:
@wraps(inner_func)
def wrapper(x: np.ndarray, y: np.ndarray, *args: Any, **kwargs: Any) -> Any:
_validate_array_1d(
x,
rules=ArrayValidationRules(
label="x",
require_1d=x_require_1d,
min_size=x_min_size,
dtype=x_dtype,
finite_only=x_finite_only,
sorted_=x_sorted,
evenly_spaced=x_evenly_spaced,
rtol=rtol,
),
)
_validate_array_1d(
y,
rules=ArrayValidationRules(
label="y",
require_1d=y_require_1d,
min_size=y_min_size,
dtype=y_dtype,
finite_only=y_finite_only,
sorted_=False,
evenly_spaced=False,
rtol=rtol,
),
)
if same_size and x.size != y.size:
raise ValueError("x and y must have the same size.")
return inner_func(x, y, *args, **kwargs)
return wrapper
if func is not None:
return decorator(func)
return decorator
def check_2d_array(
func: Callable[..., Any] | None = None,
*,
ndim: int = 2,
dtype: type | None = None,
non_constant: bool = False,
finite_only: bool = False,
) -> Callable:
"""
Decorator to check input for functions operating on 2D NumPy arrays (e.g. images).
Can be used with parentheses:
.. code-block:: python
@check_2d_array(ndim=3, dtype=np.uint8)
def process_image(image: np.ndarray) -> np.ndarray:
# Process the image
return image
Or without parentheses (default arguments):
.. code-block:: python
@check_2d_array
def process_image(image: np.ndarray) -> np.ndarray:
# Process the image
return image
Args:
ndim: Expected number of dimensions.
dtype: Expected dtype.
non_constant: Whether to check that the array has dynamic range.
finite_only: Whether to check that all values are finite.
Returns:
Decorated function with pre-checks on data.
"""
def decorator(inner_func: Callable[..., Any]) -> Callable[..., Any]:
@wraps(inner_func)
def wrapper(data: np.ndarray, *args: Any, **kwargs: Any) -> Any:
# === Check input array
if data.ndim != ndim:
raise ValueError(f"Input array must be {ndim}D, got {data.ndim}D.")
if dtype is not None and not np.issubdtype(data.dtype, dtype):
raise TypeError(
f"Input array must be of type {dtype}, got {data.dtype}."
)
if non_constant:
dmin, dmax = np.nanmin(data), np.nanmax(data)
if dmin == dmax:
raise ValueError("Input array has no dynamic range.")
if finite_only and not np.all(np.isfinite(data)):
raise ValueError("Input array contains non-finite values.")
# === Call the original function
return inner_func(data, *args, **kwargs)
return wrapper
if func is not None:
# Usage: `@check_2d_array`
return decorator(func)
# Usage: `@check_2d_array(...)`
return decorator
def normalize_kernel(kernel: np.ndarray) -> np.ndarray:
"""Normalize a convolution/deconvolution kernel if needed.
This utility function can normalize the kernel to sum to 1.0.
Args:
kernel: The kernel array to normalize.
Returns:
The normalized kernel if it's not already normalized and if its sum is not
zero, otherwise the original kernel.
Note:
A kernel is considered normalized if ``np.isclose(sum(kernel), 1.0)``.
"""
kernel_sum = np.sum(kernel)
if not np.isclose(kernel_sum, 1.0) and kernel_sum != 0.0:
return kernel / kernel_sum
return kernel
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