# Auto-generated. Do not edit.
from opendp._convert import *
from opendp._lib import *
from opendp.mod import *
from opendp.typing import *
__all__ = [
"laplacian_scale_to_accuracy",
"accuracy_to_laplacian_scale",
"gaussian_scale_to_accuracy",
"accuracy_to_gaussian_scale"
]
[docs]
def laplacian_scale_to_accuracy(
scale,
alpha,
T: RuntimeTypeDescriptor = None
) -> Any:
"""Convert a laplacian scale into an accuracy estimate (tolerance) at a statistical significance level `alpha`.
:param scale: Laplacian noise scale.
:param alpha: Statistical significance, level-`alpha`, or (1. - `alpha`)100% confidence. Must be within (0, 1].
:param T: Data type of `scale` and `alpha`
:type T: :ref:`RuntimeTypeDescriptor`
:return: Accuracy estimate. Maximum amount a value is expected to diverge at the given level-`alpha`.
:rtype: Any
:raises AssertionError: if an argument's type differs from the expected type
:raises UnknownTypeError: if a type-argument fails to parse
:raises OpenDPException: packaged error from the core OpenDP library
"""
# Standardize type arguments.
T = RuntimeType.parse_or_infer(type_name=T, public_example=scale)
# Convert arguments to c types.
scale = py_to_c(scale, c_type=ctypes.c_void_p, type_name=T)
alpha = py_to_c(alpha, c_type=ctypes.c_void_p, type_name=T)
T = py_to_c(T, c_type=ctypes.c_char_p)
# Call library function.
function = lib.opendp_accuracy__laplacian_scale_to_accuracy
function.argtypes = [ctypes.c_void_p, ctypes.c_void_p, ctypes.c_char_p]
function.restype = FfiResult
return c_to_py(unwrap(function(scale, alpha, T), AnyObjectPtr))
[docs]
def accuracy_to_laplacian_scale(
accuracy,
alpha,
T: RuntimeTypeDescriptor = None
) -> Any:
"""Convert a desired `accuracy` (tolerance) into a laplacian noise scale at a statistical significance level `alpha`.
:param accuracy: Desired accuracy. A tolerance for how far values may diverge from the input to the mechanism.
:param alpha: Statistical significance, level-`alpha`, or (1. - `alpha`)100% confidence. Must be within (0, 1].
:param T: Data type of `accuracy` and `alpha`
:type T: :ref:`RuntimeTypeDescriptor`
:return: Laplacian noise scale that meets the `accuracy` requirement at a given level-`alpha`.
:rtype: Any
:raises AssertionError: if an argument's type differs from the expected type
:raises UnknownTypeError: if a type-argument fails to parse
:raises OpenDPException: packaged error from the core OpenDP library
"""
# Standardize type arguments.
T = RuntimeType.parse_or_infer(type_name=T, public_example=accuracy)
# Convert arguments to c types.
accuracy = py_to_c(accuracy, c_type=ctypes.c_void_p, type_name=T)
alpha = py_to_c(alpha, c_type=ctypes.c_void_p, type_name=T)
T = py_to_c(T, c_type=ctypes.c_char_p)
# Call library function.
function = lib.opendp_accuracy__accuracy_to_laplacian_scale
function.argtypes = [ctypes.c_void_p, ctypes.c_void_p, ctypes.c_char_p]
function.restype = FfiResult
return c_to_py(unwrap(function(accuracy, alpha, T), AnyObjectPtr))
[docs]
def gaussian_scale_to_accuracy(
scale,
alpha,
T: RuntimeTypeDescriptor = None
) -> Any:
"""Convert a gaussian scale into an accuracy estimate (tolerance) at a statistical significance level `alpha`.
:param scale: Gaussian noise scale.
:param alpha: Statistical significance, level-`alpha`, or (1. - `alpha`)100% confidence. Must be within (0, 1].
:param T: Data type of `scale` and `alpha`
:type T: :ref:`RuntimeTypeDescriptor`
:return: Accuracy estimate. Maximum amount a value is expected to diverge at the given level-`alpha`.
:rtype: Any
:raises AssertionError: if an argument's type differs from the expected type
:raises UnknownTypeError: if a type-argument fails to parse
:raises OpenDPException: packaged error from the core OpenDP library
"""
# Standardize type arguments.
T = RuntimeType.parse_or_infer(type_name=T, public_example=scale)
# Convert arguments to c types.
scale = py_to_c(scale, c_type=ctypes.c_void_p, type_name=T)
alpha = py_to_c(alpha, c_type=ctypes.c_void_p, type_name=T)
T = py_to_c(T, c_type=ctypes.c_char_p)
# Call library function.
function = lib.opendp_accuracy__gaussian_scale_to_accuracy
function.argtypes = [ctypes.c_void_p, ctypes.c_void_p, ctypes.c_char_p]
function.restype = FfiResult
return c_to_py(unwrap(function(scale, alpha, T), AnyObjectPtr))
[docs]
def accuracy_to_gaussian_scale(
accuracy,
alpha,
T: RuntimeTypeDescriptor = None
) -> Any:
"""Convert a desired `accuracy` (tolerance) into a gaussian noise scale at a statistical significance level `alpha`.
:param accuracy: Desired accuracy. A tolerance for how far values may diverge from the input to the mechanism.
:param alpha: Statistical significance, level-`alpha`, or (1. - `alpha`)100% confidence. Must be within (0, 1).
:param T: Data type of `accuracy` and `alpha`
:type T: :ref:`RuntimeTypeDescriptor`
:return: Gaussian noise scale that meets the `accuracy` requirement at a given level-`alpha`.
:rtype: Any
:raises AssertionError: if an argument's type differs from the expected type
:raises UnknownTypeError: if a type-argument fails to parse
:raises OpenDPException: packaged error from the core OpenDP library
"""
# Standardize type arguments.
T = RuntimeType.parse_or_infer(type_name=T, public_example=accuracy)
# Convert arguments to c types.
accuracy = py_to_c(accuracy, c_type=ctypes.c_void_p, type_name=T)
alpha = py_to_c(alpha, c_type=ctypes.c_void_p, type_name=T)
T = py_to_c(T, c_type=ctypes.c_char_p)
# Call library function.
function = lib.opendp_accuracy__accuracy_to_gaussian_scale
function.argtypes = [ctypes.c_void_p, ctypes.c_void_p, ctypes.c_char_p]
function.restype = FfiResult
return c_to_py(unwrap(function(accuracy, alpha, T), AnyObjectPtr))
