# Auto-generated. Do not edit!
'''
The ``accuracy`` module provides functions for converting between accuracy and scale parameters.
'''
from opendp._convert import *
from opendp._lib import *
from opendp.mod import *
from opendp.typing import *
__all__ = [
"accuracy_to_discrete_gaussian_scale",
"accuracy_to_discrete_laplacian_scale",
"accuracy_to_gaussian_scale",
"accuracy_to_laplacian_scale",
"discrete_gaussian_scale_to_accuracy",
"discrete_laplacian_scale_to_accuracy",
"gaussian_scale_to_accuracy",
"laplacian_scale_to_accuracy"
]
[docs]
@versioned
def accuracy_to_discrete_gaussian_scale(
accuracy,
alpha,
T: Optional[RuntimeTypeDescriptor] = None
) -> Any:
r"""Convert a desired `accuracy` (tolerance) into a discrete gaussian noise scale at a statistical significance level `alpha`.
[accuracy_to_discrete_gaussian_scale in Rust documentation.](https://docs.rs/opendp/latest/opendp/accuracy/fn.accuracy_to_discrete_gaussian_scale.html)
**Proof Definition:**
[(Proof Document)](https://docs.opendp.org/en/nightly/proofs/rust/src/accuracy/accuracy_to_discrete_gaussian_scale.pdf)
: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: :py:ref:`RuntimeTypeDescriptor`
:rtype: Any
:raises TypeError: if an argument's type differs from the expected type
:raises UnknownTypeException: 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.
c_accuracy = py_to_c(accuracy, c_type=ctypes.c_void_p, type_name=T)
c_alpha = py_to_c(alpha, c_type=ctypes.c_void_p, type_name=T)
c_T = py_to_c(T, c_type=ctypes.c_char_p)
# Call library function.
lib_function = lib.opendp_accuracy__accuracy_to_discrete_gaussian_scale
lib_function.argtypes = [ctypes.c_void_p, ctypes.c_void_p, ctypes.c_char_p]
lib_function.restype = FfiResult
output = c_to_py(unwrap(lib_function(c_accuracy, c_alpha, c_T), AnyObjectPtr))
return output
[docs]
@versioned
def accuracy_to_discrete_laplacian_scale(
accuracy,
alpha,
T: Optional[RuntimeTypeDescriptor] = None
) -> Any:
r"""Convert a desired `accuracy` (tolerance) into a discrete Laplacian noise scale at a statistical significance level `alpha`.
[accuracy_to_discrete_laplacian_scale in Rust documentation.](https://docs.rs/opendp/latest/opendp/accuracy/fn.accuracy_to_discrete_laplacian_scale.html)
**Proof Definition:**
[(Proof Document)](https://docs.opendp.org/en/nightly/proofs/rust/src/accuracy/accuracy_to_discrete_laplacian_scale.pdf)
: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: :py:ref:`RuntimeTypeDescriptor`
:return: Discrete laplacian noise scale that meets the `accuracy` requirement at a given level-`alpha`.
:rtype: Any
:raises TypeError: if an argument's type differs from the expected type
:raises UnknownTypeException: 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.
c_accuracy = py_to_c(accuracy, c_type=ctypes.c_void_p, type_name=T)
c_alpha = py_to_c(alpha, c_type=ctypes.c_void_p, type_name=T)
c_T = py_to_c(T, c_type=ctypes.c_char_p)
# Call library function.
lib_function = lib.opendp_accuracy__accuracy_to_discrete_laplacian_scale
lib_function.argtypes = [ctypes.c_void_p, ctypes.c_void_p, ctypes.c_char_p]
lib_function.restype = FfiResult
output = c_to_py(unwrap(lib_function(c_accuracy, c_alpha, c_T), AnyObjectPtr))
return output
[docs]
@versioned
def accuracy_to_gaussian_scale(
accuracy,
alpha,
T: Optional[RuntimeTypeDescriptor] = None
) -> Any:
r"""Convert a desired `accuracy` (tolerance) into a gaussian noise scale at a statistical significance level `alpha`.
[accuracy_to_gaussian_scale in Rust documentation.](https://docs.rs/opendp/latest/opendp/accuracy/fn.accuracy_to_gaussian_scale.html)
: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: :py:ref:`RuntimeTypeDescriptor`
:rtype: Any
:raises TypeError: if an argument's type differs from the expected type
:raises UnknownTypeException: 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.
c_accuracy = py_to_c(accuracy, c_type=ctypes.c_void_p, type_name=T)
c_alpha = py_to_c(alpha, c_type=ctypes.c_void_p, type_name=T)
c_T = py_to_c(T, c_type=ctypes.c_char_p)
# Call library function.
lib_function = lib.opendp_accuracy__accuracy_to_gaussian_scale
lib_function.argtypes = [ctypes.c_void_p, ctypes.c_void_p, ctypes.c_char_p]
lib_function.restype = FfiResult
output = c_to_py(unwrap(lib_function(c_accuracy, c_alpha, c_T), AnyObjectPtr))
return output
[docs]
@versioned
def accuracy_to_laplacian_scale(
accuracy,
alpha,
T: Optional[RuntimeTypeDescriptor] = None
) -> Any:
r"""Convert a desired `accuracy` (tolerance) into a Laplacian noise scale at a statistical significance level `alpha`.
[accuracy_to_laplacian_scale in Rust documentation.](https://docs.rs/opendp/latest/opendp/accuracy/fn.accuracy_to_laplacian_scale.html)
: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: :py:ref:`RuntimeTypeDescriptor`
:return: Laplacian noise scale that meets the `accuracy` requirement at a given level-`alpha`.
:rtype: Any
:raises TypeError: if an argument's type differs from the expected type
:raises UnknownTypeException: 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.
c_accuracy = py_to_c(accuracy, c_type=ctypes.c_void_p, type_name=T)
c_alpha = py_to_c(alpha, c_type=ctypes.c_void_p, type_name=T)
c_T = py_to_c(T, c_type=ctypes.c_char_p)
# Call library function.
lib_function = lib.opendp_accuracy__accuracy_to_laplacian_scale
lib_function.argtypes = [ctypes.c_void_p, ctypes.c_void_p, ctypes.c_char_p]
lib_function.restype = FfiResult
output = c_to_py(unwrap(lib_function(c_accuracy, c_alpha, c_T), AnyObjectPtr))
return output
[docs]
@versioned
def discrete_gaussian_scale_to_accuracy(
scale,
alpha,
T: Optional[RuntimeTypeDescriptor] = None
) -> Any:
r"""Convert a discrete gaussian scale into an accuracy estimate (tolerance) at a statistical significance level `alpha`.
[discrete_gaussian_scale_to_accuracy in Rust documentation.](https://docs.rs/opendp/latest/opendp/accuracy/fn.discrete_gaussian_scale_to_accuracy.html)
**Proof Definition:**
[(Proof Document)](https://docs.opendp.org/en/nightly/proofs/rust/src/accuracy/discrete_gaussian_scale_to_accuracy.pdf)
: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: :py:ref:`RuntimeTypeDescriptor`
:rtype: Any
:raises TypeError: if an argument's type differs from the expected type
:raises UnknownTypeException: 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.
c_scale = py_to_c(scale, c_type=ctypes.c_void_p, type_name=T)
c_alpha = py_to_c(alpha, c_type=ctypes.c_void_p, type_name=T)
c_T = py_to_c(T, c_type=ctypes.c_char_p)
# Call library function.
lib_function = lib.opendp_accuracy__discrete_gaussian_scale_to_accuracy
lib_function.argtypes = [ctypes.c_void_p, ctypes.c_void_p, ctypes.c_char_p]
lib_function.restype = FfiResult
output = c_to_py(unwrap(lib_function(c_scale, c_alpha, c_T), AnyObjectPtr))
return output
[docs]
@versioned
def discrete_laplacian_scale_to_accuracy(
scale,
alpha,
T: Optional[RuntimeTypeDescriptor] = None
) -> Any:
r"""Convert a discrete Laplacian scale into an accuracy estimate (tolerance) at a statistical significance level `alpha`.
$\alpha = P[Y \ge accuracy]$, where $Y = | X - z |$, and $X \sim \mathcal{L}_{Z}(0, scale)$.
That is, $X$ is a discrete Laplace random variable and $Y$ is the distribution of the errors.
This function returns a float accuracy.
You can take the floor without affecting the coverage probability.
[discrete_laplacian_scale_to_accuracy in Rust documentation.](https://docs.rs/opendp/latest/opendp/accuracy/fn.discrete_laplacian_scale_to_accuracy.html)
**Proof Definition:**
[(Proof Document)](https://docs.opendp.org/en/nightly/proofs/rust/src/accuracy/discrete_laplacian_scale_to_accuracy.pdf)
:param scale: Discrete 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: :py:ref:`RuntimeTypeDescriptor`
:rtype: Any
:raises TypeError: if an argument's type differs from the expected type
:raises UnknownTypeException: 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.
c_scale = py_to_c(scale, c_type=ctypes.c_void_p, type_name=T)
c_alpha = py_to_c(alpha, c_type=ctypes.c_void_p, type_name=T)
c_T = py_to_c(T, c_type=ctypes.c_char_p)
# Call library function.
lib_function = lib.opendp_accuracy__discrete_laplacian_scale_to_accuracy
lib_function.argtypes = [ctypes.c_void_p, ctypes.c_void_p, ctypes.c_char_p]
lib_function.restype = FfiResult
output = c_to_py(unwrap(lib_function(c_scale, c_alpha, c_T), AnyObjectPtr))
return output
[docs]
@versioned
def gaussian_scale_to_accuracy(
scale,
alpha,
T: Optional[RuntimeTypeDescriptor] = None
) -> Any:
r"""Convert a gaussian scale into an accuracy estimate (tolerance) at a statistical significance level `alpha`.
[gaussian_scale_to_accuracy in Rust documentation.](https://docs.rs/opendp/latest/opendp/accuracy/fn.gaussian_scale_to_accuracy.html)
: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: :py:ref:`RuntimeTypeDescriptor`
:rtype: Any
:raises TypeError: if an argument's type differs from the expected type
:raises UnknownTypeException: 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.
c_scale = py_to_c(scale, c_type=ctypes.c_void_p, type_name=T)
c_alpha = py_to_c(alpha, c_type=ctypes.c_void_p, type_name=T)
c_T = py_to_c(T, c_type=ctypes.c_char_p)
# Call library function.
lib_function = lib.opendp_accuracy__gaussian_scale_to_accuracy
lib_function.argtypes = [ctypes.c_void_p, ctypes.c_void_p, ctypes.c_char_p]
lib_function.restype = FfiResult
output = c_to_py(unwrap(lib_function(c_scale, c_alpha, c_T), AnyObjectPtr))
return output
[docs]
@versioned
def laplacian_scale_to_accuracy(
scale,
alpha,
T: Optional[RuntimeTypeDescriptor] = None
) -> Any:
r"""Convert a Laplacian scale into an accuracy estimate (tolerance) at a statistical significance level `alpha`.
[laplacian_scale_to_accuracy in Rust documentation.](https://docs.rs/opendp/latest/opendp/accuracy/fn.laplacian_scale_to_accuracy.html)
: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: :py:ref:`RuntimeTypeDescriptor`
:rtype: Any
:raises TypeError: if an argument's type differs from the expected type
:raises UnknownTypeException: 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.
c_scale = py_to_c(scale, c_type=ctypes.c_void_p, type_name=T)
c_alpha = py_to_c(alpha, c_type=ctypes.c_void_p, type_name=T)
c_T = py_to_c(T, c_type=ctypes.c_char_p)
# Call library function.
lib_function = lib.opendp_accuracy__laplacian_scale_to_accuracy
lib_function.argtypes = [ctypes.c_void_p, ctypes.c_void_p, ctypes.c_char_p]
lib_function.restype = FfiResult
output = c_to_py(unwrap(lib_function(c_scale, c_alpha, c_T), AnyObjectPtr))
return output
