# Auto-generated. Do not edit.
from opendp._convert import *
from opendp._lib import *
from opendp.mod import *
from opendp.typing import *
__all__ = [
"make_base_discrete_gaussian",
"make_base_discrete_laplace",
"make_base_discrete_laplace_cks20",
"make_base_discrete_laplace_linear",
"make_base_gaussian",
"make_base_geometric",
"make_base_laplace",
"make_base_ptr",
"make_randomized_response",
"make_randomized_response_bool"
]
[docs]
@versioned
def make_base_discrete_gaussian(
scale,
D: RuntimeTypeDescriptor = "AtomDomain<int>",
MO: RuntimeTypeDescriptor = "ZeroConcentratedDivergence<QO>",
QI: RuntimeTypeDescriptor = "int"
) -> Measurement:
"""Make a Measurement that adds noise from the discrete_gaussian(`scale`) distribution to the input.
Set `D` to change the input data type and input metric:
| `D` | input type | `D::InputMetric` |
| ---------------------------- | ------------ | ----------------------- |
| `AtomDomain<T>` (default) | `T` | `AbsoluteDistance<QI>` |
| `VectorDomain<AtomDomain<T>>` | `Vec<T>` | `L2Distance<QI>` |
[make_base_discrete_gaussian in Rust documentation.](https://docs.rs/opendp/latest/opendp/measurements/fn.make_base_discrete_gaussian.html)
**Supporting Elements:**
* Input Domain: `D`
* Output Type: `D::Carrier`
* Input Metric: `D::InputMetric`
* Output Measure: `MO`
:param scale: Noise scale parameter for the gaussian distribution. `scale` == standard_deviation.
:param D: Domain of the data type to be privatized. Valid values are `VectorDomain<AtomDomain<T>>` or `AtomDomain<T>`.
:type D: :py:ref:`RuntimeTypeDescriptor`
:param MO: Output measure. The only valid measure is `ZeroConcentratedDivergence<QO>`, but QO can be any float.
:type MO: :py:ref:`RuntimeTypeDescriptor`
:param QI: Input distance. The type of sensitivities. Can be any integer or float.
:type QI: :py:ref:`RuntimeTypeDescriptor`
:rtype: Measurement
:raises TypeError: 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
"""
assert_features("contrib")
# Standardize type arguments.
D = RuntimeType.parse(type_name=D)
MO = RuntimeType.parse(type_name=MO, generics=["QO"])
QI = RuntimeType.parse(type_name=QI)
QO = get_atom_or_infer(MO, scale)
MO = MO.substitute(QO=QO)
# Convert arguments to c types.
c_scale = py_to_c(scale, c_type=ctypes.c_void_p, type_name=QO)
c_D = py_to_c(D, c_type=ctypes.c_char_p)
c_MO = py_to_c(MO, c_type=ctypes.c_char_p)
c_QI = py_to_c(QI, c_type=ctypes.c_char_p)
# Call library function.
lib_function = lib.opendp_measurements__make_base_discrete_gaussian
lib_function.argtypes = [ctypes.c_void_p, ctypes.c_char_p, ctypes.c_char_p, ctypes.c_char_p]
lib_function.restype = FfiResult
output = c_to_py(unwrap(lib_function(c_scale, c_D, c_MO, c_QI), Measurement))
return output
[docs]
@versioned
def make_base_discrete_laplace(
scale,
D: RuntimeTypeDescriptor = "AtomDomain<int>",
QO: RuntimeTypeDescriptor = None
) -> Measurement:
"""Make a Measurement that adds noise from the discrete_laplace(`scale`) distribution to the input.
Set `D` to change the input data type and input metric:
| `D` | input type | `D::InputMetric` |
| ---------------------------- | ------------ | ---------------------- |
| `AtomDomain<T>` (default) | `T` | `AbsoluteDistance<T>` |
| `VectorDomain<AtomDomain<T>>` | `Vec<T>` | `L1Distance<T>` |
This uses `make_base_discrete_laplace_cks20` if scale is greater than 10, otherwise it uses `make_base_discrete_laplace_linear`.
[make_base_discrete_laplace in Rust documentation.](https://docs.rs/opendp/latest/opendp/measurements/fn.make_base_discrete_laplace.html)
**Citations:**
* [GRS12 Universally Utility-Maximizing Privacy Mechanisms](https://theory.stanford.edu/~tim/papers/priv.pdf)
* [CKS20 The Discrete Gaussian for Differential Privacy](https://arxiv.org/pdf/2004.00010.pdf#subsection.5.2)
**Supporting Elements:**
* Input Domain: `D`
* Output Type: `D::Carrier`
* Input Metric: `D::InputMetric`
* Output Measure: `MaxDivergence<QO>`
:param scale: Noise scale parameter for the laplace distribution. `scale` == sqrt(2) * standard_deviation.
:param D: Domain of the data type to be privatized. Valid values are `VectorDomain<AtomDomain<T>>` or `AtomDomain<T>`
:type D: :py:ref:`RuntimeTypeDescriptor`
:param QO: Data type of the output distance and scale. `f32` or `f64`.
:type QO: :py:ref:`RuntimeTypeDescriptor`
:rtype: Measurement
:raises TypeError: 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
"""
assert_features("contrib")
# Standardize type arguments.
D = RuntimeType.parse(type_name=D)
QO = RuntimeType.parse_or_infer(type_name=QO, public_example=scale)
# Convert arguments to c types.
c_scale = py_to_c(scale, c_type=ctypes.c_void_p, type_name=QO)
c_D = py_to_c(D, c_type=ctypes.c_char_p)
c_QO = py_to_c(QO, c_type=ctypes.c_char_p)
# Call library function.
lib_function = lib.opendp_measurements__make_base_discrete_laplace
lib_function.argtypes = [ctypes.c_void_p, ctypes.c_char_p, ctypes.c_char_p]
lib_function.restype = FfiResult
output = c_to_py(unwrap(lib_function(c_scale, c_D, c_QO), Measurement))
return output
[docs]
@versioned
def make_base_discrete_laplace_cks20(
scale,
D: RuntimeTypeDescriptor = "AtomDomain<int>",
QO: RuntimeTypeDescriptor = None
) -> Measurement:
"""Make a Measurement that adds noise from the discrete_laplace(`scale`) distribution to the input,
using an efficient algorithm on rational bignums.
Set `D` to change the input data type and input metric:
| `D` | input type | `D::InputMetric` |
| ---------------------------- | ------------ | ---------------------- |
| `AtomDomain<T>` (default) | `T` | `AbsoluteDistance<T>` |
| `VectorDomain<AtomDomain<T>>` | `Vec<T>` | `L1Distance<T>` |
[make_base_discrete_laplace_cks20 in Rust documentation.](https://docs.rs/opendp/latest/opendp/measurements/fn.make_base_discrete_laplace_cks20.html)
**Citations:**
* [CKS20 The Discrete Gaussian for Differential Privacy](https://arxiv.org/pdf/2004.00010.pdf#subsection.5.2)
**Supporting Elements:**
* Input Domain: `D`
* Output Type: `D::Carrier`
* Input Metric: `D::InputMetric`
* Output Measure: `MaxDivergence<QO>`
:param scale: Noise scale parameter for the laplace distribution. `scale` == sqrt(2) * standard_deviation.
:param D: Domain of the data type to be privatized. Valid values are `VectorDomain<AtomDomain<T>>` or `AtomDomain<T>`
:type D: :py:ref:`RuntimeTypeDescriptor`
:param QO: Data type of the output distance and scale.
:type QO: :py:ref:`RuntimeTypeDescriptor`
:rtype: Measurement
:raises TypeError: 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
"""
assert_features("contrib")
# Standardize type arguments.
D = RuntimeType.parse(type_name=D)
QO = RuntimeType.parse_or_infer(type_name=QO, public_example=scale)
# Convert arguments to c types.
c_scale = py_to_c(scale, c_type=ctypes.c_void_p, type_name=QO)
c_D = py_to_c(D, c_type=ctypes.c_char_p)
c_QO = py_to_c(QO, c_type=ctypes.c_char_p)
# Call library function.
lib_function = lib.opendp_measurements__make_base_discrete_laplace_cks20
lib_function.argtypes = [ctypes.c_void_p, ctypes.c_char_p, ctypes.c_char_p]
lib_function.restype = FfiResult
output = c_to_py(unwrap(lib_function(c_scale, c_D, c_QO), Measurement))
return output
[docs]
@versioned
def make_base_discrete_laplace_linear(
scale,
bounds: Any = None,
D: RuntimeTypeDescriptor = "AtomDomain<int>",
QO: RuntimeTypeDescriptor = None
) -> Measurement:
"""Make a Measurement that adds noise from the discrete_laplace(`scale`) distribution to the input,
using a linear-time algorithm on finite data types.
This algorithm can be executed in constant time if bounds are passed.
Set `D` to change the input data type and input metric:
| `D` | input type | `D::InputMetric` |
| ---------------------------- | ------------ | ---------------------- |
| `AtomDomain<T>` (default) | `T` | `AbsoluteDistance<T>` |
| `VectorDomain<AtomDomain<T>>` | `Vec<T>` | `L1Distance<T>` |
[make_base_discrete_laplace_linear in Rust documentation.](https://docs.rs/opendp/latest/opendp/measurements/fn.make_base_discrete_laplace_linear.html)
**Citations:**
* [GRS12 Universally Utility-Maximizing Privacy Mechanisms](https://theory.stanford.edu/~tim/papers/priv.pdf)
**Supporting Elements:**
* Input Domain: `D`
* Output Type: `D::Carrier`
* Input Metric: `D::InputMetric`
* Output Measure: `MaxDivergence<QO>`
:param scale: Noise scale parameter for the distribution. `scale` == sqrt(2) * standard_deviation.
:param bounds: Set bounds on the count to make the algorithm run in constant-time.
:type bounds: Any
:param D: Domain of the data type to be privatized. Valid values are `VectorDomain<AtomDomain<T>>` or `AtomDomain<T>`
:type D: :py:ref:`RuntimeTypeDescriptor`
:param QO: Data type of the scale and output distance.
:type QO: :py:ref:`RuntimeTypeDescriptor`
:rtype: Measurement
:raises TypeError: 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
"""
assert_features("contrib")
# Standardize type arguments.
D = RuntimeType.parse(type_name=D)
QO = RuntimeType.parse_or_infer(type_name=QO, public_example=scale)
T = get_atom(D)
OptionT = RuntimeType(origin='Option', args=[RuntimeType(origin='Tuple', args=[T, T])])
# Convert arguments to c types.
c_scale = py_to_c(scale, c_type=ctypes.c_void_p, type_name=QO)
c_bounds = py_to_c(bounds, c_type=AnyObjectPtr, type_name=OptionT)
c_D = py_to_c(D, c_type=ctypes.c_char_p)
c_QO = py_to_c(QO, c_type=ctypes.c_char_p)
# Call library function.
lib_function = lib.opendp_measurements__make_base_discrete_laplace_linear
lib_function.argtypes = [ctypes.c_void_p, AnyObjectPtr, ctypes.c_char_p, ctypes.c_char_p]
lib_function.restype = FfiResult
output = c_to_py(unwrap(lib_function(c_scale, c_bounds, c_D, c_QO), Measurement))
return output
[docs]
@versioned
def make_base_gaussian(
scale,
k: int = -1074,
D: RuntimeTypeDescriptor = "AtomDomain<T>",
MO: RuntimeTypeDescriptor = "ZeroConcentratedDivergence<T>"
) -> Measurement:
"""Make a Measurement that adds noise from the gaussian(`scale`) distribution to the input.
Set `D` to change the input data type and input metric:
| `D` | input type | `D::InputMetric` |
| ---------------------------- | ------------ | ---------------------- |
| `AtomDomain<T>` (default) | `T` | `AbsoluteDistance<T>` |
| `VectorDomain<AtomDomain<T>>` | `Vec<T>` | `L2Distance<T>` |
This function takes a noise granularity in terms of 2^k.
Larger granularities are more computationally efficient, but have a looser privacy map.
If k is not set, k defaults to the smallest granularity.
[make_base_gaussian in Rust documentation.](https://docs.rs/opendp/latest/opendp/measurements/fn.make_base_gaussian.html)
**Supporting Elements:**
* Input Domain: `D`
* Output Type: `D::Carrier`
* Input Metric: `D::InputMetric`
* Output Measure: `MO`
:param scale: Noise scale parameter for the gaussian distribution. `scale` == standard_deviation.
:param k: The noise granularity in terms of 2^k.
:type k: int
:param D: Domain of the data type to be privatized. Valid values are `VectorDomain<AtomDomain<T>>` or `AtomDomain<T>`.
:type D: :py:ref:`RuntimeTypeDescriptor`
:param MO: Output Measure. The only valid measure is `ZeroConcentratedDivergence<T>`.
:type MO: :py:ref:`RuntimeTypeDescriptor`
:rtype: Measurement
:raises TypeError: 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
"""
assert_features("contrib")
# Standardize type arguments.
D = RuntimeType.parse(type_name=D, generics=["T"])
MO = RuntimeType.parse(type_name=MO, generics=["T"])
T = get_atom_or_infer(D, scale)
D = D.substitute(T=T)
MO = MO.substitute(T=T)
# Convert arguments to c types.
c_scale = py_to_c(scale, c_type=ctypes.c_void_p, type_name=T)
c_k = py_to_c(k, c_type=ctypes.c_uint32, type_name=i32)
c_D = py_to_c(D, c_type=ctypes.c_char_p)
c_MO = py_to_c(MO, c_type=ctypes.c_char_p)
# Call library function.
lib_function = lib.opendp_measurements__make_base_gaussian
lib_function.argtypes = [ctypes.c_void_p, ctypes.c_uint32, ctypes.c_char_p, ctypes.c_char_p]
lib_function.restype = FfiResult
output = c_to_py(unwrap(lib_function(c_scale, c_k, c_D, c_MO), Measurement))
return output
[docs]
@versioned
def make_base_geometric(
scale,
bounds: Any = None,
D: RuntimeTypeDescriptor = "AtomDomain<int>",
QO: RuntimeTypeDescriptor = None
) -> Measurement:
"""Deprecated.
Use `make_base_discrete_laplace` instead (more efficient).
`make_base_discrete_laplace_linear` has a similar interface with the optional constant-time bounds.
[make_base_geometric in Rust documentation.](https://docs.rs/opendp/latest/opendp/measurements/fn.make_base_geometric.html)
**Supporting Elements:**
* Input Domain: `D`
* Output Type: `D::Carrier`
* Input Metric: `D::InputMetric`
* Output Measure: `MaxDivergence<QO>`
:param scale:
:param bounds:
:type bounds: Any
:param D:
:type D: :py:ref:`RuntimeTypeDescriptor`
:param QO:
:type QO: :py:ref:`RuntimeTypeDescriptor`
:rtype: Measurement
:raises TypeError: 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
"""
assert_features("contrib")
# Standardize type arguments.
D = RuntimeType.parse(type_name=D)
QO = RuntimeType.parse_or_infer(type_name=QO, public_example=scale)
T = get_atom(D)
OptionT = RuntimeType(origin='Option', args=[RuntimeType(origin='Tuple', args=[T, T])])
# Convert arguments to c types.
c_scale = py_to_c(scale, c_type=ctypes.c_void_p, type_name=QO)
c_bounds = py_to_c(bounds, c_type=AnyObjectPtr, type_name=OptionT)
c_D = py_to_c(D, c_type=ctypes.c_char_p)
c_QO = py_to_c(QO, c_type=ctypes.c_char_p)
# Call library function.
lib_function = lib.opendp_measurements__make_base_geometric
lib_function.argtypes = [ctypes.c_void_p, AnyObjectPtr, ctypes.c_char_p, ctypes.c_char_p]
lib_function.restype = FfiResult
output = c_to_py(unwrap(lib_function(c_scale, c_bounds, c_D, c_QO), Measurement))
return output
[docs]
@versioned
def make_base_laplace(
scale,
k: int = -1074,
D: RuntimeTypeDescriptor = "AtomDomain<T>"
) -> Measurement:
"""Make a Measurement that adds noise from the laplace(`scale`) distribution to a scalar value.
Set `D` to change the input data type and input metric:
| `D` | input type | `D::InputMetric` |
| ---------------------------- | ------------ | ---------------------- |
| `AtomDomain<T>` (default) | `T` | `AbsoluteDistance<T>` |
| `VectorDomain<AtomDomain<T>>` | `Vec<T>` | `L1Distance<T>` |
This function takes a noise granularity in terms of 2^k.
Larger granularities are more computationally efficient, but have a looser privacy map.
If k is not set, k defaults to the smallest granularity.
[make_base_laplace in Rust documentation.](https://docs.rs/opendp/latest/opendp/measurements/fn.make_base_laplace.html)
**Supporting Elements:**
* Input Domain: `D`
* Output Type: `D::Carrier`
* Input Metric: `D::InputMetric`
* Output Measure: `MaxDivergence<D::Atom>`
:param scale: Noise scale parameter for the laplace distribution. `scale` == sqrt(2) * standard_deviation.
:param k: The noise granularity in terms of 2^k.
:type k: int
:param D: Domain of the data type to be privatized. Valid values are `VectorDomain<AtomDomain<T>>` or `AtomDomain<T>`
:type D: :py:ref:`RuntimeTypeDescriptor`
:rtype: Measurement
:raises TypeError: 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
"""
assert_features("contrib")
# Standardize type arguments.
D = RuntimeType.parse(type_name=D, generics=["T"])
T = get_atom_or_infer(D, scale)
D = D.substitute(T=T)
# Convert arguments to c types.
c_scale = py_to_c(scale, c_type=ctypes.c_void_p, type_name=T)
c_k = py_to_c(k, c_type=ctypes.c_uint32, type_name=i32)
c_D = py_to_c(D, c_type=ctypes.c_char_p)
# Call library function.
lib_function = lib.opendp_measurements__make_base_laplace
lib_function.argtypes = [ctypes.c_void_p, ctypes.c_uint32, ctypes.c_char_p]
lib_function.restype = FfiResult
output = c_to_py(unwrap(lib_function(c_scale, c_k, c_D), Measurement))
return output
[docs]
@versioned
def make_base_ptr(
scale,
threshold,
TK: RuntimeTypeDescriptor,
k: int = -1074,
TV: RuntimeTypeDescriptor = None
) -> Measurement:
"""Make a Measurement that uses propose-test-release to privatize a hashmap of counts.
This function takes a noise granularity in terms of 2^k.
Larger granularities are more computationally efficient, but have a looser privacy map.
If k is not set, k defaults to the smallest granularity.
[make_base_ptr in Rust documentation.](https://docs.rs/opendp/latest/opendp/measurements/fn.make_base_ptr.html)
**Supporting Elements:**
* Input Domain: `MapDomain<AtomDomain<TK>, AtomDomain<TV>>`
* Output Type: `HashMap<TK, TV>`
* Input Metric: `L1Distance<TV>`
* Output Measure: `SmoothedMaxDivergence<TV>`
:param scale: Noise scale parameter for the laplace distribution. `scale` == sqrt(2) * standard_deviation.
:param threshold: Exclude counts that are less than this minimum value.
:param k: The noise granularity in terms of 2^k.
:type k: int
:param TK: Type of Key. Must be hashable/categorical.
:type TK: :py:ref:`RuntimeTypeDescriptor`
:param TV: Type of Value. Must be float.
:type TV: :py:ref:`RuntimeTypeDescriptor`
:rtype: Measurement
:raises TypeError: 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
"""
assert_features("contrib", "floating-point")
# Standardize type arguments.
TK = RuntimeType.parse(type_name=TK)
TV = RuntimeType.parse_or_infer(type_name=TV, public_example=scale)
# Convert arguments to c types.
c_scale = py_to_c(scale, c_type=ctypes.c_void_p, type_name=TV)
c_threshold = py_to_c(threshold, c_type=ctypes.c_void_p, type_name=TV)
c_k = py_to_c(k, c_type=ctypes.c_uint32, type_name=i32)
c_TK = py_to_c(TK, c_type=ctypes.c_char_p)
c_TV = py_to_c(TV, c_type=ctypes.c_char_p)
# Call library function.
lib_function = lib.opendp_measurements__make_base_ptr
lib_function.argtypes = [ctypes.c_void_p, ctypes.c_void_p, ctypes.c_uint32, ctypes.c_char_p, ctypes.c_char_p]
lib_function.restype = FfiResult
output = c_to_py(unwrap(lib_function(c_scale, c_threshold, c_k, c_TK, c_TV), Measurement))
return output
[docs]
@versioned
def make_randomized_response(
categories: Any,
prob,
constant_time: bool = False,
T: RuntimeTypeDescriptor = None,
QO: RuntimeTypeDescriptor = None
) -> Measurement:
"""Make a Measurement that implements randomized response on a categorical value.
[make_randomized_response in Rust documentation.](https://docs.rs/opendp/latest/opendp/measurements/fn.make_randomized_response.html)
**Supporting Elements:**
* Input Domain: `AtomDomain<T>`
* Output Type: `T`
* Input Metric: `DiscreteDistance`
* Output Measure: `MaxDivergence<QO>`
:param categories: Set of valid outcomes
:type categories: Any
:param prob: Probability of returning the correct answer. Must be in `[1/num_categories, 1)`
:param constant_time: Set to true to enable constant time. Slower.
:type constant_time: bool
:param T: Data type of a category.
:type T: :py:ref:`RuntimeTypeDescriptor`
:param QO: Data type of probability and output distance.
:type QO: :py:ref:`RuntimeTypeDescriptor`
:rtype: Measurement
:raises TypeError: 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
"""
assert_features("contrib")
# Standardize type arguments.
T = RuntimeType.parse_or_infer(type_name=T, public_example=get_first(categories))
QO = RuntimeType.parse_or_infer(type_name=QO, public_example=prob)
# Convert arguments to c types.
c_categories = py_to_c(categories, c_type=AnyObjectPtr, type_name=RuntimeType(origin='Vec', args=[T]))
c_prob = py_to_c(prob, c_type=ctypes.c_void_p, type_name=QO)
c_constant_time = py_to_c(constant_time, c_type=ctypes.c_bool, type_name=bool)
c_T = py_to_c(T, c_type=ctypes.c_char_p)
c_QO = py_to_c(QO, c_type=ctypes.c_char_p)
# Call library function.
lib_function = lib.opendp_measurements__make_randomized_response
lib_function.argtypes = [AnyObjectPtr, ctypes.c_void_p, ctypes.c_bool, ctypes.c_char_p, ctypes.c_char_p]
lib_function.restype = FfiResult
output = c_to_py(unwrap(lib_function(c_categories, c_prob, c_constant_time, c_T, c_QO), Measurement))
return output
[docs]
@versioned
def make_randomized_response_bool(
prob,
constant_time: bool = False,
QO: RuntimeTypeDescriptor = None
) -> Measurement:
"""Make a Measurement that implements randomized response on a boolean value.
[make_randomized_response_bool in Rust documentation.](https://docs.rs/opendp/latest/opendp/measurements/fn.make_randomized_response_bool.html)
**Supporting Elements:**
* Input Domain: `AtomDomain<bool>`
* Output Type: `bool`
* Input Metric: `DiscreteDistance`
* Output Measure: `MaxDivergence<QO>`
:param prob: Probability of returning the correct answer. Must be in `[0.5, 1)`
:param constant_time: Set to true to enable constant time. Slower.
:type constant_time: bool
:param QO: Data type of probability and output distance.
:type QO: :py:ref:`RuntimeTypeDescriptor`
:rtype: Measurement
:raises TypeError: 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
"""
assert_features("contrib")
# Standardize type arguments.
QO = RuntimeType.parse_or_infer(type_name=QO, public_example=prob)
# Convert arguments to c types.
c_prob = py_to_c(prob, c_type=ctypes.c_void_p, type_name=QO)
c_constant_time = py_to_c(constant_time, c_type=ctypes.c_bool, type_name=bool)
c_QO = py_to_c(QO, c_type=ctypes.c_char_p)
# Call library function.
lib_function = lib.opendp_measurements__make_randomized_response_bool
lib_function.argtypes = [ctypes.c_void_p, ctypes.c_bool, ctypes.c_char_p]
lib_function.restype = FfiResult
output = c_to_py(unwrap(lib_function(c_prob, c_constant_time, c_QO), Measurement))
return output
