oat-python-sdk 0.1.1

Python client for OAT (Optimization and Analysis Tooling) database

OatClient - Python Client for OAT Database

A Python client library for interacting with the OAT (Optimization and Analysis Tooling) database backend.

Features

• ✅ Full support for logical operations (AND, OR, XOR, NOT, IMPLY, EQUIV)
• ✅ Cardinality constraints (AtLeast, AtMost, Equal)
• ✅ Linear inequality constraints (GeLineq)
• ✅ Property management with filtering
• ✅ Advanced query system with Filter helpers
• ✅ Optimization solver integration
• ✅ Buffered compilation modes (Instant/OnDemand)
• ✅ Type hints for better IDE support

Installation

pip install oat

Quick Start

from OatClient import OatClient, CompilationSettings

# Initialize client
client = OatClient(
    base_url="http://localhost:7062",
    compilation_settings=CompilationSettings.INSTANT
)

# Create primitives
x = client.set_primitive("x", bound=complex(0, 1))
y = client.set_primitive("y", bound=complex(0, 1))
z = client.set_primitive("z", bound=complex(0, 1))

# Add properties
client.set_property(x, "name", "Variable X")

# Create constraints
at_least_2 = client.set_atleast([x, y, z], 2)

# Solve
solutions = client.solve(
    roots=[at_least_2],
    objectives=[{x: -1, y: -2}],
    assume={at_least_2: complex(1, 1)},
    maximize=True
)

if solutions:
    solution = solutions[0]
    print(f"x = {solution[x]}")  # complex(lower, upper)

Available Methods

Primitive Operations

• set_primitive(id: str, bound: complex = complex(0, 1)) -> str - Create a single primitive
• set_primitives(ids: List[str], bound: complex = complex(0, 1)) -> List[str] - Create multiple primitives
• set_property(id: str, property: str, value: Any) -> None - Set node property

Logical Operations

• set_and(references: List[str]) -> str - AND operation
• set_or(references: List[str]) -> str - OR operation
• set_xor(references: List[str]) -> str - XOR operation
• set_not(references: List[str]) -> str - NOT operation
• set_imply(lhs: str, rhs: str) -> str - Implication (lhs → rhs)
• set_equiv(lhs: str, rhs: str) -> str - Equivalence (lhs ↔ rhs)

Cardinality Constraints

• set_atleast(references: List[str], value: int) -> str - At least N must be true
• set_atmost(references: List[str], value: int) -> str - At most N must be true
• set_equal(references: List[str], value: Union[int, str]) -> str - Exactly N must be true

Linear Constraints

• set_gelineq(coefficients: Dict[str, int], bias: int) -> str - Greater-or-equal linear inequality

Query Operations

• get_node_ids(filter: Optional[dict] = None) -> List[str] - Get node IDs with optional filtering
• get_properties(id: str) -> Dict[str, Any] - Get all properties for a node
• get_many_properties(ids: List[str]) -> Dict[str, Dict[str, Any]] - Get properties for multiple nodes

Solver

• solve(roots: List[str], objectives: List[Dict[str, int]], assume: Dict[str, complex] = {}, maximize: bool = True) -> List[dict] - Solve optimization problem

Utility

• health_check() -> bool - Check server health
• compile() -> None - Manually compile buffered operations
• delete_node(id: str) -> None - Delete a node

Bounds

In OatClient, bounds are represented as complex numbers where:

• real part = lower bound
• imag part = upper bound

# Bound representing [0, 1]
bound = complex(0, 1)

# Bound representing [5, 10]
bound = complex(5, 10)

# Access bounds
lower = bound.real  # 5
upper = bound.imag  # 10

Filter Helpers

Use the Filter class to build complex queries:

from OatClient import Filter

# Simple property filter
nodes = client.get_node_ids(Filter.property_equals("type", "constraint"))

# Property exists
nodes = client.get_node_ids(Filter.property_exists("name"))

# Numeric comparisons
nodes = client.get_node_ids(Filter.property_gt("priority", 5))

# Complex filters
nodes = client.get_node_ids(Filter.and_(
    Filter.property_exists("name"),
    Filter.property_gt("priority", 5),
    Filter.node_id_starts_with("task_")
))

# OR and NOT
nodes = client.get_node_ids(Filter.or_(
    Filter.property_equals("status", "active"),
    Filter.not_(Filter.property_exists("archived"))
))

Available Filter Methods

• Filter.property_equals(key: str, value: Any) - Property equals value
• Filter.property_exists(key: str) - Property exists
• Filter.property_contains(key: str, substring: str) - Property contains substring
• Filter.property_gt(key: str, value: float) - Property greater than
• Filter.property_gte(key: str, value: float) - Property greater than or equal
• Filter.property_lt(key: str, value: float) - Property less than
• Filter.property_lte(key: str, value: float) - Property less than or equal
• Filter.node_id_equals(node_id: str) - Node ID equals
• Filter.node_id_starts_with(prefix: str) - Node ID starts with prefix
• Filter.and_(*expressions) - Combine with AND
• Filter.or_(*expressions) - Combine with OR
• Filter.not_(expression) - Negate expression

Compilation Modes

Instant Compilation (Default)

Operations are compiled immediately:

client = OatClient(base_url, CompilationSettings.INSTANT)
x = client.set_primitive("x")  # Compiled immediately

On-Demand Compilation

Operations are buffered and compiled in batch:

client = OatClient(base_url, CompilationSettings.ON_DEMAND)
x = client.set_primitive("x")  # Buffered
y = client.set_primitive("y")  # Buffered
client.compile()  # Compile all buffered operations

Complete Example

from OatClient import OatClient, CompilationSettings, Filter

# Initialize
client = OatClient("http://localhost:7062", CompilationSettings.INSTANT)

# Create primitives
a = client.set_primitive("a")
b = client.set_primitive("b")
c = client.set_primitive("c")

# Add metadata
client.set_property(a, "type", "task")
client.set_property(b, "type", "task")
client.set_property(c, "type", "resource")
client.set_property(a, "priority", 10)

# Create logical constraints
and_constraint = client.set_and([a, b])
or_constraint = client.set_or([b, c])
imply_constraint = client.set_imply(a, b)  # a → b

# Create cardinality constraint
atleast_2 = client.set_atleast([a, b, c], 2)

# Query nodes
tasks = client.get_node_ids(Filter.property_equals("type", "task"))
print(f"Tasks: {tasks}")

high_priority = client.get_node_ids(Filter.property_gt("priority", 5))
print(f"High priority: {high_priority}")

# Solve with objectives
solutions = client.solve(
    roots=[atleast_2],
    objectives=[{a: -1, b: -2}],  # Minimize weighted sum
    assume={atleast_2: complex(1, 1)},  # Force constraint to be true
    maximize=True
)

if solutions:
    for i, sol in enumerate(solutions):
        print(f"\nSolution {i + 1}:")
        for var, bound in sol.items():
            print(f"  {var}: [{int(bound.real)}, {int(bound.imag)}]")

Requirements

• Python >= 3.10
• requests

License

[Your License Here]

Links

• GitHub Repository
• Documentation
• .NET Client