Integration Modules

import awkward.numba

def enable_numba():
    """
    Enable Numba integration for awkward arrays.
    
    This function registers awkward array types with Numba's type system,
    allowing awkward arrays to be used in @numba.jit decorated functions.
    """

# Numba-compilable operations
@numba.jit
def compute_with_awkward(array):
    """
    Example of Numba-compiled function working with awkward arrays.
    
    Parameters:
    - array: Awkward array that will be compiled
    
    Returns:
    Computed result with full JIT performance
    """

import awkward.jax

def register_jax():
    """
    Register awkward arrays with JAX transformation system.
    
    Enables awkward arrays to participate in JAX transformations like
    jit, grad, vmap, and pmap for automatic differentiation and 
    parallelization.
    """

# JAX transformation support
def jax_compatible_function(array):
    """
    Function that can be transformed by JAX (jit, grad, etc.).
    
    Parameters:
    - array: Awkward array compatible with JAX transformations
    
    Returns:
    Result that supports automatic differentiation
    """

def backend(array):
    """
    Get the computational backend currently used by array.
    
    Parameters:
    - array: Array to check backend for
    
    Returns:
    str indicating current backend ("cpu", "cuda", "jax", etc.)
    """

def to_backend(array, backend, highlevel=True, behavior=None):
    """
    Move array to specified computational backend.
    
    Parameters:
    - array: Array to move
    - backend: str, target backend name
        - "cpu": Standard CPU backend using NumPy
        - "cuda": CUDA backend using CuPy  
        - "jax": JAX backend for automatic differentiation
        - "typetracer": Type inference backend without data
    - highlevel: bool, if True return Array, if False return Content layout
    - behavior: dict, custom behavior for the result
    
    Returns:
    Array moved to target backend
    """

def copy_to(array, backend):
    """
    Copy array data to different backend.
    
    Parameters:
    - array: Array to copy
    - backend: str, destination backend
    
    Returns:
    Array copy on target backend
    """

import awkward.typetracer

class TypeTracer:
    """
    Lazy evaluation system for type inference without data materialization.
    
    TypeTracer arrays track type information and operations without 
    storing actual data, enabling:
    - Static type checking
    - Memory usage analysis  
    - Operation optimization
    - Schema inference
    """
    
    def touch_data(self, recursive=True):
        """
        Mark data as accessed for dependency tracking.
        
        Parameters:
        - recursive: bool, if True mark nested data as touched
        """
        
    def touch_shape(self, recursive=True):
        """
        Mark shape information as accessed.
        
        Parameters:
        - recursive: bool, if True mark nested shapes as touched
        """

def typetracer_with_report(array):
    """
    Create type tracer that generates access reports.
    
    Parameters:
    - array: Array to create type tracer for
    
    Returns:
    tuple of (TypeTracer array, report function)
    """

def typetracer_from_form(form):
    """
    Create type tracer directly from Form description.
    
    Parameters:
    - form: Form object describing array structure
    
    Returns:
    TypeTracer array matching the form
    """

import awkward.cppyy

def register_cppyy():
    """
    Register awkward types with cppyy for C++ interoperability.
    
    Enables:
    - Passing awkward arrays to C++ functions
    - Converting C++ containers to awkward arrays  
    - Integration with ROOT data analysis framework
    - Zero-copy data sharing where possible
    """

def cpp_interface(array):
    """
    Create C++-compatible interface for array.
    
    Parameters:  
    - array: Awkward array to create C++ interface for
    
    Returns:
    C++-compatible proxy object
    """

def to_cuda(array):
    """
    Move array to CUDA GPU memory.
    
    Parameters:
    - array: Array to move to GPU
    
    Returns:
    Array with data in GPU memory
    """

def from_cuda(array):
    """  
    Move array from GPU to CPU memory.
    
    Parameters:
    - array: GPU array to move to CPU
    
    Returns:
    Array with data in CPU memory
    """

def is_cuda(array):
    """
    Test if array data resides in GPU memory.
    
    Parameters:
    - array: Array to test
    
    Returns:
    bool indicating if array is on GPU
    """

def numba_array_typer(array_type):
    """
    Create Numba type signature for awkward array type.
    
    Parameters:
    - array_type: Awkward array type
    
    Returns:
    Numba type signature for compilation
    """

def jax_pytree_flatten(array):
    """
    Flatten awkward array for JAX pytree operations.
    
    Parameters:
    - array: Array to flatten
    
    Returns:
    tuple of (leaves, tree_def) for JAX pytree operations
    """

def jax_pytree_unflatten(tree_def, leaves):
    """
    Reconstruct awkward array from JAX pytree components.
    
    Parameters:
    - tree_def: Tree definition from flatten operation
    - leaves: Leaf values from flatten operation
    
    Returns:
    Reconstructed awkward array
    """

def dispatch_map():
    """
    Get mapping of operations to backend-specific implementations.
    
    Returns:
    dict mapping operation names to backend implementations
    """

def benchmark_backends(array, operation, backends=None):
    """
    Benchmark operation performance across different backends.
    
    Parameters:
    - array: Array to benchmark with
    - operation: Function to benchmark
    - backends: list of str, backends to test (None for all available)
    
    Returns:
    dict mapping backend names to timing results
    """

def memory_usage(array, backend=None):
    """
    Analyze memory usage of array on specified backend.
    
    Parameters:
    - array: Array to analyze
    - backend: str, backend to check (None for current)
    
    Returns:
    dict with memory usage statistics
    """

def optimize_for_backend(array, backend, operation_hint=None):
    """
    Optimize array layout for specific backend and operation.
    
    Parameters:
    - array: Array to optimize
    - backend: str, target backend
    - operation_hint: str, hint about intended operations
    
    Returns:
    Array optimized for target backend
    """

import awkward as ak
import numba
import numpy as np

# Enable numba integration
ak.numba.register()

@numba.jit
def fast_computation(events):
    """JIT-compiled function working with nested data."""
    total = 0.0
    for event in events:
        for particle in event.particles:
            if particle.pt > 10.0:
                total += particle.pt * particle.pt
    return total

# Use with nested data
events = ak.Array([
    {"particles": [{"pt": 15.0}, {"pt": 5.0}]},
    {"particles": [{"pt": 25.0}, {"pt": 12.0}]}
])

result = fast_computation(events)  # Runs at compiled speed

import awkward as ak
import jax
import jax.numpy as jnp

# Register awkward arrays as JAX pytrees
ak.jax.register()

def physics_calculation(events):
    """Function that can be JAX-transformed."""
    pts = events.particles.pt
    return ak.sum(pts * pts, axis=1)

# Apply JAX transformations
jit_calc = jax.jit(physics_calculation)
vectorized_calc = jax.vmap(physics_calculation)

# Automatic differentiation
def loss_function(events, weights):
    result = physics_calculation(events)
    return jnp.sum(result * weights)

gradient_fn = jax.grad(loss_function, argnums=1)

import awkward as ak
import cupy as cp

# Create array on CPU
cpu_array = ak.Array([[1, 2, 3], [4, 5]])
print(ak.backend(cpu_array))  # "cpu"

# Move to GPU
gpu_array = ak.to_backend(cpu_array, "cuda")
print(ak.backend(gpu_array))  # "cuda"

# Check if CUDA is available
if cp.cuda.is_available():
    # Perform GPU computation
    gpu_result = ak.sum(gpu_array * gpu_array)
    
    # Move result back to CPU  
    cpu_result = ak.to_backend(gpu_result, "cpu")

import awkward as ak

# Create type tracer for schema analysis
form = ak.forms.RecordForm([
    ak.forms.ListForm("i64", "i64", ak.forms.NumpyForm("float64")),
    ak.forms.NumpyForm("int32")
], ["particles", "event_id"])

tracer = ak.typetracer.typetracer_from_form(form)

def analyze_operation(data):
    """Function to analyze without data."""
    return ak.sum(data.particles, axis=1) + data.event_id

# Trace operation to understand access patterns
traced_result = analyze_operation(tracer)
print(f"Result type: {ak.type(traced_result)}")

import awkward as ak
import cppyy

# Register awkward arrays with cppyy  
ak.cppyy.register()

# Define C++ function (example)
cppyy.cppdef("""
double compute_mass(const std::vector<double>& pt, 
                   const std::vector<double>& eta) {
    double total = 0.0;
    for(size_t i = 0; i < pt.size(); ++i) {
        total += pt[i] * cosh(eta[i]);
    }
    return total;
}
""")

# Use with awkward arrays
particles = ak.Array({
    "pt": [[10.0, 20.0], [15.0]], 
    "eta": [[1.0, 0.5], [1.2]]
})

# Convert to C++ compatible format and call
for event in particles:
    mass = cppyy.gbl.compute_mass(event.pt, event.eta)
    print(f"Event mass: {mass}")

import awkward as ak
import time

# Create test data
large_array = ak.Array([
    [i + j for j in range(1000)] 
    for i in range(1000)
])

def benchmark_operation(array, backend_name):
    """Benchmark array operation on specific backend."""
    # Move to backend
    backend_array = ak.to_backend(array, backend_name)
    
    # Time the operation
    start = time.time()
    result = ak.sum(backend_array * backend_array, axis=1)
    end = time.time()
    
    return end - start

# Compare backends
backends = ["cpu"]
if ak.backend.cuda_available():
    backends.append("cuda")
if ak.backend.jax_available():
    backends.append("jax")

for backend in backends:
    duration = benchmark_operation(large_array, backend)
    print(f"{backend}: {duration:.4f} seconds")

import awkward as ak

def optimize_for_computation(array, target_backend="cpu", operation="reduction"):
    """Optimize array for specific computation pattern."""
    
    # Pack array for better memory layout
    packed = ak.to_packed(array)
    
    # Move to target backend
    backend_array = ak.to_backend(packed, target_backend)
    
    # Apply operation-specific optimizations
    if operation == "reduction" and target_backend == "cuda":
        # Use specific CUDA optimizations
        return ak.with_parameter(backend_array, "gpu_optimized", True)
    
    return backend_array

# Example usage
data = ak.Array([[1, 2, 3], [4, 5, 6, 7], [8, 9]])
optimized = optimize_for_computation(data, "cuda", "reduction")
result = ak.sum(optimized, axis=1)  # Runs with optimizations