gr801_simulation_framework.py

"""GR801 Radiation-Hardened AI SoC Simulation Framework.

Gaisler Research Artificial Intelligence NOEL-V (GRAIN) Product Line
For space-based AI applications in harsh radiation environments.

Note: This is a *simulation scaffold* intended to be lightweight and safe to import.
It avoids allocating multi-GB arrays or iterating per-CPU-cycle in Python.
"""

from __future__ import annotations

import logging
from dataclasses import dataclass
from datetime import datetime
from enum import Enum, IntEnum
from typing import Any

import numpy as np

logger = logging.getLogger("GR801_Simulator")


class GR801Config:
    """GR801 SoC Configuration Parameters."""

    # Core Configuration
    NUM_CORES = 4  # NOEL-V RISC-V cores
    CORE_FREQUENCY = 100e6  # 100 MHz (radiation-hardened)
    CACHE_LINE_SIZE = 64  # bytes
    L1_CACHE_SIZE = 32 * 1024  # 32 KB per core
    L2_CACHE_SIZE = 256 * 1024  # 256 KB shared

    # AI Accelerator
    AI_ACCELERATOR_PRESENT = True
    NEURAL_ENGINE_OPS = 8  # TOPS (Tera Operations Per Second)
    MATRIX_UNITS = 64
    VECTOR_WIDTH = 512  # bits

    # Memory Configuration
    DDR4_ECC_SIZE = 4 * 1024**3  # 4 GB address space with ECC
    SCRUB_RATE = 1e6  # scrubs per second

    # Radiation Hardening
    SEU_THRESHOLD = 37  # MeV-cm²/mg (LET threshold)
    TID_TOLERANCE = 300  # krad(Si)
    LATCHUP_IMMUNE = True

    # Power Management
    NOMINAL_POWER = 15  # Watts
    LOW_POWER_MODE = 5  # Watts
    PEAK_POWER = 25  # Watts

    # Thermal Limits
    MAX_TEMP = 125  # °C
    MIN_TEMP = -55  # °C
    OP_TEMP_RANGE = (-40, 100)  # °C


class RadiationEnvironment(Enum):
    """Space radiation environments."""

    LEO = "low_earth_orbit"
    GEO = "geostationary_orbit"
    DEEP_SPACE = "deep_space"
    SOLAR_MAX = "solar_maximum"
    JUPITER = "jupiter_radiation_belts"


@dataclass(frozen=True)
class RadiationFlux:
    """Models radiation flux in a space environment."""

    proton_flux: float  # protons/cm²/s
    heavy_ion_flux: float  # ions/cm²/s
    electron_flux: float  # electrons/cm²/s
    total_ionizing_dose_rate: float  # rad(Si)/s
    let_spectrum: dict[float, float]  # LET vs flux

    @classmethod
    def for_environment(cls, environment: RadiationEnvironment) -> RadiationFlux:
        """Get radiation flux for specific space environment."""

        fluxes: dict[RadiationEnvironment, RadiationFlux] = {
            RadiationEnvironment.LEO: cls(
                proton_flux=1e4,
                heavy_ion_flux=1e2,
                electron_flux=1e5,
                total_ionizing_dose_rate=1e-3,
                let_spectrum={0.1: 1e5, 1.0: 1e4, 10.0: 1e3, 100.0: 1e2},
            ),
            RadiationEnvironment.GEO: cls(
                proton_flux=1e5,
                heavy_ion_flux=1e3,
                electron_flux=1e6,
                total_ionizing_dose_rate=1e-2,
                let_spectrum={0.1: 1e6, 1.0: 1e5, 10.0: 1e4, 100.0: 1e3},
            ),
            RadiationEnvironment.JUPITER: cls(
                proton_flux=1e8,
                heavy_ion_flux=1e6,
                electron_flux=1e9,
                total_ionizing_dose_rate=1.0,
                let_spectrum={0.1: 1e9, 1.0: 1e8, 10.0: 1e7, 100.0: 1e6},
            ),
        }
        return fluxes.get(environment, fluxes[RadiationEnvironment.LEO])


class SEUType(IntEnum):
    """Types of Single Event Effects."""

    SEU = 1  # Single Event Upset (bit flip)
    SET = 2  # Single Event Transient
    SEFI = 3  # Single Event Functional Interrupt
    SEL = 4  # Single Event Latchup (mitigated in GR801)
    SEB = 5  # Single Event Burnout


@dataclass
class SEUEvent:
    """Single Event Upset event data."""

    timestamp: float
    location: tuple[int, int, int]  # (core, memory_type, address)
    bit_position: int
    particle_let: float  # MeV-cm²/mg
    effect_type: SEUType
    corrected: bool = False
    critical: bool = False


class GR801Core:
    """NOEL-V RISC-V radiation-hardened core model (lightweight)."""

    def __init__(self, core_id: int, frequency: float = GR801Config.CORE_FREQUENCY):
        self.core_id = int(core_id)
        self.frequency = float(frequency)
        self.power_state = "ACTIVE"
        self.temperature = 25.0  # °C
        self.error_count = 0
        self.corrected_errors = 0

        # Register file (32 registers)
        self.registers = np.zeros(32, dtype=np.uint32)

        # Cache with ECC protection (modeled, not fully implemented)
        self.cache_data = np.zeros(GR801Config.L1_CACHE_SIZE, dtype=np.uint8)
        self.cache_ecc = np.zeros(GR801Config.L1_CACHE_SIZE // 8, dtype=np.uint8)
        self.cache_tags = np.zeros(GR801Config.L1_CACHE_SIZE // 64, dtype=np.uint64)

        # Performance counters
        self.instructions_executed = 0
        self.cache_hits = 0
        self.cache_misses = 0

        # RNG (seedable per instance if needed)
        self._rng = np.random.default_rng()

    def execute_cycle(self, radiation_flux: RadiationFlux | None = None, *, cycles: int = 1):
        """Execute one or more CPU cycles with potential SEU injection.

        This method batches work to avoid per-cycle Python loops.
        """

        cycles_i = int(max(1, cycles))
        self.instructions_executed += cycles_i

        if radiation_flux is not None:
            self._simulate_radiation_effects(radiation_flux, cycles=cycles_i)

        self._update_temperature(cycles=cycles_i)

    def _simulate_radiation_effects(self, flux: RadiationFlux, *, cycles: int):
        """Simulate radiation effects on this core."""

        # Simplified cross-section model: expected upsets scale with cycles.
        # Keep lambda small; this is a toy model.
        seu_probability_per_cycle = flux.heavy_ion_flux * 1e-10
        expected_events = float(cycles) * float(seu_probability_per_cycle)
        num_events = int(self._rng.poisson(lam=max(0.0, expected_events)))

        for _ in range(num_events):
            location_type = self._rng.choice(["REGISTER", "CACHE", "CONTROL"])
            self._inject_seu(str(location_type), flux)

    def _inject_seu(self, location: str, flux: RadiationFlux):
        """Inject a simulated SEU at specified location."""

        bit_to_flip = int(self._rng.integers(0, 32))

        if location == "REGISTER":
            reg_idx = int(self._rng.integers(0, 32))
            bit_mask = np.uint32(1) << np.uint32(bit_to_flip)
            self.registers[reg_idx] ^= bit_mask
            self.error_count += 1
            return

        if location == "CACHE":
            addr = int(self._rng.integers(0, len(self.cache_data)))
            bit_mask = np.uint8(1) << np.uint8(bit_to_flip % 8)
            self.cache_data[addr] ^= bit_mask
            self.error_count += 1

            if self._ecc_can_correct(addr):
                self.corrected_errors += 1
                self.cache_data[addr] ^= bit_mask
            return

        # CONTROL / other: count as a (potentially) critical event.
        self.error_count += 1
        _ = flux

    def _ecc_can_correct(self, address: int) -> bool:
        """Check if ECC can correct the error (simplified)."""

        _ = address
        return bool(self._rng.random() < 0.95)

    def _update_temperature(self, *, cycles: int):
        """Update core temperature based on activity."""

        # Scale heating with cycles.
        base_temp_increase = 0.001 if self.power_state == "ACTIVE" else 0.0001
        self.temperature += base_temp_increase * float(cycles)

        # Simple cooling model.
        cooling_rate = 0.0005 * float(cycles)
        self.temperature -= cooling_rate * (self.temperature - 25.0)

        self.temperature = float(np.clip(self.temperature, -55, 125))


class AIAccelerator:
    """GR801 Neural Network Accelerator Model (approximate)."""

    def __init__(self):
        self.active = True
        self.temperature = 25.0
        self.power_consumption = 0.0
        self.throughput = float(GR801Config.NEURAL_ENGINE_OPS) * 1e12  # TOPS to OPS

        self.matrix_units = np.zeros(
            (GR801Config.MATRIX_UNITS, GR801Config.VECTOR_WIDTH // 32),
            dtype=np.float32,
        )

        # Keep these lightweight; treat as capacity rather than fully modeled ECC.
        self.weight_memory_size = 16 * 1024 * 1024
        self.activation_memory_size = 4 * 1024 * 1024

        self.operations_completed = 0
        self.mac_utilization = 0.0
        self.error_rate = 0.0

        self._rng = np.random.default_rng()

    def execute_inference(self, model_ops: int, input_data: np.ndarray) -> np.ndarray:
        """Execute neural network inference."""

        if not self.active:
            raise RuntimeError("AI Accelerator not active")

        model_ops_i = int(model_ops)
        _execution_time = float(model_ops_i) / self.throughput

        self.power_consumption = GR801Config.NOMINAL_POWER * 1.5

        result = self._simulate_matrix_operations(input_data)

        self.operations_completed += model_ops_i
        self.mac_utilization = float(self._rng.uniform(0.7, 0.95))

        self._update_temperature()
        return result

    def _simulate_matrix_operations(self, input_data: np.ndarray) -> np.ndarray:
        """Simulate matrix operations without allocating huge weight matrices."""

        if input_data.ndim < 2:
            raise ValueError("input_data must include a batch dimension")

        batch_size = int(input_data.shape[0])
        output_size = 1000

        flat = input_data.reshape(batch_size, -1).astype(np.float32, copy=False)

        # Downsample features to keep this fast.
        max_features = 1024
        if flat.shape[1] > max_features:
            stride = max(1, flat.shape[1] // max_features)
            flat = flat[:, ::stride][:, :max_features]

        # Deterministic-ish random projection per instance.
        weights = self._rng.standard_normal((flat.shape[1], output_size), dtype=np.float32)
        result = flat @ weights
        return np.tanh(result).astype(np.float32, copy=False)

    def _update_temperature(self):
        """Update accelerator temperature."""

        temp_increase = float(self.power_consumption) * 0.1
        self.temperature += temp_increase

        cooling_rate = 0.05
        self.temperature -= cooling_rate * (self.temperature - 25.0)

        self.temperature = float(np.clip(self.temperature, -55, 125))


class _PagedMemory:
    """Sparse paged byte-addressable memory to avoid multi-GB allocations."""

    def __init__(self, size: int, *, page_size: int = 4096):
        self.size = int(size)
        self.page_size = int(page_size)
        if self.size <= 0:
            raise ValueError("size must be positive")
        if self.page_size <= 0:
            raise ValueError("page_size must be positive")

        self._pages: dict[int, np.ndarray] = {}

    def read(self, address: int, size: int) -> np.ndarray:
        address_i = int(address)
        size_i = int(size)
        if address_i < 0 or size_i < 0 or address_i + size_i > self.size:
            raise ValueError("Memory access out of bounds")

        out = np.zeros(size_i, dtype=np.uint8)
        if size_i == 0:
            return out

        start = address_i
        end = address_i + size_i
        page_size = self.page_size

        pos = 0
        while start < end:
            page_index = start // page_size
            page_offset = start % page_size
            take = min(end - start, page_size - page_offset)

            page = self._pages.get(page_index)
            if page is not None:
                out[pos : pos + take] = page[page_offset : page_offset + take]

            pos += take
            start += take

        return out

    def write(self, address: int, data: np.ndarray):
        address_i = int(address)
        data_u8 = np.asarray(data, dtype=np.uint8)
        size_i = int(data_u8.size)

        if address_i < 0 or address_i + size_i > self.size:
            raise ValueError("Memory access out of bounds")
        if size_i == 0:
            return

        start = address_i
        end = address_i + size_i
        page_size = self.page_size

        pos = 0
        while start < end:
            page_index = start // page_size
            page_offset = start % page_size
            take = min(end - start, page_size - page_offset)

            page = self._pages.get(page_index)
            if page is None:
                page = np.zeros(page_size, dtype=np.uint8)
                self._pages[page_index] = page

            page[page_offset : page_offset + take] = data_u8[pos : pos + take]

            pos += take
            start += take

    @property
    def allocated_pages(self) -> int:
        return len(self._pages)


class MemoryController:
    """ECC-protected DDR4 Memory Controller (sparse address-space model)."""

    def __init__(self, size: int = GR801Config.DDR4_ECC_SIZE, *, page_size: int = 4096):
        self.size = int(size)
        self._mem = _PagedMemory(self.size, page_size=page_size)

        self.scrub_pointer = 0
        self.scrub_rate = float(GR801Config.SCRUB_RATE)
        self.errors_detected = 0
        self.errors_corrected = 0

        self.seu_events: list[SEUEvent] = []
        self._rng = np.random.default_rng()

    def read(self, address: int, size: int) -> np.ndarray:
        """Read memory with ECC checking (simplified)."""

        data = self._mem.read(address, size)

        # Simulate occasional ECC detection/correction. We don't store ECC bits;
        # we simulate the outcomes and counters.
        for i in range(0, int(size), 8):
            chunk_addr = int(address) + i
            if self._check_ecc(chunk_addr):
                if self._correct_ecc_error(chunk_addr):
                    self.errors_corrected += 1
                else:
                    self.errors_detected += 1
                    self._log_memory_error(chunk_addr, "UNCORRECTABLE")

        return data

    def write(self, address: int, data: np.ndarray):
        """Write memory (ECC generation is modeled, not stored)."""

        self._mem.write(address, data)

    def scrub_cycle(self, radiation_flux: RadiationFlux | None = None, *, cycles: int = 1):
        """Execute one or more scrubbing cycles."""

        cycles_i = int(max(1, cycles))
        chunk_size = 64

        # Advance pointer efficiently.
        self.scrub_pointer = int((self.scrub_pointer + chunk_size * cycles_i) % self.size)

        if radiation_flux is not None:
            self._simulate_radiation_errors(radiation_flux, cycles=cycles_i)

    def _check_ecc(self, address: int) -> bool:
        _ = address
        return bool(self._rng.random() < 0.001)

    def _correct_ecc_error(self, address: int) -> bool:
        _ = address
        return bool(self._rng.random() < 0.999)

    def _simulate_radiation_errors(self, flux: RadiationFlux, *, cycles: int):
        """Simulate radiation-induced memory errors."""

        error_probability_per_cycle = flux.heavy_ion_flux * 1e-12
        expected = float(cycles) * float(error_probability_per_cycle)
        num_events = int(self._rng.poisson(lam=max(0.0, expected)))

        for _ in range(num_events):
            error_addr = int(self._rng.integers(0, self.size))
            error_bit = int(self._rng.integers(0, 8))

            # Flip the bit in sparse memory: read-modify-write one byte.
            byte = self._mem.read(error_addr, 1)
            byte[0] ^= np.uint8(1 << error_bit)
            self._mem.write(error_addr, byte)

            self.seu_events.append(
                SEUEvent(
                    timestamp=datetime.now().timestamp(),
                    location=(0, 1, error_addr),
                    bit_position=error_bit,
                    particle_let=float(self._rng.uniform(1, 100)),
                    effect_type=SEUType.SEU,
                )
            )

    def _log_memory_error(self, address: int, error_type: str):
        logger.warning("Memory error at 0x%08X: %s", int(address), str(error_type))

    @property
    def allocated_pages(self) -> int:
        return self._mem.allocated_pages


class AIWorkload(Enum):
    """Types of AI workloads for space applications."""

    IMAGE_CLASSIFICATION = "image_classification"
    OBJECT_DETECTION = "object_detection"
    ANOMALY_DETECTION = "anomaly_detection"
    NAVIGATION = "navigation"
    COMMUNICATIONS = "communications"


@dataclass(frozen=True)
class NeuralNetworkModel:
    """Neural network model for space AI applications."""

    name: str
    input_shape: tuple[int, ...]
    output_shape: tuple[int, ...]
    operations: int
    parameters: int
    memory_footprint: int
    accuracy: float
    radiation_tolerance: float

    @classmethod
    def get_preset_model(cls, model_type: AIWorkload) -> NeuralNetworkModel:
        models: dict[AIWorkload, NeuralNetworkModel] = {
            AIWorkload.IMAGE_CLASSIFICATION: cls(
                name="EfficientNet-Space",
                input_shape=(224, 224, 3),
                output_shape=(1000,),
                operations=int(4e9),
                parameters=int(20e6),
                memory_footprint=80 * 1024 * 1024,
                accuracy=0.85,
                radiation_tolerance=0.95,
            ),
            AIWorkload.OBJECT_DETECTION: cls(
                name="YOLO-Space",
                input_shape=(416, 416, 3),
                output_shape=(13, 13, 425),
                operations=int(10e9),
                parameters=int(60e6),
                memory_footprint=240 * 1024 * 1024,
                accuracy=0.78,
                radiation_tolerance=0.90,
            ),
            AIWorkload.ANOMALY_DETECTION: cls(
                name="AutoEncoder-Space",
                input_shape=(100,),
                output_shape=(100,),
                operations=int(1e6),
                parameters=int(1e5),
                memory_footprint=400 * 1024,
                accuracy=0.92,
                radiation_tolerance=0.98,
            ),
        }
        return models.get(model_type, models[AIWorkload.IMAGE_CLASSIFICATION])


class GR801System:
    """Complete GR801 SoC System Simulation."""

    def __init__(self, environment: RadiationEnvironment = RadiationEnvironment.LEO):
        self.environment = environment
        self.radiation_flux = RadiationFlux.for_environment(environment)

        self.cores = [GR801Core(i) for i in range(GR801Config.NUM_CORES)]
        self.ai_accelerator = AIAccelerator() if GR801Config.AI_ACCELERATOR_PRESENT else None
        self.memory = MemoryController()

        self.uptime = 0.0
        self.total_power = 0.0
        self.system_temperature = 25.0
        self.total_ionizing_dose = 0.0

        self.inference_count = 0
        self.total_operations = 0
        self.system_errors = 0
        self.corrected_errors = 0

        self.current_workload: AIWorkload | None = None
        self.current_model: NeuralNetworkModel | None = None

        self.seu_log: list[SEUEvent] = []
        self.last_seu_check = 0.0

    def execute_timestep(self, dt: float = 1e-3):
        """Execute one simulation timestep."""

        dt_f = float(dt)
        if dt_f <= 0:
            raise ValueError("dt must be positive")

        self.total_ionizing_dose += float(self.radiation_flux.total_ionizing_dose_rate) * dt_f

        # Batch CPU core work (no per-cycle loops).
        cycles = int(max(1, GR801Config.CORE_FREQUENCY * dt_f))
        for core in self.cores:
            core.execute_cycle(self.radiation_flux, cycles=cycles)

        # Batch memory scrubbing.
        scrub_cycles = int(max(1, GR801Config.SCRUB_RATE * dt_f))
        self.memory.scrub_cycle(self.radiation_flux, cycles=scrub_cycles)

        self.uptime += dt_f
        self._update_power_consumption()
        self._update_temperature()
        self._check_radiation_limits()

        if int(self.uptime * 100) % 10 == 0:
            self._log_system_state()

    def run_inference(self, workload: AIWorkload, input_data: np.ndarray) -> dict[str, Any]:
        """Run AI inference on the GR801 system."""

        self.current_workload = workload
        self.current_model = NeuralNetworkModel.get_preset_model(workload)

        if not self._system_operational():
            raise RuntimeError("System not operational due to radiation damage")

        model = self.current_model
        if model is None:
            raise RuntimeError("No model selected")

        if not self._input_shape_matches(input_data, model.input_shape):
            raise ValueError(f"Input shape mismatch: expected {model.input_shape} (optionally batched)")

        start_time = self.uptime

        if self.ai_accelerator is not None:
            result = self.ai_accelerator.execute_inference(model.operations, input_data)
            self.total_power += GR801Config.NOMINAL_POWER * 0.5
        else:
            result = self._cpu_inference(input_data)

        execution_time = self.uptime - start_time
        accuracy = self._calculate_radiation_accuracy()

        self.inference_count += 1
        self.total_operations += int(model.operations)

        return {
            "result": result,
            "execution_time": execution_time,
            "estimated_accuracy": accuracy,
            "power_consumed": self.total_power * max(0.0, execution_time),
            "radiation_effects": len([e for e in self.seu_log if not e.corrected]),
        }

    def _input_shape_matches(self, input_data: np.ndarray, expected: tuple[int, ...]) -> bool:
        if tuple(input_data.shape) == tuple(expected):
            return True
        if input_data.ndim == len(expected) + 1 and tuple(input_data.shape[1:]) == tuple(expected):
            return True
        return False

    def _cpu_inference(self, input_data: np.ndarray) -> np.ndarray:
        """Fallback CPU-based inference (toy)."""

        _ = input_data
        model = self.current_model
        if model is None:
            raise RuntimeError("No model selected")

        results = [np.random.standard_normal(model.output_shape).astype(np.float32) for _core in self.cores]
        return np.mean(results, axis=0)

    def _calculate_radiation_accuracy(self) -> float:
        model = self.current_model
        if model is None:
            return 0.0

        base_accuracy = float(model.accuracy)
        radiation_tolerance = float(model.radiation_tolerance)

        uncorrected_critical = len([e for e in self.seu_log if (not e.corrected and e.critical)])
        error_impact = float(uncorrected_critical) * 0.01

        tid_impact = min(1.0, float(self.total_ionizing_dose) / float(GR801Config.TID_TOLERANCE * 1000))

        accuracy = base_accuracy * radiation_tolerance * (1.0 - error_impact) * (1.0 - tid_impact * 0.1)
        return float(max(0.0, min(1.0, accuracy)))

    def _system_operational(self) -> bool:
        if self.system_temperature > GR801Config.MAX_TEMP:
            logger.error("System temperature too high: %s°C", self.system_temperature)
            return False

        if self.system_temperature < GR801Config.MIN_TEMP:
            logger.error("System temperature too low: %s°C", self.system_temperature)
            return False

        if self.total_ionizing_dose > GR801Config.TID_TOLERANCE * 1000:
            logger.error("Total ionizing dose exceeded: %s rad", self.total_ionizing_dose)
            return False

        critical_errors = len([e for e in self.seu_log if (not e.corrected and e.critical)])
        if critical_errors > 10:
            logger.error("Too many critical errors: %s", critical_errors)
            return False

        return True

    def _update_power_consumption(self):
        base_power = float(GR801Config.NOMINAL_POWER)

        active_cores = sum(1 for core in self.cores if core.power_state == "ACTIVE")
        core_power = float(active_cores) * 2.0

        accelerator_power = 0.0
        if self.ai_accelerator is not None and self.ai_accelerator.active:
            accelerator_power = float(self.ai_accelerator.power_consumption)

        memory_power = 1.0
        temp_factor = 1.0 + 0.01 * (self.system_temperature - 25.0)

        self.total_power = (base_power + core_power + accelerator_power + memory_power) * temp_factor

    def _update_temperature(self):
        heat_generated = float(self.total_power) * 0.8
        radiation_cooling = 0.1 * (self.system_temperature**4 - 3.0**4) * 5.67e-8
        conduction_cooling = 0.05 * (self.system_temperature - 20.0)

        delta_t = (heat_generated - radiation_cooling - conduction_cooling) * 0.01
        self.system_temperature = float(
            np.clip(self.system_temperature + delta_t, GR801Config.MIN_TEMP, GR801Config.MAX_TEMP)
        )

    def _check_radiation_limits(self):
        high_let_events = [e for e in self.seu_log if e.particle_let > GR801Config.SEU_THRESHOLD]

        if high_let_events and (self.uptime - self.last_seu_check > 1.0):
            logger.warning("High LET particle detected: %s events", len(high_let_events))
            self.last_seu_check = self.uptime

            if len(high_let_events) > 5:
                self._activate_radiation_mitigation()

    def _activate_radiation_mitigation(self):
        logger.info("Activating radiation mitigation strategies")

        for core in self.cores:
            core.frequency *= 0.8

        self.memory.scrub_rate *= 2.0

        if self.ai_accelerator is not None:
            self.ai_accelerator.power_consumption *= 0.7

    def _log_system_state(self):
        state = {
            "uptime": self.uptime,
            "temperature": f"{self.system_temperature:.1f}°C",
            "power": f"{self.total_power:.1f}W",
            "tid": f"{self.total_ionizing_dose:.1f} rad",
            "inferences": self.inference_count,
            "total_operations": f"{self.total_operations:.1e}",
            "errors": self.system_errors,
            "corrected_errors": self.corrected_errors,
            "seu_rate": len(self.seu_log) / max(1.0, self.uptime),
        }

        logger.info("System State: %s", state)


class SpaceMissionSimulator:
    """Simulates a complete space mission with GR801."""

    def __init__(self, mission_duration: float = 86400.0):
        self.mission_duration = float(mission_duration)
        self.simulation_time = 0.0
        self.gr801_system = GR801System(RadiationEnvironment.LEO)

        self.mission_phase = "LAUNCH"
        self.mission_success = True
        self.science_data_collected = 0.0

        self.mission_workloads: dict[str, AIWorkload] = {
            "LAUNCH": AIWorkload.ANOMALY_DETECTION,
            "ORBIT_INSERTION": AIWorkload.NAVIGATION,
            "SCIENCE_OPS": AIWorkload.IMAGE_CLASSIFICATION,
            "COMMUNICATIONS": AIWorkload.COMMUNICATIONS,
            "DEORBIT": AIWorkload.OBJECT_DETECTION,
        }

    def run_mission(self):
        logger.info("Starting space mission simulation for %.0f seconds", self.mission_duration)

        dt = 0.1
        timesteps = int(max(1, self.mission_duration / dt))

        for step in range(timesteps):
            self.simulation_time += dt
            self._update_mission_phase()

            try:
                self.gr801_system.execute_timestep(dt)
                self._execute_mission_workload()
            except Exception as exc:  # noqa: BLE001
                logger.error("Mission failure at t=%.1fs: %s", self.simulation_time, exc)
                self.mission_success = False
                break

            if not self._mission_objectives_met():
                self.mission_success = False
                break

            if step % 1000 == 0:
                self._mission_status_report()

        self._generate_mission_report()

    def _update_mission_phase(self):
        phase_thresholds: dict[str, float] = {
            "LAUNCH": 300,
            "ORBIT_INSERTION": 1800,
            "SCIENCE_OPS": 3600,
            "COMMUNICATIONS": 7200,
            "DEORBIT": self.mission_duration - 1800,
        }

        for phase, threshold in phase_thresholds.items():
            if self.simulation_time <= threshold:
                self.mission_phase = phase
                break

    def _execute_mission_workload(self):
        workload = self.mission_workloads.get(self.mission_phase, AIWorkload.ANOMALY_DETECTION)

        if workload == AIWorkload.IMAGE_CLASSIFICATION:
            input_data = np.random.standard_normal((1, 224, 224, 3)).astype(np.float32)
        elif workload == AIWorkload.OBJECT_DETECTION:
            input_data = np.random.standard_normal((1, 416, 416, 3)).astype(np.float32)
        else:
            input_data = np.random.standard_normal((1, 100)).astype(np.float32)

        result = self.gr801_system.run_inference(workload, input_data)

        if workload == AIWorkload.IMAGE_CLASSIFICATION:
            confidence = float(np.max(result["result"]))
            self.science_data_collected += confidence * 10.0
        elif workload == AIWorkload.ANOMALY_DETECTION:
            if float(result["estimated_accuracy"]) < 0.8:
                logger.warning(
                    "Low anomaly detection accuracy: %.2f",
                    float(result["estimated_accuracy"]),
                )

    def _mission_objectives_met(self) -> bool:
        if not self.gr801_system._system_operational():
            logger.error("GR801 system failure")
            return False

        if self.mission_phase == "SCIENCE_OPS" and self.science_data_collected < 1000:
            return False

        return True

    def _mission_status_report(self):
        status = {
            "mission_time": f"{self.simulation_time:.1f}s",
            "mission_phase": self.mission_phase,
            "system_operational": self.gr801_system._system_operational(),
            "science_data": f"{self.science_data_collected:.0f} units",
            "inferences_completed": self.gr801_system.inference_count,
            "radiation_dose": f"{self.gr801_system.total_ionizing_dose:.1f} rad",
            "temperature": f"{self.gr801_system.system_temperature:.1f}°C",
        }

        logger.info("Mission Status: %s", status)

    def _generate_mission_report(self):
        report: dict[str, Any] = {
            "mission_duration": self.simulation_time,
            "mission_success": self.mission_success,
            "final_system_state": {
                "temperature": self.gr801_system.system_temperature,
                "total_ionizing_dose": self.gr801_system.total_ionizing_dose,
                "seu_events": len(self.gr801_system.seu_log),
                "uncorrected_errors": len([e for e in self.gr801_system.seu_log if not e.corrected]),
                "inferences_completed": self.gr801_system.inference_count,
                "total_operations": self.gr801_system.total_operations,
            },
            "science_return": self.science_data_collected,
            "performance_metrics": {
                "inference_rate": self.gr801_system.inference_count / max(1.0, self.simulation_time),
                "error_rate": self.gr801_system.system_errors
                / max(1.0, float(self.gr801_system.total_operations)),
                "power_efficiency": self.gr801_system.total_operations
                / max(1.0, self.gr801_system.uptime * self.gr801_system.total_power),
            },
        }

        logger.info("%s", "=" * 50)
        logger.info("MISSION REPORT")
        logger.info("%s", "=" * 50)
        for key, value in report.items():
            if isinstance(value, dict):
                logger.info("%s:", key)
                for subkey, subvalue in value.items():
                    logger.info("  %s: %s", subkey, subvalue)
            else:
                logger.info("%s: %s", key, value)

        logger.info("✓ MISSION ACCOMPLISHED" if self.mission_success else "✗ MISSION FAILED")


def simulate_gr801_mission() -> SpaceMissionSimulator:
    """Example: Simulate a 12-hour space mission with GR801."""

    simulator = SpaceMissionSimulator(mission_duration=12 * 3600)
    simulator.run_mission()
    return simulator


def test_gr801_radiation_tolerance() -> dict[str, Any]:
    """Test GR801 radiation tolerance in different environments (quick toy run)."""

    environments = [RadiationEnvironment.LEO, RadiationEnvironment.GEO, RadiationEnvironment.JUPITER]

    results: dict[str, Any] = {}
    for env in environments:
        logger.info("Testing GR801 in %s environment", env.value)
        system = GR801System(environment=env)

        # 10 seconds simulated time in 100 ms steps.
        for _ in range(100):
            system.execute_timestep(dt=0.1)

        model = NeuralNetworkModel.get_preset_model(AIWorkload.IMAGE_CLASSIFICATION)
        input_data = np.random.standard_normal((1, *model.input_shape)).astype(np.float32)

        result = system.run_inference(AIWorkload.IMAGE_CLASSIFICATION, input_data)

        results[env.value] = {
            "final_tid": system.total_ionizing_dose,
            "seu_count": len(system.seu_log),
            "inference_accuracy": result["estimated_accuracy"],
            "system_operational": system._system_operational(),
        }

    return results


if __name__ == "__main__":
    logging.basicConfig(level=logging.INFO)

    logger.info("GR801 Radiation-Hardened AI SoC Simulation Framework")
    logger.info("%s", "=" * 60)

    results = test_gr801_radiation_tolerance()
    logger.info("Radiation Tolerance Test Results:")
    for env, result in results.items():
        logger.info("%s: %s", env, result)

    logger.info("%s", "=" * 60)
    logger.info("Starting Full Space Mission Simulation")

    _mission = simulate_gr801_mission()