"""Encoding and decoding logic for :mod:`python_app.models.run_config_schema`.""" from __future__ import annotations from typing import Any from python_app.models.run_config_schema import ( ComboModel, GprRxGeometryModel, GprTxGeometryModel, PreprocessAssetModel, PreprocessNotchModel, RunConfigModel, ) from python_app.models.run_config_validation import load_ring_payload, load_switch_payload, validate_gpr_model def _as_dict(value: Any, context: str) -> dict[str, Any]: """Validate payload node is object-like, treating missing values as empty object.""" if value is None: return {} if not isinstance(value, dict): raise ValueError(f"{context} must be a JSON object") return value def _read_str(payload: dict[str, Any], key: str, default: str) -> str: """Return payload string, treating an explicit JSON `null` as missing. `payload.get(key, default)` returns `None` when the key exists with value `null`, which is then coerced into the literal string `"None"` by `str()`. """ value = payload.get(key, default) if value is None: return default return str(value) def _load_preprocess_asset(payload: dict[str, Any], target: PreprocessAssetModel) -> None: """Load preprocess asset fields into target model.""" target.set_name = _read_str(payload, "set_name", target.set_name) target.bundle_path = _read_str(payload, "bundle_path", target.bundle_path) def _load_string_list(payload: dict[str, Any], key: str, context: str) -> list[str]: """Load an optional list of strings with strict shape validation.""" raw_value = payload.get(key, []) if raw_value is None: return [] if not isinstance(raw_value, list): raise ValueError(f"{context}.{key} must be a JSON array") values: list[str] = [] for index, item in enumerate(raw_value): if not isinstance(item, str): raise ValueError(f"{context}.{key}[{index}] must be a JSON string") values.append(item) return values def _load_string_dict(payload: dict[str, Any], key: str, context: str) -> dict[str, str]: """Load an optional string-to-string dictionary with strict shape validation.""" raw_value = payload.get(key, {}) if raw_value is None: return {} if not isinstance(raw_value, dict): raise ValueError(f"{context}.{key} must be a JSON object") values: dict[str, str] = {} for item_key, item_value in raw_value.items(): if not isinstance(item_key, str) or not isinstance(item_value, str): raise ValueError(f"{context}.{key} must contain only string keys and values") values[item_key] = item_value return values def run_config_from_dict(payload: dict[str, Any]) -> RunConfigModel: """Decode JSON-like payload into :class:`RunConfigModel`.""" # Schema carries only minimal-safe fallbacks; operational defaults live in run_config.json. model = RunConfigModel() radar_payload = _as_dict(payload.get("radar"), "radar") sweep_payload = _as_dict(radar_payload.get("sweep"), "radar.sweep") switches_payload = _as_dict(payload.get("switches"), "switches") port1_payload = _as_dict(switches_payload.get("port1"), "switches.port1") port2_payload = _as_dict(switches_payload.get("port2"), "switches.port2") run_payload = _as_dict(payload.get("run"), "run") preprocess_payload = _as_dict(payload.get("preprocess"), "preprocess") gpr_payload = _as_dict(payload.get("gpr"), "gpr") rings_payload = _as_dict(payload.get("rings"), "rings") raw_ring_payload = _as_dict(rings_payload.get("raw"), "rings.raw") raw_tap_ring_payload = _as_dict(rings_payload.get("raw_tap"), "rings.raw_tap") pre_ring_payload = _as_dict(rings_payload.get("preprocessed"), "rings.preprocessed") pre_tap_ring_payload = _as_dict(rings_payload.get("preprocessed_tap"), "rings.preprocessed_tap") result_ring_payload = _as_dict(rings_payload.get("results"), "rings.results") locator_server_payload = _as_dict( run_payload.get("locator_server", run_payload.get("locator")), "run.locator_server", ) multi_device_payload = _as_dict(radar_payload.get("multi_device"), "radar.multi_device") kamil_adc_payload = _as_dict(radar_payload.get("kamil_adc"), "radar.kamil_adc") laser_control_payload = _as_dict(radar_payload.get("laser_control"), "radar.laser_control") model.radar.model = str(radar_payload.get("model", model.radar.model)) model.radar.serial = str(radar_payload.get("serial", model.radar.serial)) model.radar.remote_host = str(radar_payload.get("remote_host", model.radar.remote_host)) model.radar.remote_port = int(radar_payload.get("remote_port", model.radar.remote_port)) model.radar.driver_mode = str(radar_payload.get("driver_mode", model.radar.driver_mode)) model.radar.mock_signal_hz = float(radar_payload.get("mock_signal_hz", model.radar.mock_signal_hz)) model.radar.visa_library = str(radar_payload.get("visa_library", model.radar.visa_library)) model.radar.sweep.start_hz = float(sweep_payload.get("start_hz", model.radar.sweep.start_hz)) model.radar.sweep.stop_hz = float(sweep_payload.get("stop_hz", model.radar.sweep.stop_hz)) model.radar.sweep.points = int(sweep_payload.get("points", model.radar.sweep.points)) model.radar.sweep.if_bandwidth_hz = float( sweep_payload.get("if_bandwidth_hz", model.radar.sweep.if_bandwidth_hz) ) model.radar.sweep.power_dbm = float(sweep_payload.get("stimulus_power_dbm", model.radar.sweep.power_dbm)) slave_serials_payload = multi_device_payload.get( "slave_serials", multi_device_payload.get("slave_serial_numbers", model.radar.multi_device.slave_serials), ) if isinstance(slave_serials_payload, list): model.radar.multi_device.slave_serials = [str(value).strip() for value in slave_serials_payload if str(value).strip()] elif isinstance(slave_serials_payload, str): model.radar.multi_device.slave_serials = [ value.strip() for value in slave_serials_payload.split(",") if value.strip() ] model.radar.multi_device.force_external_reference = bool( multi_device_payload.get( "force_external_reference", model.radar.multi_device.force_external_reference, ) ) model.radar.multi_device.recovery_attempts = int( multi_device_payload.get( "recovery_attempts", model.radar.multi_device.recovery_attempts, ) ) model.radar.kamil_adc.project_dir = str( kamil_adc_payload.get("project_dir", model.radar.kamil_adc.project_dir) ) model.radar.kamil_adc.executable_path = str( kamil_adc_payload.get("executable_path", model.radar.kamil_adc.executable_path) ) model.radar.kamil_adc.tty_path = str( kamil_adc_payload.get("tty_path", model.radar.kamil_adc.tty_path) ) model.radar.kamil_adc.args = _load_string_list(kamil_adc_payload, "args", "radar.kamil_adc") model.radar.kamil_adc.env = _load_string_dict(kamil_adc_payload, "env", "radar.kamil_adc") model.radar.kamil_adc.startup_timeout_s = float( kamil_adc_payload.get("startup_timeout_s", model.radar.kamil_adc.startup_timeout_s) ) model.radar.kamil_adc.sweep_timeout_s = float( kamil_adc_payload.get("sweep_timeout_s", model.radar.kamil_adc.sweep_timeout_s) ) model.radar.kamil_adc.stop_timeout_s = float( kamil_adc_payload.get("stop_timeout_s", model.radar.kamil_adc.stop_timeout_s) ) model.radar.laser_control.enabled = bool( laser_control_payload.get("enabled", model.radar.laser_control.enabled) ) model.radar.laser_control.port = str( laser_control_payload.get("port", model.radar.laser_control.port) ) model.radar.laser_control.mode = str( laser_control_payload.get("mode", model.radar.laser_control.mode) ) model.radar.laser_control.pi_coeff1_p = int( laser_control_payload.get("pi_coeff1_p", model.radar.laser_control.pi_coeff1_p) ) model.radar.laser_control.pi_coeff1_i = int( laser_control_payload.get("pi_coeff1_i", model.radar.laser_control.pi_coeff1_i) ) model.radar.laser_control.pi_coeff2_p = int( laser_control_payload.get("pi_coeff2_p", model.radar.laser_control.pi_coeff2_p) ) model.radar.laser_control.pi_coeff2_i = int( laser_control_payload.get("pi_coeff2_i", model.radar.laser_control.pi_coeff2_i) ) laser_manual_payload = _as_dict(laser_control_payload.get("manual"), "radar.laser_control.manual") model.radar.laser_control.manual.temp1 = float( laser_manual_payload.get("temp1", model.radar.laser_control.manual.temp1) ) model.radar.laser_control.manual.temp2 = float( laser_manual_payload.get("temp2", model.radar.laser_control.manual.temp2) ) model.radar.laser_control.manual.current1 = float( laser_manual_payload.get("current1", model.radar.laser_control.manual.current1) ) model.radar.laser_control.manual.current2 = float( laser_manual_payload.get("current2", model.radar.laser_control.manual.current2) ) laser_variation_payload = _as_dict( laser_control_payload.get("variation"), "radar.laser_control.variation", ) model.radar.laser_control.variation.variation_type = str( laser_variation_payload.get( "variation_type", model.radar.laser_control.variation.variation_type, ) ) model.radar.laser_control.variation.static_temp1 = float( laser_variation_payload.get( "static_temp1", model.radar.laser_control.variation.static_temp1, ) ) model.radar.laser_control.variation.static_temp2 = float( laser_variation_payload.get( "static_temp2", model.radar.laser_control.variation.static_temp2, ) ) model.radar.laser_control.variation.static_current1 = float( laser_variation_payload.get( "static_current1", model.radar.laser_control.variation.static_current1, ) ) model.radar.laser_control.variation.static_current2 = float( laser_variation_payload.get( "static_current2", model.radar.laser_control.variation.static_current2, ) ) model.radar.laser_control.variation.min_value = float( laser_variation_payload.get("min_value", model.radar.laser_control.variation.min_value) ) model.radar.laser_control.variation.max_value = float( laser_variation_payload.get("max_value", model.radar.laser_control.variation.max_value) ) model.radar.laser_control.variation.step = float( laser_variation_payload.get("step", model.radar.laser_control.variation.step) ) model.radar.laser_control.variation.time_step = int( laser_variation_payload.get("time_step", model.radar.laser_control.variation.time_step) ) model.radar.laser_control.variation.delay_time = int( laser_variation_payload.get("delay_time", model.radar.laser_control.variation.delay_time) ) load_switch_payload(port1_payload, model.output_switch) load_switch_payload(port2_payload, model.input_switch) model.apply_device_model_constraints() model.runtime.settling_ms = int(run_payload.get("settling_ms", model.runtime.settling_ms)) model.runtime.idle_sleep_ms = int(run_payload.get("idle_sleep_ms", model.runtime.idle_sleep_ms)) model.runtime.continuous = bool(run_payload.get("continuous", model.runtime.continuous)) model.runtime.processing_live_config_path = str( run_payload.get("processing_live_config_path", model.runtime.processing_live_config_path) ) model.runtime.locator_server.device_id = int( locator_server_payload.get("device_id", model.runtime.locator_server.device_id) ) model.runtime.locator_server.protocol_version = int( locator_server_payload.get( "protocol_version", model.runtime.locator_server.protocol_version, ) ) model.runtime.locator_server.host = str( locator_server_payload.get("host", model.runtime.locator_server.host) ) model.runtime.locator_server.port = int( locator_server_payload.get("port", model.runtime.locator_server.port) ) model.runtime.locator_server.max_payload_bytes = int( locator_server_payload.get( "max_payload_bytes", model.runtime.locator_server.max_payload_bytes, ) ) model.runtime.locator_server.client_queue_size = int( locator_server_payload.get( "client_queue_size", model.runtime.locator_server.client_queue_size, ) ) model.runtime.locator_server.logger_name = str( locator_server_payload.get( "logger_name", model.runtime.locator_server.logger_name, ) ) s21_preprocess_payload = _as_dict(preprocess_payload.get("s21"), "preprocess.s21") _load_preprocess_asset( _as_dict(s21_preprocess_payload.get("calibration"), "preprocess.s21.calibration"), model.preprocess.s21.calibration, ) _load_preprocess_asset( _as_dict(s21_preprocess_payload.get("reference"), "preprocess.s21.reference"), model.preprocess.s21.reference, ) s11_preprocess_payload = _as_dict(preprocess_payload.get("s11"), "preprocess.s11") s11_calibration_payload = _as_dict(s11_preprocess_payload.get("calibration"), "preprocess.s11.calibration") _load_preprocess_asset( _as_dict(s11_calibration_payload.get("open"), "preprocess.s11.calibration.open"), model.preprocess.s11.calibration.open, ) _load_preprocess_asset( _as_dict(s11_calibration_payload.get("short"), "preprocess.s11.calibration.short"), model.preprocess.s11.calibration.short, ) _load_preprocess_asset( _as_dict(s11_calibration_payload.get("load"), "preprocess.s11.calibration.load"), model.preprocess.s11.calibration.load, ) _load_preprocess_asset( _as_dict(s11_preprocess_payload.get("reference"), "preprocess.s11.reference"), model.preprocess.s11.reference, ) notch_payload = _as_dict(preprocess_payload.get("notch"), "preprocess.notch") model.preprocess.notch = PreprocessNotchModel( enabled=bool(notch_payload.get("enabled", model.preprocess.notch.enabled)), taper_width_hz=float(notch_payload.get("taper_width_hz", model.preprocess.notch.taper_width_hz)), taper_type=str(notch_payload.get("taper_type", model.preprocess.notch.taper_type)), bands_hz=[], ) bands_payload = notch_payload.get("bands_hz", []) if isinstance(bands_payload, list): for band in bands_payload: if isinstance(band, (list, tuple)) and len(band) == 2: model.preprocess.notch.bands_hz.append((float(band[0]), float(band[1]))) model.gpr.relative_permittivity = float( gpr_payload.get("relative_permittivity", model.gpr.relative_permittivity) ) model.gpr.tx_geometry = [] tx_geometry_payload = gpr_payload.get("tx_geometry", []) if isinstance(tx_geometry_payload, list): for entry in tx_geometry_payload: entry_payload = _as_dict(entry, "gpr.tx_geometry[]") model.gpr.tx_geometry.append( GprTxGeometryModel( output_pos=int(entry_payload.get("output_pos", 0)), x_m=float(entry_payload.get("x_m", 0.0)), y_m=float(entry_payload.get("y_m", 0.0)), z_m=float(entry_payload.get("z_m", 0.0)), ) ) model.gpr.rx_geometry = [] rx_geometry_payload = gpr_payload.get("rx_geometry", []) if isinstance(rx_geometry_payload, list): for entry in rx_geometry_payload: entry_payload = _as_dict(entry, "gpr.rx_geometry[]") model.gpr.rx_geometry.append( GprRxGeometryModel( input_pos=int(entry_payload.get("input_pos", 0)), x_m=float(entry_payload.get("x_m", 0.0)), y_m=float(entry_payload.get("y_m", 0.0)), z_m=float(entry_payload.get("z_m", 0.0)), ) ) model.apply_device_model_constraints() validate_gpr_model( model.gpr, input_switch_positions=model.input_switch.positions, output_switch_positions=model.output_switch.positions, ) load_ring_payload(raw_ring_payload, model.rings.raw) load_ring_payload(raw_tap_ring_payload, model.rings.raw_tap) load_ring_payload(pre_ring_payload, model.rings.preprocessed) load_ring_payload(pre_tap_ring_payload, model.rings.preprocessed_tap) load_ring_payload(result_ring_payload, model.rings.results) combos_payload = run_payload.get("combos", []) model.combos = [] if isinstance(combos_payload, list): for combo in combos_payload: combo_payload = _as_dict(combo, "run.combos[]") model.combos.append( ComboModel( input=int(combo_payload.get("input", 0)), output=int(combo_payload.get("output", 0)), ) ) model.ensure_combos() return model def run_config_to_dict(model: RunConfigModel) -> dict[str, Any]: """Encode :class:`RunConfigModel` to C++ pipeline-compatible JSON structure.""" model.ensure_combos() sweep_payload = { "start_hz": model.radar.sweep.start_hz, "stop_hz": model.radar.sweep.stop_hz, "if_bandwidth_hz": model.radar.sweep.if_bandwidth_hz, "stimulus_power_dbm": model.radar.sweep.power_dbm, } if not model.is_kamil_adc: sweep_payload["points"] = model.radar.sweep.points return { "radar": { "model": model.radar.model, "serial": model.radar.serial, "remote_host": model.radar.remote_host, "remote_port": model.radar.remote_port, "driver_mode": model.radar.driver_mode, "mock_signal_hz": model.radar.mock_signal_hz, "visa_library": model.radar.visa_library, "multi_device": { "slave_serials": list(model.radar.multi_device.slave_serials), "force_external_reference": model.radar.multi_device.force_external_reference, "recovery_attempts": model.radar.multi_device.recovery_attempts, }, "kamil_adc": { "project_dir": model.radar.kamil_adc.project_dir, "executable_path": model.radar.kamil_adc.executable_path, "tty_path": model.radar.kamil_adc.tty_path, "args": list(model.radar.kamil_adc.args), "env": dict(model.radar.kamil_adc.env), "startup_timeout_s": model.radar.kamil_adc.startup_timeout_s, "sweep_timeout_s": model.radar.kamil_adc.sweep_timeout_s, "stop_timeout_s": model.radar.kamil_adc.stop_timeout_s, }, "laser_control": { "enabled": model.radar.laser_control.enabled, "port": model.radar.laser_control.port, "mode": model.radar.laser_control.mode, "pi_coeff1_p": model.radar.laser_control.pi_coeff1_p, "pi_coeff1_i": model.radar.laser_control.pi_coeff1_i, "pi_coeff2_p": model.radar.laser_control.pi_coeff2_p, "pi_coeff2_i": model.radar.laser_control.pi_coeff2_i, "manual": { "temp1": model.radar.laser_control.manual.temp1, "temp2": model.radar.laser_control.manual.temp2, "current1": model.radar.laser_control.manual.current1, "current2": model.radar.laser_control.manual.current2, }, "variation": { "variation_type": model.radar.laser_control.variation.variation_type, "static_temp1": model.radar.laser_control.variation.static_temp1, "static_temp2": model.radar.laser_control.variation.static_temp2, "static_current1": model.radar.laser_control.variation.static_current1, "static_current2": model.radar.laser_control.variation.static_current2, "min_value": model.radar.laser_control.variation.min_value, "max_value": model.radar.laser_control.variation.max_value, "step": model.radar.laser_control.variation.step, "time_step": model.radar.laser_control.variation.time_step, "delay_time": model.radar.laser_control.variation.delay_time, }, }, "sweep": sweep_payload, }, "switches": { "port1": { "name": model.output_switch.name, "driver_mode": model.output_switch.driver_mode, "driver": model.output_switch.driver, "radar_port": model.output_switch.radar_port, "positions": model.output_switch.positions, "default_position": model.output_switch.default_position, "gpio_chip": model.output_switch.gpio_chip, "pin_a": model.output_switch.pin_a, "pin_b": model.output_switch.pin_b, "invert_logic": model.output_switch.invert_logic, }, "port2": { "name": model.input_switch.name, "driver_mode": model.input_switch.driver_mode, "driver": model.input_switch.driver, "radar_port": model.input_switch.radar_port, "positions": model.input_switch.positions, "default_position": model.input_switch.default_position, "gpio_chip": model.input_switch.gpio_chip, "pin_a": model.input_switch.pin_a, "pin_b": model.input_switch.pin_b, "invert_logic": model.input_switch.invert_logic, }, }, "run": { "settling_ms": model.runtime.settling_ms, "idle_sleep_ms": model.runtime.idle_sleep_ms, "continuous": model.runtime.continuous, "processing_live_config_path": model.runtime.processing_live_config_path, "locator_server": { "device_id": model.runtime.locator_server.device_id, "protocol_version": model.runtime.locator_server.protocol_version, "host": model.runtime.locator_server.host, "port": model.runtime.locator_server.port, "max_payload_bytes": model.runtime.locator_server.max_payload_bytes, "client_queue_size": model.runtime.locator_server.client_queue_size, "logger_name": model.runtime.locator_server.logger_name, }, "combos": [{"input": combo.input, "output": combo.output} for combo in model.combos], }, "preprocess": { "s21": { "calibration": { "set_name": model.preprocess.s21.calibration.set_name, "bundle_path": model.preprocess.s21.calibration.bundle_path, }, "reference": { "set_name": model.preprocess.s21.reference.set_name, "bundle_path": model.preprocess.s21.reference.bundle_path, }, }, "s11": { "calibration": { "open": { "set_name": model.preprocess.s11.calibration.open.set_name, "bundle_path": model.preprocess.s11.calibration.open.bundle_path, }, "short": { "set_name": model.preprocess.s11.calibration.short.set_name, "bundle_path": model.preprocess.s11.calibration.short.bundle_path, }, "load": { "set_name": model.preprocess.s11.calibration.load.set_name, "bundle_path": model.preprocess.s11.calibration.load.bundle_path, }, }, "reference": { "set_name": model.preprocess.s11.reference.set_name, "bundle_path": model.preprocess.s11.reference.bundle_path, }, }, "notch": { "enabled": model.preprocess.notch.enabled, "bands_hz": [[low_hz, high_hz] for low_hz, high_hz in model.preprocess.notch.bands_hz], "taper_width_hz": model.preprocess.notch.taper_width_hz, "taper_type": model.preprocess.notch.taper_type, }, }, "gpr": { "relative_permittivity": model.gpr.relative_permittivity, "tx_geometry": [ { "output_pos": entry.output_pos, "x_m": entry.x_m, "y_m": entry.y_m, "z_m": entry.z_m, } for entry in model.gpr.tx_geometry ], "rx_geometry": [ { "input_pos": entry.input_pos, "x_m": entry.x_m, "y_m": entry.y_m, "z_m": entry.z_m, } for entry in model.gpr.rx_geometry ], }, "rings": { "raw": { "name": model.rings.raw.name, "capacity": model.rings.raw.capacity, "slot_size_bytes": model.rings.raw.slot_size_bytes, }, "raw_tap": { "name": model.rings.raw_tap.name, "capacity": model.rings.raw_tap.capacity, "slot_size_bytes": model.rings.raw_tap.slot_size_bytes, }, "preprocessed": { "name": model.rings.preprocessed.name, "capacity": model.rings.preprocessed.capacity, "slot_size_bytes": model.rings.preprocessed.slot_size_bytes, }, "preprocessed_tap": { "name": model.rings.preprocessed_tap.name, "capacity": model.rings.preprocessed_tap.capacity, "slot_size_bytes": model.rings.preprocessed_tap.slot_size_bytes, }, "results": { "name": model.rings.results.name, "capacity": model.rings.results.capacity, "slot_size_bytes": model.rings.results.slot_size_bytes, }, }, }