Spaces:
Sleeping
Sleeping
File size: 19,442 Bytes
12bc208 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 | # Importing the required libraries
import numpy as np
import pandas as pd
import math
from pathlib import Path
# ββ Paths βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
PROJECT_ROOT = Path(__file__).resolve().parent.parent
DATA_ROOT = PROJECT_ROOT / "data"
OUTPUT_PATH = DATA_ROOT / "synthetic_engagements.csv"
METADATA_PATH = DATA_ROOT / "aircraft_metadata.csv"
# ββ Generation config βββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
N_ROWS = 1_000_000 # Python ignores underscores in numbers, just readability
# ββ Feature ranges ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
# Launch distance: how far the missile was fired from (metres)
# Floor is 500m β below this, guns are more effective than missiles and
# the missile doesn't have enough distance to arm and track properly
# Upper bound is capped to each aircraft's missile_range at row generation
LAUNCH_DISTANCE_MIN = 500
# Your aircraft speed range - 61 m/s (TB2 drone) β 983 m/s (SR-71), full metadata range
YOUR_SPEED_RANGE = (61, 983)
# Your aircraft altitude (metres)
# 0 = ground level, 30,000m = upper combat/operational ceiling
# SR-71 operates at ~24,000m, most fighters top out around 20,000m
# 30,000m gives headroom for all 102 aircraft in metadata
YOUR_ALTITUDE_RANGE = (0, 30_000)
# Enemy aircraft altitude (metres) β same envelope as your own aircraft
# Determines vertical geometry of the engagement alongside elevation angle
ENEMY_ALTITUDE_RANGE = (0, 30_000)
# Azimuth: horizontal angle of incoming threat, clockwise from North
# 0Β° = head-on, 90Β° = right side, 180Β° = tail-chase, 270Β° = left side
AZIMUTH_RANGE = (0, 360)
# Elevation: vertical angle of incoming threat
# 0Β° = same altitude, +90Β° = directly above, -90Β° = directly below
ELEVATION_RANGE = (-90, 90)
# Maneuverability: 0 = low (bomber/transport), 1 = medium (older jets),
# 2 = high (modern fighters)
MANEUVERABILITY_VALUES = [0, 1, 2]
# Countermeasure: 0 = not deployed, 1 = deployed (flares/chaff)
COUNTERMEASURE_VALUES = [0, 1]
# ββ Runs once β loads combat-capable aircraft from metadata CSV βββββββββββββββ
def _load_metadata():
"""
Loads combat-capable aircraft from the metadata CSV (no_aa_capability == 0).
Returns:
pd.DataFrame: Filtered metadata containing only aircraft with air-to-air missile capability.
"""
if not METADATA_PATH.exists():
raise FileNotFoundError(f"Metadata CSV not found: {METADATA_PATH}")
metadata_df = pd.read_csv(METADATA_PATH)
metadata_df = metadata_df[metadata_df["no_aa_capability"]==0]
return metadata_df
# ββ Derives missile phase from how far it has already traveled ββββββββββββββββ
# phase 0 = boost (just launched), 1 = mid-course, 2 = terminal (final approach)
def _derive_missile_phase(remaining_distance, launch_distance):
"""
Derives missile flight phase from how much of its journey it has completed.
Args:
remaining_distance (float): Distance remaining between missile and target (metres).
launch_distance (float): Total distance at the moment of launch (metres).
Returns:
int: 0 = boost, 1 = mid-course, 2 = terminal.
Phase 0 β engine burning, accelerating, just launched
Phase 1 β flying toward your predicted position, guided but not actively tracking you yet
Phase 2 β active seeker on, tracking you specifically, hardest to fool
"""
traveled_distance = launch_distance - remaining_distance
ratio = traveled_distance / launch_distance
if ratio < 0.33:
return 0
elif 0.33 <= ratio < 0.66:
return 1
else:
return 2
# ββ Derives closure rate using 3D geometry (azimuth + elevation + speeds) βββββ
# How fast the gap between you and the missile is closing, in m/s
# Enemy altitude adds the vertical dimension to the engagement geometry
def _derive_closure_rate(missile_speed, your_speed,
azimuth, elevation):
"""
Derives the closure rate - how fast the gap between the missile and the target is closing (m/s).
Uses 3D geometry: azimuth accounts for horizontal approach angle,
elevation accounts for vertical approach angle.
Args:
missile_speed (float): Speed of the incoming missile (m/s).
your_speed (float): Speed of the friendly aircraft (m/s).
azimuth (float): Horizontal angle of incoming threat in degrees (0Β° = head-on).
elevation (float): Vertical angle of incoming threat in degrees (0Β° = same altitude).
Returns:
float: Closure rate in m/s.
"""
# Convert the angles into radians and prepare for cosine
azimuth = math.radians(azimuth)
elevation = math.radians(elevation)
# Extract the closure rate
closure_rate = missile_speed + (your_speed * (math.cos(azimuth) * math.cos(elevation)))
return closure_rate
"""
Note: enemy_speed is not included as a feature. This is a known simplification, in real engagements,
enemy aircraft velocity at launch contributes to effective missile speed. This can be added in a future iteration
of the physics generator.
"""
def _derive_evasion_time(remaining_distance, closure_rate,
missile_phase, enemy_generation,
your_speed, your_altitude, enemy_altitude):
"""
Derives the minimum evasion time - seconds before the missile reaches you.
Base calculation is pure kinematics: remaining_distance / closure_rate.
Four modifiers are applied to account for factors the base formula cannot capture.
Modifiers:
- missile_phase == 2 (terminal): seeker has locked on, countermeasures
need 2-3s overhead to be effective. Shrinks window by 15%. (x 0.85)
- enemy_generation == 5: HOBS (High Off-Boresight)
seeker + ECCM make the missile harder to
defeat, compressing effective reaction time by ~10%. (x 0.90)
- your_speed > 522 m/s (above median): high energy state gives more
lateral geometry per second during evasion. Slight expansion. (x 1.05)
- abs(your_altitude - enemy_altitude) > 5000m: large altitude gap pushes
engagement toward edge of missile performance envelope, degrading
terminal accuracy. Slight expansion. (x 1.10)
Args:
remaining_distance (float): Distance between missile and you right now (metres).
closure_rate (float): Combined closing speed from _derive_closure_rate() (m/s).
missile_phase (int): 0 = boost, 1 = mid-course, 2 = terminal.
enemy_generation (float): Enemy aircraft generation (3.5, 4, 4.5, or 5).
your_speed (float): Your current airspeed (m/s).
your_altitude (float): Your current altitude (metres).
enemy_altitude (float): Enemy aircraft altitude (metres).
Returns:
float: Evasion time in seconds.
"""
# Calculate the evasion time
evasion_time = remaining_distance / closure_rate
# Terminal phase - seeker locked on, countermeasures need 2β3s overhead
if missile_phase == 2:
evasion_time *= 0.85
# Gen 5 aircraft missile (HOBS + ECCM) compresses effective reaction time
if enemy_generation == 5:
evasion_time *= 0.90
# High speed β more room to maneuver
if your_speed > 522:
evasion_time *= 1.05
# Large altitude gap - missile at edge of performance envelope
if abs(your_altitude - enemy_altitude) > 5000:
evasion_time *= 1.10
return evasion_time
def _derive_hit_label(countermeasure_deployed, your_maneuverability,
azimuth, elevation, missile_phase, enemy_generation,
your_altitude, enemy_altitude):
"""
Derives whether the missile hits after an evasion attempt.
Starts from a neutral survival score of 0.5. Modifiers push it up (more
likely to hit) or down (more likely to miss). Final label is 1 if the
missile hits, 0 if it misses.
This is where azimuth and elevation belong β they affect survival after
evasion, not when the missile arrives (that is handled by closure_rate
in _derive_evasion_time).
Modifiers:
- countermeasure_deployed == 1: active countermeasures significantly
reduce hit probability. Score drops.
- your_maneuverability == 2: high maneuverability makes evasion more
effective. Score drops.
- azimuth near 0Β° (head-on): least time and geometry to evade.
Score rises.
- missile_phase == 2 (terminal): seeker locked on, hardest to defeat.
Score rises.
- enemy_generation == 5: HOBS + ECCM make the missile more lethal.
Score rises.
- elevation far from 0Β°: steep approach angle reduces evasion options.
Score rises.
- large altitude differential > 5000m: pushes missile toward edge of
performance envelope. Score drops.
Args:
countermeasure_deployed (int): 0 = not deployed, 1 = deployed.
your_maneuverability (int): 0 = low, 1 = medium, 2 = high.
azimuth (float): Horizontal angle of incoming threat in degrees (0Β° = head-on).
elevation (float): Vertical angle of incoming threat in degrees (0Β° = level).
missile_phase (int): 0 = boost, 1 = mid-course, 2 = terminal.
enemy_generation (float): Enemy aircraft generation (3.5, 4, 4.5, or 5).
your_altitude (float): Your current altitude (metres).
enemy_altitude (float): Enemy aircraft altitude (metres).
Returns:
int: 1 if missile hits, 0 if missile misses.
"""
# Neutral starting score - modifiers push it toward hit or miss
score = 0.5
# Countermeasures active - significantly reduces hit chance
if countermeasure_deployed == 1:
score -= 0.20
# High maneuverability - evasion more effective
if your_maneuverability == 2:
score -= 0.10
# Head-on approach - least time and geometry to evade
if azimuth < 30:
score += 0.15
# Terminal phase - seeker locked on, hardest to defeat
if missile_phase == 2:
score += 0.15
# Gen 5 missile - HOBS + ECCM make it more lethal
if enemy_generation == 5:
score += 0.10
# Steep approach angle - reduces evasion options
if abs(elevation) > 45:
score += 0.10
# Large altitude gap - missile at edge of performance envelope
if abs(your_altitude - enemy_altitude) > 5000:
score -= 0.10
# Hit if score crosses 0.5
return 1 if score >= 0.5 else 0
# ββ Generates one complete engagement scenario as a dict βββββββββββββββββββββ
def _generate_row(aircraft_row):
"""
Generates one synthetic engagement scenario for a given aircraft.
Samples random values for all situational features, derives computed
features using physics, and returns a complete row with labels.
Args:
aircraft_row (pd.Series): One row from the metadata CSV.
Returns:
dict: All 14 features plus evasion_time and hit labels.
"""
# Sample situational features
your_speed = np.random.uniform(*YOUR_SPEED_RANGE)
your_altitude = np.random.uniform(*YOUR_ALTITUDE_RANGE)
enemy_altitude = np.random.uniform(*ENEMY_ALTITUDE_RANGE)
azimuth = np.random.uniform(*AZIMUTH_RANGE)
elevation = np.random.uniform(*ELEVATION_RANGE)
# Sample categorical features
your_maneuverability = np.random.choice(MANEUVERABILITY_VALUES)
countermeasure_deployed = np.random.choice(COUNTERMEASURE_VALUES)
# Pull threat specs from metadata
missile_speed = aircraft_row["missile_speed"]
missile_range = aircraft_row["missile_range"]
enemy_generation = aircraft_row["enemy_generation"]
# Sample engagement distances
launch_distance = np.random.uniform(LAUNCH_DISTANCE_MIN, missile_range)
remaining_distance = np.random.uniform(0, launch_distance) # remaining_distance <= launch_distance always
# Derive computed features
missile_phase = _derive_missile_phase(remaining_distance, launch_distance)
closure_rate = _derive_closure_rate(missile_speed, your_speed,
azimuth, elevation)
# Derive labels
evasion_time = _derive_evasion_time(remaining_distance, closure_rate,
missile_phase, enemy_generation,
your_speed, your_altitude, enemy_altitude)
hit = _derive_hit_label(countermeasure_deployed, your_maneuverability,
azimuth, elevation, missile_phase, enemy_generation,
your_altitude, enemy_altitude)
# Return complete row
return {
"launch_distance": launch_distance,
"remaining_distance": remaining_distance,
"closure_rate": closure_rate,
"azimuth": azimuth,
"elevation": elevation,
"missile_phase": missile_phase,
"your_speed": your_speed,
"your_altitude": your_altitude,
"your_maneuverability": your_maneuverability,
"enemy_altitude": enemy_altitude,
"missile_speed": missile_speed,
"missile_range": missile_range,
"enemy_generation": enemy_generation,
"countermeasure_deployed": countermeasure_deployed,
"evasion_time": evasion_time,
"hit": hit
}
# ββ Saves completed DataFrame to CSV βββββββββββββββββββββββββββββββββββββββββ
def _save_dataset(df):
"""
Saves the generated dataset to a CSV file.
Args:
df (pd.DataFrame): The complete synthetic engagement dataset.
"""
# Create data directory if it doesn't exist
OUTPUT_PATH.parent.mkdir(parents=True, exist_ok=True)
# Save to CSV
df.to_csv(OUTPUT_PATH, index=False)
# ββ Public entry point β the only function the notebook calls βββββββββββββββββ
def generate_dataset():
"""
Generates 1,000,000 synthetic engagement scenarios and saves to CSV.
Loads combat-capable aircraft from metadata, samples each aircraft
equally across all rows, derives all features and labels using physics,
and writes the final dataset to disk.
Only valid threat scenarios are included (closure_rate > 0).
Returns:
pd.DataFrame: The complete synthetic engagement dataset.
"""
metadata = _load_metadata()
rows_per_aircraft = N_ROWS // len(metadata) # 1_000_000 // 56 β 17,857 rows per aircraft
rows = []
for _, aircraft_row in metadata.iterrows():
count = 0
while count < rows_per_aircraft:
row = _generate_row(aircraft_row)
if row["closure_rate"] > 0:
rows.append(row)
count += 1
# Fill remaining rows to hit exactly N_ROWS
remaining_rows = N_ROWS - len(rows)
if remaining_rows:
count = 0
while count < remaining_rows:
row = _generate_row(metadata.sample(1).iloc[0])
if row["closure_rate"] > 0:
rows.append(row)
count += 1
df = pd.DataFrame(rows)
_save_dataset(df)
print(f"Done. {len(rows):,} rows saved to {OUTPUT_PATH}")
# Inference only - assembles 14-feature array from HUD inputs and metadata for model prediction
def build_feature_array(
# From HUD sliders
launch_distance, remaining_distance,
azimuth, elevation,
your_speed, your_altitude, enemy_altitude,
countermeasure_deployed,
# From metadata lookup (enemy aircraft)
missile_speed, missile_range, enemy_generation,
# From metadata lookup (friendly aircraft)
your_maneuverability
):
"""
Assembles the 14-feature array required by the ETA and hit models at inference time.
All inputs arrive pre-collected from the HUD sliders and metadata lookups.
Closure rate and missile phase are derived here from those inputs using the
same physics helpers used during training. The returned dict is in the exact
column order the models were trained on.
Args:
launch_distance (float): Distance at moment of missile launch (metres).
remaining_distance (float): Distance remaining between missile and target (metres).
azimuth (float): Horizontal angle of incoming threat in degrees (0 = head-on).
elevation (float): Vertical angle of incoming threat in degrees (0 = level).
your_speed (float): Friendly aircraft airspeed (m/s).
your_altitude (float): Friendly aircraft altitude (metres).
enemy_altitude (float): Enemy aircraft altitude (metres).
countermeasure_deployed (int): 0 = not deployed, 1 = deployed.
missile_speed (float): Incoming missile speed (m/s), from enemy metadata.
missile_range (float): Missile maximum effective range (metres), from enemy metadata.
enemy_generation (float): Enemy aircraft generation, from enemy metadata.
your_maneuverability (int): Friendly aircraft maneuverability, from friendly metadata.
0 = low, 1 = medium, 2 = high. Maneuverability is an aircraft property
and follows the same metadata lookup pattern for both friendly and enemy platforms.
Returns:
dict: 14 features in training column order, ready for model inference.
"""
# Derive the two features that are computed from inputs rather than sourced directly
missile_phase = _derive_missile_phase(remaining_distance, launch_distance)
closure_rate = _derive_closure_rate(missile_speed, your_speed,
azimuth, elevation)
# Assemble and return all 14 features in exact training column order
return {
"launch_distance": launch_distance,
"remaining_distance": remaining_distance,
"closure_rate": closure_rate,
"azimuth": azimuth,
"elevation": elevation,
"missile_phase": missile_phase,
"your_speed": your_speed,
"your_altitude": your_altitude,
"your_maneuverability": your_maneuverability,
"enemy_altitude": enemy_altitude,
"missile_speed": missile_speed,
"missile_range": missile_range,
"enemy_generation": enemy_generation,
"countermeasure_deployed": countermeasure_deployed
} |