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Lars Bogner
2025-11-20 12:36:30 +01:00
parent dfdd200867
commit 4b071e68b5
11 changed files with 367965 additions and 459 deletions
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Code/python/all_second_lines.csv
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Code/python/notebooks/01_train_autoencoder.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "a966d12e",
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"import torch.nn.functional as F\n",
"import pandas as pd\n",
"import numpy as np\n",
"from aiRNN import preprocessors"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "8e1e0736",
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv(\"../all_second_lines.csv\", header=None, names=[\"icao\", \"r\", \"t\", \"timestamp\", \"lat\", \"lon\", \"alt\", \"ias\"])\n",
"_ = preprocessors.create_category_mappings(df)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "d4b5c6b4",
"metadata": {},
"outputs": [],
"source": [
"df = preprocessors.map_categories(df, _)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "b36505f1",
"metadata": {},
"outputs": [],
"source": [
"X_a_1 = F.one_hot(torch.tensor(df[\"R_1_IDX\"].values)).float()\n",
"X_a_2 = F.one_hot(torch.tensor(df[\"R_2_IDX\"].values)).float()\n",
"X_t = F.one_hot(torch.tensor(df[\"T_IDX\"].values)).float()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "80168e5b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"torch.Size([343796, 100]) torch.Size([343796, 50]) torch.Size([343796, 100])\n"
]
}
],
"source": [
"print(X_a_1.shape, X_a_2.shape, X_t.shape)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "af31c3e3",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch [10/500], Loss: 12.0614\n",
"Epoch [20/500], Loss: 12.0593\n"
]
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
"\u001b[31mKeyboardInterrupt\u001b[39m Traceback (most recent call last)",
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[6]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mpreprocessors\u001b[49m\u001b[43m.\u001b[49m\u001b[43mtrain_autoencoder\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_a_1\u001b[49m\u001b[43m=\u001b[49m\u001b[43mX_a_1\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mX_a_2\u001b[49m\u001b[43m=\u001b[49m\u001b[43mX_a_2\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mX_b\u001b[49m\u001b[43m=\u001b[49m\u001b[43mX_t\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_epochs\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m500\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlearning_rate\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m0.001\u001b[39;49m\u001b[43m)\u001b[49m\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/Documents/Studium/UiO/data_analysis/project3/Code/python/src/aiRNN/preprocessors.py:98\u001b[39m, in \u001b[36mtrain_autoencoder\u001b[39m\u001b[34m(X_a_1, X_a_2, X_b, num_epochs, learning_rate)\u001b[39m\n\u001b[32m 96\u001b[39m loss_B = criterion(rec_B, torch.argmax(X_b, dim=\u001b[32m1\u001b[39m))\n\u001b[32m 97\u001b[39m loss = loss_A_1 + loss_A_2 + loss_B\n\u001b[32m---> \u001b[39m\u001b[32m98\u001b[39m \u001b[43mloss\u001b[49m\u001b[43m.\u001b[49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 99\u001b[39m optimizer.step()\n\u001b[32m 100\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m (epoch + \u001b[32m1\u001b[39m) % \u001b[32m10\u001b[39m == \u001b[32m0\u001b[39m:\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/Documents/Studium/UiO/data_analysis/project3/Code/python/.venv/lib64/python3.13/site-packages/torch/_tensor.py:625\u001b[39m, in \u001b[36mTensor.backward\u001b[39m\u001b[34m(self, gradient, retain_graph, create_graph, inputs)\u001b[39m\n\u001b[32m 615\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m has_torch_function_unary(\u001b[38;5;28mself\u001b[39m):\n\u001b[32m 616\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m handle_torch_function(\n\u001b[32m 617\u001b[39m Tensor.backward,\n\u001b[32m 618\u001b[39m (\u001b[38;5;28mself\u001b[39m,),\n\u001b[32m (...)\u001b[39m\u001b[32m 623\u001b[39m inputs=inputs,\n\u001b[32m 624\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m625\u001b[39m \u001b[43mtorch\u001b[49m\u001b[43m.\u001b[49m\u001b[43mautograd\u001b[49m\u001b[43m.\u001b[49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 626\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgradient\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m=\u001b[49m\u001b[43minputs\u001b[49m\n\u001b[32m 627\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/Documents/Studium/UiO/data_analysis/project3/Code/python/.venv/lib64/python3.13/site-packages/torch/autograd/__init__.py:354\u001b[39m, in \u001b[36mbackward\u001b[39m\u001b[34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[39m\n\u001b[32m 349\u001b[39m retain_graph = create_graph\n\u001b[32m 351\u001b[39m \u001b[38;5;66;03m# The reason we repeat the same comment below is that\u001b[39;00m\n\u001b[32m 352\u001b[39m \u001b[38;5;66;03m# some Python versions print out the first line of a multi-line function\u001b[39;00m\n\u001b[32m 353\u001b[39m \u001b[38;5;66;03m# calls in the traceback and some print out the last line\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m354\u001b[39m \u001b[43m_engine_run_backward\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 355\u001b[39m \u001b[43m \u001b[49m\u001b[43mtensors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 356\u001b[39m \u001b[43m \u001b[49m\u001b[43mgrad_tensors_\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 357\u001b[39m \u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 358\u001b[39m \u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 359\u001b[39m \u001b[43m \u001b[49m\u001b[43minputs_tuple\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 360\u001b[39m \u001b[43m \u001b[49m\u001b[43mallow_unreachable\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m 361\u001b[39m \u001b[43m \u001b[49m\u001b[43maccumulate_grad\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[32m 362\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/Documents/Studium/UiO/data_analysis/project3/Code/python/.venv/lib64/python3.13/site-packages/torch/autograd/graph.py:841\u001b[39m, in \u001b[36m_engine_run_backward\u001b[39m\u001b[34m(t_outputs, *args, **kwargs)\u001b[39m\n\u001b[32m 839\u001b[39m unregister_hooks = _register_logging_hooks_on_whole_graph(t_outputs)\n\u001b[32m 840\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m841\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mVariable\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_execution_engine\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrun_backward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Calls into the C++ engine to run the backward pass\u001b[39;49;00m\n\u001b[32m 842\u001b[39m \u001b[43m \u001b[49m\u001b[43mt_outputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\n\u001b[32m 843\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# Calls into the C++ engine to run the backward pass\u001b[39;00m\n\u001b[32m 844\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[32m 845\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m attach_logging_hooks:\n",
"\u001b[31mKeyboardInterrupt\u001b[39m: "
]
}
],
"source": [
"preprocessors.train_autoencoder(X_a_1=X_a_1, X_a_2=X_a_2, X_b=X_t, num_epochs=500, learning_rate=0.001)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "adaa14bc",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "adsbpy",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
Code/python/notebooks/02_test_rnn.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "276487d8",
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"from aiRNN import dataloader, models, losses\n",
"import pathlib"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "1308b1d5",
"metadata": {},
"outputs": [],
"source": [
"file_list = pathlib.Path(\"/home/lars/Documents/Studium/UiO/data_analysis/project3/Code/cpp/known_routes_and_aircraft.csv\")\n",
"base_path = file_list.parent\n",
"file_list = file_list.read_text().splitlines()\n",
"file_list = [(base_path / f).resolve() for f in file_list]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "0d8e6107",
"metadata": {},
"outputs": [],
"source": [
"dataset = dataloader.EvenlySpacedDataset(\n",
" filepaths=file_list[:50],\n",
" n_input=30*10, # 10 minutes input\n",
" n_output=30*5, # 5 minutes output\n",
" n_windows_per_file=5,\n",
" step=1,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "595103c9",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"235"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(dataset)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "522cd04f",
"metadata": {},
"outputs": [],
"source": [
"test_model = models."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "adsbpy",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
Code/python/pyproject.toml
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@@ -12,7 +12,7 @@ dependencies = [
"ipykernel>=7.1.0",
"matplotlib>=3.10.7",
"pandas>=2.3.3",
"tensorflow>=2.20.0",
"torch>=2.9.1",
"typer>=0.20.0",
]
Code/python/src/aiRNN/autoencoder.pth
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Code/python/src/aiRNN/category_mappings.json
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{"r_1": {"02": 0, "0264": 1, "0265": 2, "0268": 3, "0270": 4, "0273": 5, "0275": 6, "0276": 7, "0278": 8, "0281": 9, "0283": 10, "0284": 11, "100001": 12, "100003": 13, "100004": 14, "100008": 15, "102002": 16, "102003": 17, "102004": 18, "102005": 19, "2": 20, "40199": 21, "40200": 22, "43": 23, "4K": 24, "4X": 25, "5601": 26, "5629": 27, "5699": 28, "83": 29, "84": 30, "84006": 31, "84007": 32, "9582": 33, "9585": 34, "9H": 35, "A6": 36, "A7": 37, "AP": 38, "B": 39, "C": 40, "CC": 41, "CS": 42, "D": 43, "EC": 44, "EI": 45, "ES": 46, "ET": 47, "EW": 48, "F": 49, "G": 50, "GND": 51, "GO": 52, "HA": 53, "HB": 54, "I": 55, "KUNTO13": 56, "KUNTO3": 57, "KUNTO7": 58, "KUNTO72": 59, "LJ": 60, "LN": 61, "LX": 62, "LY": 63, "LZ": 64, "M": 65, "MR7": 66, "N441FF": 67, "N737WK": 68, "OE": 69, "OH": 70, "OK": 71, "OM": 72, "OO": 73, "OY": 74, "Other": 75, "PH": 76, "PI": 77, "RA": 78, "SAHKO4": 79, "SAHKO8": 80, "SE": 81, "SLP": 82, "SP": 83, "SU": 84, "SX": 85, "T7": 86, "TC": 87, "TF": 88, "TWR": 89, "UK": 90, "UP": 91, "UR": 92, "VIESTI52": 93, "VN": 94, "VP": 95, "VT": 96, "YL": 97, "YR": 98, "YU": 99}, "r_2": {"ATE": 0, "ATG": 1, "ATH": 2, "ATI": 3, "DYU": 4, "ENL": 5, "ENM": 6, "ENN": 7, "ENO": 8, "ENP": 9, "ENR": 10, "ENS": 11, "ENT": 12, "ENU": 13, "ENV": 14, "Empty": 15, "LKL": 16, "LKO": 17, "LVH": 18, "LVI": 19, "LVL": 20, "LXC": 21, "LZE": 22, "LZF": 23, "LZG": 24, "LZH": 25, "LZI": 26, "LZK": 27, "LZM": 28, "LZN": 29, "LZO": 30, "LZP": 31, "LZR": 32, "LZT": 33, "LZU": 34, "Other": 35, "RDY": 36, "RGL": 37, "ROK": 38, "ROL": 39, "ROY": 40, "RUA": 41, "RUC": 42, "RUD": 43, "RUF": 44, "RUO": 45, "RZX": 46, "WEC": 47, "WIW": 48, "WIX": 49}, "t": {"A139": 0, "A169": 1, "A20N": 2, "A21N": 3, "A319": 4, "A320": 5, "A321": 6, "A332": 7, "A333": 8, "A339": 9, "A359": 10, "A35K": 11, "A388": 12, "AS32": 13, "AS50": 14, "AT43": 15, "AT45": 16, "AT72": 17, "AT75": 18, "AT76": 19, "B190": 20, "B350": 21, "B38M": 22, "B429": 23, "B734": 24, "B737": 25, "B738": 26, "B739": 27, "B744": 28, "B752": 29, "B763": 30, "B77L": 31, "B77W": 32, "B788": 33, "B789": 34, "BCS3": 35, "BE20": 36, "C130": 37, "C152": 38, "C172": 39, "C208": 40, "C25A": 41, "C25B": 42, "C25C": 43, "C30J": 44, "C56X": 45, "C68A": 46, "CL35": 47, "CL60": 48, "CRJ2": 49, "CRJ9": 50, "D228": 51, "DA40": 52, "DA42": 53, "DH8A": 54, "DH8B": 55, "DH8C": 56, "DH8D": 57, "DHC6": 58, "E190": 59, "E195": 60, "E290": 61, "E295": 62, "E35L": 63, "E550": 64, "E55P": 65, "E75L": 66, "E75S": 67, "EC35": 68, "EC45": 69, "EH10": 70, "F2TH": 71, "F50": 72, "FA7X": 73, "FA8X": 74, "G115": 75, "GLEX": 76, "GLF4": 77, "GLF5": 78, "GLF6": 79, "H25B": 80, "LJ35": 81, "LJ45": 82, "NG5": 83, "Other": 84, "P28A": 85, "PA31": 86, "PC12": 87, "PC24": 88, "R44": 89, "RV7": 90, "S92": 91, "SB20": 92, "SERV": 93, "SF34": 94, "SR22": 95, "SU95": 96, "SW4": 97, "TWR": 98, "WT9": 99}}
Code/python/src/aiRNN/dataloader.py
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@@ -1,5 +1,275 @@
import pandas as pd
import tensorflow as tf
import torch
from torch.utils.data import Dataset, DataLoader
import numpy as np
import pathlib
from typing import Sequence
import random
class BaseDataset(Dataset):
def __init__(
self,
filepaths,
n_input,
n_output,
n_windows_per_file,
step=1,
feature_columns=("lat", "lon", "alt", "ias"),
context_columns=("r","t"),
time_columns=("timestamp",),
target_columns=("lat", "lon", "alt"),
device="cpu",
):
super().__init__()
self.filepaths = [pathlib.Path(fp) for fp in filepaths]
self.n_input = n_input
self.n_output = n_output
self.window_size = n_input + n_output
self.device = device
self.n_windows_per_file = n_windows_per_file
self.step = step
# Which columns to use
sample = pd.read_csv(self.filepaths[0], nrows=5)
self.feature_cols = list(feature_columns)
self.context_cols = list(context_columns)
self.time_cols = list(time_columns)
self.target_cols = list(target_columns)
# Total samples = windows_per_file * number_of_files
self.total_windows = n_windows_per_file * len(self.filepaths)
for col_list in [self.feature_cols, self.context_cols, self.time_cols, self.target_cols]:
assert all(
col in sample.columns for col in col_list
), "Some specified columns not found in data."
def __len__(self):
return self.total_windows
def _get_start_end_indices(self, idx, n_rows):
# Max valid start index so window stays inside the file
max_start = n_rows - self.window_size * self.step
if max_start < 0:
raise ValueError("File too small for one window.")
# Evenly spaced index:
# raw_start = (n_rows / n_windows_per_file) * local_idx
# mapped into integer space
raw_start = (n_rows / self.n_windows_per_file) * idx
start_idx = int(raw_start // self.step) * self.step
# Clamp to safe zone
if start_idx > max_start:
start_idx = max_start
end_idx = start_idx + self.window_size * self.step
return start_idx, end_idx
def _modify_df(self, df):
"""Hook for subclasses to modify dataframe before slicing windows."""
return df
class EvenlySpacedDataset(BaseDataset):
def __init__(
self,
filepaths,
n_input,
n_output,
n_windows_per_file,
step=1,
feature_columns=("lat", "lon", "alt", "ias"),
context_columns=("r","t"),
time_columns=("timestamp",),
target_columns=("lat", "lon", "alt"),
device="cpu",
):
super().__init__(
filepaths=filepaths,
n_input=n_input,
n_output=n_output,
feature_columns=feature_columns,
context_columns=context_columns,
time_columns=time_columns,
target_columns=target_columns,
device=device,
step=step,
n_windows_per_file=n_windows_per_file,
)
# Preload all data into memory
self.data = []
for fp in self.filepaths:
df = pd.read_csv(fp)
n_rows = len(df)
# Extract windows
for w in range(n_windows_per_file):
try:
start_idx, end_idx = self._get_start_end_indices(w, n_rows)
window = df.iloc[start_idx:end_idx:self.step]
self.data.append(window)
except ValueError:
self.total_windows -= 1
continue
def __len__(self):
return self.total_windows
def __getitem__(self, idx):
window = self.data[idx]
X_feat = window.iloc[: self.n_input][self.feature_cols].to_numpy(np.float32)
if len(self.context_cols) > 0:
X_context = window.iloc[: self.n_input][self.context_cols].to_numpy(np.float32)
else:
X_context = None
X_time = window.iloc[: self.window_size][self.time_cols].to_numpy(np.float32)
Y_out = window.iloc[self.n_input :][self.target_cols].to_numpy(np.float32)
return (
torch.tensor(X_feat, device=self.device),
torch.tensor(X_time, device=self.device),
torch.tensor(Y_out, device=self.device),
torch.tensor(X_context, device=self.device) if X_context is not None else None,
)
class EvenlySpacedStreamingDataset(BaseDataset):
def __init__(
self,
filepaths,
n_input,
n_output,
n_windows_per_file,
step=1,
feature_columns=("lat", "lon", "alt", "ias"),
context_columns=("r","t"),
time_columns=("timestamp",),
target_columns=("lat", "lon", "alt"),
device="cpu",
):
super().__init__(
filepaths=filepaths,
n_input=n_input,
n_output=n_output,
feature_columns=feature_columns,
context_columns=context_columns,
time_columns=time_columns,
target_columns=target_columns,
device=device,
step=step,
n_windows_per_file=n_windows_per_file,
)
# Precompute row counts for each file
self.row_counts = [self._count_rows(fp) for fp in self.filepaths]
def _count_rows(self, fp):
with open(fp, "r") as f:
return sum(1 for _ in f) - 1 # minus header
def __len__(self):
return self.total_windows
def __getitem__(self, idx):
# Figure out which file we belong to
file_idx = idx // self.n_windows_per_file
local_idx = idx % self.n_windows_per_file
fp = self.filepaths[file_idx]
n_rows = self.row_counts[file_idx]
start_idx, end_idx = self._get_start_end_indices(local_idx, n_rows)
# Read only the required rows
skip = list(set(range(1, start_idx + 1)))
df = pd.read_csv(fp, skiprows=skip, nrows=self.window_size * self.step).iloc[::self.step]
# Slice into input / decoder-input / targets
X_feat = df.iloc[: self.n_input][self.feature_cols].to_numpy(np.float32)
if len(self.context_cols) > 0:
X_context = df.iloc[: self.n_input][self.context_cols].to_numpy(np.float32)
else:
X_context = None
X_time = df.iloc[: self.window_size][self.time_cols].to_numpy(np.float32)
Y_out = df.iloc[self.n_input :][self.target_cols].to_numpy(np.float32)
return (
torch.tensor(X_feat, device=self.device),
torch.tensor(X_time, device=self.device),
torch.tensor(Y_out, device=self.device),
torch.tensor(X_context, device=self.device) if X_context is not None else None,
)
def get_datasets(
files: Sequence[str],
n_input: int,
n_output: int,
n_windows_per_file: int,
step: int = 1,
feature_columns=("lat", "lon", "alt", "ias"),
context_columns=("r","t"),
time_columns=("timestamp",),
target_columns=("lat", "lon", "alt"),
device="cpu",
test_split: float = 0.2,
val_split: float = 0.1,
seed: int = 42,
streaming: bool = False,
) -> tuple[Dataset, Dataset, Dataset]:
"""Utility to create train/val/test datasets from file list."""
random.seed(seed)
files = list(files)
random.shuffle(files)
n_total = len(files)
n_test = int(n_total * test_split)
n_val = int(n_total * val_split)
test_files = files[:n_test]
val_files = files[n_test : n_test + n_val]
train_files = files[n_test + n_val :]
if not streaming:
ds = EvenlySpacedDataset
else:
ds = EvenlySpacedStreamingDataset
train_dataset = ds(
train_files,
n_input=n_input,
n_output=n_output,
n_windows_per_file=n_windows_per_file,
step=step,
feature_columns=feature_columns,
context_columns=context_columns,
time_columns=time_columns,
target_columns=target_columns,
device=device,
)
val_dataset = ds(
val_files,
n_input=n_input,
n_output=n_output,
n_windows_per_file=n_windows_per_file,
step=step,
feature_columns=feature_columns,
context_columns=context_columns,
time_columns=time_columns,
target_columns=target_columns,
device=device,
)
test_dataset = ds(
test_files,
n_input=n_input,
n_output=n_output,
n_windows_per_file=n_windows_per_file,
step=step,
feature_columns=feature_columns,
context_columns=context_columns,
time_columns=time_columns,
target_columns=target_columns,
device=device,
)
return train_dataset, val_dataset, test_dataset
Code/python/src/aiRNN/losses.py
+58
View File
@@ -0,0 +1,58 @@
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
class HaversineMSEAltitudeLoss(nn.Module):
def __init__(self, alt_const=1.0, earth_radius_km=6371.0):
super().__init__()
self.alt_const = alt_const
self.R = earth_radius_km
def forward(self, pred, target):
"""
pred: (..., 3) -> [lat, lon, alt]
target: (..., 3) -> [lat, lon, alt]
lat/lon expected in degrees.
alt in meters (or same unit in pred/target).
"""
lat1 = torch.deg2rad(pred[..., 0])
lon1 = torch.deg2rad(pred[..., 1])
lat2 = torch.deg2rad(target[..., 0])
lon2 = torch.deg2rad(target[..., 1])
dlat = lat2 - lat1
dlon = lon2 - lon1
# Haversine formula
a = (
torch.sin(dlat / 2) ** 2
+ torch.cos(lat1) * torch.cos(lat2) * torch.sin(dlon / 2) ** 2
)
c = 2 * torch.atan2(torch.sqrt(a), torch.sqrt(1 - a))
dist_km = self.R * c # great-circle distance
# MSE on distance
haversine_mse = torch.mean(dist_km ** 2)
# Altitude penalty
delta_alt = pred[..., 2] - target[..., 2]
alt_penalty = self.alt_const * torch.mean(delta_alt ** 2)
return haversine_mse + alt_penalty
class WeightedMSELoss(nn.Module):
def __init__(self, weights):
super().__init__()
self.weights = torch.tensor(weights).float()
def forward(self, pred, target):
"""
pred: (..., D)
target: (..., D)
"""
diff = pred - target
weighted_diff = diff * self.weights.to(diff.device)
return torch.mean(weighted_diff ** 2)
Code/python/src/aiRNN/models.py
+146
View File
@@ -0,0 +1,146 @@
import torch
import torch.nn as nn
class BaseRNN(nn.Module):
def __init__(self, time_in, feat_in, context_in, hidden_size, out_size, rnn_type="RNN"):
super().__init__()
self.time_in = time_in
self.feat_in = feat_in
self.context_in = context_in
self.hidden_size = hidden_size
self.out_size = out_size
# Projecters to a common RNN input size
self.time_proj = nn.Linear(time_in, hidden_size)
self.feat_proj = nn.Linear(feat_in, hidden_size)
if context_in is not None:
self.context_proj = nn.Linear(context_in, hidden_size)
# RNN cell
if rnn_type == "RNN":
self.rnn = nn.RNNCell(input_size=hidden_size, hidden_size=hidden_size)
elif rnn_type == "LSTM":
self.rnn = nn.LSTMCell(input_size=hidden_size, hidden_size=hidden_size)
else:
raise ValueError(f"Unsupported rnn_type: {rnn_type}")
# Readout: predict from hidden state (optionally conditioned on time and context too)
readout_in_size = hidden_size + time_in
if context_in is not None:
readout_in_size += context_in
self.readout = nn.Linear(readout_in_size, out_size)
def forward(self, time_seq, feat_init=None, context=None, init_steps=0, pred_steps=0, offset=0, hidden=None):
b, total_len, _ = time_seq.shape
assert init_steps <= total_len
if feat_init is not None:
assert feat_init.shape[0] == b and feat_init.shape[1] == init_steps
if hidden is None:
hidden = torch.zeros(b, self.hidden_size, device=time_seq.device)
if self.context_in is not None:
assert context is not None and context.shape[0] == b
context_proj = self.context_proj(context) # (b, hidden_size)
else:
context_proj = torch.zeros(b, self.hidden_size, device=time_seq.device)
# --- Initialization phase: feed time + feature in parallel for init_steps ---
for t in range(init_steps + offset):
t_in = time_seq[:, t, :] # (b, time_in)
t_proj = self.time_proj(t_in) # (b, hidden_size)
rnn_in = t_proj + context_proj # start with time + context
if feat_init is not None and t < init_steps:
f_in = feat_init[:, t, :] # (b, feat_in)
f_proj = self.feat_proj(f_in) # (b, hidden_size)
rnn_in = rnn_in + f_proj # combine with feature input
if isinstance(self.rnn, nn.LSTMCell):
if isinstance(hidden, tuple):
h_t, c_t = hidden
else:
h_t = hidden
c_t = torch.zeros(b, self.hidden_size, device=time_seq.device)
h_t, c_t = self.rnn(rnn_in, (h_t, c_t))
hidden = (h_t, c_t)
else:
hidden = self.rnn(rnn_in, hidden)
# --- Prediction phase: feed only time (+ context) inputs for pred_steps ---
preds = []
for p in range(pred_steps):
t_idx = init_steps + p
assert t_idx < total_len, "time_seq too short for requested pred_steps"
t_in = time_seq[:, t_idx, :] # (b, time_in)
t_proj = self.time_proj(t_in) # project time input
rnn_in = t_proj + context_proj # RNN input: time + context
if isinstance(self.rnn, nn.LSTMCell):
h_t, c_t = hidden
h_t, c_t = self.rnn(rnn_in, (h_t, c_t)) # update hidden
hidden = (h_t, c_t)
else:
hidden = self.rnn(rnn_in, hidden) # update hidden
# Readout uses hidden + raw time (+ raw context input)
read_inputs = [hidden if not isinstance(hidden, tuple) else hidden[0], t_in]
if self.context_in is not None:
read_inputs.append(context)
read = torch.cat(read_inputs, dim=-1)
out = self.readout(read) # (b, out_size)
preds.append(out.unsqueeze(1))
preds = torch.cat(preds, dim=1) # (b, pred_steps, out_size)
return preds, hidden
class TwoInputRNN(BaseRNN):
"""
Many-to-many RNN with an initialization phase that consumes both:
- time inputs (always present)
- feature inputs (only during init_steps)
After init_steps, only time inputs are provided and the model predicts a sequence.
Predictions can be compared to targets with a specified `offset`.
"""
def __init__(self, time_in, feat_in, hidden_size, out_size):
super().__init__(time_in, feat_in, context_in=None, hidden_size=hidden_size, out_size=out_size)
class ThreeInputRNN(BaseRNN):
"""
Many-to-many RNN with an initialization phase that consumes three inputs:
- time inputs (always present)
- feature inputs (only during init_steps)
- context inputs (always present + constant over time)
After init_steps, only time and context inputs are provided and the model predicts a sequence.
Predictions can be compared to targets with a specified `offset`.
"""
def __init__(self, time_in, feat_in, context_in, hidden_size, out_size):
super().__init__(time_in, feat_in, context_in=context_in, hidden_size=hidden_size, out_size=out_size)
class TwoInputLSTM(BaseRNN):
"""
Many-to-many LSTM with an initialization phase that consumes both:
- time inputs (always present)
- feature inputs (only during init_steps)
After init_steps, only time inputs are provided and the model predicts a sequence.
Predictions can be compared to targets with a specified `offset`.
"""
def __init__(self, time_in, feat_in, hidden_size, out_size):
super().__init__(time_in, feat_in, context_in=None, hidden_size=hidden_size, out_size=out_size, rnn_type="LSTM")
class ThreeInputLSTM(BaseRNN):
"""
Many-to-many LSTM with an initialization phase that consumes three inputs:
- time inputs (always present)
- feature inputs (only during init_steps)
- context inputs (always present + constant over time)
After init_steps, only time and context inputs are provided and the model predicts a sequence.
Predictions can be compared to targets with a specified `offset`.
"""
def __init__(self, time_in, feat_in, context_in, hidden_size, out_size):
super().__init__(time_in, feat_in, context_in=context_in, hidden_size=hidden_size, out_size=out_size, rnn_type="LSTM")
Code/python/src/aiRNN/preprocessors.py
+150
View File
@@ -0,0 +1,150 @@
import torch
from torch import nn
import pathlib
import json
MIN_LAT, MAX_LAT = 57.0, 72.0
MIN_LON, MAX_LON = 3.0, 32.0
MIN_ALT, MAX_ALT = -500.0, 50000.0 # in feet
MIN_IAS, MAX_IAS = 0.0, 800.0 # in knots
def min_max_normalize(value, min_val, max_val):
return (value - min_val) / (max_val - min_val)
def min_max_denormalize(norm_value, min_val, max_val):
return norm_value * (max_val - min_val) + min_val
def norm_coords(lat, lon, alt):
lat_norm = min_max_normalize(lat, MIN_LAT, MAX_LAT)
lon_norm = min_max_normalize(lon, MIN_LON, MAX_LON)
alt_norm = min_max_normalize(alt, MIN_ALT, MAX_ALT)
return lat_norm, lon_norm, alt_norm
def denorm_coords(lat_norm, lon_norm, alt_norm):
lat = min_max_denormalize(lat_norm, MIN_LAT, MAX_LAT)
lon = min_max_denormalize(lon_norm, MIN_LON, MAX_LON)
alt = min_max_denormalize(alt_norm, MIN_ALT, MAX_ALT)
return lat, lon, alt
def norm_ias(ias):
return min_max_normalize(ias, MIN_IAS, MAX_IAS)
def denorm_ias(ias_norm):
return min_max_denormalize(ias_norm, MIN_IAS, MAX_IAS)
class AutoEncoder(nn.Module):
def __init__(self, input_a_1_size, input_a_2_size, input_b_size, latent_size):
super(AutoEncoder, self).__init__()
self.input_size = input_a_1_size + input_a_2_size + input_b_size
self.input_a_1_size = input_a_1_size
self.input_a_2_size = input_a_2_size
self.input_b_size = input_b_size
self.encoder = nn.Sequential(
nn.Linear(self.input_size, 128),
nn.ReLU(),
nn.Linear(128, latent_size),
)
self.decoder_A_1 = nn.Sequential(
nn.Linear(latent_size, 128),
nn.ReLU(),
nn.Linear(128, self.input_a_1_size),
nn.Softmax(dim=1),
)
self.decoder_A_2 = nn.Sequential(
nn.Linear(latent_size, 128),
nn.ReLU(),
nn.Linear(128, self.input_a_2_size),
nn.Softmax(dim=1),
)
self.decoder_B = nn.Sequential(
nn.Linear(latent_size, 128),
nn.ReLU(),
nn.Linear(128, self.input_b_size),
nn.Softmax(dim=1),
)
def forward(self, X_a_1, X_a_2, X_b):
X = torch.cat((X_a_1, X_a_2, X_b), dim=1)
latent = self.encoder(X)
reconstructed_A_1 = self.decoder_A_1(latent)
reconstructed_A_2 = self.decoder_A_2(latent)
reconstructed_B = self.decoder_B(latent)
return reconstructed_A_1, reconstructed_A_2, reconstructed_B
category_max_limits = {
"r_1": 100,
"r_2": 50,
"t": 100,
}
autoencoder = AutoEncoder(input_a_1_size=category_max_limits["r_1"], input_a_2_size=category_max_limits["r_2"], input_b_size=category_max_limits["t"], latent_size=4)
auto_encoder_path = pathlib.Path(__file__).parent / "autoencoder.pth"
if auto_encoder_path.exists():
autoencoder.load_state_dict(torch.load(auto_encoder_path))
autoencoder.eval()
def train_autoencoder(X_a_1, X_a_2, X_b, num_epochs=100, learning_rate=1e-3):
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(autoencoder.parameters(), lr=learning_rate)
for epoch in range(num_epochs):
autoencoder.train()
optimizer.zero_grad()
rec_A_1, rec_A_2, rec_B = autoencoder(X_a_1, X_a_2, X_b)
loss_A_1 = criterion(rec_A_1, torch.argmax(X_a_1, dim=1))
loss_A_2 = criterion(rec_A_2, torch.argmax(X_a_2, dim=1))
loss_B = criterion(rec_B, torch.argmax(X_b, dim=1))
loss = loss_A_1 + loss_A_2 + loss_B
loss.backward()
optimizer.step()
if (epoch + 1) % 10 == 0:
print(f"Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}")
torch.save(autoencoder.state_dict(), auto_encoder_path)
def encode_features(X_a, X_b):
autoencoder.eval()
with torch.no_grad():
X = torch.cat((X_a, X_b), dim=1)
latent = autoencoder.encoder(X)
return latent
mappings_path = pathlib.Path(__file__).parent / "category_mappings.json"
def create_category_mappings(df):
df = df.loc[:, ["r", "t"]].dropna().reset_index(drop=True)
df.loc[:, "r_1"] = df["r"].str.split("-").str[0]
df.loc[:, "r_2"] = df["r"].str.split("-").str[1].fillna("Empty")
for col, max_unique in category_max_limits.items():
top_values = df[col].value_counts().head(max_unique-1).index.tolist()
df.loc[~df[col].isin(top_values), col] = "Other"
for col in category_max_limits.keys():
df.loc[:, col.upper()] = df[col].astype("category")
category_mappings = {}
for col in category_max_limits.keys():
category_mappings[col] = {cat: idx for idx, cat in enumerate(df[col.upper()].cat.categories)}
with open(mappings_path, "w") as f:
json.dump(category_mappings, f)
return category_mappings
def map_categories(df, category_mappings):
df = df.loc[:, ["r", "t"]].dropna().reset_index(drop=True)
df.loc[:, "r_1"] = df["r"].str.split("-").str[0]
df.loc[:, "r_2"] = df["r"].str.split("-").str[1].fillna("Empty")
for col in category_mappings.keys():
mapping = category_mappings[col]
df.loc[~df[col].isin(mapping.keys()), col] = "Other"
df.loc[:, f"{col.upper()}_IDX"] = df[col].map(mapping).astype("int64")
return df
if mappings_path.exists():
with open(mappings_path, "r") as f:
category_mappings = json.load(f)
else:
print("Category mappings file not found. Please create mappings using 'create_category_mappings' function.")
Code/python/uv.lock
Generated
+212 -457
View File
@@ -2,15 +2,6 @@ version = 1
revision = 3
requires-python = ">=3.13"
[[package]]
name = "absl-py"
version = "2.3.1"
source = { registry = "https://pypi.org/simple" }
sdist = { url = "https://files.pythonhosted.org/packages/10/2a/c93173ffa1b39c1d0395b7e842bbdc62e556ca9d8d3b5572926f3e4ca752/absl_py-2.3.1.tar.gz", hash = "sha256:a97820526f7fbfd2ec1bce83f3f25e3a14840dac0d8e02a0b71cd75db3f77fc9", size = 116588, upload-time = "2025-07-03T09:31:44.05Z" }
wheels = [
{ url = "https://files.pythonhosted.org/packages/8f/aa/ba0014cc4659328dc818a28827be78e6d97312ab0cb98105a770924dc11e/absl_py-2.3.1-py3-none-any.whl", hash = "sha256:eeecf07f0c2a93ace0772c92e596ace6d3d3996c042b2128459aaae2a76de11d", size = 135811, upload-time = "2025-07-03T09:31:42.253Z" },
]
[[package]]
name = "adsbpy"
version = "0.1.0"
@@ -23,7 +14,7 @@ dependencies = [
{ name = "ipykernel" },
{ name = "matplotlib" },
{ name = "pandas" },
{ name = "tensorflow" },
{ name = "torch" },
{ name = "typer" },
]
@@ -36,7 +27,7 @@ requires-dist = [
{ name = "ipykernel", specifier = ">=7.1.0" },
{ name = "matplotlib", specifier = ">=3.10.7" },
{ name = "pandas", specifier = ">=2.3.3" },
{ name = "tensorflow", specifier = ">=2.20.0" },
{ name = "torch", specifier = ">=2.9.1" },
{ name = "typer", specifier = ">=0.20.0" },
]
@@ -156,19 +147,6 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/d2/39/e7eaf1799466a4aef85b6a4fe7bd175ad2b1c6345066aa33f1f58d4b18d0/asttokens-3.0.1-py3-none-any.whl", hash = "sha256:15a3ebc0f43c2d0a50eeafea25e19046c68398e487b9f1f5b517f7c0f40f976a", size = 27047, upload-time = "2025-11-15T16:43:16.109Z" },
]
[[package]]
name = "astunparse"
version = "1.6.3"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "six" },
{ name = "wheel" },
]
sdist = { url = "https://files.pythonhosted.org/packages/f3/af/4182184d3c338792894f34a62672919db7ca008c89abee9b564dd34d8029/astunparse-1.6.3.tar.gz", hash = "sha256:5ad93a8456f0d084c3456d059fd9a92cce667963232cbf763eac3bc5b7940872", size = 18290, upload-time = "2019-12-22T18:12:13.129Z" }
wheels = [
{ url = "https://files.pythonhosted.org/packages/2b/03/13dde6512ad7b4557eb792fbcf0c653af6076b81e5941d36ec61f7ce6028/astunparse-1.6.3-py2.py3-none-any.whl", hash = "sha256:c2652417f2c8b5bb325c885ae329bdf3f86424075c4fd1a128674bc6fba4b8e8", size = 12732, upload-time = "2019-12-22T18:12:11.297Z" },
]
[[package]]
name = "attrs"
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