{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cell-00",
   "metadata": {},
   "source": [
    "# DeepExtractor \u2014 Training Tutorial\n",
    "\n",
    "This notebook trains a fresh `DeepExtractor_257` (U-Net 2D) model from scratch on a small synthetic\n",
    "PyCBC noise dataset, plots the training and validation losses, and then tests the trained model\n",
    "on held-out sine-Gaussian injections.\n",
    "\n",
    "**What PyCBC noise means here:** the background noise is white Gaussian noise scaled to match\n",
    "the variance of a whitened PyCBC noise realization. It is not coloured detector noise \u2014\n",
    "use `--bilby-noise` / `bilby_noise=True` for that.\n",
    "\n",
    "**Pipeline overview:**\n",
    "1. Generate synthetic time-domain data (noisy glitch + background pairs)\n",
    "2. Convert to STFT spectrograms (magnitude + phase)\n",
    "3. Train the U-Net on spectrogram pairs\n",
    "4. Plot losses\n",
    "5. Test on sine-Gaussian injections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cell-01",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from torch.optim.lr_scheduler import ReduceLROnPlateau\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", \"Wswiglal-redir-stdio\")\n",
    "\n",
    "from deepextractor.models.architectures import UNET2D\n",
    "from deepextractor.generation.generate_timeseries import (\n",
    "    generate_gaussian_noise, generate_synthetic_data, LENGTH, SAMPLE_RATE, T\n",
    ")\n",
    "from deepextractor.generation.glitch_functions import generate_sine_gaussian\n",
    "from deepextractor.utils.signal import whitened_snr_scaling\n",
    "from deepextractor.training.train_fn import train_fn\n",
    "from deepextractor.utils.io import check_accuracy\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-02",
   "metadata": {},
   "source": [
    "## Configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cell-03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device: mps\n"
     ]
    }
   ],
   "source": [
    "# Device \u2014 uses MPS on Apple Silicon, CUDA on Linux/Windows GPU, otherwise CPU\n",
    "if torch.cuda.is_available():\n",
    "    DEVICE = \"cuda\"\n",
    "elif torch.backends.mps.is_available():\n",
    "    DEVICE = \"mps\"\n",
    "else:\n",
    "    DEVICE = \"cpu\"\n",
    "print(f\"Using device: {DEVICE}\")\n",
    "\n",
    "# Dataset size \u2014 keep small for a quick demo; increase for real training\n",
    "N_TRAIN = 1000\n",
    "N_VAL   = 200\n",
    "\n",
    "# Training\n",
    "BATCH_SIZE              = 32\n",
    "EPOCHS                  = 50    # maximum epochs; early stopping may stop sooner\n",
    "LR                      = 1e-4\n",
    "LR_PATIENCE             = 4     # epochs without improvement before LR is reduced\n",
    "LR_FACTOR               = 0.1   # factor to reduce LR by\n",
    "EARLY_STOPPING_PATIENCE = 9     # epochs without improvement before training stops\n",
    "\n",
    "# STFT parameters\n",
    "# Original DeepExtractor (arXiv:2501.18423): N_FFT=512, WIN_LENGTH=64, HOP_LENGTH=32\n",
    "# \u2192 produces (257, 257) spectrograms, richer time-frequency representation\n",
    "#   but significantly slower to train.\n",
    "# Tutorial default: smaller spectrograms for faster training, but still gives decent performance (please see 129x129 model in the paper).\n",
    "N_FFT      = 256\n",
    "WIN_LENGTH = N_FFT // 2\n",
    "HOP_LENGTH = WIN_LENGTH // 2\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-04",
   "metadata": {},
   "source": [
    "## Step 1 \u2014 Generate synthetic time-domain data\n",
    "\n",
    "Each training sample is a pair:\n",
    "- **Input** `glitch`: background noise with 1\u201330 synthetic signal injections (chirps, sine-Gaussians, etc.)\n",
    "- **Target** `background`: the same noise without any injections\n",
    "\n",
    "The model learns to map glitchy strain \u2192 clean background."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cell-05",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating training noise...\n",
      "Generating pycbc noise...\n",
      "Generating validation noise...\n",
      "Generating pycbc noise...\n",
      "Generating training pairs...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Generating Synthetic Train Data: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1000/1000 [00:02<00:00, 453.94it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating validation pairs...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Generating Synthetic Val Data: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 200/200 [00:00<00:00, 374.51it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Train: (1000, 8192)  |  Val: (200, 8192)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "mean, std_dev = 0, np.sqrt(SAMPLE_RATE)\n",
    "\n",
    "print(\"Generating training noise...\")\n",
    "train_noise = generate_gaussian_noise(mean, std_dev, N_TRAIN, (LENGTH,), bilby_noise=False)\n",
    "print(\"Generating validation noise...\")\n",
    "val_noise   = generate_gaussian_noise(mean, std_dev, N_VAL,   (LENGTH,), bilby_noise=False)\n",
    "\n",
    "print(\"Generating training pairs...\")\n",
    "glitch_train, bg_train = generate_synthetic_data(train_noise, bilby_noise=False, phase=\"train\")\n",
    "print(\"Generating validation pairs...\")\n",
    "glitch_val,   bg_val   = generate_synthetic_data(val_noise,   bilby_noise=False, phase=\"val\")\n",
    "\n",
    "print(f\"\\nTrain: {glitch_train.shape}  |  Val: {glitch_val.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-06",
   "metadata": {},
   "source": [
    "## Step 2 \u2014 Scale and convert to spectrograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cell-07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spectrogram shape: torch.Size([1000, 2, 129, 129])  \u2014 (N, 2, freq_bins, time_bins)\n"
     ]
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "glitch_train_scaled = scaler.fit_transform(glitch_train.reshape(-1, 1)).reshape(glitch_train.shape)\n",
    "bg_train_scaled     = scaler.transform(bg_train.reshape(-1, 1)).reshape(bg_train.shape)\n",
    "glitch_val_scaled   = scaler.transform(glitch_val.reshape(-1, 1)).reshape(glitch_val.shape)\n",
    "bg_val_scaled       = scaler.transform(bg_val.reshape(-1, 1)).reshape(bg_val.shape)\n",
    "\n",
    "# Uncomment to save the scaler for use outside this notebook\n",
    "# import pickle, os\n",
    "# os.makedirs('/tmp/de_training_tutorial', exist_ok=True)\n",
    "# with open('/tmp/de_training_tutorial/scaler.pkl', 'wb') as f:\n",
    "#     pickle.dump(scaler, f)\n",
    "\n",
    "# Convert to STFT spectrograms (in-memory)\n",
    "window = torch.hann_window(WIN_LENGTH)\n",
    "\n",
    "def to_mag_phase(arrays):\n",
    "    \"\"\"Convert a numpy array (N, time) to a (N, 2, F, T) mag/phase tensor.\"\"\"\n",
    "    t = torch.tensor(arrays, dtype=torch.float32)\n",
    "    stft = torch.stft(t, n_fft=N_FFT, hop_length=HOP_LENGTH, win_length=WIN_LENGTH,\n",
    "                      window=window, return_complex=True)\n",
    "    mag   = torch.abs(stft)\n",
    "    phase = torch.angle(stft)\n",
    "    return torch.stack([mag, phase], dim=1)  # (N, 2, F, T)\n",
    "\n",
    "glitch_train_spec = to_mag_phase(glitch_train_scaled)\n",
    "bg_train_spec     = to_mag_phase(bg_train_scaled)\n",
    "glitch_val_spec   = to_mag_phase(glitch_val_scaled)\n",
    "bg_val_spec       = to_mag_phase(bg_val_scaled)\n",
    "\n",
    "print(f\"Spectrogram shape: {glitch_train_spec.shape}  \u2014 (N, 2, freq_bins, time_bins)\")\n",
    "\n",
    "# Uncomment to save spectrograms to disk (useful for large datasets or re-use)\n",
    "# os.makedirs('/tmp/de_training_tutorial/spectrogram_domain', exist_ok=True)\n",
    "# for name, arr in [\n",
    "#     ('glitch_train_scaled_mag_phase',     glitch_train_spec.numpy()),\n",
    "#     ('background_train_scaled_mag_phase', bg_train_spec.numpy()),\n",
    "#     ('glitch_val_scaled_mag_phase',       glitch_val_spec.numpy()),\n",
    "#     ('background_val_scaled_mag_phase',   bg_val_spec.numpy()),\n",
    "# ]:\n",
    "#     np.save(f'/tmp/de_training_tutorial/spectrogram_domain/{name}', arr)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-08",
   "metadata": {},
   "source": [
    "## Step 3 \u2014 Build model and data loaders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cell-09",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model parameters: 7,762,786\n",
      "Train batches: 32  |  Val batches: 7\n",
      "Spectrogram shape: torch.Size([1000, 2, 129, 129])  \u2014 (N, 2, freq_bins, time_bins)\n"
     ]
    }
   ],
   "source": [
    "from torch.utils.data import TensorDataset, DataLoader\n",
    "\n",
    "# Model architecture\n",
    "# Original DeepExtractor (arXiv:2501.18423): features=[64, 128, 256, 512] \u2014 ~31M parameters.\n",
    "# Use those to train a model equivalent to the published DeepExtractor.\n",
    "# Tutorial default: one fewer layer and half the filters for faster training.\n",
    "model = UNET2D(in_channels=2, out_channels=2, features=[32, 64, 128, 256]).to(DEVICE)\n",
    "print(f\"Model parameters: {sum(p.numel() for p in model.parameters()):,}\")\n",
    "\n",
    "train_ds = TensorDataset(glitch_train_spec, bg_train_spec)\n",
    "val_ds   = TensorDataset(glitch_val_spec,   bg_val_spec)\n",
    "\n",
    "train_loader = DataLoader(train_ds, batch_size=BATCH_SIZE, shuffle=True)\n",
    "val_loader   = DataLoader(val_ds,   batch_size=BATCH_SIZE, shuffle=False)\n",
    "\n",
    "print(f\"Train batches: {len(train_loader)}  |  Val batches: {len(val_loader)}\")\n",
    "print(f\"Spectrogram shape: {glitch_train_spec.shape}  \u2014 (N, 2, freq_bins, time_bins)\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-10",
   "metadata": {},
   "source": [
    "## Step 4 \u2014 Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cell-11",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on batch: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 32/32 [00:11<00:00,  2.83it/s, loss=1.95]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Loss: 2.218210\n",
      "Epoch   1/50  train=2.32153  val=2.21821  lr=1.0e-04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on batch: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 32/32 [00:10<00:00,  3.05it/s, loss=1.54]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Loss: 1.511046\n",
      "Epoch   2/50  train=1.68615  val=1.51105  lr=1.0e-04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on batch: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 32/32 [00:10<00:00,  3.02it/s, loss=1.22]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Loss: 1.312862\n",
      "Epoch   3/50  train=1.37830  val=1.31286  lr=1.0e-04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on batch: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 32/32 [00:10<00:00,  3.04it/s, loss=1.27]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Loss: 1.163939\n",
      "Epoch   4/50  train=1.18296  val=1.16394  lr=1.0e-04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on batch: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 32/32 [00:10<00:00,  3.10it/s, loss=0.918]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Loss: 1.020379\n",
      "Epoch   5/50  train=1.06407  val=1.02038  lr=1.0e-04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on batch: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 32/32 [00:10<00:00,  3.10it/s, loss=0.928]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Loss: 0.980715\n",
      "Epoch   6/50  train=0.97128  val=0.98071  lr=1.0e-04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on batch: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 32/32 [00:10<00:00,  3.06it/s, loss=0.951]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Loss: 0.869840\n",
      "Epoch   7/50  train=0.91569  val=0.86984  lr=1.0e-04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on batch: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 32/32 [00:10<00:00,  3.04it/s, loss=0.877]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Loss: 0.817595\n",
      "Epoch   8/50  train=0.85876  val=0.81759  lr=1.0e-04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on batch: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 32/32 [00:10<00:00,  3.01it/s, loss=0.706]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Loss: 0.806517\n",
      "Epoch   9/50  train=0.81324  val=0.80652  lr=1.0e-04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on batch: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 32/32 [00:10<00:00,  2.93it/s, loss=0.732]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Loss: 0.753120\n",
      "Epoch  10/50  train=0.78160  val=0.75312  lr=1.0e-04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on batch: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 32/32 [00:11<00:00,  2.89it/s, loss=0.743]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Loss: 0.719149\n",
      "Epoch  11/50  train=0.75014  val=0.71915  lr=1.0e-04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on batch: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 32/32 [00:11<00:00,  2.80it/s, loss=0.76] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Loss: 0.698354\n",
      "Epoch  12/50  train=0.72427  val=0.69835  lr=1.0e-04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on batch: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 32/32 [00:11<00:00,  2.69it/s, loss=0.601]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Loss: 0.680848\n",
      "Epoch  13/50  train=0.69808  val=0.68085  lr=1.0e-04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on batch: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 32/32 [00:12<00:00,  2.59it/s, loss=0.682]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Loss: 0.660272\n",
      "Epoch  14/50  train=0.68013  val=0.66027  lr=1.0e-04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on batch: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 32/32 [00:12<00:00,  2.50it/s, loss=0.595]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Loss: 0.647760\n",
      "Epoch  15/50  train=0.66030  val=0.64776  lr=1.0e-04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on batch: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 32/32 [00:13<00:00,  2.43it/s, loss=0.686]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Loss: 0.632859\n",
      "Epoch  16/50  train=0.64636  val=0.63286  lr=1.0e-04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on batch: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 32/32 [00:13<00:00,  2.40it/s, loss=0.585]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Loss: 0.617714\n",
      "Epoch  17/50  train=0.63055  val=0.61771  lr=1.0e-04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on batch: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 32/32 [00:13<00:00,  2.44it/s, loss=0.729]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Loss: 0.841430\n",
      "Epoch  18/50  train=0.62186  val=0.84143  lr=1.0e-04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on batch: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 32/32 [00:13<00:00,  2.46it/s, loss=0.575]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Loss: 0.603988\n",
      "Epoch  19/50  train=0.62017  val=0.60399  lr=1.0e-04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on batch: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 32/32 [00:13<00:00,  2.45it/s, loss=0.646]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Loss: 0.593803\n",
      "Epoch  20/50  train=0.60578  val=0.59380  lr=1.0e-04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on batch: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 32/32 [00:12<00:00,  2.48it/s, loss=0.629]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Loss: 0.581474\n",
      "Epoch  21/50  train=0.59351  val=0.58147  lr=1.0e-04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on batch: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 32/32 [00:12<00:00,  2.50it/s, loss=0.575]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Loss: 0.572586\n",
      "Epoch  22/50  train=0.58487  val=0.57259  lr=1.0e-04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on batch: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 32/32 [00:12<00:00,  2.52it/s, loss=0.645]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Loss: 0.564201\n",
      "Epoch  23/50  train=0.58049  val=0.56420  lr=1.0e-04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on batch: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 32/32 [00:12<00:00,  2.51it/s, loss=0.73] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Loss: 0.562453\n",
      "Epoch  24/50  train=0.57640  val=0.56245  lr=1.0e-04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on batch: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 32/32 [00:12<00:00,  2.52it/s, loss=0.725]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Loss: 0.556875\n",
      "Epoch  25/50  train=0.57033  val=0.55688  lr=1.0e-04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on batch: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 32/32 [00:12<00:00,  2.51it/s, loss=0.604]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Loss: 0.556285\n",
      "Epoch  26/50  train=0.56327  val=0.55629  lr=1.0e-04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on batch: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 32/32 [00:13<00:00,  2.36it/s, loss=0.475]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Loss: 0.547908\n",
      "Epoch  27/50  train=0.55664  val=0.54791  lr=1.0e-04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on batch: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 32/32 [00:14<00:00,  2.19it/s, loss=0.487]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Loss: 0.545304\n",
      "Epoch  28/50  train=0.55267  val=0.54530  lr=1.0e-04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on batch: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 32/32 [00:13<00:00,  2.44it/s, loss=0.504]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Loss: 0.542404\n",
      "Epoch  29/50  train=0.54966  val=0.54240  lr=1.0e-04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on batch: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 32/32 [00:13<00:00,  2.44it/s, loss=0.455]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Loss: 0.539977\n",
      "Epoch  30/50  train=0.54552  val=0.53998  lr=1.0e-04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on batch: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 32/32 [00:13<00:00,  2.32it/s, loss=0.641]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Loss: 0.536445\n",
      "Epoch  31/50  train=0.54668  val=0.53645  lr=1.0e-04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on batch: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 32/32 [00:13<00:00,  2.40it/s, loss=0.534]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Loss: 0.535496\n",
      "Epoch  32/50  train=0.54160  val=0.53550  lr=1.0e-04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on batch: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 32/32 [00:13<00:00,  2.42it/s, loss=0.608]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Loss: 0.533340\n",
      "Epoch  33/50  train=0.54091  val=0.53334  lr=1.0e-04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on batch: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 32/32 [00:13<00:00,  2.43it/s, loss=0.613]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Loss: 0.530971\n",
      "Epoch  34/50  train=0.53817  val=0.53097  lr=1.0e-04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on batch: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 32/32 [00:13<00:00,  2.31it/s, loss=0.42] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Loss: 0.530778\n",
      "Epoch  35/50  train=0.53090  val=0.53078  lr=1.0e-04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on batch: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 32/32 [00:15<00:00,  2.06it/s, loss=0.641]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Loss: 0.527804\n",
      "Epoch  36/50  train=0.53390  val=0.52780  lr=1.0e-04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on batch: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 32/32 [00:16<00:00,  1.95it/s, loss=0.527]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Loss: 0.527331\n",
      "Epoch  37/50  train=0.52809  val=0.52733  lr=1.0e-04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on batch: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 32/32 [00:15<00:00,  2.13it/s, loss=0.664]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Loss: 0.526076\n",
      "Epoch  38/50  train=0.52855  val=0.52608  lr=1.0e-04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on batch: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 32/32 [00:13<00:00,  2.31it/s, loss=0.641]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Loss: 0.525271\n",
      "Epoch  39/50  train=0.52609  val=0.52527  lr=1.0e-04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on batch: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 32/32 [00:13<00:00,  2.34it/s, loss=0.409]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Loss: 0.529516\n",
      "Epoch  40/50  train=0.51858  val=0.52952  lr=1.0e-04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on batch: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 32/32 [00:13<00:00,  2.34it/s, loss=0.44] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Loss: 0.523367\n",
      "Epoch  41/50  train=0.51629  val=0.52337  lr=1.0e-04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on batch: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 32/32 [00:13<00:00,  2.35it/s, loss=0.568]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Loss: 0.522603\n",
      "Epoch  42/50  train=0.51454  val=0.52260  lr=1.0e-04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on batch: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 32/32 [00:13<00:00,  2.36it/s, loss=0.557]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Loss: 0.521652\n",
      "Epoch  43/50  train=0.51073  val=0.52165  lr=1.0e-04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on batch: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 32/32 [00:14<00:00,  2.27it/s, loss=0.514]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Loss: 0.521917\n",
      "Epoch  44/50  train=0.50676  val=0.52192  lr=1.0e-04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on batch: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 32/32 [00:13<00:00,  2.37it/s, loss=0.467]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Loss: 0.520662\n",
      "Epoch  45/50  train=0.50248  val=0.52066  lr=1.0e-04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on batch: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 32/32 [00:13<00:00,  2.29it/s, loss=0.586]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Loss: 0.521084\n",
      "Epoch  46/50  train=0.50213  val=0.52108  lr=1.0e-04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on batch: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 32/32 [00:13<00:00,  2.29it/s, loss=0.424]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Loss: 0.520615\n",
      "Epoch  47/50  train=0.49446  val=0.52061  lr=1.0e-04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on batch: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 32/32 [00:13<00:00,  2.37it/s, loss=0.399]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Loss: 0.521620\n",
      "Epoch  48/50  train=0.48950  val=0.52162  lr=1.0e-04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on batch: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 32/32 [00:13<00:00,  2.32it/s, loss=0.521]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Loss: 0.520201\n",
      "Epoch  49/50  train=0.48736  val=0.52020  lr=1.0e-04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training on batch: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 32/32 [00:13<00:00,  2.33it/s, loss=0.614]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Loss: 0.521240\n",
      "Epoch  50/50  train=0.48693  val=0.52124  lr=1.0e-04\n",
      "\n",
      "Training complete.\n"
     ]
    }
   ],
   "source": [
    "loss_fn   = nn.MSELoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=LR)\n",
    "scheduler = ReduceLROnPlateau(optimizer, mode=\"min\", factor=LR_FACTOR, patience=LR_PATIENCE)\n",
    "amp_scaler = torch.amp.GradScaler(\"cuda\") if DEVICE == \"cuda\" else torch.amp.GradScaler(\"cpu\")\n",
    "\n",
    "train_losses, val_losses = [], []\n",
    "best_val_loss           = float(\"inf\")\n",
    "early_stopping_counter  = 0\n",
    "\n",
    "for epoch in range(EPOCHS):\n",
    "    train_loss, _, _ = train_fn(\n",
    "        train_loader, model, \"DeepExtractor_257\", optimizer, loss_fn, amp_scaler, DEVICE\n",
    "    )\n",
    "    val_loss, _, _ = check_accuracy(val_loader, model, \"DeepExtractor_257\", device=DEVICE)\n",
    "\n",
    "    train_losses.append(train_loss)\n",
    "    val_losses.append(val_loss)\n",
    "    scheduler.step(val_loss)\n",
    "\n",
    "    current_lr = optimizer.param_groups[0]['lr']\n",
    "    print(f\"Epoch {epoch+1:>3}/{EPOCHS}  train={train_loss:.5f}  val={val_loss:.5f}  lr={current_lr:.1e}\")\n",
    "\n",
    "    if val_loss < best_val_loss:\n",
    "        best_val_loss          = val_loss\n",
    "        early_stopping_counter = 0\n",
    "    else:\n",
    "        early_stopping_counter += 1\n",
    "        if early_stopping_counter >= EARLY_STOPPING_PATIENCE:\n",
    "            print(f\"\\nEarly stopping at epoch {epoch+1} (no improvement for {EARLY_STOPPING_PATIENCE} epochs).\")\n",
    "            break\n",
    "\n",
    "print(\"\\nTraining complete.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-12",
   "metadata": {},
   "source": [
    "## Step 5 \u2014 Plot losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cell-13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "epochs_ran = range(1, len(train_losses) + 1)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 4))\n",
    "ax.plot(epochs_ran, train_losses, label=\"Train\",      color=\"C0\")\n",
    "ax.plot(epochs_ran, val_losses,   label=\"Validation\", color=\"C1\")\n",
    "ax.set_xlabel(\"Epoch\")\n",
    "ax.set_ylabel(\"MSE Loss\")\n",
    "ax.set_title(\"Training and Validation Loss\")\n",
    "ax.legend()\n",
    "ax.grid(True)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-14",
   "metadata": {},
   "source": [
    "## Step 6 \u2014 Test on sine-Gaussian injections\n",
    "\n",
    "We generate fresh test examples \u2014 PyCBC (numpy) noise with a sine-Gaussian injected at a range of SNRs \u2014\n",
    "and run them through the trained model.\n",
    "The model was never trained on these specific examples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cell-15",
   "metadata": {},
   "outputs": [],
   "source": [
    "def reconstruct(noisy_signal, model, scaler, device, n_fft, hop_length, win_length):\n",
    "    \"\"\"Scale \u2192 STFT \u2192 U-Net \u2192 iSTFT \u2192 unscale \u2192 subtract background.\"\"\"\n",
    "    # Scale\n",
    "    scaled = scaler.transform(noisy_signal.reshape(-1, 1)).reshape(noisy_signal.shape)\n",
    "\n",
    "    # STFT\n",
    "    window = torch.hann_window(win_length)\n",
    "    t = torch.tensor(scaled, dtype=torch.float32).unsqueeze(0)  # (1, time)\n",
    "    stft = torch.stft(t, n_fft=n_fft, hop_length=hop_length, win_length=win_length,\n",
    "                      window=window, return_complex=True)\n",
    "    mag   = torch.abs(stft)\n",
    "    phase = torch.angle(stft)\n",
    "    spec  = torch.stack([mag, phase], dim=1)  # (1, 2, F, T)\n",
    "\n",
    "    # U-Net inference\n",
    "    model.eval()\n",
    "    with torch.no_grad():\n",
    "        bg_spec = model(spec.to(device)).cpu()  # predicted background spectrogram\n",
    "\n",
    "    # iSTFT\n",
    "    bg_mag   = bg_spec[:, 0, :, :]\n",
    "    bg_phase = bg_spec[:, 1, :, :]\n",
    "    bg_complex = bg_mag * torch.exp(1j * bg_phase)\n",
    "    bg_td = torch.istft(bg_complex, n_fft=n_fft, hop_length=hop_length,\n",
    "                        win_length=win_length, window=window,\n",
    "                        length=noisy_signal.shape[-1])\n",
    "\n",
    "    # Unscale and subtract background to recover signal\n",
    "    bg_unscaled = scaler.inverse_transform(bg_td.numpy().reshape(-1, 1)).reshape(-1)\n",
    "    noisy_unscaled = noisy_signal.copy()\n",
    "    reconstruction = noisy_unscaled - bg_unscaled\n",
    "    return reconstruction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cell-16",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating pycbc noise...\n",
      "Generating pycbc noise...\n",
      "Generating pycbc noise...\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "T_INJ = T / 2\n",
    "SNR_VALUES = [10, 30, 100]\n",
    "\n",
    "def overlap(a, b):\n",
    "    \"\"\"Normalised time-domain overlap (match) between two real signals.\n",
    "    Equivalent to the PyCBC match on whitened data (flat PSD).\"\"\"\n",
    "    return np.dot(a, b) / np.sqrt(np.dot(a, a) * np.dot(b, b))\n",
    "\n",
    "fig, axes = plt.subplots(len(SNR_VALUES), 1, figsize=(12, 4 * len(SNR_VALUES)))\n",
    "t_axis = np.linspace(0, T, LENGTH)\n",
    "\n",
    "for ax, snr in zip(axes, SNR_VALUES):\n",
    "    noise = generate_gaussian_noise(mean, std_dev, 1, (LENGTH,), bilby_noise=False)[0]\n",
    "\n",
    "    _, wavelet = generate_sine_gaussian(duration=0.5, freq_max=1024)\n",
    "    wavelet = wavelet - np.mean(wavelet)\n",
    "    wavelet = whitened_snr_scaling(wavelet, snr=snr)\n",
    "\n",
    "    len_glitch = len(wavelet)\n",
    "    id_start   = int(T_INJ * SAMPLE_RATE) - len_glitch // 2\n",
    "    noisy      = noise.copy()\n",
    "    noisy[id_start : id_start + len_glitch] += wavelet\n",
    "\n",
    "    injected = np.zeros(LENGTH)\n",
    "    injected[id_start : id_start + len_glitch] = wavelet\n",
    "\n",
    "    reconstructed = reconstruct(noisy, model, scaler, DEVICE, N_FFT, HOP_LENGTH, WIN_LENGTH)\n",
    "\n",
    "    match    = overlap(injected, reconstructed)\n",
    "    mismatch = 1.0 - match\n",
    "\n",
    "    ax.plot(t_axis, injected,      color=\"black\", lw=1.5, linestyle=\"--\", label=\"Injected (ground truth)\")\n",
    "    ax.plot(t_axis, reconstructed, color=\"C0\",    lw=1.5, label=\"Reconstructed\")\n",
    "    ax.plot(t_axis, noisy,         color=\"gray\",  lw=0.8, alpha=0.4, label=\"Noisy input\")\n",
    "    ax.set_title(\n",
    "        f\"Sine-Gaussian  SNR={snr}  |  \"\n",
    "        f\"Match $= {match:.3f}$,  $\\\\mathcal{{M}} = {mismatch:.3f}$\"\n",
    "    )\n",
    "    ax.set_xlabel(\"Time (s)\")\n",
    "    ax.set_ylabel(\"Amplitude\")\n",
    "    ax.legend(loc=\"upper right\")\n",
    "    ax.grid(True)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cdf8b4d1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "deepextractor-test",
   "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": "3.11.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}