{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qKH_dZ2eOYjj"
      },
      "source": [
        "# Synthetic Skewed Gaussian Pulse\n",
        "\n",
        "## Skewed Gaussian Function  \n",
        "\n",
        "The pulse is modeled using a **skewed Gaussian function**:\n",
        "\n",
        "$$\n",
        "f(x) =\n",
        "\\begin{cases}\n",
        "A \\exp\\left(-\\frac{(x - \\mu)^2}{2\\sigma_{\\text{left}}^2}\\right), & x < \\mu \\\\  \n",
        "A \\exp\\left(-\\frac{(x - \\mu)^2}{2\\sigma_{\\text{right}}^2}\\right), & x \\geq \\mu  \n",
        "\\end{cases}\n",
        "$$\n",
        "\n",
        "where:  \n",
        "\n",
        "- Peak position:  $ \\mu $  \n",
        "\n",
        "- Amplitude:  $ A $  \n",
        "\n",
        "- Standard deviation on the left side:  $ \\sigma_{\\text{left}} $  \n",
        "\n",
        "- Standard deviation on the right side:  $ \\sigma_{\\text{right}} $  \n",
        "\n",
        "---\n",
        "\n",
        "## Threshold Calculation  \n",
        "\n",
        "The theoretical positions where intensity falls to **10%** of \\( A \\) are derived from:  \n",
        "\n",
        "$$\n",
        "A \\exp\\left(-\\frac{(x - \\mu)^2}{2\\sigma^2}\\right) = 0.1A\n",
        "$$\n",
        "\n",
        "Dividing by \\( A \\) and taking the **natural logarithm**:  \n",
        "\n",
        "$$\n",
        "-\\frac{(x - \\mu)^2}{2\\sigma^2} = \\ln(0.1)\n",
        "$$\n",
        "\n",
        "Solving for \\( x \\):  \n",
        "\n",
        "$$\n",
        "|x - \\mu| = \\sqrt{2 \\sigma^2 (-\\ln 0.1)}\n",
        "$$\n",
        "\n",
        "Thus, the **left and right threshold positions** are:  \n",
        "\n",
        "$$\n",
        "x_{\\text{left}} = \\mu - \\sqrt{2 \\sigma_{\\text{left}}^2 (-\\ln 0.1)}\n",
        "$$  \n",
        "\n",
        "$$\n",
        "x_{\\text{right}} = \\mu + \\sqrt{2 \\sigma_{\\text{right}}^2 (-\\ln 0.1)}\n",
        "$$  \n",
        "\n",
        "---\n",
        "\n",
        "## Noise Addition  \n",
        "\n",
        "Gaussian noise is added to the pulse:  \n",
        "\n",
        "$$\n",
        "y_{\\text{noisy}} = f(x) + \\mathcal{N}(0, \\sigma_{\\text{noise}})\n",
        "$$\n",
        "\n",
        "where  $ \\mathcal{N}(0, \\sigma_{\\text{noise}}) $  represents **Gaussian noise** with zero mean and standard deviation $\\sigma_{\\text{noise}}$.  \n",
        "\n",
        "---\n",
        "\n",
        "## Visualization  \n",
        "\n",
        "- The pulse is plotted as both a **line plot** and **scatter plot**.  \n",
        "- Vertical lines are drawn at:  \n",
        "\n",
        "  - **Peak position** ( $ \\mu $ ), in **red**  \n",
        "  - **Left 10% threshold** ( $ x_{\\text{left}} $ ), in **green**  \n",
        "  - **Right 10% threshold** ( $ x_{\\text{right}} $ ), in **orange**  \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "8PuBtDc4-C6J",
        "outputId": "74841d58-95b6-48c9-8294-95f346f9c76d"
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Peak (μ): 0.7385180675426091\n",
            "Left 10% position: 0.6608272561975099\n",
            "Right 10% position: 0.7952341688561271\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# np.random.seed(42)\n",
        "\n",
        "\n",
        "# Define a skewed Gaussian function\n",
        "def skewed_gaussian(x, mu, sigma_left, sigma_right, A):\n",
        "    \"\"\"\n",
        "    Returns a skewed Gaussian pulse:\n",
        "      For x < mu:  A * exp(-((x-mu)^2) / (2*sigma_left^2))\n",
        "      For x >= mu: A * exp(-((x-mu)^2) / (2*sigma_right^2))\n",
        "    \"\"\"\n",
        "    return np.where(x < mu,\n",
        "                    A * np.exp(-((x - mu)**2) / (2 * sigma_left**2)),\n",
        "                    A * np.exp(-((x - mu)**2) / (2 * sigma_right**2)))\n",
        "\n",
        "def generate_pulse(window_length=200, T=1.0, noise_std=0.05):\n",
        "    \"\"\"\n",
        "    Generates a synthetic pulse in a discrete window.\n",
        "\n",
        "    Parameters:\n",
        "      window_length: number of discrete time points\n",
        "      T: total duration (time range from 0 to T)\n",
        "      noise_std: standard deviation of additive Gaussian noise\n",
        "\n",
        "    Returns:\n",
        "      x: time vector\n",
        "      noisy_signal: synthetic noisy intensity values\n",
        "      mu: true peak position\n",
        "      left_thresh: theoretical left 10% threshold position\n",
        "      right_thresh: theoretical right 10% threshold position\n",
        "    \"\"\"\n",
        "    # Create time axis (discrete samples)\n",
        "    x = np.linspace(0, T, window_length)\n",
        "\n",
        "    # Randomly choose a peak position (avoid edges so thresholds are in-bound)\n",
        "    mu = np.random.uniform(T*0.2, T*0.8)\n",
        "    # Random amplitude around 1\n",
        "    A = np.random.uniform(0.8, 1.2)\n",
        "    # Random standard deviations for left and right side\n",
        "    sigma_left = np.random.uniform(0.01, 0.05)\n",
        "    sigma_right = np.random.uniform(0.01, 0.05)\n",
        "\n",
        "    # Generate the clean pulse signal\n",
        "    clean_signal = skewed_gaussian(x, mu, sigma_left, sigma_right, A)\n",
        "\n",
        "    # Calculate theoretical positions where intensity falls to 10% of A.\n",
        "    # Solve: exp(-((x-mu)^2)/(2*sigma^2)) = 0.1  ==>  |x-mu| = sqrt(2 * sigma^2 * (-ln(0.1)))\n",
        "    left_thresh = mu - np.sqrt(2 * sigma_left**2 * (-np.log(0.1)))\n",
        "    right_thresh = mu + np.sqrt(2 * sigma_right**2 * (-np.log(0.1)))\n",
        "\n",
        "    # Add Gaussian noise to the clean signal\n",
        "    noisy_signal = clean_signal + np.random.normal(0, noise_std, size=window_length)\n",
        "\n",
        "    # Plotting the results with both connected lines and individual points\n",
        "    plt.figure(figsize=(8, 4))\n",
        "\n",
        "    # Connected line plots\n",
        "    plt.plot(x, noisy_signal, label=\"Noisy Pulse (Line)\", color='blue', alpha=0.7)\n",
        "    # plt.plot(x, clean_signal, label=\"Clean Pulse (Line)\", linestyle=\"--\", color='gray', alpha=0.7)\n",
        "\n",
        "    # Scatter plot for unconnected points\n",
        "    plt.scatter(x, noisy_signal, color='blue', s=15, label=\"Noisy Pulse (Points)\", zorder=3)\n",
        "    # plt.scatter(x, clean_signal, color='gray', s=15, label=\"Clean Pulse (Points)\", zorder=3, alpha=0.5)\n",
        "\n",
        "    # Mark key positions with vertical lines\n",
        "    plt.axvline(mu, color='red', linestyle='--', label=\"Peak (μ)\")\n",
        "    plt.axvline(left_thresh, color='green', linestyle='--', label=\"Left 10%\")\n",
        "    plt.axvline(right_thresh, color='orange', linestyle='--', label=\"Right 10%\")\n",
        "\n",
        "    plt.xlabel(\"Time\")\n",
        "    plt.ylabel(\"Intensity\")\n",
        "    plt.title(\"Synthetic Skewed Gaussian Pulse with Discrete Points\")\n",
        "    plt.legend()\n",
        "    plt.show()\n",
        "\n",
        "    return x, noisy_signal, mu, left_thresh, right_thresh\n",
        "\n",
        "# Generate and plot one synthetic pulse with both line and scatter plots\n",
        "x, noisy_signal, mu, left_thresh, right_thresh = generate_pulse(window_length=200, T=1)\n",
        "print(\"Peak (μ):\", mu)\n",
        "print(\"Left 10% position:\", left_thresh)\n",
        "print(\"Right 10% position:\", right_thresh)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2a9QQbWgL9_R",
        "outputId": "34b32f25-74aa-42b2-8502-edeea8e20bf3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Dataset saved to synthetic_pulses_data.h5\n",
            "Dataset saved to synthetic_pulses_data_test.h5\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import h5py\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Set a global random seed for reproducibility\n",
        "np.random.seed(42)\n",
        "\n",
        "# Define a skewed Gaussian function\n",
        "def skewed_gaussian(x, mu, sigma_left, sigma_right, A):\n",
        "    \"\"\"\n",
        "    Returns a skewed Gaussian pulse:\n",
        "      For x < mu:  A * exp(-((x-mu)^2) / (2*sigma_left^2))\n",
        "      For x >= mu: A * exp(-((x-mu)^2) / (2*sigma_right^2))\n",
        "    \"\"\"\n",
        "    return np.where(x < mu,\n",
        "                    A * np.exp(-((x - mu)**2) / (2 * sigma_left**2)),\n",
        "                    A * np.exp(-((x - mu)**2) / (2 * sigma_right**2)))\n",
        "\n",
        "#Removed plotting\n",
        "def generate_pulse(window_length, T, noise_std):\n",
        "    \"\"\"\n",
        "    Generates a synthetic pulse in a discrete window.\n",
        "\n",
        "    Parameters:\n",
        "      window_length: number of discrete time points for this pulse\n",
        "      T: total duration (time range from 0 to T) for this pulse\n",
        "      noise_std: standard deviation of additive Gaussian noise for this pulse\n",
        "\n",
        "    Returns:\n",
        "      x: time vector for the pulse\n",
        "      noisy_signal: synthetic noisy intensity values (discrete samples)\n",
        "      mu: true peak position\n",
        "      left_thresh: theoretical left 10% threshold position\n",
        "      right_thresh: theoretical right 10% threshold position\n",
        "    \"\"\"\n",
        "    x = np.linspace(0, T, window_length)\n",
        "    mu = np.random.uniform(T * 0.2, T * 0.8)\n",
        "    A = np.random.uniform(0.8, 1.2)\n",
        "    sigma_left = np.random.uniform(0.01, 0.05)\n",
        "    sigma_right = np.random.uniform(0.01, 0.05)\n",
        "    clean_signal = np.where(x < mu,\n",
        "                            A * np.exp(-((x - mu)**2) / (2 * sigma_left**2)),\n",
        "                            A * np.exp(-((x - mu)**2) / (2 * sigma_right**2)))\n",
        "    left_thresh = mu - np.sqrt(2 * sigma_left**2 * (-np.log(0.1)))\n",
        "    right_thresh = mu + np.sqrt(2 * sigma_right**2 * (-np.log(0.1)))\n",
        "    noisy_signal = clean_signal + np.random.normal(0, noise_std, size=window_length)\n",
        "    return x, noisy_signal, mu, left_thresh, right_thresh\n",
        "\n",
        "# --- New function to generate and save the dataset ---\n",
        "def generate_and_save_data(filename, n_samples, window_length=200, T=1, noise_std_range=[0.1, 0.5]):\n",
        "    \"\"\"\n",
        "    Generates n_samples synthetic pulses using the generate_pulse function and saves them to an HDF5 file.\n",
        "\n",
        "    Parameters:\n",
        "      filename: Name of the file to save the dataset.\n",
        "      n_samples: Number of pulses to generate.\n",
        "      window_length: Number of discrete time points for each pulse.\n",
        "      T: Total time duration for pulses.\n",
        "      noise_std_range: List containing the minimum and maximum noise standard deviation.\n",
        "    \"\"\"\n",
        "    xs = []      # Store time vectors\n",
        "    signals = [] # Store noisy pulse signals\n",
        "    mus = []     # Store true peak positions\n",
        "    lefts = []   # Store left 10% threshold positions\n",
        "    rights = []  # Store right 10% threshold positions\n",
        "\n",
        "    for i in range(n_samples):\n",
        "        noise_val = np.random.uniform(noise_std_range[0], noise_std_range[1])\n",
        "        x, signal, mu, left_thresh, right_thresh = generate_pulse(window_length, T, noise_val)\n",
        "        xs.append(x)\n",
        "        signals.append(signal)\n",
        "        mus.append(mu)\n",
        "        lefts.append(left_thresh)\n",
        "        rights.append(right_thresh)\n",
        "\n",
        "    # Convert lists to numpy arrays for storage\n",
        "    mus = np.array(mus)\n",
        "    lefts = np.array(lefts)\n",
        "    rights = np.array(rights)\n",
        "\n",
        "    # Save the dataset in HDF5 format\n",
        "    with h5py.File(filename, \"w\") as hf:\n",
        "        pulse_grp = hf.create_group(\"pulses\")\n",
        "        time_grp = hf.create_group(\"times\")\n",
        "\n",
        "        for i, (x, signal) in enumerate(zip(xs, signals)):\n",
        "            pulse_grp.create_dataset(f\"pulse_{i}\", data=signal)\n",
        "            time_grp.create_dataset(f\"time_{i}\", data=x)\n",
        "\n",
        "        hf.create_dataset(\"mus\", data=mus)\n",
        "        hf.create_dataset(\"lefts\", data=lefts)\n",
        "        hf.create_dataset(\"rights\", data=rights)\n",
        "\n",
        "    print(f\"Dataset saved to {filename}\")\n",
        "\n",
        "# Generate training dataset (50,000 samples)\n",
        "generate_and_save_data(\"synthetic_pulses_data.h5\", 50000)\n",
        "\n",
        "# Generate testing dataset (5,000 samples)\n",
        "generate_and_save_data(\"synthetic_pulses_data_test.h5\", 5000)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        },
        "id": "LMFEEjkWN_Bf",
        "outputId": "55ed074e-0bb2-4960-ba08-e4b6111567da"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Pulse index (n): 10010\n",
            "Peak (μ): 0.7458824634273584\n",
            "Left 10% threshold: 0.654567708896075\n",
            "Right 10% threshold: 0.8022667505686524\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import h5py\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Open the file for reading\n",
        "with h5py.File(\"synthetic_pulses_data.h5\", \"r\") as hf:\n",
        "    pulse_grp = hf[\"pulses\"]\n",
        "    time_grp = hf[\"times\"]\n",
        "    mus = hf[\"mus\"][:]    # Load as numpy array\n",
        "    lefts = hf[\"lefts\"][:]\n",
        "    rights = hf[\"rights\"][:]\n",
        "\n",
        "    # Example: Load the nth pulse (e.g., n = 1000)\n",
        "    n = 10010\n",
        "    nth_signal = pulse_grp[f\"pulse_{n}\"][:]  # Convert dataset to numpy array\n",
        "    nth_x = time_grp[f\"time_{n}\"][:]\n",
        "    nth_peak = mus[n]\n",
        "    nth_left = lefts[n]\n",
        "    nth_right = rights[n]\n",
        "\n",
        "print(\"Pulse index (n):\", n)\n",
        "# print(\"Noisy signal for pulse n:\", nth_signal)\n",
        "print(\"Peak (μ):\", nth_peak)\n",
        "print(\"Left 10% threshold:\", nth_left)\n",
        "print(\"Right 10% threshold:\", nth_right)\n",
        "\n",
        "# Plot the nth pulse with its thresholds\n",
        "plt.figure(figsize=(8, 4))\n",
        "plt.plot(nth_x, nth_signal, label=\"Noisy Pulse (Line)\", color='blue', alpha=0.7)\n",
        "plt.scatter(nth_x, nth_signal, color='blue', s=15, label=\"Noisy Pulse (Points)\")\n",
        "plt.axvline(nth_peak, color='red', linestyle='--', label=\"Peak (μ)\")\n",
        "plt.axvline(nth_left, color='green', linestyle='--', label=\"Left 10%\")\n",
        "plt.axvline(nth_right, color='orange', linestyle='--', label=\"Right 10%\")\n",
        "plt.xlabel(\"Time\")\n",
        "plt.ylabel(\"Intensity\")\n",
        "plt.title(f\"Pulse number {n}\")\n",
        "plt.legend()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s4mAo46jbWAh",
        "outputId": "b7ea3da1-3241-46ff-eb72-d4b6d97770f1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "device(type='cuda')"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import torch\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "device"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PsnDtnAEaCR_"
      },
      "source": [
        "# MAKE AND TRAIN MODEL\n",
        "# Architecture:\n",
        "## Overview\n",
        "\n",
        "- **Input**: A batch of 1D sequences with shape `(batch, sequence_length)`.\n",
        "- **CNN Component**: Extracts local features using a convolution layer, followed by a non-linear activation (ReLU) and down-sampling via max pooling.\n",
        "- **LSTM Component**: Processes the extracted features as a sequence, capturing temporal dependencies.\n",
        "- **Fully Connected Layer**: Maps the LSTM’s hidden state to a 3-dimensional output for regression.\n",
        "\n",
        "---\n",
        "\n",
        "## CNN Component\n",
        "\n",
        "### 1D Convolution Layer\n",
        "\n",
        "- **Layer Definition**:  \n",
        "  ```python\n",
        "  self.conv1 = nn.Conv1d(in_channels=1, out_channels=cnn_channels, kernel_size=5, padding=2)\n",
        "  ```\n",
        "- **Input Transformation**:  \n",
        "  The input tensor `x` originally has shape `(batch, sequence_length)`. We first add a channel dimension to obtain `(batch, 1, sequence_length)`.\n",
        "\n",
        "- **Mathematical Operation**:  \n",
        "  For an input signal \\( x \\) and a filter \\( w \\) of size \\( k = 5 \\) with padding \\( p = 2 \\), the convolution at position \\( i \\) is given by:\n",
        "  $$\n",
        "  y[i] = \\sum_{j=-2}^{2} w[j] \\cdot x[i+j]\n",
        "  $$\n",
        "  Here, the padding ensures that the output has the same length as the input.\n",
        "\n",
        "### ReLU Activation\n",
        "\n",
        "- **Operation**:  \n",
        "  The ReLU (Rectified Linear Unit) activation function is applied element-wise:\n",
        "  $$\n",
        "  \\text{ReLU}(z) = \\max(0, z)\n",
        "  $$\n",
        "  This introduces non-linearity into the model.\n",
        "\n",
        "### Max Pooling\n",
        "\n",
        "- **Layer Definition**:  \n",
        "  ```python\n",
        "  self.pool = nn.MaxPool1d(kernel_size=2)\n",
        "  ```\n",
        "- **Effect on Dimensions**:  \n",
        "  After pooling, the output sequence length is reduced by approximately a factor of 2 (assuming the sequence length is even), i.e., from \\( L \\) to $ \\frac{L}{2} $.\n",
        "\n",
        "---\n",
        "\n",
        "## LSTM Component\n",
        "\n",
        "### Input Preparation\n",
        "\n",
        "- **Permutation**:  \n",
        "  The CNN output has shape `(batch, cnn_channels, sequence_length/2)`. It is permuted to `(batch, sequence_length/2, cnn_channels)` to match the expected input shape for the LSTM, where:\n",
        "  - **Sequence length**: $ T = \\frac{L}{2} $\n",
        "  - **Feature size**: F = `cnn_channels`\n",
        "\n",
        "### LSTM Layer\n",
        "\n",
        "- **Layer Definition**:  \n",
        "  ```python\n",
        "  self.lstm = nn.LSTM(input_size=cnn_channels, hidden_size=lstm_hidden_size, num_layers=lstm_layers, batch_first=True)\n",
        "  ```\n",
        "- **Operation**:  \n",
        "  The LSTM processes the sequence data and captures temporal dependencies. For each time step \\( t \\), the LSTM cell performs the following operations:\n",
        "\n",
        "  1. **Forget Gate**:\n",
        "     $$\n",
        "     f_t = \\sigma(W_f x_t + U_f h_{t-1} + b_f)\n",
        "     $$\n",
        "  2. **Input Gate**:\n",
        "     $$\n",
        "     i_t = \\sigma(W_i x_t + U_i h_{t-1} + b_i)\n",
        "     $$\n",
        "  3. **Candidate Cell State**:\n",
        "     $$\n",
        "     c_t = \\tanh(W_c x_t + U_c h_{t-1} + b_c)\n",
        "     $$\n",
        "  4. **Cell State Update**:\n",
        "     $$\n",
        "     c_t = f_t \\odot c_{t-1} + i_t \\odot c_t\n",
        "     $$\n",
        "  5. **Output Gate and Hidden State**:\n",
        "     $$\n",
        "     o_t = \\sigma(W_o x_t + U_o h_{t-1} + b_o)\n",
        "     $$\n",
        "     $$\n",
        "     h_t = o_t \\odot \\tanh(c_t)\n",
        "     $$\n",
        "  \n",
        "  Here, $ \\sigma $ is the sigmoid function, and $ \\odot $ denotes element-wise multiplication. The LSTM outputs:\n",
        "  - **Hidden states** for all time steps.\n",
        "  - **Final hidden state** $ h_n $ (taken from the last LSTM layer) is used for prediction.\n",
        "\n",
        "---\n",
        "\n",
        "## Fully Connected Layer\n",
        "\n",
        "- **Layer Definition**:  \n",
        "  ```python\n",
        "  self.fc = nn.Linear(lstm_hidden_size, 3)\n",
        "  ```\n",
        "- **Operation**:  \n",
        "  The last hidden state $h_n[-1] $ from the LSTM is passed through the fully connected layer:\n",
        "  $$\n",
        "  y = W_{fc} h + b_{fc}\n",
        "  $$\n",
        "  This linear transformation maps the LSTM output to a 3-dimensional output vector.\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "## Training Setup\n",
        "\n",
        "- **Loss Function**:  \n",
        "  Mean Squared Error (MSE) loss is used for regression:\n",
        "  $$\n",
        "  \\text{MSE} = \\frac{1}{N} \\sum_{i=1}^{N} (y_i - \\hat{y}_i)^2\n",
        "  $$\n",
        "- **Optimizer**:  \n",
        "  The Adam optimizer is applied with a learning rate of 0.001:\n",
        "  ```python\n",
        "  optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
        "  ```\n",
        "\n",
        "---\n",
        "\n",
        "## REFERENCE TIME:\n",
        "- training time = `24m 18s` on `RTX3050 6GB laptop`\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wxLdXj5mOT25",
        "outputId": "21e278da-95f7-4389-9a1b-a0f5c385a68c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Starting epoch 1\n",
            "Epoch 1/100 Train Loss: 0.029682847\n",
            "Epoch 1/100 Test Loss: 0.001227332\n",
            "Starting epoch 2\n",
            "Epoch 2/100 Train Loss: 0.001227523\n",
            "Epoch 2/100 Test Loss: 0.000770838\n",
            "Starting epoch 3\n",
            "Epoch 3/100 Train Loss: 0.000626989\n",
            "Epoch 3/100 Test Loss: 0.000612079\n",
            "Starting epoch 4\n",
            "Epoch 4/100 Train Loss: 0.001092168\n",
            "Epoch 4/100 Test Loss: 0.000973888\n",
            "Starting epoch 5\n",
            "Epoch 5/100 Train Loss: 0.000724640\n",
            "Epoch 5/100 Test Loss: 0.000890696\n",
            "Starting epoch 6\n",
            "Epoch 6/100 Train Loss: 0.000532634\n",
            "Epoch 6/100 Test Loss: 0.000480608\n",
            "Starting epoch 7\n",
            "Epoch 7/100 Train Loss: 0.000518919\n",
            "Epoch 7/100 Test Loss: 0.000343433\n",
            "Starting epoch 8\n",
            "Epoch 8/100 Train Loss: 0.000346682\n",
            "Epoch 8/100 Test Loss: 0.000211814\n",
            "Starting epoch 9\n",
            "Epoch 9/100 Train Loss: 0.000274600\n",
            "Epoch 9/100 Test Loss: 0.000260043\n",
            "Starting epoch 10\n",
            "Epoch 10/100 Train Loss: 0.000238073\n",
            "Epoch 10/100 Test Loss: 0.000274929\n",
            "Starting epoch 11\n",
            "Epoch 11/100 Train Loss: 0.000251168\n",
            "Epoch 11/100 Test Loss: 0.000292592\n",
            "Starting epoch 12\n",
            "Epoch 12/100 Train Loss: 0.000191451\n",
            "Epoch 12/100 Test Loss: 0.000270360\n",
            "Starting epoch 13\n",
            "Epoch 13/100 Train Loss: 0.000195192\n",
            "Epoch 13/100 Test Loss: 0.000189403\n",
            "Starting epoch 14\n",
            "Epoch 14/100 Train Loss: 0.000196613\n",
            "Epoch 14/100 Test Loss: 0.000153912\n",
            "Starting epoch 15\n",
            "Epoch 15/100 Train Loss: 0.000190569\n",
            "Epoch 15/100 Test Loss: 0.000244839\n",
            "Starting epoch 16\n",
            "Epoch 16/100 Train Loss: 0.000188055\n",
            "Epoch 16/100 Test Loss: 0.000168312\n",
            "Starting epoch 17\n",
            "Epoch 17/100 Train Loss: 0.000185131\n",
            "Epoch 17/100 Test Loss: 0.000186096\n",
            "Starting epoch 18\n",
            "Epoch 18/100 Train Loss: 0.000208689\n",
            "Epoch 18/100 Test Loss: 0.000190901\n",
            "Starting epoch 19\n",
            "Epoch 19/100 Train Loss: 0.000171738\n",
            "Epoch 19/100 Test Loss: 0.000160824\n",
            "Starting epoch 20\n",
            "Epoch 20/100 Train Loss: 0.000168138\n",
            "Epoch 20/100 Test Loss: 0.000159897\n",
            "Starting epoch 21\n",
            "Epoch 21/100 Train Loss: 0.000179767\n",
            "Epoch 21/100 Test Loss: 0.000169581\n",
            "Starting epoch 22\n",
            "Epoch 22/100 Train Loss: 0.000153919\n",
            "Epoch 22/100 Test Loss: 0.000155552\n",
            "Starting epoch 23\n",
            "Epoch 23/100 Train Loss: 0.000139975\n",
            "Epoch 23/100 Test Loss: 0.000126421\n",
            "Starting epoch 24\n",
            "Epoch 24/100 Train Loss: 0.000131942\n",
            "Epoch 24/100 Test Loss: 0.000195342\n",
            "Starting epoch 25\n",
            "Epoch 25/100 Train Loss: 0.000130733\n",
            "Epoch 25/100 Test Loss: 0.000237950\n",
            "Starting epoch 26\n",
            "Epoch 26/100 Train Loss: 0.000130730\n",
            "Epoch 26/100 Test Loss: 0.000168897\n",
            "Starting epoch 27\n",
            "Epoch 27/100 Train Loss: 0.000123671\n",
            "Epoch 27/100 Test Loss: 0.000112072\n",
            "Starting epoch 28\n",
            "Epoch 28/100 Train Loss: 0.000120701\n",
            "Epoch 28/100 Test Loss: 0.000131934\n",
            "Starting epoch 29\n",
            "Epoch 29/100 Train Loss: 0.000125131\n",
            "Epoch 29/100 Test Loss: 0.000109867\n",
            "Starting epoch 30\n",
            "Epoch 30/100 Train Loss: 0.000116443\n",
            "Epoch 30/100 Test Loss: 0.000117057\n",
            "Starting epoch 31\n",
            "Epoch 31/100 Train Loss: 0.000123669\n",
            "Epoch 31/100 Test Loss: 0.000197826\n",
            "Starting epoch 32\n",
            "Epoch 32/100 Train Loss: 0.000129155\n",
            "Epoch 32/100 Test Loss: 0.000157149\n",
            "Starting epoch 33\n",
            "Epoch 33/100 Train Loss: 0.000122993\n",
            "Epoch 33/100 Test Loss: 0.000138528\n",
            "Starting epoch 34\n",
            "Epoch 34/100 Train Loss: 0.000136764\n",
            "Epoch 34/100 Test Loss: 0.000212278\n",
            "Starting epoch 35\n",
            "Epoch 35/100 Train Loss: 0.000121237\n",
            "Epoch 35/100 Test Loss: 0.000138444\n",
            "Starting epoch 36\n",
            "Epoch 36/100 Train Loss: 0.000113317\n",
            "Epoch 36/100 Test Loss: 0.000120758\n",
            "Starting epoch 37\n",
            "Epoch 37/100 Train Loss: 0.000134946\n",
            "Epoch 37/100 Test Loss: 0.000148952\n",
            "Starting epoch 38\n",
            "Epoch 38/100 Train Loss: 0.000112742\n",
            "Epoch 38/100 Test Loss: 0.000159996\n",
            "Starting epoch 39\n",
            "Epoch 39/100 Train Loss: 0.000109651\n",
            "Epoch 39/100 Test Loss: 0.000114667\n",
            "Starting epoch 40\n",
            "Epoch 40/100 Train Loss: 0.000118546\n",
            "Epoch 40/100 Test Loss: 0.000163693\n",
            "Starting epoch 41\n",
            "Epoch 41/100 Train Loss: 0.000125150\n",
            "Epoch 41/100 Test Loss: 0.000168482\n",
            "Starting epoch 42\n",
            "Epoch 42/100 Train Loss: 0.000124396\n",
            "Epoch 42/100 Test Loss: 0.000107010\n",
            "Starting epoch 43\n",
            "Epoch 43/100 Train Loss: 0.000109609\n",
            "Epoch 43/100 Test Loss: 0.000139655\n",
            "Starting epoch 44\n",
            "Epoch 44/100 Train Loss: 0.000115829\n",
            "Epoch 44/100 Test Loss: 0.000148732\n",
            "Starting epoch 45\n",
            "Epoch 45/100 Train Loss: 0.000108392\n",
            "Epoch 45/100 Test Loss: 0.000122998\n",
            "Starting epoch 46\n",
            "Epoch 46/100 Train Loss: 0.000108654\n",
            "Epoch 46/100 Test Loss: 0.000109543\n",
            "Starting epoch 47\n",
            "Epoch 47/100 Train Loss: 0.000111466\n",
            "Epoch 47/100 Test Loss: 0.000118676\n",
            "Starting epoch 48\n",
            "Epoch 48/100 Train Loss: 0.000107514\n",
            "Epoch 48/100 Test Loss: 0.000132431\n",
            "Starting epoch 49\n",
            "Epoch 49/100 Train Loss: 0.000132061\n",
            "Epoch 49/100 Test Loss: 0.000136781\n",
            "Starting epoch 50\n",
            "Epoch 50/100 Train Loss: 0.000107361\n",
            "Epoch 50/100 Test Loss: 0.000186607\n",
            "Starting epoch 51\n",
            "Epoch 51/100 Train Loss: 0.000107049\n",
            "Epoch 51/100 Test Loss: 0.000146175\n",
            "Starting epoch 52\n",
            "Epoch 52/100 Train Loss: 0.000108122\n",
            "Epoch 52/100 Test Loss: 0.000109759\n",
            "Starting epoch 53\n",
            "Epoch 53/100 Train Loss: 0.000108206\n",
            "Epoch 53/100 Test Loss: 0.000113214\n",
            "Starting epoch 54\n",
            "Epoch 54/100 Train Loss: 0.000106427\n",
            "Epoch 54/100 Test Loss: 0.000170124\n",
            "Starting epoch 55\n",
            "Epoch 55/100 Train Loss: 0.000143551\n",
            "Epoch 55/100 Test Loss: 0.000157748\n",
            "Starting epoch 56\n",
            "Epoch 56/100 Train Loss: 0.000115754\n",
            "Epoch 56/100 Test Loss: 0.000150456\n",
            "Starting epoch 57\n",
            "Epoch 57/100 Train Loss: 0.000111199\n",
            "Epoch 57/100 Test Loss: 0.000146636\n",
            "Starting epoch 58\n",
            "Epoch 58/100 Train Loss: 0.000106412\n",
            "Epoch 58/100 Test Loss: 0.000156057\n",
            "Starting epoch 59\n",
            "Epoch 59/100 Train Loss: 0.000112638\n",
            "Epoch 59/100 Test Loss: 0.000177300\n",
            "Starting epoch 60\n",
            "Epoch 60/100 Train Loss: 0.000114868\n",
            "Epoch 60/100 Test Loss: 0.000163531\n",
            "Starting epoch 61\n",
            "Epoch 61/100 Train Loss: 0.000106959\n",
            "Epoch 61/100 Test Loss: 0.000139443\n",
            "Starting epoch 62\n",
            "Epoch 62/100 Train Loss: 0.000106155\n",
            "Epoch 62/100 Test Loss: 0.000129563\n",
            "Starting epoch 63\n",
            "Epoch 63/100 Train Loss: 0.000115991\n",
            "Epoch 63/100 Test Loss: 0.000138841\n",
            "Starting epoch 64\n",
            "Epoch 64/100 Train Loss: 0.000107517\n",
            "Epoch 64/100 Test Loss: 0.000131209\n",
            "Starting epoch 65\n",
            "Epoch 65/100 Train Loss: 0.000104705\n",
            "Epoch 65/100 Test Loss: 0.000118441\n",
            "Starting epoch 66\n",
            "Epoch 66/100 Train Loss: 0.000106013\n",
            "Epoch 66/100 Test Loss: 0.000130974\n",
            "Starting epoch 67\n",
            "Epoch 67/100 Train Loss: 0.000105235\n",
            "Epoch 67/100 Test Loss: 0.000137253\n",
            "Starting epoch 68\n",
            "Epoch 68/100 Train Loss: 0.000107993\n",
            "Epoch 68/100 Test Loss: 0.000097970\n",
            "Starting epoch 69\n",
            "Epoch 69/100 Train Loss: 0.000104387\n",
            "Epoch 69/100 Test Loss: 0.000130209\n",
            "Starting epoch 70\n",
            "Epoch 70/100 Train Loss: 0.000105732\n",
            "Epoch 70/100 Test Loss: 0.000106107\n",
            "Starting epoch 71\n",
            "Epoch 71/100 Train Loss: 0.000104088\n",
            "Epoch 71/100 Test Loss: 0.000105227\n",
            "Starting epoch 72\n",
            "Epoch 72/100 Train Loss: 0.000103897\n",
            "Epoch 72/100 Test Loss: 0.000103894\n",
            "Starting epoch 73\n",
            "Epoch 73/100 Train Loss: 0.000104190\n",
            "Epoch 73/100 Test Loss: 0.000098232\n",
            "Starting epoch 74\n",
            "Epoch 74/100 Train Loss: 0.000115722\n",
            "Epoch 74/100 Test Loss: 0.000108532\n",
            "Starting epoch 75\n",
            "Epoch 75/100 Train Loss: 0.000107660\n",
            "Epoch 75/100 Test Loss: 0.000126059\n",
            "Starting epoch 76\n",
            "Epoch 76/100 Train Loss: 0.000104644\n",
            "Epoch 76/100 Test Loss: 0.000132481\n",
            "Starting epoch 77\n",
            "Epoch 77/100 Train Loss: 0.000103055\n",
            "Epoch 77/100 Test Loss: 0.000135365\n",
            "Starting epoch 78\n",
            "Epoch 78/100 Train Loss: 0.000103136\n",
            "Epoch 78/100 Test Loss: 0.000130939\n",
            "Starting epoch 79\n",
            "Epoch 79/100 Train Loss: 0.000103502\n",
            "Epoch 79/100 Test Loss: 0.000162923\n",
            "Starting epoch 80\n",
            "Epoch 80/100 Train Loss: 0.000109153\n",
            "Epoch 80/100 Test Loss: 0.000127586\n",
            "Starting epoch 81\n",
            "Epoch 81/100 Train Loss: 0.000117516\n",
            "Epoch 81/100 Test Loss: 0.000141762\n",
            "Starting epoch 82\n",
            "Epoch 82/100 Train Loss: 0.000102707\n",
            "Epoch 82/100 Test Loss: 0.000152215\n",
            "Starting epoch 83\n",
            "Epoch 83/100 Train Loss: 0.000101065\n",
            "Epoch 83/100 Test Loss: 0.000152776\n",
            "Starting epoch 84\n",
            "Epoch 84/100 Train Loss: 0.000104908\n",
            "Epoch 84/100 Test Loss: 0.000173936\n",
            "Starting epoch 85\n",
            "Epoch 85/100 Train Loss: 0.000109144\n",
            "Epoch 85/100 Test Loss: 0.000105824\n",
            "Starting epoch 86\n",
            "Epoch 86/100 Train Loss: 0.000102257\n",
            "Epoch 86/100 Test Loss: 0.000110908\n",
            "Starting epoch 87\n",
            "Epoch 87/100 Train Loss: 0.000102155\n",
            "Epoch 87/100 Test Loss: 0.000114763\n",
            "Starting epoch 88\n",
            "Epoch 88/100 Train Loss: 0.000114440\n",
            "Epoch 88/100 Test Loss: 0.000131471\n",
            "Starting epoch 89\n",
            "Epoch 89/100 Train Loss: 0.000104703\n",
            "Epoch 89/100 Test Loss: 0.000109799\n",
            "Starting epoch 90\n",
            "Epoch 90/100 Train Loss: 0.000102012\n",
            "Epoch 90/100 Test Loss: 0.000123744\n",
            "Starting epoch 91\n",
            "Epoch 91/100 Train Loss: 0.000101392\n",
            "Epoch 91/100 Test Loss: 0.000122576\n",
            "Starting epoch 92\n",
            "Epoch 92/100 Train Loss: 0.000101849\n",
            "Epoch 92/100 Test Loss: 0.000115593\n",
            "Starting epoch 93\n",
            "Epoch 93/100 Train Loss: 0.000100880\n",
            "Epoch 93/100 Test Loss: 0.000157653\n",
            "Starting epoch 94\n",
            "Epoch 94/100 Train Loss: 0.000102430\n",
            "Epoch 94/100 Test Loss: 0.000111086\n",
            "Starting epoch 95\n",
            "Epoch 95/100 Train Loss: 0.000101599\n",
            "Epoch 95/100 Test Loss: 0.000122228\n",
            "Starting epoch 96\n",
            "Epoch 96/100 Train Loss: 0.000100923\n",
            "Epoch 96/100 Test Loss: 0.000096381\n",
            "Starting epoch 97\n",
            "Epoch 97/100 Train Loss: 0.000100431\n",
            "Epoch 97/100 Test Loss: 0.000134801\n",
            "Starting epoch 98\n",
            "Epoch 98/100 Train Loss: 0.000100555\n",
            "Epoch 98/100 Test Loss: 0.000116502\n",
            "Starting epoch 99\n",
            "Epoch 99/100 Train Loss: 0.000100135\n",
            "Epoch 99/100 Test Loss: 0.000117025\n",
            "Starting epoch 100\n",
            "Epoch 100/100 Train Loss: 0.000101404\n",
            "Epoch 100/100 Test Loss: 0.000100457\n",
            "Training complete. Model saved as 'pulse_detection_model_final_2.pth'.\n",
            "Training history and predictions saved to 'training_history.json'.\n"
          ]
        }
      ],
      "source": [
        "import h5py\n",
        "import numpy as np\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from torch.utils.data import DataLoader, Dataset\n",
        "import json  # For saving the history as JSON\n",
        "\n",
        "# ---------------------------\n",
        "# Define a custom Dataset for HDF5 data\n",
        "# ---------------------------\n",
        "class PulseDataset(Dataset):\n",
        "    def __init__(self, h5_filename):\n",
        "        # Open the file in read mode\n",
        "        self.h5_file = h5py.File(h5_filename, 'r')\n",
        "        self.pulses_group = self.h5_file['pulses']\n",
        "        # Load target arrays (assumed to be homogeneous)\n",
        "        self.mus = self.h5_file['mus'][:]    # shape: (n_samples,)\n",
        "        self.lefts = self.h5_file['lefts'][:]\n",
        "        self.rights = self.h5_file['rights'][:]\n",
        "        self.n_samples = self.mus.shape[0]\n",
        "\n",
        "    def __len__(self):\n",
        "        return self.n_samples\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        # Each pulse is stored under the key \"pulse_{idx}\"\n",
        "        pulse = self.pulses_group[f\"pulse_{idx}\"][:]\n",
        "        # Convert the pulse to a torch tensor (1D tensor of fixed length)\n",
        "        pulse_tensor = torch.tensor(pulse, dtype=torch.float32)\n",
        "        # Build the target tensor: [mu, left_threshold, right_threshold]\n",
        "        target = torch.tensor([self.mus[idx], self.lefts[idx], self.rights[idx]], dtype=torch.float32)\n",
        "        return pulse_tensor, target\n",
        "\n",
        "\n",
        "# Create datasets and DataLoaders for training and testing data\n",
        "train_dataset = PulseDataset(\"synthetic_pulses_data.h5\")\n",
        "test_dataset  = PulseDataset(\"synthetic_pulses_data_test.h5\")\n",
        "\n",
        "# Using default collate since pulses are of fixed length\n",
        "train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)\n",
        "test_loader  = DataLoader(test_dataset, batch_size=32, shuffle=False)\n",
        "\n",
        "# ---------------------------\n",
        "# Define the Hybrid CNN-LSTM Model\n",
        "# ---------------------------\n",
        "class HybridCNNLSTM(nn.Module):\n",
        "    def __init__(self, cnn_channels=32, lstm_hidden_size=64, lstm_layers=1):\n",
        "        super(HybridCNNLSTM, self).__init__()\n",
        "        # 1D convolution: expects input of shape (batch, 1, sequence_length)\n",
        "        self.conv1 = nn.Conv1d(in_channels=1, out_channels=cnn_channels, kernel_size=5, padding=2)\n",
        "        self.relu = nn.ReLU()\n",
        "        self.pool = nn.MaxPool1d(kernel_size=2)\n",
        "        # LSTM: input size = cnn_channels (features from the CNN layer)\n",
        "        self.lstm = nn.LSTM(input_size=cnn_channels, hidden_size=lstm_hidden_size,\n",
        "                            num_layers=lstm_layers, batch_first=True)\n",
        "        # Fully connected layer mapping from LSTM hidden state to 3 regression outputs\n",
        "        self.fc = nn.Linear(lstm_hidden_size, 3)\n",
        "\n",
        "    def forward(self, x):\n",
        "        # x shape: (batch, sequence_length)\n",
        "        # Add a channel dimension: becomes (batch, 1, sequence_length)\n",
        "        x = x.unsqueeze(1)\n",
        "        # Apply convolution, activation, and pooling:\n",
        "        x = self.conv1(x)      # -> (batch, cnn_channels, sequence_length)\n",
        "        x = self.relu(x)\n",
        "        x = self.pool(x)       # -> (batch, cnn_channels, sequence_length/2)\n",
        "        # Permute to (batch, sequence_length/2, cnn_channels) for LSTM input\n",
        "        x = x.permute(0, 2, 1)\n",
        "        # LSTM processing\n",
        "        out, (h_n, _) = self.lstm(x)\n",
        "        # Use the last layer's hidden state for prediction\n",
        "        x = self.fc(h_n[-1])\n",
        "        return x\n",
        "\n",
        "# Set device\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "# Instantiate model, loss function, and optimizer\n",
        "model = HybridCNNLSTM().to(device)\n",
        "criterion = nn.MSELoss()\n",
        "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
        "\n",
        "# ---------------------------\n",
        "# Setup Loss History and Prediction Storage for Specific Pulses\n",
        "# ---------------------------\n",
        "train_loss_history = []    # List to record average train loss per epoch\n",
        "test_loss_history  = []    # List to record average test loss per epoch\n",
        "\n",
        "# For training set: record pulse 1000 and 2000 (their predictions and actual targets)\n",
        "predictions_train_1000 = []\n",
        "actuals_train_1000 = []\n",
        "predictions_train_2000 = []\n",
        "actuals_train_2000 = []\n",
        "\n",
        "# For test set: record pulse 1000 and 2000\n",
        "predictions_test_1000 = []\n",
        "actuals_test_1000 = []\n",
        "predictions_test_2000 = []\n",
        "actuals_test_2000 = []\n",
        "\n",
        "num_epochs = 100  # Total number of training epochs\n",
        "\n",
        "# ---------------------------\n",
        "# Training Loop with Test Evaluation\n",
        "# ---------------------------\n",
        "for epoch in range(num_epochs):\n",
        "    print(f\"Starting epoch {epoch+1}\", flush=True)\n",
        "\n",
        "    # --- Training Phase ---\n",
        "    model.train()  # Ensure the model is in training mode\n",
        "    epoch_train_loss = 0.0\n",
        "    for batch_signals, batch_targets in train_loader:\n",
        "        batch_signals, batch_targets = batch_signals.to(device), batch_targets.to(device)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "        outputs = model(batch_signals)\n",
        "        loss = criterion(outputs, batch_targets)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        epoch_train_loss += loss.item()\n",
        "\n",
        "    avg_train_loss = epoch_train_loss / len(train_loader)\n",
        "    train_loss_history.append(avg_train_loss)\n",
        "    print(f\"Epoch {epoch+1}/{num_epochs} Train Loss: {avg_train_loss:.9f}\", flush=True)\n",
        "\n",
        "    # --- Testing Phase (evaluation on test dataset, without training) ---\n",
        "    model.eval()  # Switch to evaluation mode\n",
        "    epoch_test_loss = 0.0\n",
        "    with torch.inference_mode():\n",
        "        for batch_signals, batch_targets in test_loader:\n",
        "            batch_signals, batch_targets = batch_signals.to(device), batch_targets.to(device)\n",
        "            outputs = model(batch_signals)\n",
        "            loss = criterion(outputs, batch_targets)\n",
        "            epoch_test_loss += loss.item()\n",
        "    avg_test_loss = epoch_test_loss / len(test_loader)\n",
        "    test_loss_history.append(avg_test_loss)\n",
        "    print(f\"Epoch {epoch+1}/{num_epochs} Test Loss: {avg_test_loss:.9f}\", flush=True)\n",
        "\n",
        "    # --- Evaluate and record predictions for specific pulses from both train and test sets ---\n",
        "    with torch.inference_mode():\n",
        "        # Check if the indices exist in the training set\n",
        "        if len(train_dataset) > 2000:\n",
        "            # For training pulse 1000:\n",
        "            pulse_1000, target_1000 = train_dataset[1000]\n",
        "            pulse_1000 = pulse_1000.unsqueeze(0).to(device)  # Add batch dimension\n",
        "            pred_1000 = model(pulse_1000)\n",
        "            predictions_train_1000.append(pred_1000.cpu().numpy())\n",
        "            actuals_train_1000.append(target_1000.cpu().numpy())\n",
        "\n",
        "            # For training pulse 2000:\n",
        "            pulse_2000, target_2000 = train_dataset[2000]\n",
        "            pulse_2000 = pulse_2000.unsqueeze(0).to(device)\n",
        "            pred_2000 = model(pulse_2000)\n",
        "            predictions_train_2000.append(pred_2000.cpu().numpy())\n",
        "            actuals_train_2000.append(target_2000.cpu().numpy())\n",
        "\n",
        "        # Check if the indices exist in the test set\n",
        "        if len(test_dataset) > 2000:\n",
        "            # For test pulse 1000:\n",
        "            pulse_1000_test, target_1000_test = test_dataset[1000]\n",
        "            pulse_1000_test = pulse_1000_test.unsqueeze(0).to(device)\n",
        "            pred_1000_test = model(pulse_1000_test)\n",
        "            predictions_test_1000.append(pred_1000_test.cpu().numpy())\n",
        "            actuals_test_1000.append(target_1000_test.cpu().numpy())\n",
        "\n",
        "            # For test pulse 2000:\n",
        "            pulse_2000_test, target_2000_test = test_dataset[2000]\n",
        "            pulse_2000_test = pulse_2000_test.unsqueeze(0).to(device)\n",
        "            pred_2000_test = model(pulse_2000_test)\n",
        "            predictions_test_2000.append(pred_2000_test.cpu().numpy())\n",
        "            actuals_test_2000.append(target_2000_test.cpu().numpy())\n",
        "\n",
        "# Save the final model\n",
        "torch.save(model.state_dict(), \"pulse_detection_model_final_2.pth\")\n",
        "print(\"Training complete. Model saved as 'pulse_detection_model_final_2.pth'.\")\n",
        "\n",
        "# ---------------------------\n",
        "# Save Training History and Predictions to a JSON file\n",
        "# ---------------------------\n",
        "history_dict = {\n",
        "    \"train_loss_history\": train_loss_history,\n",
        "    \"test_loss_history\": test_loss_history,\n",
        "    \"predictions_train_1000\": [pred.tolist() for pred in predictions_train_1000],\n",
        "    \"actuals_train_1000\": [act.tolist() for act in actuals_train_1000],\n",
        "    \"predictions_train_2000\": [pred.tolist() for pred in predictions_train_2000],\n",
        "    \"actuals_train_2000\": [act.tolist() for act in actuals_train_2000],\n",
        "    \"predictions_test_1000\": [pred.tolist() for pred in predictions_test_1000],\n",
        "    \"actuals_test_1000\": [act.tolist() for act in actuals_test_1000],\n",
        "    \"predictions_test_2000\": [pred.tolist() for pred in predictions_test_2000],\n",
        "    \"actuals_test_2000\": [act.tolist() for act in actuals_test_2000],\n",
        "}\n",
        "\n",
        "with open(\"training_history.json\", \"w\") as f:\n",
        "    json.dump(history_dict, f, indent=4)\n",
        "\n",
        "print(\"Training history and predictions saved to 'training_history.json'.\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DTt7EKjwZHKR"
      },
      "source": [
        "# TRIAL WITH GPU\n",
        "### Reference values:\n",
        "\n",
        "- `619 µs ± 11.5 µs per loop` (mean ± std. dev. of 7 runs, 1000 loops each) ( `T4 colab` )\n",
        "- `3.08 ms ± 169 μs per loop` (mean ± std. dev. of 7 runs, 100 loops each) (`local RTX3050 6gb laptop`)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "- load model if not loaded with"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model loaded successfully.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\bijay\\AppData\\Local\\Temp\\ipykernel_23032\\2264321294.py:6: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
            "  model.load_state_dict(torch.load(\"hybrid_cnn_lstm_model_final_1.pth\"))\n"
          ]
        }
      ],
      "source": [
        "\n",
        "# To load the model later:\n",
        "# 1. Initialize the model instance with the same architecture\n",
        "model = HybridCNNLSTM()\n",
        "model = model.to(device)\n",
        "# 2. Load the saved state_dict into the model\n",
        "model.load_state_dict(torch.load(\"hybrid_cnn_lstm_model_final_1.pth\"))\n",
        "model.eval()  # Set the model to evaluation mode\n",
        "print(\"Model loaded successfully.\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        },
        "id": "0McOVgPfPw2c",
        "outputId": "79f92bf2-93fb-44bf-d0aa-61465c13c9cd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "3.08 ms ± 169 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
            "Original Targets:\n",
            "Peak (μ): 0.30222816977256917\n",
            "Left 10% threshold: 0.2539789520212513\n",
            "Right 10% threshold: 0.33101207766640073\n",
            "\n",
            "Model Prediction:\n",
            "Predicted Peak: 0.3077761\n",
            "Predicted Left 10%: 0.24402879\n",
            "Predicted Right 10%: 0.35137707\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import h5py\n",
        "import torch\n",
        "import matplotlib.pyplot as plt\n",
        "import timeit\n",
        "# Open the file for reading\n",
        "with h5py.File(\"synthetic_pulses_data_test.h5\", \"r\") as hf:\n",
        "    pulse_grp = hf[\"pulses\"]\n",
        "    time_grp = hf[\"times\"]\n",
        "    mus = hf[\"mus\"][:]    # Load as numpy array\n",
        "    lefts = hf[\"lefts\"][:]\n",
        "    rights = hf[\"rights\"][:]\n",
        "\n",
        "    # Example: Load the nth pulse (e.g., n = 1000)\n",
        "    n = 1010\n",
        "    nth_signal = pulse_grp[f\"pulse_{n}\"][:]  # Convert dataset to numpy array\n",
        "    nth_time = time_grp[f\"time_{n}\"][:]\n",
        "    nth_peak = mus[n]\n",
        "    nth_left = lefts[n]\n",
        "    nth_right = rights[n]\n",
        "\n",
        "\n",
        "# Convert the sample signal to a torch tensor and run through the model\n",
        "model.eval()\n",
        "with torch.no_grad():\n",
        "    sample_tensor = torch.tensor(nth_signal, dtype=torch.float32).unsqueeze(0)  # add batch dimension\n",
        "    %timeit model(sample_tensor.to(device)).squeeze().cpu().numpy()\n",
        "    pred = model(sample_tensor.to(device)).squeeze().cpu().numpy()\n",
        "\n",
        "# Print original and predicted values\n",
        "print(\"Original Targets:\")\n",
        "print(\"Peak (μ):\", nth_peak)\n",
        "print(\"Left 10% threshold:\", nth_left)\n",
        "print(\"Right 10% threshold:\", nth_right)\n",
        "print(\"\\nModel Prediction:\")\n",
        "print(\"Predicted Peak:\", pred[0])\n",
        "print(\"Predicted Left 10%:\", pred[1])\n",
        "print(\"Predicted Right 10%:\", pred[2])\n",
        "\n",
        "# Plotting the nth pulse with original thresholds\n",
        "plt.figure(figsize=(8, 4))\n",
        "plt.plot(nth_time, nth_signal, label=\"Noisy Pulse (Line)\", color='grey', alpha=0.7)\n",
        "plt.scatter(nth_time, nth_signal, color='grey', s=15, label=\"Noisy Pulse (Points)\")\n",
        "plt.axvline(nth_peak, color='red', linestyle='--', label=\"Original Peak (μ)\")\n",
        "plt.axvline(nth_left, color='red', linestyle='--', label=\"Original Left 10%\")\n",
        "plt.axvline(nth_right, color='red', linestyle='--', label=\"Original Right 10%\")\n",
        "plt.axvline(pred[0], color='green', linestyle='--', label=\"Predicted Peak (μ)\")\n",
        "plt.axvline(pred[1], color='green', linestyle='--', label=\"Predicted Left 10%\")\n",
        "plt.axvline(pred[2], color='green', linestyle='--', label=\"Predicted Right 10%\")\n",
        "\n",
        "plt.xlabel(\"Time\")\n",
        "plt.ylabel(\"Intensity\")\n",
        "plt.title(f\"Pulse number {n}\")\n",
        "plt.legend()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vOFa3v2OZe-4"
      },
      "source": [
        "# TRIAL WITH CPU\n",
        "### Reference data:\n",
        "- `3.6 ms ± 82.6 μs per loop` (mean ± std. dev. of 7 runs, 100 loops each)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "B6G0o0K7ArJW"
      },
      "outputs": [],
      "source": [
        "import h5py\n",
        "import numpy as np\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from torch.utils.data import DataLoader, Dataset\n",
        "from torch.nn.utils.rnn import pad_sequence\n",
        "\n",
        "# ---------------------------\n",
        "# Define a custom Dataset for HDF5 data\n",
        "# ---------------------------\n",
        "class PulseDataset(Dataset):\n",
        "    def __init__(self, h5_filename):\n",
        "        # Open the file in read mode\n",
        "        self.h5_file = h5py.File(h5_filename, 'r')\n",
        "        self.pulses_group = self.h5_file['pulses']\n",
        "        # Load target arrays (assumed to be homogeneous)\n",
        "        self.mus = self.h5_file['mus'][:]    # shape: (n_samples,)\n",
        "        self.lefts = self.h5_file['lefts'][:]\n",
        "        self.rights = self.h5_file['rights'][:]\n",
        "        self.n_samples = self.mus.shape[0]\n",
        "\n",
        "    def __len__(self):\n",
        "        return self.n_samples\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        # Each pulse is stored under the key \"pulse_{idx}\"\n",
        "        pulse = self.pulses_group[f\"pulse_{idx}\"][:]\n",
        "        # Convert the pulse to a torch tensor (1D tensor of fixed length)\n",
        "        pulse_tensor = torch.tensor(pulse, dtype=torch.float32)\n",
        "        # Build the target tensor: [mu, left_threshold, right_threshold]\n",
        "        target = torch.tensor([self.mus[idx], self.lefts[idx], self.rights[idx]], dtype=torch.float32)\n",
        "        return pulse_tensor, target\n",
        "\n",
        "\n",
        "# Create the dataset and DataLoader\n",
        "dataset = PulseDataset(\"synthetic_pulses_data.h5\")\n",
        "dataloader = DataLoader(dataset, batch_size=32, shuffle=True)\n",
        "\n",
        "# ---------------------------\n",
        "# Define the Hybrid CNN-LSTM Model\n",
        "# ---------------------------\n",
        "class HybridCNNLSTM(nn.Module):\n",
        "    def __init__(self, cnn_channels=32, lstm_hidden_size=64, lstm_layers=1):\n",
        "        super(HybridCNNLSTM, self).__init__()\n",
        "        # 1D convolution: expects input of shape (batch, 1, sequence_length)\n",
        "        self.conv1 = nn.Conv1d(in_channels=1, out_channels=cnn_channels, kernel_size=5, padding=2)\n",
        "        self.relu = nn.ReLU()\n",
        "        self.pool = nn.MaxPool1d(kernel_size=2)\n",
        "        # LSTM: input size = cnn_channels (features from the CNN layer)\n",
        "        self.lstm = nn.LSTM(input_size=cnn_channels, hidden_size=lstm_hidden_size,\n",
        "                            num_layers=lstm_layers, batch_first=True)\n",
        "        # Fully connected layer mapping from LSTM hidden state to 3 regression outputs\n",
        "        self.fc = nn.Linear(lstm_hidden_size, 3)\n",
        "\n",
        "    def forward(self, x):\n",
        "        # x shape: (batch, sequence_length)\n",
        "        # Add a channel dimension: becomes (batch, 1, sequence_length)\n",
        "        x = x.unsqueeze(1)\n",
        "        # Apply convolution, activation, and pooling:\n",
        "        x = self.conv1(x)      # -> (batch, cnn_channels, sequence_length)\n",
        "        x = self.relu(x)\n",
        "        x = self.pool(x)       # -> (batch, cnn_channels, sequence_length/2)\n",
        "        # Permute to (batch, sequence_length/2, cnn_channels) for LSTM input\n",
        "        x = x.permute(0, 2, 1)\n",
        "        # LSTM processing\n",
        "        out, (h_n, _) = self.lstm(x)\n",
        "        # Use the last layer's hidden state for prediction\n",
        "        x = self.fc(h_n[-1])\n",
        "        return x\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dCJbYeqOQEKp",
        "outputId": "dfb5f2ca-343b-439d-ec73-924b146d8b13"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model loaded successfully.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\bijay\\AppData\\Local\\Temp\\ipykernel_23032\\1354752151.py:5: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
            "  model.load_state_dict(torch.load(\"pulse_detection_model_final_2.pth\", map_location=torch.device('cpu')))\n"
          ]
        }
      ],
      "source": [
        "\n",
        "# To load the model later:\n",
        "# 1. Initialize the model instance with the same architecture\n",
        "model = HybridCNNLSTM()\n",
        "# 2. Load the saved state_dict into the model\n",
        "model.load_state_dict(torch.load(\"pulse_detection_model_final_2.pth\", map_location=torch.device('cpu')))\n",
        "model.eval()  # Set the model to evaluation mode\n",
        "print(\"Model loaded successfully.\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        },
        "id": "b9K1MSK-Uc11",
        "outputId": "8b18a104-2150-4d35-9acf-1271ff3f324b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "3.48 ms ± 84 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
            "Original Targets:\n",
            "Peak (μ): 0.7959898707788462\n",
            "Left 10% threshold: 0.7278428238091137\n",
            "Right 10% threshold: 0.8435561234832507\n",
            "\n",
            "Model Prediction:\n",
            "Predicted Peak: 0.791826\n",
            "Predicted Left 10%: 0.72988665\n",
            "Predicted Right 10%: 0.8471111\n",
            "0.006906270980834961\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import time\n",
        "\n",
        "np.random.seed(np.random.randint(0,1000))\n",
        "nth_time, nth_signal, nth_peak, nth_left, nth_right = generate_pulse(200, 1, 0.15)\n",
        "end_fit = 0\n",
        "# Convert the sample signal to a torch tensor and run through the model\n",
        "model.eval()\n",
        "with torch.inference_mode():\n",
        "    sample_tensor = torch.tensor(nth_signal, dtype=torch.float32).unsqueeze(0)  # add batch dimension\n",
        "    %timeit pred = model(sample_tensor).squeeze().numpy()\n",
        "    start_fit = time.time()\n",
        "    pred = model(sample_tensor).squeeze().numpy()\n",
        "    end_fit = time.time() - start_fit\n",
        "\n",
        "print(\"Original Targets:\")\n",
        "print(\"Peak (μ):\", nth_peak)\n",
        "print(\"Left 10% threshold:\", nth_left)\n",
        "print(\"Right 10% threshold:\", nth_right)\n",
        "print(\"\\nModel Prediction:\")\n",
        "print(\"Predicted Peak:\", pred[0])\n",
        "print(\"Predicted Left 10%:\", pred[1])\n",
        "print(\"Predicted Right 10%:\", pred[2])\n",
        "\n",
        "print(end_fit)\n",
        "\n",
        "# Plotting the nth pulse with original thresholds\n",
        "plt.figure(figsize=(8, 4))\n",
        "plt.plot(nth_time, nth_signal, label=\"Noisy Pulse (Line)\", color='grey', alpha=0.7)\n",
        "plt.scatter(nth_time, nth_signal, color='grey', s=15, label=\"Noisy Pulse (Points)\")\n",
        "plt.axvline(nth_peak, color='red', linestyle='--', label=\"Original Peak (μ)\")\n",
        "plt.axvline(nth_left, color='red', linestyle='--', label=\"Original Left 10%\")\n",
        "plt.axvline(nth_right, color='red', linestyle='--', label=\"Original Right 10%\")\n",
        "plt.axvline(pred[0], color='green', linestyle='--', label=\"Predicted Peak (μ)\")\n",
        "plt.axvline(pred[1], color='green', linestyle='--', label=\"Predicted Left 10%\")\n",
        "plt.axvline(pred[2], color='green', linestyle='--', label=\"Predicted Right 10%\")\n",
        "\n",
        "plt.xlabel(\"Time\")\n",
        "plt.ylabel(\"Intensity\")\n",
        "plt.title(f\"Pulse number {n}\")\n",
        "plt.legend()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sv6KrW6zZudg"
      },
      "source": [
        "# COMPARISION WITH GAUSSIAN REGRESSION (CPU)\n",
        "### Reference data:\n",
        "- GPR Fit Time: `487 ms ± 98 ms per loop` (mean ± std. dev. of 7 runs, 1 loop each)\n",
        "- GPR Prediction Time: `34.9 ms ± 827 μs per loop` (mean ± std. dev. of 7 runs, 10 loops each)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 709
        },
        "id": "8J0XCHuqxZOY",
        "outputId": "ba675fa8-0c48-4c6f-c71d-4fb4cb08c0c3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "487 ms ± 98 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
            "34.9 ms ± 827 μs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
            "GPR Fit Time: 3.9677 seconds\n",
            "GPR Prediction Time: 2.8425 seconds\n",
            "\n",
            "True Values:\n",
            "Peak: 0.5004, Left Threshold: 0.4360, Right Threshold: 0.5347\n",
            "\n",
            "Predicted Values:\n",
            "Peak: 0.3508, Left Threshold: 0.2779, Right Threshold: 0.4329\n"
          ]
        },
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import numpy as np\n",
        "import h5py\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.gaussian_process import GaussianProcessRegressor\n",
        "from sklearn.gaussian_process.kernels import RBF, WhiteKernel\n",
        "import time\n",
        "\n",
        "# # Load the dataset\n",
        "# n = 1000  # Example pulse index\n",
        "# with h5py.File(\"synthetic_pulses_data.h5\", \"r\") as hf:\n",
        "#     pulse_grp = hf[\"pulses\"]\n",
        "#     time_grp = hf[\"times\"]\n",
        "#     mus = hf[\"mus\"][:]\n",
        "#     lefts = hf[\"lefts\"][:]\n",
        "#     rights = hf[\"rights\"][:]\n",
        "\n",
        "#     nth_signal = pulse_grp[f\"pulse_{n}\"][:]\n",
        "#     nth_x = time_grp[f\"time_{n}\"][:]\n",
        "#     nth_peak_true = mus[n]\n",
        "#     nth_left_true = lefts[n]\n",
        "#     nth_right_true = rights[n]\n",
        "\n",
        "nth_x = nth_time\n",
        "nth_peak_true = nth_peak\n",
        "nth_left_true = nth_left\n",
        "nth_right_true = nth_right\n",
        "# Prepare data for GPR\n",
        "X = nth_x.reshape(-1, 1)\n",
        "y = nth_signal.ravel()\n",
        "\n",
        "# Define the kernel with initial parameters and bounds\n",
        "kernel = RBF(length_scale=0.05) + WhiteKernel(noise_level=0.1)\n",
        "gpr = GaussianProcessRegressor(kernel=kernel, n_restarts_optimizer=10)\n",
        "\n",
        "# Time the fitting process\n",
        "start_fit = time.time()\n",
        "%timeit gpr.fit(X, y)\n",
        "fit_time = time.time() - start_fit\n",
        "\n",
        "# Generate a dense time grid for prediction\n",
        "x_dense = np.linspace(nth_x.min(), nth_x.max(), 10000).reshape(-1, 1)\n",
        "\n",
        "# Time the prediction\n",
        "start_pred = time.time()\n",
        "%timeit y_mean, y_std = gpr.predict(x_dense, return_std=True)\n",
        "pred_time = time.time() - start_pred\n",
        "\n",
        "# Find peak coordinates\n",
        "peak_idx = np.argmax(y_mean)\n",
        "peak_amp = y_mean[peak_idx]\n",
        "peak_time = x_dense[peak_idx, 0]\n",
        "threshold = 0.1 * peak_amp\n",
        "\n",
        "# Function to find threshold crossing with interpolation\n",
        "def find_threshold(x_values, y_values, threshold):\n",
        "    # Check if all values are above threshold\n",
        "    if np.all(y_values > threshold):\n",
        "        return None\n",
        "    # Find where the values cross the threshold\n",
        "    below = y_values <= threshold\n",
        "    # Get the indices where the transition happens\n",
        "    transition = np.where(below[:-1] != below[1:])[0]\n",
        "    if len(transition) == 0:\n",
        "        return None\n",
        "    # Take the first transition from above to below\n",
        "    for idx in transition:\n",
        "        if not below[idx] and below[idx+1]:\n",
        "            left_idx = idx\n",
        "            right_idx = idx + 1\n",
        "            x_left = x_values[left_idx]\n",
        "            y_left = y_values[left_idx]\n",
        "            x_right = x_values[right_idx]\n",
        "            y_right = y_values[right_idx]\n",
        "            # Linear interpolation\n",
        "            alpha = (threshold - y_right) / (y_left - y_right)\n",
        "            x_thresh = x_right + alpha * (x_left - x_right)\n",
        "            return x_thresh\n",
        "    return None\n",
        "# Find left threshold (to the left of the peak)\n",
        "left_mask = x_dense < peak_time\n",
        "x_left = x_dense[left_mask].flatten()\n",
        "y_left = y_mean[left_mask.flatten()]  # Fix here\n",
        "# Reverse to start from peak and go left\n",
        "x_left_rev = x_left[::-1]\n",
        "y_left_rev = y_left[::-1]\n",
        "left_threshold = find_threshold(x_left_rev, y_left_rev, threshold)\n",
        "\n",
        "# Find right threshold (to the right of the peak)\n",
        "right_mask = x_dense > peak_time\n",
        "x_right = x_dense[right_mask].flatten()\n",
        "y_right = y_mean[right_mask.flatten()]  # Fix here\n",
        "right_threshold = find_threshold(x_right, y_right, threshold)\n",
        "\n",
        "\n",
        "# Output results\n",
        "print(f\"GPR Fit Time: {fit_time:.4f} seconds\")\n",
        "print(f\"GPR Prediction Time: {pred_time:.4f} seconds\")\n",
        "print(\"\\nTrue Values:\")\n",
        "print(f\"Peak: {nth_peak_true:.4f}, Left Threshold: {nth_left_true:.4f}, Right Threshold: {nth_right_true:.4f}\")\n",
        "print(\"\\nPredicted Values:\")\n",
        "print(f\"Peak: {peak_time:.4f}, Left Threshold: {left_threshold:.4f}, Right Threshold: {right_threshold:.4f}\")\n",
        "\n",
        "# Plotting\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.scatter(X, y, c='k', s=10, label=\"Noisy Data\")\n",
        "plt.plot(x_dense, y_mean, 'r-', lw=2, label=\"GPR Prediction\")\n",
        "plt.axvline(peak_time, color='b', linestyle='--', label=\"Predicted Peak\")\n",
        "plt.axvline(left_threshold, color='g', linestyle=':', label=\"Predicted Left 10%\")\n",
        "plt.axvline(right_threshold, color='orange', linestyle=':', label=\"Predicted Right 10%\")\n",
        "plt.axvline(nth_peak_true, color='m', linestyle='--', alpha=0.5, label=\"True Peak\")\n",
        "plt.axvline(nth_left_true, color='c', linestyle=':', alpha=0.5, label=\"True Left 10%\")\n",
        "plt.axvline(nth_right_true, color='y', linestyle=':', alpha=0.5, label=\"True Right 10%\")\n",
        "plt.xlabel(\"Time\")\n",
        "plt.ylabel(\"Intensity\")\n",
        "plt.title(f\"Gaussian Process Regression Fit for Pulse {n}\")\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Visualizations:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Saved training_animation_1000.gif\n",
            "Saved training_animation_2000.gif\n"
          ]
        }
      ],
      "source": [
        "import json\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.animation as animation\n",
        "\n",
        "# Load the training history\n",
        "with open(\"training_history.json\", \"r\") as f:\n",
        "    history = json.load(f)\n",
        "\n",
        "# Function to prepare data for a specific pulse\n",
        "def prepare_data(pulse_number):\n",
        "    actual_train = np.array(history[f\"actuals_train_{pulse_number}\"][0])\n",
        "    # Remove extra dimension so predictions become (num_epochs, 3)\n",
        "    pred_train = np.squeeze(np.array(history[f\"predictions_train_{pulse_number}\"]), axis=1)\n",
        "    actual_test = np.array(history[f\"actuals_test_{pulse_number}\"][0])\n",
        "    pred_test = np.squeeze(np.array(history[f\"predictions_test_{pulse_number}\"]), axis=1)\n",
        "    return actual_train, pred_train, actual_test, pred_test\n",
        "\n",
        "# Prepare data for pulses 1000 and 2000\n",
        "pulse_1000_data = prepare_data(1000)\n",
        "pulse_2000_data = prepare_data(2000)\n",
        "\n",
        "# Extract loss histories\n",
        "train_loss = history[\"train_loss_history\"]\n",
        "test_loss = history[\"test_loss_history\"]\n",
        "\n",
        "# Function to create animation for a specific pulse\n",
        "def create_animation(pulse_number, pulse_data, output_filename):\n",
        "    actual_train, pred_train, actual_test, pred_test = pulse_data\n",
        "    num_epochs = len(train_loss)\n",
        "    \n",
        "    fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(14, 10))\n",
        "    fig.suptitle(f'Training Progress for Pulse {pulse_number}', fontsize=16)\n",
        "    \n",
        "    # Training Loss (log scale)\n",
        "    ax1.set_title(\"Training Loss (log scale)\")\n",
        "    ax1.set_xlabel(\"Epoch\")\n",
        "    ax1.set_ylabel(\"Loss\")\n",
        "    ax1.grid(True)\n",
        "    line_train_loss, = ax1.semilogy([], [], lw=2, color='tab:blue')\n",
        "    \n",
        "    # Train Predictions\n",
        "    ax2.set_title(f\"Train Predictions (Pulse {pulse_number})\")\n",
        "    ax2.set_xlabel(\"Epoch\")\n",
        "    ax2.set_ylabel(\"Threshold Value\")\n",
        "    ax2.grid(True)\n",
        "    line_train_mu, = ax2.plot([], [], 'b-', label='Predicted μ')\n",
        "    line_train_left, = ax2.plot([], [], 'g-', label='Predicted Left')\n",
        "    line_train_right, = ax2.plot([], [], 'r-', label='Predicted Right')\n",
        "    ax2.axhline(actual_train[0], color='b', ls='--', label='Actual μ')\n",
        "    ax2.axhline(actual_train[1], color='g', ls='--', label='Actual Left')\n",
        "    ax2.axhline(actual_train[2], color='r', ls='--', label='Actual Right')\n",
        "    ax2.legend(loc='upper right', fontsize='small')\n",
        "    \n",
        "    # Test Loss (log scale)\n",
        "    ax3.set_title(\"Test Loss (log scale)\")\n",
        "    ax3.set_xlabel(\"Epoch\")\n",
        "    ax3.set_ylabel(\"Loss\")\n",
        "    ax3.grid(True)\n",
        "    line_test_loss, = ax3.semilogy([], [], lw=2, color='tab:orange')\n",
        "    \n",
        "    # Test Predictions\n",
        "    ax4.set_title(f\"Test Predictions (Pulse {pulse_number})\")\n",
        "    ax4.set_xlabel(\"Epoch\")\n",
        "    ax4.set_ylabel(\"Threshold Value\")\n",
        "    ax4.grid(True)\n",
        "    line_test_mu, = ax4.plot([], [], 'b-', label='Predicted μ')\n",
        "    line_test_left, = ax4.plot([], [], 'g-', label='Predicted Left')\n",
        "    line_test_right, = ax4.plot([], [], 'r-', label='Predicted Right')\n",
        "    ax4.axhline(actual_test[0], color='b', ls='--', label='Actual μ')\n",
        "    ax4.axhline(actual_test[1], color='g', ls='--', label='Actual Left')\n",
        "    ax4.axhline(actual_test[2], color='r', ls='--', label='Actual Right')\n",
        "    ax4.legend(loc='upper right', fontsize='small')\n",
        "    \n",
        "    plt.tight_layout(rect=[0, 0.03, 1, 0.95])\n",
        "    \n",
        "    # Initialization function\n",
        "    def init():\n",
        "        line_train_loss.set_data([], [])\n",
        "        line_train_mu.set_data([], [])\n",
        "        line_train_left.set_data([], [])\n",
        "        line_train_right.set_data([], [])\n",
        "        line_test_loss.set_data([], [])\n",
        "        line_test_mu.set_data([], [])\n",
        "        line_test_left.set_data([], [])\n",
        "        line_test_right.set_data([], [])\n",
        "        return (line_train_loss, line_train_mu, line_train_left, line_train_right,\n",
        "                line_test_loss, line_test_mu, line_test_left, line_test_right)\n",
        "    \n",
        "    # Update function for each frame\n",
        "    def update(epoch):\n",
        "        x = np.arange(epoch+1)\n",
        "        # Update training loss\n",
        "        line_train_loss.set_data(x, train_loss[:epoch+1])\n",
        "        ax1.relim()\n",
        "        ax1.autoscale_view()\n",
        "        \n",
        "        # Update train predictions\n",
        "        line_train_mu.set_data(x, pred_train[:epoch+1, 0])\n",
        "        line_train_left.set_data(x, pred_train[:epoch+1, 1])\n",
        "        line_train_right.set_data(x, pred_train[:epoch+1, 2])\n",
        "        ax2.relim()\n",
        "        ax2.autoscale_view()\n",
        "        \n",
        "        # Update test loss\n",
        "        line_test_loss.set_data(x, test_loss[:epoch+1])\n",
        "        ax3.relim()\n",
        "        ax3.autoscale_view()\n",
        "        \n",
        "        # Update test predictions\n",
        "        line_test_mu.set_data(x, pred_test[:epoch+1, 0])\n",
        "        line_test_left.set_data(x, pred_test[:epoch+1, 1])\n",
        "        line_test_right.set_data(x, pred_test[:epoch+1, 2])\n",
        "        ax4.relim()\n",
        "        ax4.autoscale_view()\n",
        "        \n",
        "        fig.suptitle(f'Epoch {epoch + 1}', fontsize=16)\n",
        "        return (line_train_loss, line_train_mu, line_train_left, line_train_right,\n",
        "                line_test_loss, line_test_mu, line_test_left, line_test_right)\n",
        "    \n",
        "    # Create the animation\n",
        "    ani = animation.FuncAnimation(fig, update, frames=num_epochs, init_func=init, blit=True)\n",
        "    \n",
        "    # Save as GIF\n",
        "    ani.save(output_filename, writer='pillow', fps=10, dpi=100)\n",
        "    plt.close()\n",
        "    print(f\"Saved {output_filename}\")\n",
        "\n",
        "# Create and save animations for both pulses\n",
        "create_animation(1000, pulse_1000_data, \"training_animation_1000.gif\")\n",
        "create_animation(2000, pulse_2000_data, \"training_animation_2000.gif\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1400x1000 with 4 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "TRAINING DATA:\n",
            "Pulse index (n): 1000\n",
            "Actual Peak (μ): 0.6762750043878725\n",
            "Actual Left 10% threshold: 0.5987246857378838\n",
            "Actual Right 10% threshold: 0.7204484776551425\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "TEST DATA:\n",
            "Pulse index (n): 1000\n",
            "Actual Peak (μ): 0.22683187569347033\n",
            "Actual Left 10% threshold: 0.1754641245939691\n",
            "Actual Right 10% threshold: 0.28295542561956927\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import json\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import h5py\n",
        "\n",
        "# ======================================================\n",
        "# PART 1: Loss Graphs & Training/Test Predictions vs Epochs\n",
        "# ======================================================\n",
        "\n",
        "# Load the training history JSON file\n",
        "with open(\"training_history.json\", \"r\") as f:\n",
        "    history = json.load(f)\n",
        "\n",
        "def prepare_data(pulse_number):\n",
        "    \"\"\"\n",
        "    Extracts the actual target values and prediction arrays for a given pulse.\n",
        "    Assumes the JSON keys are in the form:\n",
        "       \"actuals_train_{pulse_number}\"\n",
        "       \"predictions_train_{pulse_number}\"\n",
        "       \"actuals_test_{pulse_number}\"\n",
        "       \"predictions_test_{pulse_number}\"\n",
        "    If the predictions are stored as arrays of shape (epochs, 1, 3),\n",
        "    they are squeezed to shape (epochs, 3).\n",
        "    \"\"\"\n",
        "    # Actual targets: stored with an extra [0] index in JSON\n",
        "    actual_train = np.array(history[f\"actuals_train_{pulse_number}\"][0])\n",
        "    actual_test  = np.array(history[f\"actuals_test_{pulse_number}\"][0])\n",
        "    \n",
        "    # Predictions: might be of shape (epochs, 1, 3)\n",
        "    pred_train_raw = np.array(history[f\"predictions_train_{pulse_number}\"])\n",
        "    pred_test_raw  = np.array(history[f\"predictions_test_{pulse_number}\"])\n",
        "    \n",
        "    if pred_train_raw.ndim == 3 and pred_train_raw.shape[1] == 1:\n",
        "        pred_train = np.squeeze(pred_train_raw, axis=1)\n",
        "    else:\n",
        "        pred_train = pred_train_raw\n",
        "\n",
        "    if pred_test_raw.ndim == 3 and pred_test_raw.shape[1] == 1:\n",
        "        pred_test = np.squeeze(pred_test_raw, axis=1)\n",
        "    else:\n",
        "        pred_test = pred_test_raw\n",
        "\n",
        "    return actual_train, pred_train, actual_test, pred_test\n",
        "\n",
        "# Choose a pulse number for which predictions were stored in JSON\n",
        "# (Make sure the keys \"actuals_train_<n>\", etc. exist in your JSON file)\n",
        "pulse_number = 1000  \n",
        "actual_train, pred_train, actual_test, pred_test = prepare_data(pulse_number)\n",
        "\n",
        "# Loss histories (assumed to be lists over epochs)\n",
        "train_loss = np.array(history[\"train_loss_history\"])\n",
        "test_loss  = np.array(history[\"test_loss_history\"])\n",
        "num_epochs = len(train_loss)\n",
        "epochs = np.arange(1, num_epochs + 1)\n",
        "\n",
        "# --- Loss Graphs ---\n",
        "plt.figure(figsize=(14, 10))\n",
        "\n",
        "# Subplot 1: Training Loss (log scale)\n",
        "plt.subplot(2, 2, 1)\n",
        "plt.semilogy(epochs, train_loss, lw=2, color='tab:blue', label=\"Train Loss\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"Loss (log scale)\")\n",
        "plt.title(\"Training Loss\")\n",
        "plt.grid(True)\n",
        "plt.legend()\n",
        "\n",
        "# Subplot 2: Train Predictions over Epochs\n",
        "plt.subplot(2, 2, 2)\n",
        "plt.plot(epochs, pred_train[:, 0], 'b-', label=\"Predicted Peak (μ)\")\n",
        "plt.plot(epochs, pred_train[:, 1], 'g-', label=\"Predicted Left 10%\")\n",
        "plt.plot(epochs, pred_train[:, 2], 'r-', label=\"Predicted Right 10%\")\n",
        "plt.axhline(actual_train[0], color='b', linestyle='--', label=\"Actual Peak (μ)\")\n",
        "plt.axhline(actual_train[1], color='g', linestyle='--', label=\"Actual Left 10%\")\n",
        "plt.axhline(actual_train[2], color='r', linestyle='--', label=\"Actual Right 10%\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"Threshold Value\")\n",
        "plt.title(\"Train Predictions\")\n",
        "plt.grid(True)\n",
        "plt.legend()\n",
        "\n",
        "# Subplot 3: Test Loss (log scale)\n",
        "plt.subplot(2, 2, 3)\n",
        "plt.semilogy(epochs, test_loss, lw=2, color='tab:orange', label=\"Test Loss\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"Loss (log scale)\")\n",
        "plt.title(\"Test Loss\")\n",
        "plt.grid(True)\n",
        "plt.legend()\n",
        "\n",
        "# Subplot 4: Test Predictions over Epochs\n",
        "plt.subplot(2, 2, 4)\n",
        "plt.plot(epochs, pred_test[:, 0], 'b-', label=\"Predicted Peak (μ)\")\n",
        "plt.plot(epochs, pred_test[:, 1], 'g-', label=\"Predicted Left 10%\")\n",
        "plt.plot(epochs, pred_test[:, 2], 'r-', label=\"Predicted Right 10%\")\n",
        "plt.axhline(actual_test[0], color='b', linestyle='--', label=\"Actual Peak (μ)\")\n",
        "plt.axhline(actual_test[1], color='g', linestyle='--', label=\"Actual Left 10%\")\n",
        "plt.axhline(actual_test[2], color='r', linestyle='--', label=\"Actual Right 10%\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"Threshold Value\")\n",
        "plt.title(\"Test Predictions\")\n",
        "plt.grid(True)\n",
        "plt.legend()\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "# Extract final predictions (from the last epoch) for overlaying on the pulse signals\n",
        "final_pred_train = pred_train[-1, :]  # [pred_peak, pred_left, pred_right]\n",
        "final_pred_test  = pred_test[-1, :]\n",
        "\n",
        "# ======================================================\n",
        "# PART 2: Overlay Predictions on Signal Data from h5 Files\n",
        "# ======================================================\n",
        "\n",
        "# --- Training Data Overlay ---\n",
        "with h5py.File(\"synthetic_pulses_data.h5\", \"r\") as hf:\n",
        "    pulse_grp = hf[\"pulses\"]\n",
        "    time_grp = hf[\"times\"]\n",
        "    mus = hf[\"mus\"][:]    # True thresholds stored as numpy arrays\n",
        "    lefts = hf[\"lefts\"][:]\n",
        "    rights = hf[\"rights\"][:]\n",
        "    \n",
        "    # Load the pulse data for the chosen index (pulse_number)\n",
        "    nth_signal = pulse_grp[f\"pulse_{pulse_number}\"][:]  \n",
        "    nth_x = time_grp[f\"time_{pulse_number}\"][:]\n",
        "    nth_peak = mus[pulse_number]\n",
        "    nth_left = lefts[pulse_number]\n",
        "    nth_right = rights[pulse_number]\n",
        "\n",
        "print(\"TRAINING DATA:\")\n",
        "print(\"Pulse index (n):\", pulse_number)\n",
        "print(\"Actual Peak (μ):\", nth_peak)\n",
        "print(\"Actual Left 10% threshold:\", nth_left)\n",
        "print(\"Actual Right 10% threshold:\", nth_right)\n",
        "\n",
        "plt.figure(figsize=(8, 4))\n",
        "plt.plot(nth_x, nth_signal, label=\"Noisy Pulse (Line)\", color='blue', alpha=0.7)\n",
        "plt.scatter(nth_x, nth_signal, color='blue', s=15, label=\"Noisy Pulse (Points)\")\n",
        "\n",
        "# Plot actual thresholds from h5 file (solid dashed lines)\n",
        "plt.axvline(nth_peak, color='red', linestyle='--', label=\"Actual Peak (μ)\")\n",
        "plt.axvline(nth_left, color='green', linestyle='--', label=\"Actual Left 10%\")\n",
        "plt.axvline(nth_right, color='orange', linestyle='--', label=\"Actual Right 10%\")\n",
        "\n",
        "# Overlay predictions (final epoch from JSON) with a different linestyle\n",
        "plt.axvline(final_pred_train[0], color='magenta', linestyle='-.', label=\"Predicted Peak (μ)\")\n",
        "plt.axvline(final_pred_train[1], color='cyan', linestyle='-.', label=\"Predicted Left 10%\")\n",
        "plt.axvline(final_pred_train[2], color='purple', linestyle='-.', label=\"Predicted Right 10%\")\n",
        "\n",
        "plt.xlabel(\"Time\")\n",
        "plt.ylabel(\"Intensity\")\n",
        "plt.title(f\"Training Pulse Overlay: Pulse {pulse_number}\")\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "\n",
        "# --- Test Data Overlay ---\n",
        "with h5py.File(\"synthetic_pulses_data_test.h5\", \"r\") as hf:\n",
        "    pulse_grp = hf[\"pulses\"]\n",
        "    time_grp = hf[\"times\"]\n",
        "    mus_test = hf[\"mus\"][:]    # True thresholds for test data\n",
        "    lefts_test = hf[\"lefts\"][:]\n",
        "    rights_test = hf[\"rights\"][:]\n",
        "    \n",
        "    # Load the pulse data for the chosen index (pulse_number)\n",
        "    nth_signal_test = pulse_grp[f\"pulse_{pulse_number}\"][:]  \n",
        "    nth_x_test = time_grp[f\"time_{pulse_number}\"][:]\n",
        "    nth_peak_test = mus_test[pulse_number]\n",
        "    nth_left_test = lefts_test[pulse_number]\n",
        "    nth_right_test = rights_test[pulse_number]\n",
        "\n",
        "print(\"TEST DATA:\")\n",
        "print(\"Pulse index (n):\", pulse_number)\n",
        "print(\"Actual Peak (μ):\", nth_peak_test)\n",
        "print(\"Actual Left 10% threshold:\", nth_left_test)\n",
        "print(\"Actual Right 10% threshold:\", nth_right_test)\n",
        "\n",
        "plt.figure(figsize=(8, 4))\n",
        "plt.plot(nth_x_test, nth_signal_test, label=\"Noisy Pulse (Line)\", color='blue', alpha=0.7)\n",
        "plt.scatter(nth_x_test, nth_signal_test, color='blue', s=15, label=\"Noisy Pulse (Points)\")\n",
        "\n",
        "# Plot actual thresholds from test h5 file\n",
        "plt.axvline(nth_peak_test, color='red', linestyle='--', label=\"Actual Peak (μ)\")\n",
        "plt.axvline(nth_left_test, color='green', linestyle='--', label=\"Actual Left 10%\")\n",
        "plt.axvline(nth_right_test, color='orange', linestyle='--', label=\"Actual Right 10%\")\n",
        "\n",
        "# Overlay predictions (final epoch from JSON for test)\n",
        "plt.axvline(final_pred_test[0], color='magenta', linestyle='-.', label=\"Predicted Peak (μ)\")\n",
        "plt.axvline(final_pred_test[1], color='cyan', linestyle='-.', label=\"Predicted Left 10%\")\n",
        "plt.axvline(final_pred_test[2], color='purple', linestyle='-.', label=\"Predicted Right 10%\")\n",
        "\n",
        "plt.xlabel(\"Time\")\n",
        "plt.ylabel(\"Intensity\")\n",
        "plt.title(f\"Test Pulse Overlay: Pulse {pulse_number}\")\n",
        "plt.legend()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {},
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.animation as animation\n",
        "import json\n",
        "from matplotlib.gridspec import GridSpec\n",
        "\n",
        "# Load the training history\n",
        "with open('training_history.json', 'r') as f:\n",
        "    history = json.load(f)\n",
        "\n",
        "# Load sample pulses from datasets\n",
        "with h5py.File(\"synthetic_pulses_data.h5\", \"r\") as hf:\n",
        "    train_pulse_1000 = hf[\"pulses\"][\"pulse_1000\"][:]\n",
        "    train_time_1000 = hf[\"times\"][\"time_1000\"][:]\n",
        "    train_true_vals_1000 = [\n",
        "        hf[\"mus\"][1000],\n",
        "        hf[\"lefts\"][1000],\n",
        "        hf[\"rights\"][1000]\n",
        "    ]\n",
        "\n",
        "with h5py.File(\"synthetic_pulses_data_test.h5\", \"r\") as hf:\n",
        "    test_pulse_1000 = hf[\"pulses\"][\"pulse_1000\"][:]\n",
        "    test_time_1000 = hf[\"times\"][\"time_1000\"][:]\n",
        "    test_true_vals_1000 = [\n",
        "        hf[\"mus\"][1000],\n",
        "        hf[\"lefts\"][1000],\n",
        "        hf[\"rights\"][1000]\n",
        "    ]\n",
        "\n",
        "# Create the animation figure\n",
        "fig = plt.figure(figsize=(15, 10))\n",
        "gs = GridSpec(2, 2, figure=fig)\n",
        "ax1 = fig.add_subplot(gs[0, :])  # Loss history plot (top)\n",
        "ax2 = fig.add_subplot(gs[1, 0])  # Training pulse plot (bottom left)\n",
        "ax3 = fig.add_subplot(gs[1, 1])  # Test pulse plot (bottom right)\n",
        "\n",
        "def animate(frame):\n",
        "    # Clear previous frame\n",
        "    ax1.clear()\n",
        "    ax2.clear()\n",
        "    ax3.clear()\n",
        "    \n",
        "    # Plot loss history\n",
        "    train_loss = history['train_loss_history'][:frame+1]\n",
        "    test_loss = history['test_loss_history'][:frame+1]\n",
        "    epochs = range(frame + 1)\n",
        "    \n",
        "    ax1.plot(epochs, train_loss, 'b-', label='Training Loss')\n",
        "    ax1.plot(epochs, test_loss, 'r-', label='Test Loss')\n",
        "    ax1.set_xlabel('Epoch')\n",
        "    ax1.set_ylabel('Loss')\n",
        "    ax1.set_title('Training and Test Loss History')\n",
        "    ax1.legend()\n",
        "    ax1.grid(True)\n",
        "    \n",
        "    # Plot training pulse\n",
        "    ax2.plot(train_time_1000, train_pulse_1000, 'gray', alpha=0.5, label='Signal')\n",
        "    if frame > 0:\n",
        "        pred = history['predictions_train_1000'][frame-1][0]  # Get prediction for current frame\n",
        "        ax2.axvline(pred[0], color='r', linestyle='--', label='Peak')\n",
        "        ax2.axvline(pred[1], color='g', linestyle='--', label='Left')\n",
        "        ax2.axvline(pred[2], color='b', linestyle='--', label='Right')\n",
        "        # Plot true values\n",
        "        ax2.axvline(train_true_vals_1000[0], color='r', linestyle=':', alpha=0.5)\n",
        "        ax2.axvline(train_true_vals_1000[1], color='g', linestyle=':', alpha=0.5)\n",
        "        ax2.axvline(train_true_vals_1000[2], color='b', linestyle=':', alpha=0.5)\n",
        "    ax2.set_xlabel('Time')\n",
        "    ax2.set_ylabel('Intensity')\n",
        "    ax2.set_title('Training Pulse (1000)')\n",
        "    ax2.legend()\n",
        "    ax2.grid(True)\n",
        "    \n",
        "    # Plot test pulse\n",
        "    ax3.plot(test_time_1000, test_pulse_1000, 'gray', alpha=0.5, label='Signal')\n",
        "    if frame > 0:\n",
        "        pred = history['predictions_test_1000'][frame-1][0]  # Get prediction for current frame\n",
        "        ax3.axvline(pred[0], color='r', linestyle='--', label='Peak')\n",
        "        ax3.axvline(pred[1], color='g', linestyle='--', label='Left')\n",
        "        ax3.axvline(pred[2], color='b', linestyle='--', label='Right')\n",
        "        # Plot true values\n",
        "        ax3.axvline(test_true_vals_1000[0], color='r', linestyle=':', alpha=0.5)\n",
        "        ax3.axvline(test_true_vals_1000[1], color='g', linestyle=':', alpha=0.5)\n",
        "        ax3.axvline(test_true_vals_1000[2], color='b', linestyle=':', alpha=0.5)\n",
        "    ax3.set_xlabel('Time')\n",
        "    ax3.set_ylabel('Intensity')\n",
        "    ax3.set_title('Test Pulse (1000)')\n",
        "    ax3.legend()\n",
        "    ax3.grid(True)\n",
        "    \n",
        "    plt.tight_layout()\n",
        "\n",
        "# Create the animation\n",
        "anim = animation.FuncAnimation(\n",
        "    fig, \n",
        "    animate, \n",
        "    frames=len(history['train_loss_history']),\n",
        "    interval=100,  # 100ms between frames\n",
        "    repeat=True\n",
        ")\n",
        "\n",
        "# Save the animation\n",
        "anim.save('training_history.gif', writer='pillow')\n",
        "\n",
        "plt.close()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {},
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.animation as animation\n",
        "import json\n",
        "from matplotlib.gridspec import GridSpec\n",
        "\n",
        "# Load the training history\n",
        "with open('training_history.json', 'r') as f:\n",
        "    history = json.load(f)\n",
        "\n",
        "# Load sample pulses from datasets\n",
        "with h5py.File(\"synthetic_pulses_data.h5\", \"r\") as hf:\n",
        "    train_pulse_1000 = hf[\"pulses\"][\"pulse_1000\"][:]\n",
        "    train_time_1000 = hf[\"times\"][\"time_1000\"][:]\n",
        "    train_true_vals_1000 = [\n",
        "        hf[\"mus\"][1000],\n",
        "        hf[\"lefts\"][1000],\n",
        "        hf[\"rights\"][1000]\n",
        "    ]\n",
        "\n",
        "with h5py.File(\"synthetic_pulses_data_test.h5\", \"r\") as hf:\n",
        "    test_pulse_1000 = hf[\"pulses\"][\"pulse_1000\"][:]\n",
        "    test_time_1000 = hf[\"times\"][\"time_1000\"][:]\n",
        "    test_true_vals_1000 = [\n",
        "        hf[\"mus\"][1000],\n",
        "        hf[\"lefts\"][1000],\n",
        "        hf[\"rights\"][1000]\n",
        "    ]\n",
        "\n",
        "# Create the animation figure\n",
        "fig = plt.figure(figsize=(15, 10))\n",
        "gs = GridSpec(2, 2, figure=fig)\n",
        "ax1 = fig.add_subplot(gs[0, :])  # Loss history plot (top)\n",
        "ax2 = fig.add_subplot(gs[1, 0])  # Training pulse plot (bottom left)\n",
        "ax3 = fig.add_subplot(gs[1, 1])  # Test pulse plot (bottom right)\n",
        "\n",
        "def animate(frame):\n",
        "    # Clear previous frame\n",
        "    ax1.clear()\n",
        "    ax2.clear()\n",
        "    ax3.clear()\n",
        "    \n",
        "    # Plot loss history\n",
        "    train_loss = history['train_loss_history'][:frame+1]\n",
        "    test_loss = history['test_loss_history'][:frame+1]\n",
        "    epochs = range(frame + 1)\n",
        "    \n",
        "    ax1.plot(epochs, train_loss, 'g-', label='Training Loss')\n",
        "    ax1.plot(epochs, test_loss, 'r-', label='Test Loss')\n",
        "    ax1.set_xlabel('Epoch')\n",
        "    ax1.set_ylabel('Loss')\n",
        "    ax1.set_title('Training and Test Loss History')\n",
        "    ax1.legend()\n",
        "    ax1.grid(True)\n",
        "    ax1.set_yscale('log')  # Set y-axis to logarithmic scale\n",
        "    \n",
        "    # Plot training pulse\n",
        "    ax2.plot(train_time_1000, train_pulse_1000, 'gray', alpha=0.5, label='Signal')\n",
        "    if frame > 0:\n",
        "        pred = history['predictions_train_1000'][frame-1][0]  # Get prediction for current frame\n",
        "        ax2.axvline(pred[0], color='orange', linestyle='--', label='Peak')\n",
        "        ax2.axvline(pred[1], color='g', linestyle='--', label='Left')\n",
        "        ax2.axvline(pred[2], color='purple', linestyle='--', label='Right')\n",
        "        # Plot true values\n",
        "        ax2.axvline(train_true_vals_1000[0], color='red', alpha=0.5)\n",
        "        ax2.axvline(train_true_vals_1000[1], color='green', alpha=0.5)\n",
        "        ax2.axvline(train_true_vals_1000[2], color='blue', alpha=0.5)\n",
        "    ax2.set_xlabel('Time')\n",
        "    ax2.set_ylabel('Intensity')\n",
        "    ax2.set_title('Training Pulse (1000)')\n",
        "    ax2.legend()\n",
        "    ax2.grid(True)\n",
        "    \n",
        "    # Plot test pulse\n",
        "    ax3.plot(test_time_1000, test_pulse_1000, 'gray', alpha=0.5, label='Signal')\n",
        "    if frame > 0:\n",
        "        pred = history['predictions_test_1000'][frame-1][0]  # Get prediction for current frame\n",
        "        ax3.axvline(pred[0], color='orange', linestyle='--', label='Peak')\n",
        "        ax3.axvline(pred[1], color='g', linestyle='--', label='Left')\n",
        "        ax3.axvline(pred[2], color='purple', linestyle='--', label='Right')\n",
        "        # Plot true values\n",
        "        ax3.axvline(test_true_vals_1000[0], color='red', alpha=0.5)\n",
        "        ax3.axvline(test_true_vals_1000[1], color='green', alpha=0.5)\n",
        "        ax3.axvline(test_true_vals_1000[2], color='blue', alpha=0.5)\n",
        "    ax3.set_xlabel('Time')\n",
        "    ax3.set_ylabel('Intensity')\n",
        "    ax3.set_title('Test Pulse (1000)')\n",
        "    ax3.legend()\n",
        "    ax3.grid(True)\n",
        "    \n",
        "    plt.tight_layout()\n",
        "\n",
        "\n",
        "# Create the animation\n",
        "anim = animation.FuncAnimation(\n",
        "    fig, \n",
        "    animate, \n",
        "    frames=len(history['train_loss_history']),\n",
        "    interval=100,  # 100ms between frames\n",
        "    repeat=True\n",
        ")\n",
        "\n",
        "# Save the animation\n",
        "anim.save('training_history_3.gif', writer='pillow')\n",
        "\n",
        "plt.close()"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [
        "DTt7EKjwZHKR",
        "vOFa3v2OZe-4",
        "Sv6KrW6zZudg"
      ],
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.12.7"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}