From b2bf1b0e3153c288e0aaa66acf8adaa6fe8b5403 Mon Sep 17 00:00:00 2001
From: nuluh <dam.ar@outlook.com>
Date: Thu, 28 Aug 2025 07:10:41 +0700
Subject: [PATCH] feat(notebooks): Add type hints for data lists, create
 preview function for STFT visualization, and save plot for Sensor B.
 Introduce test section with AU data processing.

---
 code/notebooks/stft.ipynb | 82 ++++++++++++++++++++++++++++++++++++---
 1 file changed, 76 insertions(+), 6 deletions(-)

diff --git a/code/notebooks/stft.ipynb b/code/notebooks/stft.ipynb
index 6e9e48d..145c7e5 100644
--- a/code/notebooks/stft.ipynb
+++ b/code/notebooks/stft.ipynb
@@ -342,7 +342,7 @@
     "import os\n",
     "import pandas as pd\n",
     "\n",
-    "ready_data1a = []\n",
+    "ready_data1a: list[pd.DataFrame] = []\n",
     "for file in os.listdir('D:/thesis/data/converted/raw/sensor1'):\n",
     "    ready_data1a.append(pd.read_csv(os.path.join('D:/thesis/data/converted/raw/sensor1', file), skiprows=1))"
    ]
@@ -354,7 +354,7 @@
    "outputs": [],
    "source": [
     "# Load the processed data for Sensor 2\n",
-    "ready_data2a = []\n",
+    "ready_data2a: list[pd.DataFrame] = []\n",
     "for file in os.listdir('D:/thesis/data/converted/raw/sensor2'):\n",
     "    ready_data2a.append(pd.read_csv(os.path.join('D:/thesis/data/converted/raw/sensor2', file), skiprows=1))"
    ]
@@ -414,6 +414,33 @@
     "import matplotlib.pyplot as plt"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def preview_stft(data: pd.DataFrame, x_num_ticks: int = 6, y_num_ticks: int = 5):\n",
+    "    \"\"\"\n",
+    "    Preview the Short-Time Fourier Transform (STFT) of the given data.\n",
+    "\n",
+    "    Parameters:\n",
+    "    -------\n",
+    "    data (pd.DataFrame): The STFT data to be visualized.\n",
+    "    x_num_ticks (int): Number of ticks on the x-axis (time frames). Defaults to 6.\n",
+    "    y_num_ticks (int): Number of ticks on the y-axis (frequency bins). Defaults to 5.\n",
+    "    \"\"\"\n",
+    "    plt.figure(dpi=300)  # Set figure size and DPI\n",
+    "    plt.pcolormesh(data.transpose(), cmap='jet', vmax=0.03, vmin=0.0)\n",
+    "    # plt.title('STFT Preview')\n",
+    "    plt.colorbar(label='Magnitude')\n",
+    "    plt.xlabel('Segmen Waktu')\n",
+    "    plt.ylabel('Sampel Frekuensi (Hz)')\n",
+    "    plt.xticks(np.linspace(0, len(data)-1, x_num_ticks))  # Set x-ticks at regular intervals\n",
+    "    plt.yticks(np.linspace(0, len(data.columns)-1, y_num_ticks))  # Set y-ticks at regular intervals\n",
+    "    plt.show()"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -422,10 +449,7 @@
    "source": [
     "# colormesh give title x is frequency and y is time and rotate/transpose the data\n",
     "# Plotting the STFT Data\n",
-    "plt.figure(dpi=300)  # Set figure size and DPI\n",
-    "plt.pcolormesh(ready_data1a[0].transpose(), cmap='jet', vmax=0.03, vmin=0.0)\n",
-    "plt.title('STFT of Sensor A Dataset A Label 0 Undamaged')\n",
-    "plt.show()"
+    "preview_stft(ready_data1a[0])  # Preview for Sensor 1"
    ]
   },
   {
@@ -437,6 +461,7 @@
     "plt.figure(dpi=300)  # Set figure size and DPI\n",
     "plt.pcolormesh(ready_data2a[0].transpose(), cmap='jet', vmax=0.03, vmin=0.0)\n",
     "plt.title('STFT of Sensor B Dataset A Label 0 Undamaged')\n",
+    "plt.savefig(\"stft-sensor-b-dataset-a-undamaged.png\", dpi=300)\n",
     "plt.show()"
    ]
   },
@@ -1005,6 +1030,51 @@
     "print(\"Accuracy on Dataset B:\", accuracy_score(y, y_pred_svm))\n",
     "print(classification_report(y, y_pred_svm))"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Test with AU"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "file_path = 'D:/thesis/data/dataset_B/zzzBU.TXT'\n",
+    "df = pd.read_csv(file_path, delim_whitespace=True, skiprows=10, header=0, memory_map=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df1= df.iloc[:, [1]]\n",
+    "df2 = df.iloc[:, [26]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.signal.windows import hann\n",
+    "from scipy.signal import stft\n",
+    "\n",
+    "window = 1024\n",
+    "hop = 512\n",
+    "\n",
+    "stft1 = stft(df1.values.flatten(), window=hann(window), nperseg=window, noverlap=hop, fs=window)\n",
+    "stft2 = stft(df2.values.flatten(), window=hann(window), nperseg=window, noverlap=hop, fs=window)\n"
+   ]
   }
  ],
  "metadata": {