feat(notebooks): Enhance STFT preview functionality and improve plotting
- Updated `preview_stft` function to accept both DataFrame and list of DataFrames. - Added support for multiple subplots when a list of DataFrames is provided. - Improved color mapping and axis labeling in plots. - Adjusted figure saving options for better output formats. - Refactored code to reduce redundancy in plotting logic for Sensor A and Sensor B. - Added predictions using SVM models for processed data.
This commit is contained in:
nuluh
@@ -402,7 +402,14 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Preview Plot"
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"##### Preview Plot"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"###### Function plot"
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]
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},
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{
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@@ -411,7 +418,8 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt"
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"import matplotlib.pyplot as plt\n",
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"from typing import Union"
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]
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},
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{
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@@ -420,7 +428,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"def preview_stft(data: pd.DataFrame, x_num_ticks: int = 6, y_num_ticks: int = 5):\n",
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"def preview_stft(data: Union[pd.DataFrame, list[pd.DataFrame]], x_num_ticks: int = 6, y_num_ticks: int = 5, cols: int=3):\n",
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" \"\"\"\n",
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" Preview the Short-Time Fourier Transform (STFT) of the given data.\n",
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"\n",
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@@ -430,15 +438,54 @@
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" x_num_ticks (int): Number of ticks on the x-axis (time frames). Defaults to 6.\n",
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" y_num_ticks (int): Number of ticks on the y-axis (frequency bins). Defaults to 5.\n",
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" \"\"\"\n",
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" plt.figure(dpi=300) # Set figure size and DPI\n",
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" plt.pcolormesh(data.transpose(), cmap='jet', vmax=0.03, vmin=0.0)\n",
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" # plt.title('STFT Preview')\n",
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" plt.colorbar(label='Magnitude')\n",
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" plt.xlabel('Segmen Waktu')\n",
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" plt.ylabel('Sampel Frekuensi (Hz)')\n",
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" plt.xticks(np.linspace(0, len(data)-1, x_num_ticks)) # Set x-ticks at regular intervals\n",
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" plt.yticks(np.linspace(0, len(data.columns)-1, y_num_ticks)) # Set y-ticks at regular intervals\n",
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" plt.show()"
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" if type(data) == pd.DataFrame:\n",
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" plt.figure(dpi=300) # Set figure size and DPI\n",
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" plt.pcolormesh(data.transpose(), cmap='jet', vmax=0.03, vmin=0.0, rasterized=True)\n",
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" # plt.title('STFT Preview')\n",
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" plt.colorbar(label='Magnitude ($m/s^2$)')\n",
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" plt.xlabel('Segmen Waktu')\n",
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" plt.ylabel('Sampel Frekuensi (Hz)')\n",
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" plt.xticks(np.linspace(0, len(data)-1, x_num_ticks)) # Set x-ticks at regular intervals\n",
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" plt.yticks(np.linspace(0, len(data.columns)-1, y_num_ticks)) # Set y-ticks at regular intervals\n",
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" plt.savefig(\"output_single.svg\", format=\"svg\", dpi=100)\n",
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" plt.show()\n",
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"\n",
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" elif type(data) == list and len(data) > 1:\n",
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" # Create a figure and subplots\n",
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" fig, axes = plt.subplots(len(data)//cols, cols, figsize=(15, 8), sharex=True, sharey=True)\n",
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"\n",
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" # Flatten the axes array for easier iteration\n",
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" axes = axes.flatten()\n",
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"\n",
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" # Loop through each subplot and plot the data\n",
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" for i in range(len(data)):\n",
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" pcm = axes[i].pcolormesh(data[i].transpose(), cmap='jet', vmax=0.03, vmin=0.0, rasterized=True)\n",
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" axes[i].set_title(f'd_{i+1}', fontsize=12)\n",
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" axes[i].set_xticks(np.linspace(0, len(data[i])-1, 6)) # Set x-ticks at regular intervals\n",
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" axes[i].set_yticks(np.linspace(0, len(data[i].columns)-1, 9)) # Set y-ticks at regular intervals\n",
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"\n",
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" # Add a single color bar for all subplots\n",
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" # Use the first `pcolormesh` object (or any valid one) for the color bar\n",
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" cbar = fig.colorbar(pcm, ax=axes, orientation='vertical')\n",
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" cbar.set_label('Magnitude ($m/s^2$)', fontsize=12)\n",
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"\n",
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" # Set shared labels\n",
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" fig.text(0.5, 0.04, 'Segmen Waktu', ha='center', fontsize=12)\n",
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" fig.text(0.04, 0.5, 'Sampel Frekuensi (Hz)', va='center', rotation='vertical', fontsize=12)\n",
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" # fig.suptitle('STFT of Sensor A Damage Case (d1--d6) Dataset A', fontsize=16)\n",
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"\n",
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" # Adjust layout\n",
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" # plt.tight_layout(rect=[0.05, 0.05, 1, 1]) # Leave space for shared labels\n",
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" plt.subplots_adjust(left=0.1, right=0.75, top=0.9, bottom=0.1, wspace=0.2, hspace=0.2)\n",
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" plt.savefig(\"output_multiple.svg\", format=\"svg\", dpi=80)\n",
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" plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"###### Sensor A"
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]
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},
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{
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@@ -449,7 +496,14 @@
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"source": [
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"# colormesh give title x is frequency and y is time and rotate/transpose the data\n",
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"# Plotting the STFT Data\n",
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"preview_stft(ready_data1a[0]) # Preview for Sensor 1"
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"preview_stft(ready_data1a[0], y_num_ticks=9) # Preview for Sensor 1"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"###### Sensor B"
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]
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},
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{
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@@ -458,11 +512,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"plt.figure(dpi=300) # Set figure size and DPI\n",
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"plt.pcolormesh(ready_data2a[0].transpose(), cmap='jet', vmax=0.03, vmin=0.0)\n",
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"plt.title('STFT of Sensor B Dataset A Label 0 Undamaged')\n",
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"plt.savefig(\"stft-sensor-b-dataset-a-undamaged.png\", dpi=300)\n",
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"plt.show()"
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"preview_stft(ready_data2a[0], y_num_ticks=9) # Preview for Sensor 2"
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]
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},
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{
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@@ -483,7 +533,14 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Preview Plot"
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"##### Preview Plot"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"###### Sensor A"
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]
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},
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{
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@@ -492,33 +549,14 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"# Create a figure and subplots\n",
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"fig, axes = plt.subplots(2, 3, figsize=(15, 8), sharex=True, sharey=True)\n",
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"\n",
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"# Flatten the axes array for easier iteration\n",
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"axes = axes.flatten()\n",
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"\n",
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"# Loop through each subplot and plot the data\n",
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"for i in range(6):\n",
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" pcm = axes[i].pcolormesh(ready_data1a[i+1].transpose(), cmap='jet', vmax=0.03, vmin=0.0)\n",
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" axes[i].set_title(f'Label {i+1}', fontsize=12)\n",
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"\n",
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"# Add a single color bar for all subplots\n",
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"# Use the first `pcolormesh` object (or any valid one) for the color bar\n",
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"cbar = fig.colorbar(pcm, ax=axes, orientation='vertical')\n",
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"# cbar.set_label('Magnitude')\n",
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"\n",
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"# Set shared labels\n",
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"fig.text(0.5, 0.04, 'Time Frames', ha='center', fontsize=12)\n",
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"fig.text(0.04, 0.5, 'Frequency [Hz]', va='center', rotation='vertical', fontsize=12)\n",
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"fig.suptitle('STFT of Sensor A Damage Case (d1--d6) Dataset A', fontsize=16)\n",
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"\n",
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"# Adjust layout\n",
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"# plt.tight_layout(rect=[0.05, 0.05, 1, 1]) # Leave space for shared labels\n",
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"plt.subplots_adjust(left=0.1, right=0.75, top=0.9, bottom=0.1, wspace=0.2, hspace=0.2)\n",
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"\n",
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"plt.show()"
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"preview_stft(ready_data1a[1:], cols=3, y_num_ticks=9) # Preview for Sensor 1 Damage Cases (d1--d6)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"###### Sensor B"
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]
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},
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{
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@@ -527,33 +565,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"from cmcrameri import cm\n",
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"# Create a figure and subplots\n",
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"fig, axes = plt.subplots(2, 3, figsize=(15, 8), sharex=True, sharey=True)\n",
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"\n",
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"# Flatten the axes array for easier iteration\n",
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"axes = axes.flatten()\n",
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"\n",
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"# Loop through each subplot and plot the data\n",
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"for i in range(6):\n",
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" pcm = axes[i].pcolormesh(ready_data2a[i+1].transpose(), cmap='jet', vmax=0.03, vmin=0.0)\n",
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" axes[i].set_title(f'Label {i+1}', fontsize=12)\n",
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"\n",
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"# Add a single color bar for all subplots\n",
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"# Use the first `pcolormesh` object (or any valid one) for the color bar\n",
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"cbar = fig.colorbar(pcm, ax=axes, orientation='vertical')\n",
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"# cbar.set_label('Magnitude')\n",
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"\n",
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"# Set shared labels\n",
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"fig.text(0.5, 0.04, 'Time Frames', ha='center', fontsize=12)\n",
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"fig.text(0.04, 0.5, 'Frequency [Hz]', va='center', rotation='vertical', fontsize=12)\n",
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"fig.suptitle('STFT of Sensor B Damage Case (d1--d6) Dataset A', fontsize=16)\n",
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"\n",
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"# Adjust layout\n",
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"# plt.tight_layout(rect=[0.05, 0.05, 1, 1]) # Leave space for shared labels\n",
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"plt.subplots_adjust(left=0.1, right=0.75, top=0.9, bottom=0.1, wspace=0.2, hspace=0.2)\n",
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"\n",
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"plt.show()"
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"preview_stft(ready_data2a[1:], cols=3, y_num_ticks=9) # Preview for Sensor 1 Damage Cases (d1--d6)"
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]
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},
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{
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@@ -1072,8 +1084,57 @@
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"window = 1024\n",
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"hop = 512\n",
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"\n",
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"stft1 = stft(df1.values.flatten(), window=hann(window), nperseg=window, noverlap=hop, fs=window)\n",
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"stft2 = stft(df2.values.flatten(), window=hann(window), nperseg=window, noverlap=hop, fs=window)\n"
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"_, _, stft1 = stft(df1.values.flatten(), window=hann(window), nperseg=window, noverlap=hop, fs=window)\n",
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"_, _, stft2 = stft(df2.values.flatten(), window=hann(window), nperseg=window, noverlap=hop, fs=window)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"a = pd.DataFrame(np.abs(stft1.T), columns=[f\"Freq_{freq:.2f}\" for freq in np.linspace(0, window/2, stft1.shape[1])]).rename(columns={a.columns[0]: \"00\"})\n",
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"b = pd.DataFrame(np.abs(stft2.T), columns=[f\"Freq_{freq:.2f}\" for freq in np.linspace(0, window/2, stft2.shape[1])]).rename(columns={b.columns[0]: \"00\"})"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"y = [0]*len(a)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from joblib import load\n",
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"\n",
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"svm_model_a = load('D:/thesis/models/sensor1/SVM with StandardScaler and PCA.joblib')\n",
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"svm_model_b = load('D:/thesis/models/sensor2/SVM with StandardScaler and PCA.joblib')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"y_pred_svm = svm_model_b.predict(b)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"y_pred_svm"
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]
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}
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],
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