.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "example_gallery/plot_rf_snr.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. .. rst-class:: sphx-glr-example-title .. _sphx_glr_example_gallery_plot_rf_snr.py: Signal-to-Noise Ratio (SNR) Metric for Raw RF Data ==================================================== This example demonstrates how to: 1. Load raw RF data from the PICMUS dataset 2. Visualize the RF signal to identify regions of interest 3. Automatically detect signal and noise regions 4. (Optional) Manually adjust parameters based on what you see 5. Compute and print the RF SNR This workflow uses real raw RF data from the PICMUS challenge dataset [1]_. The SNR calculation follows standard ultrasound analysis practices, with automated region detection inspired by analysis tools like ultraspy [2]_. References ---------- .. [1] H. Liebgott, A. Rodriguez-Molares, F. Cervenansky, J. A. Jensen and O. Bernard, "Plane-Wave Imaging Challenge in Medical Ultrasound," 2016 IEEE International Ultrasonics Symposium (IUS), Tours, France, 2016, pp. 1-4, doi: 10.1109/ULTSYM.2016.7728908. .. [2] P. Ecarlat, E. Carcreff, F. Varray, H. Liebgott and B. Nicolas, "Get Ready to Spy on Ultrasound: Meet ultraspy," 2023 IEEE International Ultrasonics Symposium (IUS), Montreal, QC, Canada, 2023, pp. 1-4, doi: 10.1109/IUS51837.2023.10307778. Example ------- .. GENERATED FROM PYTHON SOURCE LINES 33-35 1. Import required modules and functions ------------------------------------------------------------------------ .. GENERATED FROM PYTHON SOURCE LINES 35-41 .. code-block:: Python import matplotlib.pyplot as plt from ultrasound_metrics.data.uff import inspect_dataset, load_dataset from ultrasound_metrics.metrics.rf_snr import compute_rf_snr, find_signal_and_noise .. GENERATED FROM PYTHON SOURCE LINES 42-44 2. Load the ultrasound dataset and extract raw RF data ------------------------------------------------------------------------ .. GENERATED FROM PYTHON SOURCE LINES 44-69 .. code-block:: Python # Load raw RF data directly from the PICMUS dataset dataset_info = inspect_dataset("picmus_resolution_experiment") print(f"Dataset: {dataset_info['name']}") print(f"Description: {dataset_info['description']}") # Load raw RF data from channel_data print("\n=== Loading Raw RF Data ===") channel_data = load_dataset("picmus_resolution_experiment", key="/channel_data") print(f"Raw channel data shape: {channel_data.shape}") print(f"Raw channel data type: {channel_data.dtype}") # Extract a single channel for RF SNR analysis # Channel data shape: (samples, channels, frames) samples, channels, frames = channel_data.shape channel_idx = channels // 2 # Middle channel frame_idx = 0 # First frame # Extract single channel RF data rf_data = channel_data[:, channel_idx, frame_idx] print(f"Extracted RF data shape: {rf_data.shape}") print(f"Channel index: {channel_idx} (out of {channels})") print(f"Frame index: {frame_idx} (out of {frames})") print(f"Number of samples: {samples}") .. rst-class:: sphx-glr-script-out .. code-block:: none Dataset: picmus_resolution_experiment Description: PICMUS challenge resolution/distortion test (experiment) === Loading Raw RF Data === Raw channel data shape: (3328, 128, 75) Raw channel data type: float32 Extracted RF data shape: (3328,) Channel index: 64 (out of 128) Frame index: 0 (out of 75) Number of samples: 3328 .. GENERATED FROM PYTHON SOURCE LINES 70-72 3. Visualize the RF data to help identify regions ------------------------------------------------------------------------ .. GENERATED FROM PYTHON SOURCE LINES 72-102 .. code-block:: Python plt.figure(figsize=(15, 8)) # Plot the full RF signal plt.subplot(2, 1, 1) plt.plot(rf_data, "b-", linewidth=0.5, alpha=0.8) plt.title(f"Full RF Signal - Channel {channel_idx}") plt.xlabel("Sample Index") plt.ylabel("Amplitude") plt.grid(True, alpha=0.3) # Add some reference lines to help identify regions plt.axhline(y=0, color="k", linestyle="--", alpha=0.5) plt.axhline(y=rf_data.std(), color="g", linestyle=":", alpha=0.7, label=f"±1std = ±{rf_data.std():.3f}") plt.axhline(y=-rf_data.std(), color="g", linestyle=":", alpha=0.7) plt.legend() # Plot a zoomed view of the first 500 samples to see startup artifacts plt.subplot(2, 1, 2) zoom_samples = min(500, len(rf_data)) plt.plot(rf_data[:zoom_samples], "r-", linewidth=1) plt.title(f"Zoomed View - First {zoom_samples} Samples") plt.xlabel("Sample Index") plt.ylabel("Amplitude") plt.grid(True, alpha=0.3) plt.axhline(y=0, color="k", linestyle="--", alpha=0.5) plt.tight_layout() plt.show() .. image-sg:: /example_gallery/images/sphx_glr_plot_rf_snr_001.png :alt: Full RF Signal - Channel 64, Zoomed View - First 500 Samples :srcset: /example_gallery/images/sphx_glr_plot_rf_snr_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 103-105 4. Manual parameter adjustment based on visualization ------------------------------------------------------------------------ .. GENERATED FROM PYTHON SOURCE LINES 105-125 .. code-block:: Python print("\n=== Manual Parameter Adjustment ===") print("Based on the visualization above, you can adjust these parameters:") # Current parameters - users can modify these based on what they see ignore_until = 10 # Skip initial startup artifacts signal_width = 50 # Width of signal region around maximum noise_offset = 100 # Gap between signal and noise regions print("Current parameters:") print(f"- ignore_until: {ignore_until} (skip first {ignore_until} samples)") print(f"- signal_width: {signal_width} (signal region width)") print(f"- noise_offset: {noise_offset} (gap between signal and noise)") print("\nTo adjust these parameters:") print("1. Look at the RF signal plot above") print("2. Identify where startup artifacts end (usually first 50-200 samples)") print("3. Modify the 'ignore_until' parameter below") print("4. Re-run the analysis") .. rst-class:: sphx-glr-script-out .. code-block:: none === Manual Parameter Adjustment === Based on the visualization above, you can adjust these parameters: Current parameters: - ignore_until: 10 (skip first 10 samples) - signal_width: 50 (signal region width) - noise_offset: 100 (gap between signal and noise) To adjust these parameters: 1. Look at the RF signal plot above 2. Identify where startup artifacts end (usually first 50-200 samples) 3. Modify the 'ignore_until' parameter below 4. Re-run the analysis .. GENERATED FROM PYTHON SOURCE LINES 126-128 5. Automatic region detection with user-defined parameters ------------------------------------------------------------------------ .. GENERATED FROM PYTHON SOURCE LINES 128-145 .. code-block:: Python print("\n=== Automatic Region Detection ===") print(f"Using parameters: ignore_until={ignore_until}, signal_width={signal_width}, noise_offset={noise_offset}") signal_data_auto, noise_data_auto = find_signal_and_noise( rf_data, signal_width=signal_width, noise_offset=noise_offset, ignore_until=ignore_until, show=True ) # Get the automatic region boundaries (approximate calculation) # Note: This is an approximation since find_signal_and_noise doesn't return exact indices auto_signal_samples = len(signal_data_auto) auto_noise_samples = len(noise_data_auto) print("\nAutomatic region summary:") print(f"- Signal: {auto_signal_samples} samples") print(f"- Noise: {auto_noise_samples} samples") .. image-sg:: /example_gallery/images/sphx_glr_plot_rf_snr_002.png :alt: Automatic Signal and Noise Region Detection :srcset: /example_gallery/images/sphx_glr_plot_rf_snr_002.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none === Automatic Region Detection === Using parameters: ignore_until=10, signal_width=50, noise_offset=100 Automatic region summary: - Signal: 50 samples - Noise: 3172 samples .. GENERATED FROM PYTHON SOURCE LINES 146-148 6. Manual adjustment of automatic regions (OPTIONAL) ------------------------------------------------------------------------ .. GENERATED FROM PYTHON SOURCE LINES 148-176 .. code-block:: Python print("\n=== Manual Adjustment of Automatic Regions ===") print("The automatic detection may not capture the ideal regions.") print("You can manually adjust the bounds based on what you see in the plot above.") # Manual adjustment parameters (modify these based on visualization) print("\n=== Manual Adjustment Parameters ===") print("Modify these values based on the visualization above:") # Example: Restrict noise to samples up to the 200th sample manual_signal_start = 20 # Start of signal region (big spike) manual_signal_end = 40 # End of signal region manual_noise_start = 40 # Start of noise region manual_noise_end = 200 # Limit noise region to first 200 samples print("Manual adjustments:") print(f"- Signal: samples {manual_signal_start}-{manual_signal_end} (manual selection)") print(f"- Noise: samples {manual_noise_start}-{manual_noise_end} (limited to 200)") # Extract manually adjusted regions signal_data = rf_data[manual_signal_start:manual_signal_end] noise_data = rf_data[manual_noise_start:manual_noise_end] print("\nAdjusted regions:") print(f"- Signal: {len(signal_data)} samples") print(f"- Noise: {len(noise_data)} samples") .. rst-class:: sphx-glr-script-out .. code-block:: none === Manual Adjustment of Automatic Regions === The automatic detection may not capture the ideal regions. You can manually adjust the bounds based on what you see in the plot above. === Manual Adjustment Parameters === Modify these values based on the visualization above: Manual adjustments: - Signal: samples 20-40 (manual selection) - Noise: samples 40-200 (limited to 200) Adjusted regions: - Signal: 20 samples - Noise: 160 samples .. GENERATED FROM PYTHON SOURCE LINES 177-179 7. Print region summary ------------------------------------------------------------------------ .. GENERATED FROM PYTHON SOURCE LINES 179-187 .. code-block:: Python signal_samples = len(signal_data) noise_samples = len(noise_data) print("\n=== Region Selection Summary ===") print(f"Signal region: {signal_samples} samples (manually adjusted)") print(f"Noise region: {noise_samples} samples (manually adjusted)") .. rst-class:: sphx-glr-script-out .. code-block:: none === Region Selection Summary === Signal region: 20 samples (manually adjusted) Noise region: 160 samples (manually adjusted) .. GENERATED FROM PYTHON SOURCE LINES 188-190 8. Print basic statistics ------------------------------------------------------------------------ .. GENERATED FROM PYTHON SOURCE LINES 190-197 .. code-block:: Python print("\n=== Region Statistics ===") print(f"Signal mean: {signal_data.mean():.3f}") print(f"Signal std: {signal_data.std():.3f}") print(f"Noise mean: {noise_data.mean():.3f}") print(f"Noise std: {noise_data.std():.3f}") .. rst-class:: sphx-glr-script-out .. code-block:: none === Region Statistics === Signal mean: -0.006 Signal std: 0.190 Noise mean: 0.000 Noise std: 0.018 .. GENERATED FROM PYTHON SOURCE LINES 198-200 9. Compute and print RF SNR ------------------------------------------------------------------------ .. GENERATED FROM PYTHON SOURCE LINES 200-207 .. code-block:: Python snr_db = compute_rf_snr(signal_data, noise_data, show=True) print("\n=== RF SNR Results ===") print(f"RF SNR: {snr_db:.2f} dB") print("Data source: Raw RF data from /channel_data (manually adjusted regions)") .. image-sg:: /example_gallery/images/sphx_glr_plot_rf_snr_003.png :alt: Signal and Noise Data (SNR = 20.60 dB) :srcset: /example_gallery/images/sphx_glr_plot_rf_snr_003.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none === RF SNR Results === RF SNR: 20.60 dB Data source: Raw RF data from /channel_data (manually adjusted regions) .. GENERATED FROM PYTHON SOURCE LINES 208-210 10. Compare different SNR calculation methods ------------------------------------------------------------------------ .. GENERATED FROM PYTHON SOURCE LINES 210-224 .. code-block:: Python print("\n=== SNR Method Comparison ===") # Standard SNR (centered, average power) snr_standard = compute_rf_snr(signal_data, noise_data, center=True, max_power=False) print(f"Standard SNR (centered, avg power): {snr_standard:.2f} dB") # SNR with max power snr_max_power = compute_rf_snr(signal_data, noise_data, center=True, max_power=True) print(f"SNR with max power: {snr_max_power:.2f} dB") # SNR without centering snr_uncentered = compute_rf_snr(signal_data, noise_data, center=False, max_power=False) print(f"SNR without centering: {snr_uncentered:.2f} dB") .. rst-class:: sphx-glr-script-out .. code-block:: none === SNR Method Comparison === Standard SNR (centered, avg power): 20.60 dB SNR with max power: 24.96 dB SNR without centering: 20.60 dB .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.465 seconds) .. _sphx_glr_download_example_gallery_plot_rf_snr.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_rf_snr.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_rf_snr.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_rf_snr.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_