Visualization

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"""# Visualization and Scene.

[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/DeepMIMO/DeepMIMO/blob/main/docs/tutorials/2_visualization.py)
 
[![GitHub](https://img.shields.io/badge/Open_on-GitHub-181717?logo=github&style=for-the-badge)](https://github.com/DeepMIMO/DeepMIMO/blob/main/docs/tutorials/2_visualization.py)

---

**Tutorial Overview:**
- Coverage Maps - Visualizing signal coverage
- Rays - Ray propagation visualization
- Path Plots - Visualization of different path components
- Scene & Materials - 3D scene and material visualization
- Plot Overlays - Combining different visualizations

**Related Video:** [Visualization Video](https://youtu.be/MO7h2shBhsc)

---
"""

"""# Visualization and Scene. [](https://colab.research.google.com/github/DeepMIMO/DeepMIMO/blob/main/docs/tutorials/2_visualization.py)   [](https://github.com/DeepMIMO/DeepMIMO/blob/main/docs/tutorials/2_visualization.py) --- **Tutorial Overview:** - Coverage Maps - Visualizing signal coverage - Rays - Ray propagation visualization - Path Plots - Visualization of different path components - Scene & Materials - 3D scene and material visualization - Plot Overlays - Combining different visualizations **Related Video:** [Visualization Video](https://youtu.be/MO7h2shBhsc) --- """

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# Import libraries
import matplotlib.pyplot as plt
import numpy as np

import deepmimo as dm

# Import libraries import matplotlib.pyplot as plt import numpy as np import deepmimo as dm

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# Load dataset
scen_name = "asu_campus_3p5"
dm.download(scen_name)
dataset = dm.load(scen_name)

# Load dataset scen_name = "asu_campus_3p5" dm.download(scen_name) dataset = dm.load(scen_name)

Coverage Maps¶

Visualize signal coverage across the scenario.

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# Plot power coverage
dataset.power.plot()
plt.title("Power Coverage Map")
plt.show()

# Plot power coverage dataset.power.plot() plt.title("Power Coverage Map") plt.show()

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# Plot pathloss coverage
dataset.pathloss.plot()
plt.title("Pathloss Coverage Map")
plt.show()

# Plot pathloss coverage dataset.pathloss.plot() plt.title("Pathloss Coverage Map") plt.show()

Rays¶

Visualize ray propagation paths.

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# Plot rays for a user with line-of-sight
u_idx = np.where(dataset.los == 1)[0][100]
dataset.plot_rays(u_idx, proj_3D=False, dpi=100)
plt.title("Ray Propagation Paths")
plt.show()

# Plot rays for a user with line-of-sight u_idx = np.where(dataset.los == 1)[0][100] dataset.plot_rays(u_idx, proj_3D=False, dpi=100) plt.title("Ray Propagation Paths") plt.show()

Path Plots¶

Visualize different path components and characteristics.

Percentage of Power¶

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# Plot power percentage per path
if hasattr(dataset, "power"):
    # Calculate power percentage
    linear_power = 10 ** (dataset.power / 10)
    total_power = np.sum(linear_power, axis=1, keepdims=True)
    power_pct = (linear_power / (total_power + 1e-20)) * 100

    plt.figure(figsize=(10, 6))
    plt.hist(power_pct[power_pct > 0].flatten(), bins=50)
    plt.xlabel("Power Percentage (%)")
    plt.ylabel("Count")
    plt.title("Distribution of Power Percentage per Path")
    plt.show()

# Plot power percentage per path if hasattr(dataset, "power"): # Calculate power percentage linear_power = 10 ** (dataset.power / 10) total_power = np.sum(linear_power, axis=1, keepdims=True) power_pct = (linear_power / (total_power + 1e-20)) * 100 plt.figure(figsize=(10, 6)) plt.hist(power_pct[power_pct > 0].flatten(), bins=50) plt.xlabel("Power Percentage (%)") plt.ylabel("Count") plt.title("Distribution of Power Percentage per Path") plt.show()

Number of Interactions¶

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# Visualize number of interactions per path
if hasattr(dataset, "interactions"):
    interactions = dataset.interactions
    # Count digits in interaction codes
    num_interactions = np.array(
        [[len(str(int(x))) if x > 0 else 0 for x in row] for row in interactions]
    )

    plt.figure(figsize=(10, 6))
    plt.hist(num_interactions.flatten(), bins=range(10))
    plt.xlabel("Number of Interactions")
    plt.ylabel("Count")
    plt.title("Distribution of Interaction Counts")
    plt.show()

# Visualize number of interactions per path if hasattr(dataset, "interactions"): interactions = dataset.interactions # Count digits in interaction codes num_interactions = np.array( [[len(str(int(x))) if x > 0 else 0 for x in row] for row in interactions] ) plt.figure(figsize=(10, 6)) plt.hist(num_interactions.flatten(), bins=range(10)) plt.xlabel("Number of Interactions") plt.ylabel("Count") plt.title("Distribution of Interaction Counts") plt.show()

Scene & Materials¶

Explore the 3D scene and materials.

Scene Visualization¶

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# Plot the 3D scene
if hasattr(dataset, "scene"):
    dataset.scene.plot()
    plt.title("3D Scene")
    plt.show()
else:
    print("Scene data not available for this scenario")

# Plot the 3D scene if hasattr(dataset, "scene"): dataset.scene.plot() plt.title("3D Scene") plt.show() else: print("Scene data not available for this scenario")

Materials¶

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# Display material properties
if hasattr(dataset, "materials"):
    print("Available Materials:")
    print(dataset.materials)
else:
    print("Material data not available for this scenario")

# Display material properties if hasattr(dataset, "materials"): print("Available Materials:") print(dataset.materials) else: print("Material data not available for this scenario")

Plot Overlays¶

Combine different visualizations for comprehensive analysis.

2D Scene, Coverage & Rays Overlay¶

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# Overlay coverage map with rays
fig, ax = plt.subplots(figsize=(12, 8))

# Plot coverage as background
dataset.power.plot(ax=ax)

# Overlay rays for a selected user
los_users = np.where(dataset.los == 1)[0]
if len(los_users) > 0:
    dataset.plot_rays(los_users[50], ax=ax, proj_3D=False)

plt.title("Coverage Map with Ray Overlay")
plt.show()

# Overlay coverage map with rays fig, ax = plt.subplots(figsize=(12, 8)) # Plot coverage as background dataset.power.plot(ax=ax) # Overlay rays for a selected user los_users = np.where(dataset.los == 1)[0] if len(los_users) > 0: dataset.plot_rays(los_users[50], ax=ax, proj_3D=False) plt.title("Coverage Map with Ray Overlay") plt.show()

3D Scene & Rays Overlay¶

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# 3D visualization with scene and rays
if hasattr(dataset, "scene"):
    # Plot rays first, then overlay the scene
    ax = dataset.plot_rays(los_users[50])
    dataset.scene.plot(ax=ax)
    ax.legend().set_visible(False)

    plt.title("3D Scene with Rays")
    plt.show()
else:
    print("3D scene not available for this scenario")

# 3D visualization with scene and rays if hasattr(dataset, "scene"): # Plot rays first, then overlay the scene ax = dataset.plot_rays(los_users[50]) dataset.scene.plot(ax=ax) ax.legend().set_visible(False) plt.title("3D Scene with Rays") plt.show() else: print("3D scene not available for this scenario")

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Next Steps¶

Continue with:

• Tutorial 3: Detailed Channel Generation - Deep dive into channel generation parameters
• Tutorial 4: User Selection and Dataset Manipulation - Learn how to filter and sample users