Convert & Upload Ray-tracing Dataset.¶

 

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Tutorial Overview:

• From Wireless InSite - Convert from Wireless InSite ray tracer
• From Sionna RT - Convert from Sionna RT
• From AODT - Convert from AODT format
• Upload to DeepMIMO - Upload your datasets to the DeepMIMO database

Related Videos:

• Convert Video
• Upload Video

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In [ ]:

From Wireless InSite¶

Convert ray tracing data from Wireless InSite to DeepMIMO format.

In [1]:

# Example: Convert from Wireless InSite
# NOTE: This requires Wireless InSite output files

# Define paths to Wireless InSite files
# insite_path = "/path/to/insite/output"
# output_path = "/path/to/deepmimo/output"

# Convert using the converter
# dm.convert(
#     source="wireless_insite",
#     input_path=insite_path,
#     output_path=output_path,
#     scenario_name="my_insite_scenario"
# )

print("Wireless InSite converter example (requires InSite files)")
print("See documentation for detailed usage")

# Example: Convert from Wireless InSite # NOTE: This requires Wireless InSite output files # Define paths to Wireless InSite files # insite_path = "/path/to/insite/output" # output_path = "/path/to/deepmimo/output" # Convert using the converter # dm.convert( # source="wireless_insite", # input_path=insite_path, # output_path=output_path, # scenario_name="my_insite_scenario" # ) print("Wireless InSite converter example (requires InSite files)") print("See documentation for detailed usage")

Wireless InSite converter example (requires InSite files)
See documentation for detailed usage

From Sionna RT¶

Convert from Sionna Ray Tracer.

In [2]:

# Example: Export from Sionna RT to DeepMIMO
# NOTE: This requires Sionna RT to be installed and configured

# In your Sionna RT script, use the exporter:
example_code = """
# In Sionna:
from deepmimo.converters import sionna_exporter

# After running your Sionna RT simulation:
paths = scene.compute_paths()

# Export to DeepMIMO format
sionna_exporter(
    paths=paths,
    output_dir="./deepmimo_output",
    scenario_name="my_sionna_scenario"
)
"""

print("Sionna RT exporter example:")
print(example_code)

# Example: Export from Sionna RT to DeepMIMO # NOTE: This requires Sionna RT to be installed and configured # In your Sionna RT script, use the exporter: example_code = """ # In Sionna: from deepmimo.converters import sionna_exporter # After running your Sionna RT simulation: paths = scene.compute_paths() # Export to DeepMIMO format sionna_exporter( paths=paths, output_dir="./deepmimo_output", scenario_name="my_sionna_scenario" ) """ print("Sionna RT exporter example:") print(example_code)

Sionna RT exporter example:

# In Sionna:
from deepmimo.converters import sionna_exporter

# After running your Sionna RT simulation:
paths = scene.compute_paths()

# Export to DeepMIMO format
sionna_exporter(
    paths=paths,
    output_dir="./deepmimo_output",
    scenario_name="my_sionna_scenario"
)

From AODT¶

Convert from AODT (Autonomous Driving) dataset format.

In [3]:

# Example: Convert from AODT
# NOTE: This requires AODT dataset files

# Define paths
# aodt_path = "/path/to/aodt/dataset"
# output_path = "/path/to/deepmimo/output"

# Convert using the AODT converter
# dm.convert(
#     source="aodt",
#     input_path=aodt_path,
#     output_path=output_path,
#     scenario_name="my_aodt_scenario"
# )

print("AODT converter example (requires AODT files)")
print("See documentation for detailed usage")

# Example: Convert from AODT # NOTE: This requires AODT dataset files # Define paths # aodt_path = "/path/to/aodt/dataset" # output_path = "/path/to/deepmimo/output" # Convert using the AODT converter # dm.convert( # source="aodt", # input_path=aodt_path, # output_path=output_path, # scenario_name="my_aodt_scenario" # ) print("AODT converter example (requires AODT files)") print("See documentation for detailed usage")

AODT converter example (requires AODT files)
See documentation for detailed usage

Upload to DeepMIMO¶

Upload your converted scenarios to the DeepMIMO database.

Upload Scenario Files¶

In [4]:

# Example: Upload scenario to DeepMIMO database
# NOTE: This requires authentication credentials

# Upload scenario
# dm.upload(
#     scenario_path="/path/to/deepmimo/scenario",
#     scenario_name="my_custom_scenario",
#     description="Description of the scenario",
#     tags=["outdoor", "urban", "5G"],
#     api_key="your_api_key_here"
# )

print("Upload scenario example (requires API key)")
print("Visit deepmimo.net to get your API key")

# Example: Upload scenario to DeepMIMO database # NOTE: This requires authentication credentials # Upload scenario # dm.upload( # scenario_path="/path/to/deepmimo/scenario", # scenario_name="my_custom_scenario", # description="Description of the scenario", # tags=["outdoor", "urban", "5G"], # api_key="your_api_key_here" # ) print("Upload scenario example (requires API key)") print("Visit deepmimo.net to get your API key")

Upload scenario example (requires API key)
Visit deepmimo.net to get your API key

Upload Additional Images¶

In [5]:

# Example: Upload scenario images/visualizations
# dm.upload_images(
#     scenario_name="my_custom_scenario",
#     image_paths=[
#         "/path/to/coverage_map.png",
#         "/path/to/3d_scene.png"
#     ],
#     api_key="your_api_key_here"
# )

print("Upload images example")

# Example: Upload scenario images/visualizations # dm.upload_images( # scenario_name="my_custom_scenario", # image_paths=[ # "/path/to/coverage_map.png", # "/path/to/3d_scene.png" # ], # api_key="your_api_key_here" # ) print("Upload images example")

Upload images example

Upload Ray Tracing Source Files¶

In [6]:

# Example: Upload original ray tracing source files
# dm.upload_rt_source(
#     scenario_name="my_custom_scenario",
#     source_files=[
#         "/path/to/scene.xml",
#         "/path/to/materials.mat"
#     ],
#     ray_tracer="wireless_insite",  # or "sionna", "aodt"
#     api_key="your_api_key_here"
# )

print("Upload ray tracing source example")

# Example: Upload original ray tracing source files # dm.upload_rt_source( # scenario_name="my_custom_scenario", # source_files=[ # "/path/to/scene.xml", # "/path/to/materials.mat" # ], # ray_tracer="wireless_insite", # or "sionna", "aodt" # api_key="your_api_key_here" # ) print("Upload ray tracing source example")

Upload ray tracing source example

Converter Workflow Example¶

Complete workflow from ray tracing to DeepMIMO format.

In [7]:

# Step 1: Run ray tracing simulation
print("Step 1: Run your ray tracing simulation")
print("  - Configure scenario geometry")
print("  - Set TX/RX positions")
print("  - Run simulation")

# Step 2: Convert to DeepMIMO format
print("\nStep 2: Convert to DeepMIMO format")
print("  - Use appropriate converter (InSite, Sionna, AODT)")
print("  - Specify input/output paths")
print("  - Run conversion")

# Step 3: Validate converted data
print("\nStep 3: Validate converted data")
print("  - Load dataset with dm.load()")
print("  - Check data integrity")
print("  - Visualize results")

# Step 4: Upload to DeepMIMO (optional)
print("\nStep 4: Upload to DeepMIMO database (optional)")
print("  - Prepare scenario metadata")
print("  - Upload scenario files")
print("  - Share with community")

# Step 1: Run ray tracing simulation print("Step 1: Run your ray tracing simulation") print(" - Configure scenario geometry") print(" - Set TX/RX positions") print(" - Run simulation") # Step 2: Convert to DeepMIMO format print("\nStep 2: Convert to DeepMIMO format") print(" - Use appropriate converter (InSite, Sionna, AODT)") print(" - Specify input/output paths") print(" - Run conversion") # Step 3: Validate converted data print("\nStep 3: Validate converted data") print(" - Load dataset with dm.load()") print(" - Check data integrity") print(" - Visualize results") # Step 4: Upload to DeepMIMO (optional) print("\nStep 4: Upload to DeepMIMO database (optional)") print(" - Prepare scenario metadata") print(" - Upload scenario files") print(" - Share with community")

Step 1: Run your ray tracing simulation
  - Configure scenario geometry
  - Set TX/RX positions
  - Run simulation

Step 2: Convert to DeepMIMO format
  - Use appropriate converter (InSite, Sionna, AODT)
  - Specify input/output paths
  - Run conversion

Step 3: Validate converted data
  - Load dataset with dm.load()
  - Check data integrity
  - Visualize results

Step 4: Upload to DeepMIMO database (optional)
  - Prepare scenario metadata
  - Upload scenario files
  - Share with community

Validation After Conversion¶

In [8]:

# After conversion, validate your data
# converted_scen = "my_converted_scenario"
# dataset = dm.load(converted_scen)

# Check basic properties
# print(f"Number of users: {len(dataset.power)}")
# print(f"Number of paths: {dataset.power.shape[1]}")
# print(f"Data matrices available: {dir(dataset)}")

# Visualize
# dataset.power.plot()
# plt.title('Converted Scenario - Power Coverage')
# plt.show()

print("Validation example (after successful conversion)")

# After conversion, validate your data # converted_scen = "my_converted_scenario" # dataset = dm.load(converted_scen) # Check basic properties # print(f"Number of users: {len(dataset.power)}") # print(f"Number of paths: {dataset.power.shape[1]}") # print(f"Data matrices available: {dir(dataset)}") # Visualize # dataset.power.plot() # plt.title('Converted Scenario - Power Coverage') # plt.show() print("Validation example (after successful conversion)")

Validation example (after successful conversion)

Supported Ray Tracers¶

DeepMIMO supports conversion from:

1. Wireless InSite

   • Full 3D ray tracing
   • Supports all interaction types
   • Detailed material properties

2. Sionna RT

   • GPU-accelerated ray tracing
   • Differentiable ray tracer
   • TensorFlow integration

3. AODT

   • Autonomous driving scenarios
   • V2X communication
   • Mobility models

Best Practices¶

1. Organize your data: Keep source files well-organized
2. Document your scenario: Include metadata and descriptions
3. Validate thoroughly: Check converted data before uploading
4. Use meaningful names: Choose descriptive scenario names
5. Include visualizations: Upload coverage maps and 3D views

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Next Steps¶

• Tutorial 8: Migration Guide - Migrating from DeepMIMO v3 to v4
• Return to Tutorial 1: Getting Started for more basic operations