Visualization

The visualization module provides tools for visualizing DeepMIMO datasets, including coverage maps, ray paths, and channel characteristics.

More visualization examples

See the Visualization section of the DeepMIMO Manual for additional examples.

Coverage Maps

Create coverage map visualization for user positions.

import deepmimo as dm

# Load a scenario and select the txrx pair with a receiver grid
dataset = dm.load('asu_campus_3p5')[0]

# Example plot LoS status with default settings
dm.plot_coverage(dataset.rx_pos, dataset.los)

# Customize visualization
ax, cbar = dm.plot_coverage(
    rxs,                   # User positions (N×3)
    cov_map,               # Coverage values
    dpi=100,               # Plot resolution
    figsize=(6, 4),        # Figure size
    cbar_title='Power',    # Colorbar title
    title=True,            # Show title
    scat_sz=0.5,           # Marker size
    bs_pos=bs_position,    # Base station position
    bs_ori=bs_orientation, # Base station orientation
    legend=True,           # Show legend
    proj_3D=False,         # 2D/3D projection
    cmap='viridis'         # Color map
)

# Plot multiple features
features = ['aoa_az', 'aoa_el', 'aod_az', 'aod_el', 
            'delay', 'power', 'phase', 'los', 'num_paths']

for key in features:
    plt_var = dataset[key][:,0] if dataset[key].ndim == 2 else dataset[key]
    dataset.plot_coverage(plt_var, title=key)  # wrapper to plot_coverage(dataset.rx_pos)

Generate coverage map visualization for user positions.

This function creates a customizable plot showing user positions colored by coverage values, with optional base station position and orientation indicators.

Parameters:

Name	Type	Description	Default
rxs	ndarray	User position array with shape (n_users, 3)	required
cov_map	tuple[float, ...] | list[float] | ndarray	Coverage map values for coloring	required
dpi	int	Plot resolution in dots per inch. Defaults to 300.	100
figsize	tuple[int, int]	Figure dimensions (width, height) in inches. Defaults to (6,4).	(6, 4)
cbar_title	str	Title for the colorbar. Defaults to ''.	''
title	bool | str	Plot title. Defaults to False.	False
scat_sz	float	Size of scatter markers. Defaults to 0.5.	0.5
bs_pos	Optional[ndarray]	Base station position coordinates. Defaults to None.	None
bs_ori	Optional[ndarray]	Base station orientation angles. Defaults to None.	None
legend	bool	Whether to show plot legend. Defaults to False.	False
lims	Optional[tuple[float, float]]	Color scale limits (min, max). Defaults to None.	None
proj_3d	bool	Whether to create 3D projection. Defaults to False.	False
equal_aspect	bool	Whether to maintain equal axis scaling. Defaults to False.	False
tight	bool	Whether to set tight axis limits around data points. Defaults to True.	True
cmap	str | list	Matplotlib colormap name or list of colors. Defaults to 'viridis'.	'viridis'
cbar_labels	Optional[list[str]]	List of labels for the colorbar. Defaults to None.	None
ax	Optional[Axes]	Matplotlib Axes object. Defaults to None.	None
**kwargs	Any	Additional keyword-only options; accepts proj_3D alias.	{}

Returns:

Type	Description
Figure	Tuple containing:
Axes	matplotlib Figure object
Colorbar	matplotlib Axes object
tuple[Figure, Axes, Colorbar]	matplotlib Colorbar object

Rays

Plot ray paths between transmitter and receiver with interaction points.

import deepmimo as dm

# Load a scenario and select the txrx pair with a receiver grid
dataset = dm.load('asu_campus_3p5')[0]

# Plot ray paths
fig, ax = dm.plot_rays(
    dataset.rx_pos,      # Receiver location
    dataset.tx_pos,      # Transmitter location
    dataset.inter_pos,   # Interaction positions
    dataset.inter,       # Interaction types
    figsize=(10, 8),     # Figure size
    dpi=100,             # Plot resolution
    proj_3D=True,        # 3D projection
    color_by_type=True   # Color by interaction type
)

# Plot ray paths with wrapper
dataset.plot_rays(10)  # user index

Plot ray paths between transmitter and receiver with interaction points.

For a given user, plots all ray paths connecting TX and RX through their respective interaction points. Each path is drawn as a sequence of segments connecting TX -> interaction points -> RX.

Parameters:

Name	Type	Description	Default
rx_loc	ndarray	Receiver location array with shape [3,]	required
tx_loc	ndarray	Transmitter location array with shape [3,]	required
inter_pos	ndarray	Interaction positions with shape [n_paths, max_interactions, 3] where n_paths is the number of rays for this user	required
inter	ndarray	Interaction types with shape [n_paths,] where each path's value contains digits representing interaction types (e.g., 211 means type 2 for first bounce, type 1 for second and third)	required
figsize	tuple	Figure size in inches. Defaults to (10,8).	(10, 8)
dpi	int	Resolution in dots per inch. Defaults to 300.	100
proj_3d	bool	Whether to create 3D projection. Defaults to True.	True
color_by_type	bool	Whether to color interaction points by their type. Defaults to False.	True
inter_objects	Optional[ndarray]	Object ids at each interaction point. Defaults to None. If provided, will color the interaction points by the object id, and ignore the interaction type.	None
inter_obj_labels	Optional[list[str]]	Labels for the interaction objects. Defaults to None. If provided, will use these labels instead of the object ids.	None
color_rays_by_pwr	bool	Whether to color rays by their power. Defaults to False.	False
powers	Optional[ndarray]	Power values for each path. Required if color_rays_by_pwr is True.	None
show_cbar	bool	Whether to show the colorbar. Defaults to False.	False
limits	Optional[tuple[float, float]]	Power limits for coloring (min, max). If None, uses relative scaling.	None
ax	Optional[Axes]	Matplotlib Axes object. Defaults to None. When provided, the figure and axes are not created.	None
**kwargs	Any	Additional keyword-only options; accepts proj_3D alias.	{}

Returns:

Type	Description
Figure	Tuple containing:
Axes	matplotlib Figure object
tuple[Figure, Axes]	matplotlib Axes object