FDSVisMap

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FDSVisMap is a Python tool for waypoint-based assessment of visibility in the context of performance-based fire safety design.

It provides methods for analyzing and visualizing visibility maps (Vismaps) derived from FDS (Fire Dynamics Simulator) output data.

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Installation & Setup

pip install fdsvismap

Installation with uv

# Install dependencies
uv sync

# Install in editable mode with dev dependencies
uv sunc --extra dev

Running Tests Locally

To run code quality checks, you can use the following command locally:

# Run all quality checks (linting, formatting, type checking)
# as well as the tests.
./scripts/ci.sh

Citation

To cite this work refer to

@article{BORGER2024104269,
title = {A waypoint based approach to visibility in performance based fire safety design},
author = {Kristian Börger and Alexander Belt and Lukas Arnold},
journal = {Fire Safety Journal},
volume = {150},
pages = {104269},
year = {2024},
issn = {0379-7112},
doi = {https://doi.org/10.1016/j.firesaf.2024.104269},
url = {https://www.sciencedirect.com/science/article/pii/S0379711224001826},
}

FDS Slice File (SLCF) Requirements

FDSVisMap requires specific slice file data from your FDS simulation. The tool uses soot extinction coefficient or soot optical density to calculate visibility.

Required FDS Input

Add the following to your FDS input file (.fds):

&SLCF QUANTITY='SOOT EXTINCTION COEFFICIENT', PBZ=2.0, /

Or for optical density:

&SLCF QUANTITY='SOOT OPTICAL DENSITY', PBZ=2.0, /

• PBZ=2.0 sets the z-coordinate (position) of the slice plane (adjust as needed).
• The slice plane height (z-coordinate, corresponding to PBZ in FDS) is selected in Python via fds_slc_height.

Supported Quantities

Python quantity value	FDS Quantity
ext_coef_C (default)	SOOT EXTINCTION COEFFICIENT
ext_coef_C0.9H0.1	SOOT EXTINCTION COEFFICIENT
OD_C	SOOT OPTICAL DENSITY
OD_C0.9H0.1	SOOT OPTICAL DENSITY

You can set vis.quantity either to these Python-side names (recommended) or to the corresponding FDS QUANTITY strings (for example, 'SOOT EXTINCTION COEFFICIENT' or 'SOOT OPTICAL DENSITY'); both forms are accepted as aliases.

vis = VisMap()

# Default: uses 'SOOT EXTINCTION COEFFICIENT' (Python-side name: "ext_coef_C")
vis.read_fds_data(sim_dir, fds_slc_height=2.0)

# Or explicitly set the quantity using the Python-side name
vis.quantity = "ext_coef_C"
vis.read_fds_data(sim_dir, fds_slc_height=2.0)

# Or use optical density (Python-side name: "OD_C")
vis.quantity = "OD_C"
vis.read_fds_data(sim_dir, fds_slc_height=2.0)

Integration with fdsreader

FDSVisMap uses fdsreader internally to read FDS output files. The read_fds_data() method automatically handles:

• Loading the simulation directory via fds.Simulation(sim_dir)
• Finding the appropriate slice file by quantity and height
• Extracting grid coordinates and time points

No manual fdsreader usage is required.

Usage Example

"""Example script to create visibility maps."""

from fdsvismap import VisMap
from pathlib import Path
import matplotlib.pyplot as plt

project_root = Path(__file__).parent
bg_img = project_root / "misc" / "floorplan.png"

# Set path for FDS simulation directory and background image.
sim_dir = str(project_root / "fds_data")

# Create instance of VisMap class
vis = VisMap()

# Read data from FDS simulation directory.
vis.read_fds_data(sim_dir, fds_slc_height=2)

# Add background image.
vis.add_background_image(bg_img)

# Set starpoint and waypoints along escape route.
vis.set_start_point(1, 9)
vis.set_waypoint(1, 8.4, 4.8, 3, 0)
vis.set_waypoint(2, 9.8, 4, 3, 270)
vis.set_waypoint(3, 17, 10, 3, 180)

# Set times when the simulation should be evaluated.
times = range(0, 500, 50)
vis.set_time_points(times)

# Add a visual obstruction that affects visibility calculations.
vis.add_visual_obstruction(8, 8.8, 4.6, 4.8)

# Do the required calculations to create the Vismap.
vis.compute_all()

# # Plot ASET map based on Vismaps and save as pdf.
fig, ax = vis.create_aset_map_plot(plot_obstructions=True)
ax.set_xlim(0, 20)
ax.set_ylim(0, 10)
plt.savefig('aset_map.pdf', dpi=300)
plt.close()

# # Plot time and waypoint aggregated Vismap and save as pdf.
fig, ax = vis.create_time_agg_wp_agg_vismap_plot()
ax.set_xlim(0, 20)
ax.set_ylim(0, 10)
plt.savefig('time_agg_wp_agg_vismap.pdf', dpi=300)
plt.close()

# Set parameters for local evaluations.
time = 500
x = 2
y = 4
c = 3
waypoint_id = 2

print("\n")

# Check if waypoint is visible from given location at given time.
wp_is_visible = vis.wp_is_visible(time, x, y, waypoint_id)
print(f"Is waypoint {waypoint_id} visible at {time} s at coordinates X/Y = ({x},{y})?: {wp_is_visible}")

# Get distance from waypoint to given location.
distance_top_wp = vis.get_distance_to_wp(x, y, waypoint_id)
print(f"The distance from waypoint {waypoint_id} to location X/Y = ({x},{y}) is {distance_top_wp} m.")

# Calculate local visibility at given location and time, considering a specific c factor.
local_visibility = vis.get_local_visibility(time, x, y, c)
print(f"The local visibility at time {time} s and location X/Y = ({x},{y}) is {local_visibility:.2f} m.")

# Calculate visibility at given location and time relative to a waypoint, considering a specific c factor.
visibility = vis.get_visibility_to_wp(time, x, y, waypoint_id)
print(f"The visibility at time {time} s and location X/Y = ({x},{y}) relative to waypoint {waypoint_id} is {visibility:.2f} m.")