Particles

Collection of Particle objects.

Classes:

Particle(class_name, quantities, color)

Container to store particle data from particle simulations with FDS.

class fdsreader.part.particle.Particle(class_name: str, quantities: List[Quantity], color: Tuple[float, float, float])

Container to store particle data from particle simulations with FDS.

Variables:

• class_name – Name of the particle class defined in the FDS input-file.

• quantities – List of all quantities for which data has been written out.

• color – Color assigned to the particle.

• n_particles – Number of existing particles for each timestep per mesh.

• lower_bounds – Dictionary with lower bounds for each timestep with quantities as keys.

• upper_bounds – Dictionary with upper bounds for each timestep with quantities as keys.

Methods:

clear_cache()	Remove all data from the internal cache that has been loaded so far to free memory.
filter_by_tag(tag)	Filter all particles by a single one with the specified tag.

Attributes:

data	Dictionary with quantities as keys and a list with a numpy array for each timestep which contains data for each particle in that timestep.
id
positions	List with a numpy array for each timestep which contains the position of each particle in that timestep.
tags	List with a numpy array for each timestep which contains a tag for each particle in that timestep.

clear_cache()

Remove all data from the internal cache that has been loaded so far to free memory.

property data: Dict[str, List[ndarray]]

Dictionary with quantities as keys and a list with a numpy array for each timestep which contains data for each particle in that timestep.

filter_by_tag(tag: int)

Filter all particles by a single one with the specified tag.

property id

property positions: List[ndarray]

List with a numpy array for each timestep which contains the position of each particle in that timestep.

property tags: List[ndarray]

List with a numpy array for each timestep which contains a tag for each particle in that timestep.