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Track gen

track_gen.py Documentation#

Overview#

The track_gen.py module provides functionality to simulate foci dynamics in space, particularly within cell simulations. It includes a Track_generator class that generates tracks of foci movements in a simulated cell space, with options for transitions between different diffusion coefficients and Hurst exponents.

Classes#

Track_generator#

A class to generate tracks of foci movements in a simulated cell space.

Parameters#
  • cell (CellType):
  • A cell object defining the space for track generation.
  • cycle_count (int):
  • The number of frames for the simulation.
  • exposure_time (int | float):
  • Exposure time in milliseconds.
  • interval_time (int | float):
  • Interval time between frames in milliseconds.
  • oversample_motion_time (int | float):
  • Time for oversampling motion in milliseconds.
Methods#
  • __init__(self, cell: CellType, cycle_count: int, exposure_time: int | float, interval_time: int | float, oversample_motion_time: int | float) -> None:

  • Initializes the Track_generator object.

  • _allowable_cell_types(self):

  • Checks if the cell type is supported (currently only rectangular cells are supported).

  • track_generation_no_transition(self, diffusion_coefficient: float, hurst_exponent: float, track_length: int, initials: np.ndarray, start_time: int | float) -> dict:

  • Simulates track generation with no transition between diffusion coefficients and Hurst exponents.

  • track_generation_with_transition(self, diffusion_transition_matrix: np.ndarray | list, hurst_transition_matrix: np.ndarray | list, diffusion_parameters: np.ndarray | list, hurst_parameters: np.ndarray | list, diffusion_state_probability: np.ndarray | list, hurst_state_probability: np.ndarray | list, track_length: int, initials: np.ndarray, start_time: int | float) -> dict:

  • Generates track data with transitions between diffusion coefficients and Hurst exponents.

  • track_generation_constant(self, track_length: int, initials: np.ndarray, start_time: int) -> dict:

  • Generates a constant track (no movement).

  • _convert_diffcoef_um2s_um2xms(self, diffusion_coefficient: float | np.ndarray | list) -> float | np.ndarray | list:

  • Converts diffusion coefficient from um^2/s to um^2/x ms.

  • _convert_time_to_frame(self, time: int) -> int:

  • Converts time in milliseconds to frame number.

  • _convert_frame_to_time(self, frame: int) -> int:

  • Converts frame number to time in milliseconds.
Private Methods#
  • _allowable_cell_types(self):
  • Ensures that only rectangular cells are supported for track generation.

Functions#

  • _initialize_points_per_time(total_time: int, oversample_motion_time: int) -> dict:

  • Initializes an empty dictionary with keys for each time point.

  • _update_points_per_time(points_per_time: dict, track: dict) -> None:

  • Updates the points per time dictionary with new track data.

  • _generate_constant_tracks(track_generator: Track_generator, track_lengths: list | np.ndarray | int, initial_positions: np.ndarray, starting_frames: int = 0) -> tuple[dict, dict]:

  • Generates tracks with constant parameters.

  • _generate_no_transition_tracks(track_generator: Track_generator, track_lengths: list | np.ndarray | int, initial_positions: np.ndarray, starting_frames: int, diffusion_parameters: np.ndarray, hurst_parameters: np.ndarray) -> tuple[dict, dict]:

  • Generates tracks without state transitions.

  • _generate_transition_tracks(track_generator: Track_generator, track_lengths: list | np.ndarray | int, initial_positions: np.ndarray, starting_frames: int, diffusion_parameters: np.ndarray, hurst_parameters: np.ndarray, diffusion_transition_matrix: np.ndarray, hurst_transition_matrix: np.ndarray, diffusion_state_probability: np.ndarray, hurst_state_probability: np.ndarray) -> tuple[dict, dict]:

  • Generates tracks with state transitions.

  • _convert_tracks_to_trajectory(tracks: dict) -> dict:

  • Converts tracks to a trajectory format.

Dependencies#

  • random
  • numpy (np)
  • RectangularCell, RodCell, SphericalCell from ..cells
  • FBM_BP from .movement.fbm_BP

Usage#

To use the Track_generator class, instantiate it with a cell object and the necessary parameters. Then, use the provided methods to generate tracks with or without transitions.

from track_gen import Track_generator
from ..cells.rectangular_cell import RectangularCell

# Example usage
cell = RectangularCell()
track_generator = Track_generator(cell, cycle_count=100, exposure_time=10, interval_time=5, oversample_motion_time=2)

# Generate a track with no transitions
track_data = track_generator.track_generation_no_transition(
    diffusion_coefficient=1.0,
    hurst_exponent=0.5,
    track_length=100,
    initials=np.array([0, 0, 0]),
    start_time=0
)

print(track_data)

This will output a dictionary containing the generated track data.