2. Create videos

This tutorial demonstrates how to generate fluorescence microscopy videos of the AnDi trajectories using the function transform_to_video.

1. Setup

Importing the dependencies needed to run this tutorial.

import numpy as np
import random
import imageio
import matplotlib.pyplot as plt
import deeptrack as dt
from andi_datasets.models_phenom import models_phenom

2. Defining example diffusion model

As an example, We generate the trajectories of dimerization model from models_phenom.

2.1. Dimerization

Defining simulation parameters.

T = 50 # number of time steps (frames)
N = 50 # number of particles (trajectories)
L = 1.5 * 128 # length of the box (pixels) -> extending fov by 1.5 times
D = 0.1 # diffusion coefficient (pixels^2/frame)

trajs, labels = models_phenom().dimerization(
    N=N,
    L=L,
    T=T,
    alphas=[1.2, 0.7],
    Ds=[10 * D, 0.1 * D],
    r=1,  # radius of the particles
    Pb=1,  # binding probability
    Pu=0,  # unbinding probability
)

Plotting trajectories.

for traj in np.moveaxis(trajs, 0, 1):
    plt.plot(traj[:,0], traj[:,1])
plt.show()

3. Generating videos

3.1. Import functions

For generating videos we import transform_to_video function from andi_datasets package. Additionally we import play_video function to display the videos within the jupyter notebook.

from andi_datasets.utils_videos import transform_to_video, play_video

3.2. Usage

The trajectory data generated can be directly passed through transform_to_video to generate fluorescence videos of the particles.

3.2.1. Generating a sample video

video = transform_to_video(
    trajs,
)

play_video(video)

Once Loop Reflect