Particle Tracker¶

class particle_tracker_one_d.ParticleTracker(frames, time, automatic_update=True)[source]¶

Dynamic Particle tracker object which finds trajectories in the frames. Trajectories are automatically updated when properties are changed.

Parameters:

frames: np.array: The frames in which trajectories are to be found. The shape of the np.array should be (nFrames,xPixels). The intensity of the frames should be normalised according to \(I_n = (I-I_{min})/(I_{max}-I_{min})\), where \(I\) is the intensity of the frames, \(I_{min}\), \(I_{max}\) are the global intensity minima and maxima of the frames.
time: np.array: The corresponding time of each frame.

Attributes:

frames: np.array:
time: np.array:
boxcar_width: int:
integration_radius_of_intensity_peaks: int:
particle_detection_threshold: float:
maximum_number_of_frames_a_particle_can_disappear_and_still_be_linked_to_other_particles: int:
maximum_distance_a_particle_can_travel_between_frames: int:
particle_positions: list:

Methods

`change_cost_coefficients`([a, b, c, d])	Change the coefficients of the cost function \(c(p_1,p_2) = a\cdot (x_{p_1} - x_{p_2})^2 + b \cdot (m_0(p_1)-m_0(p_2))^2 + b \cdot (m_2(p_1)-m_2(p_2))^2) + d \cdot (t_{p_1}-t_{p_2})^2\)
`get_frame_at_time`(time)	time: float
`normalise_intensity`(frames)	frames: np.array
`plot_all_frames`([ax])	ax: matplotlib axes instance
`plot_all_particles`([ax])	ax: matplotlib axes instance
`plot_frame`(frame_index[, ax])	frame_index: index
`plot_frame_at_time`(time[, ax])	time: float
`plot_moments`([ax])	ax: matplotlib axes instance

boxcar_width¶

int:: Number of values used in the boxcar averaging of the frames.

change_cost_coefficients(a=1, b=1, c=1, d=1)[source]¶

Change the coefficients of the cost function \(c(p_1,p_2) = a\cdot (x_{p_1} - x_{p_2})^2 + b \cdot (m_0(p_1)-m_0(p_2))^2 + b \cdot (m_2(p_1)-m_2(p_2))^2) + d \cdot (t_{p_1}-t_{p_2})^2\)

a: float

b: float

c: float

d: float

frames¶

np.array:: The frames which the particle tracker tries to find trajectories in. If the property boxcar_width!=0 it will return the smoothed frames.

get_frame_at_time(time)[source]¶

time: float: Time of the frame which you want to get.

Returns:	np.array Returns the frame which corresponds to the input time.

integration_radius_of_intensity_peaks¶

int:: Number of pixels used when integrating the intensity peaks. No particles closer than twice this value will be found. If two peaks are found within twice this value, the one with highest intensity moment will be kept.

maximum_distance_a_particle_can_travel_between_frames¶

int:: Max number of pixels a particle can travel between two consecutive frames.

maximum_number_of_frames_a_particle_can_disappear_and_still_be_linked_to_other_particles¶

int:: Number of frames a particle can be invisible and still be linked in a trajectory.

static normalise_intensity(frames)[source]¶

frames: np.array: Normalises the intensity of the frames according to \(I_n = (I-I_{min})/(I_{max}-I_{min})\), where \(I\) is the intensity of the frames, \(I_{min}\), \(I_{max}\) are the global intensity minima and maxima of the frames.

Returns:	np.array The normalised intensity.

particle_detection_threshold¶

float:: Defines the threshold value for finding intensity peaks. Local maximas below this threshold will not be considered as particles. Should be a value between 0 and 1.

particle_positions¶

list:: List with numpy arrays containing all particle positions.

plot_all_frames(ax=None, **kwargs)[source]¶

ax: matplotlib axes instance: The axes which you want the frames to plotted on. If none is provided a new instance will be created.
**kwargs:: Plot settings, any settings which can be used in matplotlib.pyplot.imshow method.

Returns:	matplotlib axes instance Returns the axes input argument or creates and returns a new instance of an matplotlib axes object.

plot_all_particles(ax=None, **kwargs)[source]¶

ax: matplotlib axes instance: The axes which you want the particle detections to be plotted on. If none is provided a new instance will be created.
**kwargs:: Plot settings, any settings which can be used in matplotlib.pyplot.scatter method.

Returns:	matplotlib axes instance Returns the axes input argument or creates and returns a new instance of an matplotlib axes object.

plot_frame(frame_index, ax=None, **kwargs)[source]¶

frame_index: index: The index of the frame you want to plot.
ax: matplotlib axes instance: The axes which you want the frames to plotted on. If none is provided a new instance will be created.
**kwargs:: Plot settings, any settings which can be used in matplotlib.pyplot.plot method.

Returns:	matplotlib axes instance Returns the axes input argument or creates and returns a new instance of an matplotlib axes object.

plot_frame_at_time(time, ax=None, **kwargs)[source]¶

time: float: The time of the frame you want to plot.
ax: matplotlib axes instance: The axes which you want the frames to plotted on. If none is provided a new instance will be created.
**kwargs:: Plot settings, any settings which can be used in matplotlib.pyplot.plot method.

Returns:	matplotlib axes instance Returns the axes input argument or creates and returns a new instance of an matplotlib axes object.

plot_moments(ax=None, **kwargs)[source]¶

ax: matplotlib axes instance: The axes which you want the moments to plotted on. If none is provided a new instance will be created.
**kwargs:: Plot settings, any settings which can be used in matplotlib.pyplot.scatter method.

Returns:	matplotlib axes instance Returns the axes input argument or creates and returns a new instance of an matplotlib axes object.

time¶

np.array:: The time for each frame.

trajectories¶

list:: Returns a list with all found trajectories of type class: Trajectory.