Shortest Path Finder

class particle_tracker_one_d.ShortestPathFinder(frames, time, automatic_update=True)

Class for finding shortest path between points in a set of frames.

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.

automatic_update: bool

Choose if the class should update itself when changing properties.

Attributes:

frames

np.array:

time

boxcar_width

int:

integration_radius_of_intensity_peaks

int:

particle_detection_threshold

float:

particle_positions

np.array:

static_points

start_point

tuple:

end_point

tuple:

Methods

change_cost_coefficients([a, b, c])	Change the coefficients of the cost function \(c(p_1,p_2) = a\cdot (x_{p_1} - x_{p_2})^2 + b \cdot (m_0(p_1)-m_0(p_2))^2 + b \cdot (m_2(p_1)-m_2(p_2))^2)\)
plot_all_frames([ax])	ax: matplotlib axes instance
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)

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

a: float

b: float

c: float

end_point

tuple:

(frame_index, position_index), The end point of the path you want to find.

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.

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.

particle_detection_threshold

float:

Defines the threshold value for finding intensity peaks. Local maximas below this threshold will not be considered as particles.

particle_positions

np.array:

Numpy array with all particle positions on the form np.array((nParticles,), dtype=[(‘frame_index’, np.int16), (‘time’, np.float32),(‘integer_position’, np.int16), (‘refined_position’, np.float32)])

plot_all_frames(ax=None, **kwargs)

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_moments(ax=None, **kwargs)

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.scatter method.

Returns:

matplotlib axes instance

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

shortest_path

dict:

The shortest path between the start and end point, defined by the cost function. Cost, length and association matrix.

start_point

tuple:

(frame_index, position_index), The start point of the path you want to find.

trajectory

trajectory:

The shortest path between the start and end point represented as a trajectory.