PYME.simulation.locify module

PYME.simulation.locify.FitResultR(x, y, z, I, t, b2, z_err_mult=3)

PYME.simulation.locify.eventify(*args, **kwargs)

PYME.simulation.locify.eventify2(x, y, meanIntensity, meanDuration, backGroundIntensity, meanEventNumber, sf=2, tm=10000, z=0, z_err_scale=1.0, paint_mode=True)

PAINT version of eventify

PYME.simulation.locify.locify(im, pixelSize=1, pointsPerPixel=0.1)

Create a set of point positions with a density corresponding to the input image im. Useful for generating localisation microscopy images from conventional images. Assumes im is a 2D array with values between 0 and 1 and interprets this value as a probability. pointsPerPixel gives the point density for a prob. of 1.

PYME.simulation.locify.points_from_sdf(sdf, r_max=1, centre=(0, 0, 0), dx_min=1, p=0.1)

Generate points from a signed distance function. Effectively does octree-like subdivision of the function domain to assign points on a regular grid, then passes through a Monte-Carlo acceptance function to simulate labelling efficiency

Parameters

sdffunction

the signed distance function. Should be of the form dist = sdf(pts) where pts is a 3xN ndarray/

r_max: float

The maximum radius of the object (from centre)

centre3-tuple / array of float

The centre of the object

dx_minfloat

The target side length of a voxel. Effectively a density parameter (density = 1/dx_min^3).

pfloat

Monte-Carlo acceptance probability.

Returns

verts3xN ndarray of fluorophore positions

PYME.simulation.locify.testPattern()

generate a test pattern