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.rand(d0, d1, ..., dn)

Random values in a given shape.

Note

This is a convenience function for users porting code from Matlab, and wraps random_sample. That function takes a tuple to specify the size of the output, which is consistent with other NumPy functions like numpy.zeros and numpy.ones.

Create an array of the given shape and populate it with random samples from a uniform distribution over [0, 1).

Parameters

d0, d1, …, dnint, optional

The dimensions of the returned array, must be non-negative. If no argument is given a single Python float is returned.

Returns

outndarray, shape (d0, d1, ..., dn)

Random values.

See also

random

Examples

>>> np.random.rand(3,2)
array([[ 0.14022471,  0.96360618],  #random
       [ 0.37601032,  0.25528411],  #random
       [ 0.49313049,  0.94909878]]) #random

PYME.simulation.locify.testPattern()

generate a test pattern