PYME.simulation.locify module

PYME.simulation.locify.FitResultR(x, y, z, I, t, b2, z_err_mult=3)
PYME.simulation.locify.eventify(x, y, meanIntensity, meanDuration, backGroundIntensity, meanEventNumber, sf=2, tm=2000, z=0, z_err_scale=1.0)
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.rand(d0, d1, ..., dn)

Random values in a given shape.

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


d0, d1, ..., dn : int, optional

The dimensions of the returned array, should all be positive. If no argument is given a single Python float is returned.


out : ndarray, shape (d0, d1, ..., dn)

Random values.

See also



This is a convenience function. If you want an interface that takes a shape-tuple as the first argument, refer to np.random.random_sample .


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

generate a test pattern