# PYME.Analysis.BleachProfile.deMod module¶

PYME.Analysis.BleachProfile.deMod.dBdt(B, t, kasc, kdis, kod, kdo, kbl, th, I)
PYME.Analysis.BleachProfile.deMod.flF2(N, t, argS)
PYME.Analysis.BleachProfile.deMod.flF2Mod(p, t)
PYME.Analysis.BleachProfile.deMod.flFpow(N, t, argS)
PYME.Analysis.BleachProfile.deMod.flFsq(N, t, argS, Ia)
PYME.Analysis.BleachProfile.deMod.flFsq1(N, t, argS)
PYME.Analysis.BleachProfile.deMod.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).

Parameters: 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.

random

Notes

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 .

Examples

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

PYME.Analysis.BleachProfile.deMod.randn(d0, d1, ..., dn)

Return a sample (or samples) from the “standard normal” distribution.

If positive, int_like or int-convertible arguments are provided, randn generates an array of shape (d0, d1, ..., dn), filled with random floats sampled from a univariate “normal” (Gaussian) distribution of mean 0 and variance 1 (if any of the $$d_i$$ are floats, they are first converted to integers by truncation). A single float randomly sampled from the distribution is returned if no argument is provided.

This is a convenience function. If you want an interface that takes a tuple as the first argument, use numpy.random.standard_normal instead.

Parameters: d0, d1, ..., dn : int, optional The dimensions of the returned array, should be all positive. If no argument is given a single Python float is returned. Z : ndarray or float A (d0, d1, ..., dn)-shaped array of floating-point samples from the standard normal distribution, or a single such float if no parameters were supplied.

random.standard_normal
Similar, but takes a tuple as its argument.

Notes

For random samples from $$N(\mu, \sigma^2)$$, use:

sigma * np.random.randn(...) + mu

Examples

>>> np.random.randn()
2.1923875335537315 #random


Two-by-four array of samples from N(3, 6.25):

>>> 2.5 * np.random.randn(2, 4) + 3
array([[-4.49401501,  4.00950034, -1.81814867,  7.29718677],  #random
[ 0.39924804,  4.68456316,  4.99394529,  4.84057254]]) #random

PYME.Analysis.BleachProfile.deMod.thiolInt(t, B0, kasc, kdis, kod, kdo, kbl, th, I)