Suggested Applications

If applying polydispersion to parameters describing particle sizes, consider using the Lognormal or Schulz distributions.

If applying polydispersion to parameters describing interfacial thicknesses or angular orientations, consider using the Gaussian or Boltzmann distributions.

If applying polydispersion to parameters describing angles, use the Uniform distribution. Beware of using distributions that are always positive (eg, the Lognormal) because angles can be negative!

The array distribution provides a very simple means of implementing a user- defined distribution, but without any fittable parameters. Greater flexibility is conferred by the user-defined distribution.

Uniform Distribution

The Uniform Distribution is defined as

where $bar x$ ($x_text{mean}$ in the figure) is the mean of the distribution, $sigma$ is the half-width, and Norm is a normalization factor which is determined during the numerical calculation.

The polydispersity in sasmodels is given by

Uniform distribution.

The value $N_sigma$ is ignored for this distribution.

Rectangular Distribution

The Rectangular Distribution is defined as

where $bar x$ ($x_text{mean}$ in the figure) is the mean of the distribution, $w$ is the half-width, and Norm is a normalization factor which is determined during the numerical calculation.

Note that the standard deviation and the half width $w$ are different!

The standard deviation is

whilst the polydispersity in sasmodels is given by

Rectangular distribution.

Gaussian Distribution

The Gaussian Distribution is defined as

where $bar x$ ($x_text{mean}$ in the figure) is the mean of the distribution and Norm is a normalization factor which is determined during the numerical calculation.

The polydispersity in sasmodels is given by

Normal distribution.

Boltzmann Distribution

The Boltzmann Distribution is defined as

where $bar x$ ($x_text{mean}$ in the figure) is the mean of the distribution and Norm is a normalization factor which is determined during the numerical calculation.

The width is defined as

which is the inverse Boltzmann factor, where $k$ is the Boltzmann constant, $T$ the temperature in Kelvin and $E$ a characteristic energy per particle.

Boltzmann distribution.

Lognormal Distribution

The Lognormal Distribution describes a function of $x$ where $ln (x)$ has a normal distribution. The result is a distribution that is skewed towards larger values of $x$.

The Lognormal Distribution is defined as

where Norm is a normalization factor which will be determined during the numerical calculation, $mu=ln(x_text{med})$ and $x_text{med}$ is the median value of the lognormal distribution, but $sigma$ is a parameter describing the width of the underlying normal distribution.

$x_text{med}$ will be the value given for the respective size parameter in sasmodels, for example, radius=60.

The polydispersity in sasmodels is given by

The mean value of the distribution is given by $bar x = exp(mu+ sigma^2/2)$ and the peak value by $max x = exp(mu - sigma^2)$.

The variance (the square of the standard deviation) of the lognormal distribution is given by

Note that larger values of PD might need a larger number of points and $N_sigma$.

Lognormal distribution for PD=0.1.

For further information on the Lognormal distribution see: http://en.wikipedia.org/wiki/Log-normal_distribution and http://mathworld.wolfram.com/LogNormalDistribution.html

Schulz Distribution

The Schulz (sometimes written Schultz) distribution is similar to the Lognormal distribution, in that it is also skewed towards larger values of $x$, but which has computational advantages over the Lognormal distribution.

The Schulz distribution is defined as

where $bar x$ ($x_text{mean}$ in the figure) is the mean of the distribution, Norm is a normalization factor which is determined during the numerical calculation, and $z$ is a measure of the width of the distribution such that

where $p$ is the polydispersity in sasmodels given by

and $sigma$ is the RMS deviation from $bar x$.

Note that larger values of PD might need a larger number of points and $N_sigma$. For example, for PD=0.7 with radius=60 |Ang|, at least Npts>=160 and Nsigmas>=15 are required.

Schulz distribution.

For further information on the Schulz distribution see: M Kotlarchyk & S-H Chen, J Chem Phys, (1983), 79, 2461 and M Kotlarchyk, RB Stephens, and JS Huang, J Phys Chem, (1988), 92, 1533

Array Distribution

This user-definable distribution should be given as a simple ASCII text file where the array is defined by two columns of numbers: $x$ and $f(x)$. The $f(x)$ will be normalized to 1 during the computation.

Example of what an array distribution file should look like:

Only these array values are used computation, therefore the parameter value given for the model will have no affect, and will be ignored when computing the average. This means that any parameter with an array distribution will not be fitable.

User-defined Distributions

You can also define your own distribution by creating a python file defining a Distribution object with a _weights method. The _weights method takes center, sigma, lb and ub as arguments, and can access self.npts and self.nsigmas from the distribution. They are interpreted as follows:

• center the value of the shape parameter (for size dispersity) or zero if it is an angular dispersity. This parameter may be fitted.
• sigma the width of the distribution, which is the polydispersity parameter times the center for size dispersity, or the polydispersity parameter alone for angular dispersity. This parameter may be fitted.
• lb, ub are the parameter limits (lower & upper bounds) given in the model definition file. For example, a radius parameter has lb equal to zero. A volume fraction parameter would have lb equal to zero and ub equal to one.
• self.nsigmas the distance to go into the tails when evaluating the distribution. For a two parameter distribution, this value could be co-opted to use for the second parameter, though it will not be available for fitting.
• self.npts the number of points to use when evaluating the distribution. The user will adjust this to trade calculation time for accuracy, but the distribution code is free to return more or fewer, or use it for the third parameter in a three parameter distribution.

As an example, the code following wraps the Laplace distribution from scipy stats:

import numpy as np
from scipy.stats import laplace
from sasmodels import weights
class Dispersion(weights.Dispersion):
    r"""
    Laplace distribution
    .. math::
        w(x) = e^{-\sigma |x - \mu|}
    """
    type = "laplace"
    default = dict(npts=35, width=0, nsigmas=3)  # default values
    def _weights(self, center, sigma, lb, ub):
        x = self._linspace(center, sigma, lb, ub)
        wx = laplace.pdf(x, center, sigma)
        return x, wx

You can plot the weights for a given value and width using the following:

from numpy import inf
from matplotlib import pyplot as plt
from sasmodels import weights
# reload the user-defined weights
weights.load_weights()
x, wx = weights.get_weights('laplace', n=35, width=0.1, nsigmas=3, value=50,
                            limits=[0, inf], relative=True)
# plot the weights
plt.interactive(True)
plt.plot(x, wx, 'x')

The self.nsigmas and self.npts parameters are normally used to control the accuracy of the distribution integral. The self._linspace function uses them to define the x values (along with the center, sigma, lb, and ub which are passed as parameters). If you repurpose npts or nsigmas you will need to generate your own x. Be sure to honour the limits lb and ub, for example to disallow a negative radius or constrain the volume fraction to lie between zero and one.

To activate a user-defined distribution, put it in a file such as distname.py in the SAS_WEIGHTS_PATH folder. This is defined with an environment variable, defaulting to:

SAS_WEIGHTS_PATH=~/.sasview/weights

The weights path is loaded on startup. To update the distribution definition in a running application you will need to enter the following python commands:

import sasmodels.weights
sasmodels.weights.load_weights('path/to/distname.py')

Note about DLS polydispersity

Several measures of polydispersity abound in Dynamic Light Scattering (DLS) and it should not be assumed that any of the following can be simply equated with the polydispersity PD parameter used in SasView.

The dimensionless Polydispersity Index (PI) is a measure of the width of the distribution of autocorrelation function decay rates (not the distribution of particle sizes itself, though the two are inversely related) and is defined by ISO 22412:2017 as

where $mu_text{2}$ is the second cumulant, and $bar Gamma^2$ is the intensity-weighted average value, of the distribution of decay rates.

If the distribution of decay rates is Gaussian then

where $sigma$ is the standard deviation, allowing a Relative Polydispersity (RP) to be defined as

PI values smaller than 0.05 indicate a highly monodisperse system. Values greater than 0.7 indicate significant polydispersity.

The size polydispersity P-parameter is defined as the relative standard deviation coefficient of variation

where $nu$ is the variance of the distribution and $bar R$ is the mean value of $R$. Here, the product $P bar R$ is equal to the standard deviation of the Lognormal distribution.

P values smaller than 0.13 indicate a monodisperse system.

For more information see:

ISO 22412:2017, International Standards Organisation (2017).

Polydispersity: What does it mean for DLS and Chromatography.

Dynamic Light Scattering: Common Terms Defined, Whitepaper WP111214. Malvern Instruments (2011).

S King, C Washington & R Heenan, Phys Chem Chem Phys, (2005), 7, 143.

T Allen, in Particle Size Measurement, 4th Edition, Chapman & Hall, London (1990).

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