[ff7119b] | 1 | """ |
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| 2 | SAS distributions for polydispersity. |
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| 3 | """ |
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[ce27e21] | 4 | # TODO: include dispersion docs with the disperser models |
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[6cefbc9] | 5 | from __future__ import division, print_function |
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| 6 | |
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[7ae2b7f] | 7 | from math import sqrt # type: ignore |
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[6cefbc9] | 8 | from collections import OrderedDict |
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| 9 | |
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[7ae2b7f] | 10 | import numpy as np # type: ignore |
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| 11 | from scipy.special import gammaln # type: ignore |
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[14de349] | 12 | |
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[733a3e1] | 13 | # TODO: include dispersion docs with the disperser models |
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| 14 | |
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[ce27e21] | 15 | class Dispersion(object): |
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| 16 | """ |
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| 17 | Base dispersion object. |
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| 18 | |
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| 19 | Subclasses should define *_weights(center, sigma, lb, ub)* |
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| 20 | which returns the x points and their corresponding weights. |
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| 21 | """ |
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| 22 | type = "base disperser" |
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| 23 | default = dict(npts=35, width=0, nsigmas=3) |
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| 24 | def __init__(self, npts=None, width=None, nsigmas=None): |
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| 25 | self.npts = self.default['npts'] if npts is None else npts |
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| 26 | self.width = self.default['width'] if width is None else width |
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| 27 | self.nsigmas = self.default['nsigmas'] if nsigmas is None else nsigmas |
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[14de349] | 28 | |
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| 29 | def get_pars(self): |
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[5c962df] | 30 | """ |
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| 31 | Return the parameters to the disperser as a dictionary. |
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| 32 | """ |
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[ce27e21] | 33 | pars = {'type': self.type} |
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| 34 | pars.update(self.__dict__) |
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| 35 | return pars |
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| 36 | |
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[3c56da87] | 37 | # pylint: disable=no-self-use |
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[ce27e21] | 38 | def set_weights(self, values, weights): |
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[5c962df] | 39 | """ |
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| 40 | Set the weights on the disperser if it is :class:`ArrayDispersion`. |
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| 41 | """ |
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[ce27e21] | 42 | raise RuntimeError("set_weights is only available for ArrayDispersion") |
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| 43 | |
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| 44 | def get_weights(self, center, lb, ub, relative): |
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| 45 | """ |
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| 46 | Return the weights for the distribution. |
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| 47 | |
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| 48 | *center* is the center of the distribution |
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[14de349] | 49 | |
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[823e620] | 50 | *lb*, *ub* are the min and max allowed values |
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[ce27e21] | 51 | |
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| 52 | *relative* is True if the distribution width is proportional to the |
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| 53 | center value instead of absolute. For polydispersity use relative. |
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| 54 | For orientation parameters use absolute. |
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| 55 | """ |
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| 56 | sigma = self.width * center if relative else self.width |
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[5d4777d] | 57 | if sigma == 0 or self.npts < 2: |
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| 58 | if lb <= center <= ub: |
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| 59 | return np.array([center], 'd'), np.array([1.], 'd') |
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| 60 | else: |
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| 61 | return np.array([], 'd'), np.array([], 'd') |
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[6cefbc9] | 62 | x, px = self._weights(center, sigma, lb, ub) |
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| 63 | return x, px |
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[ce27e21] | 64 | |
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| 65 | def _weights(self, center, sigma, lb, ub): |
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| 66 | """actual work of computing the weights""" |
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| 67 | raise NotImplementedError |
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| 68 | |
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| 69 | def _linspace(self, center, sigma, lb, ub): |
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| 70 | """helper function to provide linear spaced weight points within range""" |
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| 71 | npts, nsigmas = self.npts, self.nsigmas |
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| 72 | x = center + np.linspace(-nsigmas*sigma, +nsigmas*sigma, npts) |
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| 73 | x = x[(x >= lb) & (x <= ub)] |
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| 74 | return x |
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| 75 | |
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| 76 | |
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| 77 | class GaussianDispersion(Dispersion): |
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[5c962df] | 78 | r""" |
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| 79 | Gaussian dispersion, with 1-\ $\sigma$ width. |
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| 80 | |
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| 81 | .. math:: |
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| 82 | |
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| 83 | w = \exp\left(-\tfrac12 (x - c)^2/\sigma^2\right) |
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| 84 | """ |
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[ce27e21] | 85 | type = "gaussian" |
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| 86 | default = dict(npts=35, width=0, nsigmas=3) |
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| 87 | def _weights(self, center, sigma, lb, ub): |
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[6cefbc9] | 88 | # TODO: sample high probability regions more densely |
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| 89 | # i.e., step uniformly in cumulative density rather than x value |
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| 90 | # so weight = 1/Npts for all weights, but values are unevenly spaced |
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[ce27e21] | 91 | x = self._linspace(center, sigma, lb, ub) |
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[14de349] | 92 | px = np.exp((x-center)**2 / (-2.0 * sigma * sigma)) |
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| 93 | return x, px |
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| 94 | |
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[ce27e21] | 95 | |
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| 96 | class RectangleDispersion(Dispersion): |
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[5c962df] | 97 | r""" |
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| 98 | Uniform dispersion, with width $\sqrt{3}\sigma$. |
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| 99 | |
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| 100 | .. math:: |
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| 101 | |
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| 102 | w = 1 |
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| 103 | """ |
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[ce27e21] | 104 | type = "rectangle" |
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| 105 | default = dict(npts=35, width=0, nsigmas=1.70325) |
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| 106 | def _weights(self, center, sigma, lb, ub): |
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[bb46723] | 107 | x = self._linspace(center, sigma, lb, ub) |
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| 108 | x = x[np.fabs(x-center) <= np.fabs(sigma)*sqrt(3.0)] |
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| 109 | return x, np.ones_like(x) |
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[ce27e21] | 110 | |
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| 111 | |
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| 112 | class LogNormalDispersion(Dispersion): |
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[5c962df] | 113 | r""" |
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| 114 | log Gaussian dispersion, with 1-\ $\sigma$ width. |
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| 115 | |
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| 116 | .. math:: |
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| 117 | |
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| 118 | w = \frac{\exp\left(-\tfrac12 (\ln x - c)^2/\sigma^2\right)}{x\sigma} |
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| 119 | """ |
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[ce27e21] | 120 | type = "lognormal" |
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| 121 | default = dict(npts=80, width=0, nsigmas=8) |
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| 122 | def _weights(self, center, sigma, lb, ub): |
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[823e620] | 123 | x = self._linspace(center, sigma, max(lb, 1e-8), max(ub, 1e-8)) |
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[f1a8811] | 124 | # sigma in the lognormal function is in ln(R) space, thus needs converting |
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| 125 | sig = np.fabs(sigma/center) |
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| 126 | px = np.exp(-0.5*((np.log(x)-np.log(center))/sig)**2)/(x*sig) |
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[ce27e21] | 127 | return x, px |
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| 128 | |
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| 129 | |
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| 130 | class SchulzDispersion(Dispersion): |
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[5c962df] | 131 | r""" |
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| 132 | Schultz dispersion, with 1-\ $\sigma$ width. |
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| 133 | |
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| 134 | .. math:: |
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| 135 | |
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| 136 | w = \frac{z^z\,R^{z-1}}{e^{Rz}\,c \Gamma(z)} |
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| 137 | |
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| 138 | where $c$ is the center of the distribution, $R = x/c$ and $z=(c/\sigma)^2$. |
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| 139 | |
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| 140 | This is evaluated using logarithms as |
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| 141 | |
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| 142 | .. math:: |
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| 143 | |
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| 144 | w = \exp\left(z \ln z + (z-1)\ln R - Rz - \ln c - \ln \Gamma(z) \right) |
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| 145 | """ |
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[ce27e21] | 146 | type = "schulz" |
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| 147 | default = dict(npts=80, width=0, nsigmas=8) |
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| 148 | def _weights(self, center, sigma, lb, ub): |
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[823e620] | 149 | x = self._linspace(center, sigma, max(lb, 1e-8), max(ub, 1e-8)) |
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| 150 | R = x/center |
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[ce27e21] | 151 | z = (center/sigma)**2 |
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| 152 | arg = z*np.log(z) + (z-1)*np.log(R) - R*z - np.log(center) - gammaln(z) |
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| 153 | px = np.exp(arg) |
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| 154 | return x, px |
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| 155 | |
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| 156 | |
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| 157 | class ArrayDispersion(Dispersion): |
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[5c962df] | 158 | r""" |
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| 159 | Empirical dispersion curve. |
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| 160 | |
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| 161 | Use :meth:`set_weights` to set $w = f(x)$. |
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| 162 | """ |
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[ce27e21] | 163 | type = "array" |
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| 164 | default = dict(npts=35, width=0, nsigmas=1) |
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| 165 | def __init__(self, npts=None, width=None, nsigmas=None): |
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| 166 | Dispersion.__init__(self, npts, width, nsigmas) |
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| 167 | self.values = np.array([0.], 'd') |
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| 168 | self.weights = np.array([1.], 'd') |
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| 169 | |
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| 170 | def set_weights(self, values, weights): |
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[5c962df] | 171 | """ |
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| 172 | Set the weights for the given x values. |
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| 173 | """ |
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[ce27e21] | 174 | self.values = np.ascontiguousarray(values, 'd') |
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| 175 | self.weights = np.ascontiguousarray(weights, 'd') |
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| 176 | self.npts = len(values) |
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| 177 | |
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| 178 | def _weights(self, center, sigma, lb, ub): |
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[6cefbc9] | 179 | # TODO: rebin the array dispersion using npts |
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| 180 | # TODO: use a distribution that can be recentered and scaled |
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| 181 | x = self.values |
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| 182 | #x = center + self.values*sigma |
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[823e620] | 183 | idx = (x >= lb) & (x <= ub) |
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[ce27e21] | 184 | x = x[idx] |
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| 185 | px = self.weights[idx] |
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| 186 | return x, px |
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| 187 | |
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| 188 | |
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[ff7119b] | 189 | # dispersion name -> disperser lookup table. |
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[6cefbc9] | 190 | # Maintain order since this is used by sasview GUI to order the options in |
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| 191 | # the dispersion type combobox. |
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| 192 | MODELS = OrderedDict((d.type, d) for d in ( |
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| 193 | RectangleDispersion, |
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| 194 | ArrayDispersion, |
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| 195 | LogNormalDispersion, |
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| 196 | GaussianDispersion, |
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| 197 | SchulzDispersion, |
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[ce27e21] | 198 | )) |
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| 199 | |
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[1780d59] | 200 | |
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| 201 | def get_weights(disperser, n, width, nsigmas, value, limits, relative): |
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[ff7119b] | 202 | """ |
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| 203 | Return the set of values and weights for a polydisperse parameter. |
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| 204 | |
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| 205 | *disperser* is the name of the disperser. |
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| 206 | |
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| 207 | *n* is the number of points in the weight vector. |
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| 208 | |
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| 209 | *width* is the width of the disperser distribution. |
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| 210 | |
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| 211 | *nsigmas* is the number of sigmas to span for the dispersion convolution. |
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| 212 | |
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| 213 | *value* is the value of the parameter in the model. |
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| 214 | |
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[6cefbc9] | 215 | *limits* is [lb, ub], the lower and upper bound on the possible values. |
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[ff7119b] | 216 | |
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| 217 | *relative* is true if *width* is defined in proportion to the value |
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| 218 | of the parameter, and false if it is an absolute width. |
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| 219 | |
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[823e620] | 220 | Returns *(value, weight)*, where *value* and *weight* are vectors. |
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[ff7119b] | 221 | """ |
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[6cefbc9] | 222 | if disperser == "array": |
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| 223 | raise NotImplementedError("Don't handle arrays through get_weights; use values and weights directly") |
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[fa5fd8d] | 224 | cls = MODELS[disperser] |
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[1780d59] | 225 | obj = cls(n, width, nsigmas) |
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[823e620] | 226 | v, w = obj.get_weights(value, limits[0], limits[1], relative) |
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| 227 | return v, w |
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[6cefbc9] | 228 | |
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| 229 | |
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| 230 | def plot_weights(model_info, pairs): |
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| 231 | # type: (ModelInfo, List[Tuple[np.ndarray, np.ndarray]]) -> None |
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| 232 | """ |
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| 233 | Plot the weights returned by :func:`get_weights`. |
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| 234 | |
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| 235 | *model_info* is |
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| 236 | :param model_info: |
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| 237 | :param pairs: |
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| 238 | :return: |
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| 239 | """ |
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| 240 | import pylab |
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| 241 | |
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| 242 | if any(len(values)>1 for values, weights in pairs): |
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| 243 | labels = [p.name for p in model_info.parameters.call_parameters] |
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| 244 | pylab.interactive(True) |
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| 245 | pylab.figure() |
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| 246 | for (v,w), s in zip(pairs, labels): |
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| 247 | if len(v) > 1: |
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| 248 | #print("weights for", s, v, w) |
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| 249 | pylab.plot(v, w, '-o', label=s) |
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| 250 | pylab.grid(True) |
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| 251 | pylab.legend() |
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[f1a8811] | 252 | #pylab.show() |
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