Changes in / [1d9998c:7609046] in sasmodels

Files:

• doc/guide/pd/polydispersity.rst (modified) (4 diffs)
• example/weights/cyclic_gaussian.py (added)
• example/weights/laplace.py (added)
• sasmodels/compare.py (modified) (3 diffs)
• sasmodels/sasview_model.py (modified) (1 diff)
• sasmodels/weights.py (modified) (1 diff)

doc/guide/pd/polydispersity.rst

@@ -33 +33 @@
 sigmas $N_\sigma$ to include from the tails of the distribution, and the
 number of points used to compute the average. The center of the distribution
-is set by the value of the model parameter. The meaning of a polydispersity 
-parameter *PD* (not to be confused with a molecular weight distributions 
-in polymer science) in a model depends on the type of parameter it is being 
+is set by the value of the model parameter. The meaning of a polydispersity
+parameter *PD* (not to be confused with a molecular weight distributions
+in polymer science) in a model depends on the type of parameter it is being
 applied too.
 
-The distribution width applied to *volume* (ie, shape-describing) parameters 
-is relative to the center value such that $\sigma = \mathrm{PD} \cdot \bar x$. 
-However, the distribution width applied to *orientation* (ie, angle-describing) 
+The distribution width applied to *volume* (ie, shape-describing) parameters
+is relative to the center value such that $\sigma = \mathrm{PD} \cdot \bar x$.
+However, the distribution width applied to *orientation* (ie, angle-describing)
 parameters is just $\sigma = \mathrm{PD}$.
 
@@ -64 +64 @@
 *  *Schulz Distribution*
 *  *Array Distribution*
+*  *User-defined Distributions*
 
 These are all implemented as *number-average* distributions.
 
-Additional distributions are under consideration.
 
 .. note:: In 2009 IUPAC decided to introduce the new term 'dispersity' to replace 
@@ -87 +87 @@
 or angular orientations, use 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 
+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 allows a user-defined distribution to be applied.
+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. 
 
 .. ZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZ
@@ -329 +331 @@
 .. ZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZ
 
+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, set the following environment variable:
+
+    SASMODELS_WEIGHTS=path/to/folder/name_of_distribution.py
+
+.. ZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZ
+
 Note about DLS polydispersity
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

sasmodels/compare.py

@@ -41 +41 @@
 from . import kerneldll
 from . import kernelcl
+from . import weights
 from .data import plot_theory, empty_data1D, empty_data2D, load_data
 from .direct_model import DirectModel, get_mesh
 from .generate import FLOAT_RE, set_integration_size
-from .weights import plot_weights
 
 # pylint: disable=unused-import
@@ -783 +783 @@
             model_info = base._kernel.info
             dim = base._kernel.dim
-            plot_weights(model_info, get_mesh(model_info, base_pars, dim=dim))
+            weights.plot_weights(model_info, get_mesh(model_info, base_pars, dim=dim))
         if opts['show_profile']:
             import pylab
@@ -1439 +1439 @@
     #import pprint; pprint.pprint(model_info)
 
+    # Hack to load user-defined distributions; run through all parameters
+    # and make sure any pd_type parameter is a defined distribution.
+    if (any(p.endswith('pd_type') and v not in weights.MODELS
+            for p, v in pars.items()) or
+        any(p.endswith('pd_type') and v not in weights.MODELS
+            for p, v in pars2.items())):
+       weights.load_weights()
+
     if opts['show_pars']:
         if model_info.name != model_info2.name or pars != pars2:

sasmodels/sasview_model.py

@@ -30 +30 @@
 from . import modelinfo
 from .details import make_kernel_args, dispersion_mesh
+
+# Hack: load in any custom distributions
+# Uses ~/.sasview/weights/*.py unless SASMODELS_WEIGHTS is set in the environ.
+# Override with weights.load_weights(pattern="<weights_path>/*.py")
+weights.load_weights()
 
 # pylint: disable=unused-import

sasmodels/weights.py

@@ -231 +231 @@
 ))
 
+SASMODELS_WEIGHTS = "~/.sasview/weights/*.py"
+def load_weights(pattern=None):
+    # type: (str) -> None
+    """
+    Load dispersion distributions matching the given pattern
+    """
+    import logging
+    import os
+    import os.path
+    import glob
+    import traceback
+    from .custom import load_custom_kernel_module
+    if pattern is None:
+        pattern = os.environ.get("SASMODELS_WEIGHTS", SASMODELS_WEIGHTS)
+    for filename in sorted(glob.glob(os.path.expanduser(pattern))):
+        try:
+            #print("loading weights from", filename)
+            module = load_custom_kernel_module(filename)
+            MODELS[module.Dispersion.type] = module.Dispersion
+        except Exception as exc:
+            logging.error(traceback.format_exc(exc))
 
 def get_weights(disperser, n, width, nsigmas, value, limits, relative):