Changes in / [6ab64c9:706f466] in sasmodels

Files:

• doc/guide/magnetism/magnetism.rst (modified) (1 diff)
• doc/guide/pd/polydispersity.rst (modified) (3 diffs)
• doc/guide/resolution.rst (modified) (2 diffs)
• doc/guide/scripting.rst (modified) (2 diffs)
• example/latex_smeared_out_0.txt (added)
• example/latex_smeared_out_1.txt (added)
• example/oriented_usans.py (modified) (1 diff)
• example/simul_fit.py (modified) (2 diffs)
• sasmodels/bumps_model.py (modified) (2 diffs)
• sasmodels/compare.py (modified) (1 diff)
• sasmodels/data.py (modified) (2 diffs)

doc/guide/magnetism/magnetism.rst

@@ -77 +77 @@
 ===========   ================================================================
  M0_sld        = $D_M M_0$
- Up_theta      = $\theta_\mathrm{up}$
+ Up_theta      = $\theta_{up}$
  M_theta       = $\theta_M$
  M_phi         = $\phi_M$

doc/guide/pd/polydispersity.rst

@@ -95 +95 @@
            \exp\left(-\frac{(x - \bar x)^2}{2\sigma^2}\right)
 
-where $\bar x$ is the mean of the distribution and *Norm* is a normalization
-factor which is determined during the numerical calculation.
+where $\bar x$ is the mean of the distribution and *Norm* is a normalization factor
+which is determined during the numerical calculation.
 
 The polydispersity is
@@ -122 +122 @@
 during the numerical calculation.
 
-The median value for the distribution will be the value given for the
-respective size parameter, for example, *radius=60*.
+The median value for the distribution will be the value given for the respective
+size parameter, for example, *radius=60*.
 
 The polydispersity is given by $\sigma$
@@ -208 +208 @@
 
 Many commercial Dynamic Light Scattering (DLS) instruments produce a size
-polydispersity parameter, sometimes even given the symbol $p$\ ! This
+polydispersity parameter, sometimes even given the symbol $p$ This
 parameter is defined as the relative standard deviation coefficient of
 variation of the size distribution and is NOT the same as the polydispersity

doc/guide/resolution.rst

@@ -17 +17 @@
 resolution contribution into a model calculation/simulation (which by definition
 will be exact) to make it more representative of what has been measured
-experimentally - a process called *smearing*. Sasmodels does the latter.
+experimentally - a process called *smearing*. sasmodels does the latter.
 
 Both smearing and desmearing rely on functions to describe the resolution
-effect. Sasmodels provides three smearing algorithms:
+effect. sasmodels provides three smearing algorithms:
 
 *  *Slit Smearing*
@@ -99 +99 @@
 
 For discrete $q$ values, at the $q$ values of the data points and at the $q$
-values extended up to $q_N = q_i + \Delta q_u$ the smeared
+values extended up to $q_N = q_i + \Delta q_v$ the smeared
 intensity can be approximately calculated as
 

doc/guide/scripting.rst

@@ -69 +69 @@
     $ bumps example/model.py --preview
 
-Note that bumps and sasmodels are included as part of the SasView
-distribution.  On windows, bumps can be called from the cmd prompt
-as follows::
-
-    SasViewCom bumps.cli example/model.py --preview
-
 Using sasmodels directly
 ========================
@@ -111 +105 @@
     plt.loglog(q, Iq)
     plt.show()
-
-On windows, this can be called from the cmd prompt using sasview as::
-
-    SasViewCom example/cylinder_eval.py

example/oriented_usans.py

@@ -6 +6 @@
 
 # Spherical particle data, not ellipsoids
-sans, usans = load_data('latex_smeared.xml', index='all')
+sans, usans = load_data('../../sasview/sasview/test/1d_data/latex_smeared.xml')
 usans.qmin, usans.qmax = np.min(usans.x), np.max(usans.x)
 usans.mask = (usans.x < 0.0)

example/simul_fit.py

@@ +1 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# To Sasview/documents/scripts
+
 from bumps.names import *
 from sasmodels.core import load_model
@@ -4 +9 @@
 from sasmodels.data import load_data, plot_data
 
-# latex data, same sample usans and sans
-# particles radius ~2300, uniform dispersity
-datasets = load_data('latex_smeared.xml', index='all')
-#[print(data) for data in datasets]
 
-# A single sphere model to share between the datasets.  We will use
-# FreeVariables below to set the parameters that are independent between
-# the datasets.
-kernel = load_model('sphere')
-pars = dict(scale=0.01, background=0.0, sld=5.0, sld_solvent=0.0, radius=1500.,
-            #radius_pd=0.1, radius_pd_n=35,
-            )
+""" IMPORT THE DATA USED """
+datafiles = ['latex_smeared_out_0.txt', 'latex_smeared_out_1.txt']
+datasets = [load_data(el) for el in datafiles]
+
+for data in datasets:
+    data.qmin = 0.0
+    data.qmax = 10.0
+
+#sphere model
+kernel = load_model('sphere', dtype="single")
+pars = dict(scale=0.01, background=0.0, sld=1.0, sld_solvent=6.0, radius=1500.)
 model = Model(kernel, **pars)
+model.radius.range(0, inf)
+#model.background.range(-inf, inf)
+#model.scale.range(0, inf)
+model.sld.range(-inf, inf)
+model.sld_solvent.range(-inf, inf)
 
-# radius and polydispersity (if any) are shared
-model.radius.range(0, inf)
-#model.radius_pd.range(0, 1)
-
-# Contrast and dilution are the same for both measurements, but are not
-# separable with a single measurement (i.e., I(q) ~ F(q) contrast^2 Vf),
-# so fit one of scale, sld or solvent sld.  With absolute scaling from
-# data reduction, can use the same parameter for both datasets.
-model.scale.range(0, inf)
-#model.sld.range(-inf, inf)
-#model.sld_solvent.range(-inf, inf)
-
-# Background is different for sans and usans so set it as a free variable
-# in the model.
 free = FreeVariables(
-    names=[data.run[0] for data in datasets],
+    names=[data.filename for data in datasets],
     background=model.background,
+    scale=model.scale,
     )
 free.background.range(-inf, inf)
+free.scale.range(0, inf)
 
-# Note: can access the parameters for the individual models using
-# free.background[0] and free.background[1], setting constraints or
-# ranges as appropriate.
-
-# For more complex systems where different datasets require independent models,
-# separate models can be defined, with parameters tied together using
-# constraint expressions.  For example, the following could be used to fit
-# data set 1 to spheres and data set 2 to cylinders of the same volume:
-#    model1 = Model(load_model('sphere'))
-#    model2 = Model(load_model('cylinder'))
-#    model1.sld = model2.sld
-#    model1.sld_solvent = model2.sld_solvent
-#    model1.scale = model2.scale
-#    # set cylinders and spheres to the same volume
-#    model1.radius = (3/4*model2.radius**2*model2.length)**(1/3)
-#    model1.background.range(0, 2)
-#    model2.background.range(0, 2)
-
-# Setup the experiments, sharing the same model across all datasets.
-M = [Experiment(data=data, model=model, name=data.run[0]) for data in datasets]
+M = [Experiment(data=data, model=model) for data in datasets]
 
 problem = FitProblem(M, freevars=free)
+
+print(problem._parameters)

sasmodels/bumps_model.py

@@ -133 +133 @@
     """
     _cache = None # type: Dict[str, np.ndarray]
-    def __init__(self, data, model, cutoff=1e-5, name=None):
+    def __init__(self, data, model, cutoff=1e-5):
         # type: (Data, Model, float) -> None
         # remember inputs so we can inspect from outside
-        self.name = data.filename if name is None else name
         self.model = model
         self.cutoff = cutoff
@@ -205 +204 @@
         """
         data, theory, resid = self._data, self.theory(), self.residuals()
-        # TODO: hack to display oriented usans 2-D pattern
-        Iq_calc = self.Iq_calc if isinstance(self.Iq_calc, tuple) else None
-        plot_theory(data, theory, resid, view, Iq_calc=Iq_calc)
+        plot_theory(data, theory, resid, view, Iq_calc=self.Iq_calc)
 
     def simulate_data(self, noise=None):

sasmodels/compare.py

@@ -533 +533 @@
                 % (pd, n, nsigma, nsigma, pdtype)
     if M0 != 0.:
-        line += "  M0:%.3f  mtheta:%.1f  mphi:%.1f" % (M0, mtheta, mphi)
+        line += "  M0:%.3f  mphi:%.1f  mtheta:%.1f" % (M0, mphi, mtheta)
     return line
 

sasmodels/data.py

@@ -44 +44 @@
     Data = Union["Data1D", "Data2D", "SesansData"]
 
-def load_data(filename, index=0):
+def load_data(filename):
     # type: (str) -> Data
     """
@@ -55 +55 @@
         filename, indexstr = filename[:-1].split('[')
         index = int(indexstr)
+    else:
+        index = None
     datasets = loader.load(filename)
-    if not datasets:  # None or []
+    if datasets is None:
         raise IOError("Data %r could not be loaded" % filename)
     if not isinstance(datasets, list):
         datasets = [datasets]
-    for data in datasets:
-        if hasattr(data, 'x'):
-            data.qmin, data.qmax = data.x.min(), data.x.max()
-            data.mask = (np.isnan(data.y) if data.y is not None
-                        else np.zeros_like(data.x, dtype='bool'))
-        elif hasattr(data, 'qx_data'):
-            data.mask = ~data.mask
-    return datasets[index] if index != 'all' else datasets
+    if index is None and len(datasets) > 1:
+        raise ValueError("Need to specify filename[index] for multipart data")
+    data = datasets[index if index is not None else 0]
+    if hasattr(data, 'x'):
+        data.qmin, data.qmax = data.x.min(), data.x.max()
+        data.mask = (np.isnan(data.y) if data.y is not None
+                     else np.zeros_like(data.x, dtype='bool'))
+    elif hasattr(data, 'qx_data'):
+        data.mask = ~data.mask
+    return data