Changeset a3412a6 in sasmodels


Ignore:
Timestamp:
Mar 6, 2019 4:08:08 PM (5 years ago)
Author:
Paul Kienzle <pkienzle@…>
Branches:
ticket_1156
Children:
bd91e8f
Parents:
f752b9b (diff), 9150036 (diff)
Note: this is a merge changeset, the changes displayed below correspond to the merge itself.
Use the (diff) links above to see all the changes relative to each parent.
Message:

Merge branch 'beta_approx' into ticket_1156

Files:
15 edited

Legend:

Unmodified
Added
Removed

  • doc/guide/plugin.rst

    raa8c6e0 r9150036  
    272272structure factor to account for interactions between particles.  See 
    273273`Form_Factors`_ for more details. 
     274 
     275**model_info = ...** lets you define a model directly, for example, by 
     276loading and modifying existing models.  This is done implicitly by 
     277:func:`sasmodels.core.load_model_info`, which can create a mixture model 
     278from a pair of existing models.  For example:: 
     279 
     280    from sasmodels.core import load_model_info 
     281    model_info = load_model_info('sphere+cylinder') 
     282 
     283See :class:`sasmodels.modelinfo.ModelInfo` for details about the model 
     284attributes that are defined. 
    274285 
    275286Model Parameters 
    … …  
    894905             - \frac{\sin(x)}{x}\left(\frac{1}{x} - \frac{3!}{x^3} + \frac{5!}{x^5} - \frac{7!}{x^7}\right) 
    895906 
    896         For small arguments , 
     907        For small arguments, 
    897908 
    898909        .. math:: 

  • example/multiscatfit.py

    r49d1f8b8 r2c4a190  
    1515 
    1616    # Show the model without fitting 
    17     PYTHONPATH=..:../explore:../../bumps:../../sasview/src python multiscatfit.py 
     17    PYTHONPATH=..:../../bumps:../../sasview/src python multiscatfit.py 
    1818 
    1919    # Run the fit 
    20     PYTHONPATH=..:../explore:../../bumps:../../sasview/src ../../bumps/run.py \ 
     20    PYTHONPATH=..:../../bumps:../../sasview/src ../../bumps/run.py \ 
    2121    multiscatfit.py --store=/tmp/t1 
    2222 
    … …  
    5555    ) 
    5656 
     57# Tie the model to the data 
     58M = Experiment(data=data, model=model) 
     59 
     60# Stack mulitple scattering on top of the existing resolution function. 
     61M.resolution = MultipleScattering(resolution=M.resolution, probability=0.) 
     62 
    5763# SET THE FITTING PARAMETERS 
    5864model.radius_polar.range(15, 3000) 
    … …  
    6571model.scale.range(0, 0.1) 
    6672 
    67 # Mulitple scattering probability parameter 
    68 # HACK: the probability is stuffed in as an extra parameter to the experiment. 
    69 probability = Parameter(name="probability", value=0.0) 
    70 probability.range(0.0, 0.9) 
     73# The multiple scattering probability parameter is in the resolution function 
     74# instead of the scattering function, so access it through M.resolution 
     75M.scattering_probability.range(0.0, 0.9) 
    7176 
    72 M = Experiment(data=data, model=model, extra_pars={'probability': probability}) 
    73  
    74 # Stack mulitple scattering on top of the existing resolution function. 
    75 # Because resolution functions in sasview don't have fitting parameters, 
    76 # we instead allow the multiple scattering calculator to take a function 
    77 # instead of a probability.  This function returns the current value of 
    78 # the parameter. ** THIS IS TEMPORARY ** when multiple scattering is 
    79 # properly integrated into sasmodels and sasview, its fittable parameter 
    80 # will be treated like the model parameters. 
    81 M.resolution = MultipleScattering(resolution=M.resolution, 
    82                                   probability=lambda: probability.value, 
    83                                   ) 
    84 M._kernel_inputs = M.resolution.q_calc 
     77# Let bumps know that we are fitting this experiment 
    8578problem = FitProblem(M) 
    8679 

  • sasmodels/__init__.py

    ra1ec908 r37f38ff  
    1414defining new models. 
    1515""" 
    16 __version__ = "0.98" 
     16__version__ = "0.99" 
    1717 
    1818def data_files(): 

  • sasmodels/bumps_model.py

    r49d1f8b8 r2c4a190  
    3535    # when bumps is not on the path. 
    3636    from bumps.names import Parameter # type: ignore 
     37    from bumps.parameter import Reference # type: ignore 
    3738except ImportError: 
    3839    pass 
    … …  
    139140    def __init__(self, data, model, cutoff=1e-5, name=None, extra_pars=None): 
    140141        # type: (Data, Model, float) -> None 
     142        # Allow resolution function to define fittable parameters.  We do this 
     143        # by creating reference parameters within the resolution object rather 
     144        # than modifying the object itself to use bumps parameters.  We need 
     145        # to reset the parameters each time the object has changed.  These 
     146        # additional parameters need to be returned from the fitting engine. 
     147        # To make them available to the user, they are added as top-level 
     148        # attributes to the experiment object.  The only change to the 
     149        # resolution function is that it needs an optional 'fittable' attribute 
     150        # which maps the internal name to the user visible name for the 
     151        # for the parameter. 
     152        self._resolution = None 
     153        self._resolution_pars = {} 
    141154        # remember inputs so we can inspect from outside 
    142155        self.name = data.filename if name is None else name 
    … …  
    145158        self._interpret_data(data, model.sasmodel) 
    146159        self._cache = {} 
     160        # CRUFT: no longer need extra parameters 
     161        # Multiple scattering probability is now retrieved directly from the 
     162        # multiple scattering resolution function. 
    147163        self.extra_pars = extra_pars 
    148164 
    … …  
    162178        return len(self.Iq) 
    163179 
     180    @property 
     181    def resolution(self): 
     182        return self._resolution 
     183 
     184    @resolution.setter 
     185    def resolution(self, value): 
     186        self._resolution = value 
     187 
     188        # Remove old resolution fitting parameters from experiment 
     189        for name in self._resolution_pars: 
     190            delattr(self, name) 
     191 
     192        # Create new resolution fitting parameters 
     193        res_pars = getattr(self._resolution, 'fittable', {}) 
     194        self._resolution_pars = { 
     195            name: Reference(self._resolution, refname, name=name) 
     196            for refname, name in res_pars.items() 
     197        } 
     198 
     199        # Add new resolution fitting parameters as experiment attributes 
     200        for name, ref in self._resolution_pars.items(): 
     201            setattr(self, name, ref) 
     202 
    164203    def parameters(self): 
    165204        # type: () -> Dict[str, Parameter] 
    … …  
    168207        """ 
    169208        pars = self.model.parameters() 
    170         if self.extra_pars: 
     209        if self.extra_pars is not None: 
    171210            pars.update(self.extra_pars) 
     211        pars.update(self._resolution_pars) 
    172212        return pars 
    173213 

  • sasmodels/direct_model.py

    rc1799d3 r9150036  
    224224            else: 
    225225                Iq, dIq = None, None 
    226             #self._theory = np.zeros_like(q) 
    227             q_vectors = [res.q_calc] 
    228226        elif self.data_type == 'Iqxy': 
    229227            #if not model.info.parameters.has_2d: 
    … …  
    242240            res = resolution2d.Pinhole2D(data=data, index=index, 
    243241                                         nsigma=3.0, accuracy=accuracy) 
    244             #self._theory = np.zeros_like(self.Iq) 
    245             q_vectors = res.q_calc 
    246242        elif self.data_type == 'Iq': 
    247243            index = (data.x >= data.qmin) & (data.x <= data.qmax) 
    … …  
    268264            else: 
    269265                res = resolution.Perfect1D(data.x[index]) 
    270  
    271             #self._theory = np.zeros_like(self.Iq) 
    272             q_vectors = [res.q_calc] 
    273266        elif self.data_type == 'Iq-oriented': 
    274267            index = (data.x >= data.qmin) & (data.x <= data.qmax) 
    … …  
    286279                                      qx_width=data.dxw[index], 
    287280                                      qy_width=data.dxl[index]) 
    288             q_vectors = res.q_calc 
    289281        else: 
    290282            raise ValueError("Unknown data type") # never gets here 
    … …  
    292284        # Remember function inputs so we can delay loading the function and 
    293285        # so we can save/restore state 
    294         self._kernel_inputs = q_vectors 
    295286        self._kernel = None 
    296287        self.Iq, self.dIq, self.index = Iq, dIq, index 
    … …  
    329320        # type: (ParameterSet, float) -> np.ndarray 
    330321        if self._kernel is None: 
    331             self._kernel = self._model.make_kernel(self._kernel_inputs) 
     322            # TODO: change interfaces so that resolution returns kernel inputs 
     323            # Maybe have resolution always return a tuple, or maybe have 
     324            # make_kernel accept either an ndarray or a pair of ndarrays. 
     325            kernel_inputs = self.resolution.q_calc 
     326            if isinstance(kernel_inputs, np.ndarray): 
     327                kernel_inputs = (kernel_inputs,) 
     328            self._kernel = self._model.make_kernel(kernel_inputs) 
    332329 
    333330        # Need to pull background out of resolution for multiple scattering 

  • sasmodels/multiscat.py

    rb3703f5 r2c4a190  
    342342 
    343343    *probability* is related to the expected number of scattering 
    344     events in the sample $\lambda$ as $p = 1 = e^{-\lambda}$.  As a 
    345     hack to allow probability to be a fitted parameter, the "value" 
    346     can be a function that takes no parameters and returns the current 
    347     value of the probability.  *coverage* determines how many scattering 
    348     steps to consider.  The default is 0.99, which sets $n$ such that 
    349     $1 \ldots n$ covers 99% of the Poisson probability mass function. 
     344    events in the sample $\lambda$ as $p = 1 - e^{-\lambda}$. 
     345    *coverage* determines how many scattering steps to consider.  The 
     346    default is 0.99, which sets $n$ such that $1 \ldots n$ covers 99% 
     347    of the Poisson probability mass function. 
    350348 
    351349    *is2d* is True then 2D scattering is used, otherwise it accepts 
    … …  
    399397        self.qmin = qmin 
    400398        self.nq = nq 
    401         self.probability = probability 
     399        self.probability = 0. if probability is None else probability 
    402400        self.coverage = coverage 
    403401        self.is2d = is2d 
    … …  
    456454        self.Iqxy = None # type: np.ndarray 
    457455 
     456        # Label probability as a fittable parameter, and give its external name 
     457        # Note that the external name must be a valid python identifier, since 
     458        # is will be set as an experiment attribute. 
     459        self.fittable = {'probability': 'scattering_probability'} 
     460 
    458461    def apply(self, theory): 
    459462        if self.is2d: 
    … …  
    463466        Iq_calc = Iq_calc.reshape(self.nq, self.nq) 
    464467 
     468        # CRUFT: don't need probability as a function anymore 
    465469        probability = self.probability() if callable(self.probability) else self.probability 
    466470        coverage = self.coverage 

  • sasmodels/sasview_model.py

    ra8a1d48 r9150036  
    2525from . import core 
    2626from . import custom 
     27from . import kernelcl 
    2728from . import product 
    2829from . import generate 
    … …  
    3031from . import modelinfo 
    3132from .details import make_kernel_args, dispersion_mesh 
     33from .kernelcl import reset_environment 
    3234 
    3335# pylint: disable=unused-import 
    … …  
    6870#: has changed since we last reloaded. 
    6971_CACHED_MODULE = {}  # type: Dict[str, "module"] 
     72 
     73def reset_environment(): 
     74    # type: () -> None 
     75    """ 
     76    Clear the compute engine context so that the GUI can change devices. 
     77 
     78    This removes all compiled kernels, even those that are active on fit 
     79    pages, but they will be restored the next time they are needed. 
     80    """ 
     81    kernelcl.reset_environment() 
     82    for model in MODELS.values(): 
     83        model._model = None 
    7084 
    7185def find_model(modelname): 
    … …  
    696710    def _calculate_Iq(self, qx, qy=None): 
    697711        if self._model is None: 
    698             self._model = core.build_model(self._model_info) 
     712            # Only need one copy of the compiled kernel regardless of how many 
     713            # times it is used, so store it in the class.  Also, to reset the 
     714            # compute engine, need to clear out all existing compiled kernels, 
     715            # which is much easier to do if we store them in the class. 
     716            self.__class__._model = core.build_model(self._model_info) 
    699717        if qy is not None: 
    700718            q_vectors = [np.asarray(qx), np.asarray(qy)] 

  • explore/realspace.py

    r362a66f r5778279  
    99import numpy as np 
    1010from numpy import pi, radians, sin, cos, sqrt 
    11 from numpy.random import poisson, uniform, randn, rand 
     11from numpy.random import poisson, uniform, randn, rand, randint 
    1212from numpy.polynomial.legendre import leggauss 
    1313from scipy.integrate import simps 
    … …  
    7878 
    7979 
     80I3 = np.matrix([[1., 0, 0], [0, 1, 0], [0, 0, 1]]) 
     81 
    8082class Shape: 
    81     rotation = np.matrix([[1., 0, 0], [0, 1, 0], [0, 0, 1]]) 
     83    rotation = I3 
    8284    center = np.array([0., 0., 0.])[:, None] 
    8385    r_max = None 
     86    lattice_size = np.array((1, 1, 1)) 
     87    lattice_spacing = np.array((1., 1., 1.)) 
     88    lattice_distortion = 0.0 
     89    lattice_rotation = 0.0 
     90    lattice_type = "" 
    8491 
    8592    def volume(self): 
    … …  
    96103 
    97104    def rotate(self, theta, phi, psi): 
    98         self.rotation = rotation(theta, phi, psi) * self.rotation 
     105        if theta != 0. or phi != 0. or psi != 0.: 
     106            self.rotation = rotation(theta, phi, psi) * self.rotation 
    99107        return self 
    100108 
    … …  
    103111        return self 
    104112 
     113    def lattice(self, size=(1, 1, 1), spacing=(2, 2, 2), type="sc", 
     114                distortion=0.0, rotation=0.0): 
     115        self.lattice_size = np.asarray(size, 'i') 
     116        self.lattice_spacing = np.asarray(spacing, 'd') 
     117        self.lattice_type = type 
     118        self.lattice_distortion = distortion 
     119        self.lattice_rotation = rotation 
     120 
    105121    def _adjust(self, points): 
    106         points = np.asarray(self.rotation * np.matrix(points.T)) + self.center 
     122        if self.rotation is I3: 
     123            points = points.T + self.center 
     124        else: 
     125            points = np.asarray(self.rotation * np.matrix(points.T)) + self.center 
     126        if self.lattice_type: 
     127            points = self._apply_lattice(points) 
    107128        return points.T 
    108129 
    109     def r_bins(self, q, over_sampling=1, r_step=0.): 
    110         r_max = min(2 * pi / q[0], self.r_max) 
     130    def r_bins(self, q, over_sampling=10, r_step=0.): 
     131        if self.lattice_type: 
     132            r_max = np.sqrt(np.sum(self.lattice_size*self.lattice_spacing*self.dims)**2)/2 
     133        else: 
     134            r_max = self.r_max 
     135        #r_max = min(2 * pi / q[0], r_max) 
    111136        if r_step == 0.: 
    112137            r_step = 2 * pi / q[-1] / over_sampling 
    113138        #r_step = 0.01 
    114139        return np.arange(r_step, r_max, r_step) 
     140 
     141    def _apply_lattice(self, points): 
     142        """Spread points to different lattice positions""" 
     143        size = self.lattice_size 
     144        spacing = self.lattice_spacing 
     145        shuffle = self.lattice_distortion 
     146        rotate = self.lattice_rotation 
     147        lattice = self.lattice_type 
     148 
     149        if rotate != 0: 
     150            # To vectorize the rotations we will need to unwrap the matrix multiply 
     151            raise NotImplementedError("don't handle rotations yet") 
     152 
     153        # Determine the number of lattice points in the lattice 
     154        shapes_per_cell = 2 if lattice == "bcc" else 4 if lattice == "fcc" else 1 
     155        number_of_lattice_points = np.prod(size) * shapes_per_cell 
     156 
     157        # For each point in the original shape, figure out which lattice point 
     158        # to translate it to.  This is both cell index (i*ny*nz + j*nz  + k) as 
     159        # well as the point in the cell (corner, body center or face center). 
     160        nsamples = points.shape[1] 
     161        lattice_point = randint(number_of_lattice_points, size=nsamples) 
     162 
     163        # Translate the cell index into the i,j,k coordinates of the senter 
     164        cell_index = lattice_point // shapes_per_cell 
     165        center = np.vstack((cell_index//(size[1]*size[2]), 
     166                            (cell_index%(size[1]*size[2]))//size[2], 
     167                            cell_index%size[2])) 
     168        center = np.asarray(center, dtype='d') 
     169        if lattice == "bcc": 
     170            center[:, lattice_point % shapes_per_cell == 1] += [[0.5], [0.5], [0.5]] 
     171        elif lattice == "fcc": 
     172            center[:, lattice_point % shapes_per_cell == 1] += [[0.0], [0.5], [0.5]] 
     173            center[:, lattice_point % shapes_per_cell == 2] += [[0.5], [0.0], [0.5]] 
     174            center[:, lattice_point % shapes_per_cell == 3] += [[0.5], [0.5], [0.0]] 
     175 
     176        # Each lattice point has its own displacement from the ideal position. 
     177        # Not checking that shapes do not overlap if displacement is too large. 
     178        offset = shuffle*(randn(3, number_of_lattice_points) if shuffle < 0.3 
     179                          else rand(3, number_of_lattice_points)) 
     180        center += offset[:, cell_index] 
     181 
     182        # Each lattice point has its own rotation.  Rotate the point prior to 
     183        # applying any displacement. 
     184        # rotation = rotate*(randn(size=(shapes, 3)) if shuffle < 30 else rand(size=(nsamples, 3))) 
     185        # for k in shapes: points[k] = rotation[k]*points[k] 
     186        points += center*(np.array([spacing])*np.array(self.dims)).T 
     187        return points 
    115188 
    116189class Composite(Shape): 
    … …  
    669742    Iq = 100 * np.ones_like(qx) 
    670743    data = Data2D(x=qx, y=qy, z=Iq, dx=None, dy=None, dz=np.sqrt(Iq)) 
    671     data.x_bins = qx[0,:] 
    672     data.y_bins = qy[:,0] 
     744    data.x_bins = qx[0, :] 
     745    data.y_bins = qy[:, 0] 
    673746    data.filename = "fake data" 
    674747 
    … …  
    695768    return shape, fn, fn_xy 
    696769 
    697 def build_sphere(radius=125, rho=2): 
     770DEFAULT_SPHERE_RADIUS = 125 
     771DEFAULT_SPHERE_CONTRAST = 2 
     772def build_sphere(radius=DEFAULT_SPHERE_RADIUS, rho=DEFAULT_SPHERE_CONTRAST): 
    698773    shape = TriaxialEllipsoid(radius, radius, radius, rho) 
    699774    fn = lambda q: sphere_Iq(q, radius)*rho**2 
    … …  
    751826    return shape, fn, fn_xy 
    752827 
    753 def build_cubic_lattice(shape, nx=1, ny=1, nz=1, dx=2, dy=2, dz=2, 
    754                   shuffle=0, rotate=0): 
     828def build_sc_lattice(shape, nx=1, ny=1, nz=1, dx=2, dy=2, dz=2, 
     829                        shuffle=0, rotate=0): 
    755830    a, b, c = shape.dims 
    756     shapes = [copy(shape) 
     831    corners= [copy(shape) 
    757832              .shift((ix+(randn() if shuffle < 0.3 else rand())*shuffle)*dx*a, 
    758833                     (iy+(randn() if shuffle < 0.3 else rand())*shuffle)*dy*b, 
    … …  
    762837              for iy in range(ny) 
    763838              for iz in range(nz)] 
    764     lattice = Composite(shapes) 
     839    lattice = Composite(corners) 
    765840    return lattice 
    766841 
     842def build_bcc_lattice(shape, nx=1, ny=1, nz=1, dx=2, dy=2, dz=2, 
     843                      shuffle=0, rotate=0): 
     844    a, b, c = shape.dims 
     845    corners = [copy(shape) 
     846               .shift((ix+(randn() if shuffle < 0.3 else rand())*shuffle)*dx*a, 
     847                      (iy+(randn() if shuffle < 0.3 else rand())*shuffle)*dy*b, 
     848                      (iz+(randn() if shuffle < 0.3 else rand())*shuffle)*dz*c) 
     849               .rotate(*((randn(3) if rotate < 30 else rand(3))*rotate)) 
     850               for ix in range(nx) 
     851               for iy in range(ny) 
     852               for iz in range(nz)] 
     853    centers = [copy(shape) 
     854               .shift((ix+0.5+(randn() if shuffle < 0.3 else rand())*shuffle)*dx*a, 
     855                      (iy+0.5+(randn() if shuffle < 0.3 else rand())*shuffle)*dy*b, 
     856                      (iz+0.5+(randn() if shuffle < 0.3 else rand())*shuffle)*dz*c) 
     857               .rotate(*((randn(3) if rotate < 30 else rand(3))*rotate)) 
     858               for ix in range(nx) 
     859               for iy in range(ny) 
     860               for iz in range(nz)] 
     861    lattice = Composite(corners + centers) 
     862    return lattice 
     863 
     864def build_fcc_lattice(shape, nx=1, ny=1, nz=1, dx=2, dy=2, dz=2, 
     865                      shuffle=0, rotate=0): 
     866    a, b, c = shape.dims 
     867    corners = [copy(shape) 
     868               .shift((ix+(randn() if shuffle < 0.3 else rand())*shuffle)*dx*a, 
     869                      (iy+(randn() if shuffle < 0.3 else rand())*shuffle)*dy*b, 
     870                      (iz+(randn() if shuffle < 0.3 else rand())*shuffle)*dz*c) 
     871               .rotate(*((randn(3) if rotate < 30 else rand(3))*rotate)) 
     872               for ix in range(nx) 
     873               for iy in range(ny) 
     874               for iz in range(nz)] 
     875    faces_a = [copy(shape) 
     876               .shift((ix+0.0+(randn() if shuffle < 0.3 else rand())*shuffle)*dx*a, 
     877                      (iy+0.5+(randn() if shuffle < 0.3 else rand())*shuffle)*dy*b, 
     878                      (iz+0.5+(randn() if shuffle < 0.3 else rand())*shuffle)*dz*c) 
     879               .rotate(*((randn(3) if rotate < 30 else rand(3))*rotate)) 
     880               for ix in range(nx) 
     881               for iy in range(ny) 
     882               for iz in range(nz)] 
     883    faces_b = [copy(shape) 
     884               .shift((ix+0.5+(randn() if shuffle < 0.3 else rand())*shuffle)*dx*a, 
     885                      (iy+0.0+(randn() if shuffle < 0.3 else rand())*shuffle)*dy*b, 
     886                      (iz+0.5+(randn() if shuffle < 0.3 else rand())*shuffle)*dz*c) 
     887               .rotate(*((randn(3) if rotate < 30 else rand(3))*rotate)) 
     888               for ix in range(nx) 
     889               for iy in range(ny) 
     890               for iz in range(nz)] 
     891    faces_c = [copy(shape) 
     892               .shift((ix+0.5+(randn() if shuffle < 0.3 else rand())*shuffle)*dx*a, 
     893                      (iy+0.5+(randn() if shuffle < 0.3 else rand())*shuffle)*dy*b, 
     894                      (iz+0.0+(randn() if shuffle < 0.3 else rand())*shuffle)*dz*c) 
     895               .rotate(*((randn(3) if rotate < 30 else rand(3))*rotate)) 
     896               for ix in range(nx) 
     897               for iy in range(ny) 
     898               for iz in range(nz)] 
     899    lattice = Composite(corners + faces_a + faces_b + faces_c) 
     900    return lattice 
    767901 
    768902SHAPE_FUNCTIONS = OrderedDict([ 
    … …  
    775909]) 
    776910SHAPES = list(SHAPE_FUNCTIONS.keys()) 
     911LATTICE_FUNCTIONS = OrderedDict([ 
     912    ("sc", build_sc_lattice), 
     913    ("bcc", build_bcc_lattice), 
     914    ("fcc", build_fcc_lattice), 
     915]) 
     916LATTICE_TYPES = list(LATTICE_FUNCTIONS.keys()) 
    777917 
    778918def check_shape(title, shape, fn=None, show_points=False, 
    … …  
    783923    r = shape.r_bins(q, r_step=r_step) 
    784924    sampling_density = samples / shape.volume 
     925    print("sampling points") 
    785926    rho, points = shape.sample(sampling_density) 
     927    print("calculating Pr") 
    786928    t0 = time.time() 
    787929    Pr = calc_Pr(r, rho-rho_solvent, points) 
    … …  
    792934    import pylab 
    793935    if show_points: 
    794          plot_points(rho, points); pylab.figure() 
     936        plot_points(rho, points); pylab.figure() 
    795937    plot_calc(r, Pr, q, Iq, theory=theory, title=title) 
    796938    pylab.gcf().canvas.set_window_title(title) 
    … …  
    806948    Qx, Qy = np.meshgrid(qx, qy) 
    807949    sampling_density = samples / shape.volume 
     950    print("sampling points") 
    808951    t0 = time.time() 
    809952    rho, points = shape.sample(sampling_density) 
    … …  
    844987                        help='lattice size') 
    845988    parser.add_argument('-z', '--spacing', type=str, default='2,2,2', 
    846                         help='lattice spacing') 
     989                        help='lattice spacing (relative to shape)') 
     990    parser.add_argument('-t', '--type', choices=LATTICE_TYPES, 
     991                        default=LATTICE_TYPES[0], 
     992                        help='lattice type') 
    847993    parser.add_argument('-r', '--rotate', type=float, default=0., 
    848994                        help="rotation relative to lattice, gaussian < 30 degrees, uniform otherwise") 
    … …  
    8581004    nx, ny, nz = [int(v) for v in opts.lattice.split(',')] 
    8591005    dx, dy, dz = [float(v) for v in opts.spacing.split(',')] 
    860     shuffle, rotate = opts.shuffle, opts.rotate 
     1006    distortion, rotation = opts.shuffle, opts.rotate 
    8611007    shape, fn, fn_xy = SHAPE_FUNCTIONS[opts.shape](**pars) 
    862     if nx > 1 or ny > 1 or nz > 1: 
    863         shape = build_cubic_lattice(shape, nx, ny, nz, dx, dy, dz, shuffle, rotate) 
     1008    view = tuple(float(v) for v in opts.view.split(',')) 
     1009    # If comparing a sphere in a cubic lattice, compare against the 
     1010    # corresponding paracrystalline model. 
     1011    if opts.shape == "sphere" and dx == dy == dz and nx*ny*nz > 1: 
     1012        radius = pars.get('radius', DEFAULT_SPHERE_RADIUS) 
     1013        model_name = opts.type + "_paracrystal" 
     1014        model_pars = { 
     1015            "scale": 1., 
     1016            "background": 0., 
     1017            "lattice_spacing": 2*radius*dx, 
     1018            "lattice_distortion": distortion, 
     1019            "radius": radius, 
     1020            "sld": pars.get('rho', DEFAULT_SPHERE_CONTRAST), 
     1021            "sld_solvent": 0., 
     1022            "theta": view[0], 
     1023            "phi": view[1], 
     1024            "psi": view[2], 
     1025        } 
     1026        fn, fn_xy = wrap_sasmodel(model_name, **model_pars) 
     1027    if nx*ny*nz > 1: 
     1028        if rotation != 0: 
     1029            print("building %s lattice"%opts.type) 
     1030            build_lattice = LATTICE_FUNCTIONS[opts.type] 
     1031            shape = build_lattice(shape, nx, ny, nz, dx, dy, dz, 
     1032                                  distortion, rotation) 
     1033        else: 
     1034            shape.lattice(size=(nx, ny, nz), spacing=(dx, dy, dz), 
     1035                          type=opts.type, 
     1036                          rotation=rotation, distortion=distortion) 
     1037 
    8641038    title = "%s(%s)" % (opts.shape, " ".join(opts.pars)) 
    8651039    if opts.dim == 1: 
    … …  
    8671041                    mesh=opts.mesh, qmax=opts.qmax, samples=opts.samples) 
    8681042    else: 
    869         view = tuple(float(v) for v in opts.view.split(',')) 
    8701043        check_shape_2d(title, shape, fn_xy, view=view, show_points=opts.plot, 
    8711044                       mesh=opts.mesh, qmax=opts.qmax, samples=opts.samples) 

  • sasmodels/convert.py

    r610ef23 rb3f4831  
    166166        oldpars = _hand_convert_3_1_2_to_4_1(name, oldpars) 
    167167    if version < (4, 2, 0): 
     168        oldpars = _hand_convert_4_1_to_4_2(name, oldpars) 
    168169        oldpars = _rename_magnetic_pars(oldpars) 
     170    return oldpars 
     171 
     172def _hand_convert_4_1_to_4_2(name, oldpars): 
     173    if name in ('bcc_paracrystal', 'fcc_paracrystal', 'sc_paracrystal'): 
     174        oldpars['lattice_spacing'] = oldpars.pop('dnn') 
     175        oldpars['lattice_distortion'] = oldpars.pop('d_factor') 
    169176    return oldpars 
    170177 

  • sasmodels/models/bcc_paracrystal.c

    r108e70e r642046e  
    11static double 
    2 bcc_Zq(double qa, double qb, double qc, double dnn, double d_factor) 
     2bcc_Zq(double qa, double qb, double qc, double lattice_spacing, double lattice_distortion) 
    33{ 
    44    // Equations from Matsuoka 26-27-28, multiplied by |q| 
    … …  
    1717    //         => exp(a)^2 - 2 cos(d ak) exp(a) + 1) 
    1818    //         => (exp(a) - 2 cos(d ak)) * exp(a) + 1 
    19     const double arg = -0.5*square(dnn*d_factor)*(a1*a1 + a2*a2 + a3*a3); 
     19    const double arg = -0.5*square(lattice_spacing*lattice_distortion)*(a1*a1 + a2*a2 + a3*a3); 
    2020    const double exp_arg = exp(arg); 
    2121    const double Zq = -cube(expm1(2.0*arg)) 
    22         / ( ((exp_arg - 2.0*cos(dnn*a1))*exp_arg + 1.0) 
    23           * ((exp_arg - 2.0*cos(dnn*a2))*exp_arg + 1.0) 
    24           * ((exp_arg - 2.0*cos(dnn*a3))*exp_arg + 1.0)); 
     22        / ( ((exp_arg - 2.0*cos(lattice_spacing*a1))*exp_arg + 1.0) 
     23          * ((exp_arg - 2.0*cos(lattice_spacing*a2))*exp_arg + 1.0) 
     24          * ((exp_arg - 2.0*cos(lattice_spacing*a3))*exp_arg + 1.0)); 
    2525 
    2626#elif 0 
    … …  
    3636    //            = tanh(-a) / [1 - cos(d a_k)/cosh(-a)] 
    3737    // 
    38     const double arg = 0.5*square(dnn*d_factor)*(a1*a1 + a2*a2 + a3*a3); 
     38    const double arg = 0.5*square(lattice_spacing*lattice_distortion)*(a1*a1 + a2*a2 + a3*a3); 
    3939    const double sinh_qd = sinh(arg); 
    4040    const double cosh_qd = cosh(arg); 
    41     const double Zq = sinh_qd/(cosh_qd - cos(dnn*a1)) 
    42                     * sinh_qd/(cosh_qd - cos(dnn*a2)) 
    43                     * sinh_qd/(cosh_qd - cos(dnn*a3)); 
     41    const double Zq = sinh_qd/(cosh_qd - cos(lattice_spacing*a1)) 
     42                    * sinh_qd/(cosh_qd - cos(lattice_spacing*a2)) 
     43                    * sinh_qd/(cosh_qd - cos(lattice_spacing*a3)); 
    4444#else 
    45     const double arg = 0.5*square(dnn*d_factor)*(a1*a1 + a2*a2 + a3*a3); 
     45    const double arg = 0.5*square(lattice_spacing*lattice_distortion)*(a1*a1 + a2*a2 + a3*a3); 
    4646    const double tanh_qd = tanh(arg); 
    4747    const double cosh_qd = cosh(arg); 
    48     const double Zq = tanh_qd/(1.0 - cos(dnn*a1)/cosh_qd) 
    49                     * tanh_qd/(1.0 - cos(dnn*a2)/cosh_qd) 
    50                     * tanh_qd/(1.0 - cos(dnn*a3)/cosh_qd); 
     48    const double Zq = tanh_qd/(1.0 - cos(lattice_spacing*a1)/cosh_qd) 
     49                    * tanh_qd/(1.0 - cos(lattice_spacing*a2)/cosh_qd) 
     50                    * tanh_qd/(1.0 - cos(lattice_spacing*a3)/cosh_qd); 
    5151#endif 
    5252 
    … …  
    5757// occupied volume fraction calculated from lattice symmetry and sphere radius 
    5858static double 
    59 bcc_volume_fraction(double radius, double dnn) 
     59bcc_volume_fraction(double radius, double lattice_spacing) 
    6060{ 
    61     return 2.0*sphere_volume(sqrt(0.75)*radius/dnn); 
     61    return 2.0*sphere_volume(radius/lattice_spacing); 
    6262} 
    6363 
    … …  
    6969 
    7070 
    71 static double Iq(double q, double dnn, 
    72     double d_factor, double radius, 
     71static double Iq(double q, double lattice_spacing, 
     72    double lattice_distortion, double radius, 
    7373    double sld, double solvent_sld) 
    7474{ 
    … …  
    9494            const double qa = qab*cos_phi; 
    9595            const double qb = qab*sin_phi; 
    96             const double form = bcc_Zq(qa, qb, qc, dnn, d_factor); 
     96            const double form = bcc_Zq(qa, qb, qc, lattice_spacing, lattice_distortion); 
    9797            inner_sum += GAUSS_W[j] * form; 
    9898        } 
    … …  
    103103    const double Zq = outer_sum/(4.0*M_PI); 
    104104    const double Pq = sphere_form(q, radius, sld, solvent_sld); 
    105     return bcc_volume_fraction(radius, dnn) * Pq * Zq; 
     105    return bcc_volume_fraction(radius, lattice_spacing) * Pq * Zq; 
    106106} 
    107107 
    108108 
    109109static double Iqabc(double qa, double qb, double qc, 
    110     double dnn, double d_factor, double radius, 
     110    double lattice_spacing, double lattice_distortion, double radius, 
    111111    double sld, double solvent_sld) 
    112112{ 
    113113    const double q = sqrt(qa*qa + qb*qb + qc*qc); 
    114     const double Zq = bcc_Zq(qa, qb, qc, dnn, d_factor); 
     114    const double Zq = bcc_Zq(qa, qb, qc, lattice_spacing, lattice_distortion); 
    115115    const double Pq = sphere_form(q, radius, sld, solvent_sld); 
    116     return bcc_volume_fraction(radius, dnn) * Pq * Zq; 
     116    return bcc_volume_fraction(radius, lattice_spacing) * Pq * Zq; 
    117117} 

  • sasmodels/models/bcc_paracrystal.py

    rda7b26b rb3f4831  
    3434.. math:: 
    3535 
    36     V_\text{lattice} = \frac{16\pi}{3} \frac{R^3}{\left(D\sqrt{2}\right)^3} 
     36    V_\text{lattice} = \frac{8\pi}{3} \frac{R^3}{\left(2D/\sqrt{3}\right)^3} 
    3737 
    3838 
    … …  
    104104 
    105105* **Author:** NIST IGOR/DANSE **Date:** pre 2010 
    106 * **Last Modified by:** Paul Butler **Date:** September 29, 2016 
    107 * **Last Reviewed by:** Richard Heenan **Date:** March 21, 2016 
     106* **Last Modified by:** Paul Butler **Date:** September 16, 2018 
     107* **Last Reviewed by:** Paul Butler **Date:** September 16, 2018 
    108108""" 
    109109 
    … …  
    127127# pylint: disable=bad-whitespace, line-too-long 
    128128#             ["name", "units", default, [lower, upper], "type","description" ], 
    129 parameters = [["dnn",         "Ang",       220,    [-inf, inf], "",            "Nearest neighbour distance"], 
    130               ["d_factor",    "",            0.06, [-inf, inf], "",            "Paracrystal distortion factor"], 
     129parameters = [["lattice_spacing",         "Ang",       220,    [-inf, inf], "",            "Lattice spacing"], 
     130              ["lattice_distortion",    "",            0.06, [-inf, inf], "",            "Paracrystal distortion factor"], 
    131131              ["radius",      "Ang",        40,    [0, inf],    "volume",      "Particle radius"], 
    132132              ["sld",         "1e-6/Ang^2",  4,    [-inf, inf], "sld",         "Particle scattering length density"], 
    … …  
    149149    # in this range 'cuz its easy. 
    150150    radius = 10**np.random.uniform(1.3, 4) 
    151     d_factor = 10**np.random.uniform(-2, -0.7)  # sigma_d in 0.01-0.7 
    152     dnn_fraction = np.random.beta(a=10, b=1) 
    153     dnn = radius*4/np.sqrt(3)/dnn_fraction 
     151    lattice_distortion = 10**np.random.uniform(-2, -0.7)  # sigma_d in 0.01-0.7 
     152    lattice_spacing_fraction = np.random.beta(a=10, b=1) 
     153    lattice_spacing = radius*4/np.sqrt(3)/lattice_spacing_fraction 
    154154    pars = dict( 
    155155        #sld=1, sld_solvent=0, scale=1, background=1e-32, 
    156         dnn=dnn, 
    157         d_factor=d_factor, 
     156        lattice_spacing=lattice_spacing, 
     157        lattice_distortion=lattice_distortion, 
    158158        radius=radius, 
    159159    ) 

  • sasmodels/models/fcc_paracrystal.c

    r71b751d rcc8b183  
    11static double 
    2 fcc_Zq(double qa, double qb, double qc, double dnn, double d_factor) 
     2fcc_Zq(double qa, double qb, double qc, double lattice_spacing, double lattice_distortion) 
    33{ 
    44    // Equations from Matsuoka 17-18-19, multiplied by |q| 
    … …  
    1616    //         => exp(a)^2 - 2 cos(d ak) exp(a) + 1) 
    1717    //         => (exp(a) - 2 cos(d ak)) * exp(a) + 1 
    18     const double arg = -0.5*square(dnn*d_factor)*(a1*a1 + a2*a2 + a3*a3); 
     18    const double arg = -0.5*square(lattice_spacing*lattice_distortion)*(a1*a1 + a2*a2 + a3*a3); 
    1919    const double exp_arg = exp(arg); 
    2020    const double Zq = -cube(expm1(2.0*arg)) 
    21         / ( ((exp_arg - 2.0*cos(dnn*a1))*exp_arg + 1.0) 
    22           * ((exp_arg - 2.0*cos(dnn*a2))*exp_arg + 1.0) 
    23           * ((exp_arg - 2.0*cos(dnn*a3))*exp_arg + 1.0)); 
     21        / ( ((exp_arg - 2.0*cos(lattice_spacing*a1))*exp_arg + 1.0) 
     22          * ((exp_arg - 2.0*cos(lattice_spacing*a2))*exp_arg + 1.0) 
     23          * ((exp_arg - 2.0*cos(lattice_spacing*a3))*exp_arg + 1.0)); 
    2424 
    2525    return Zq; 
    … …  
    2929// occupied volume fraction calculated from lattice symmetry and sphere radius 
    3030static double 
    31 fcc_volume_fraction(double radius, double dnn) 
     31fcc_volume_fraction(double radius, double lattice_spacing) 
    3232{ 
    33     return 4.0*sphere_volume(M_SQRT1_2*radius/dnn); 
     33    return 4.0*sphere_volume(radius/lattice_spacing); 
    3434} 
    3535 
    … …  
    4141 
    4242 
    43 static double Iq(double q, double dnn, 
    44   double d_factor, double radius, 
     43static double Iq(double q, double lattice_spacing, 
     44  double lattice_distortion, double radius, 
    4545  double sld, double solvent_sld) 
    4646{ 
    … …  
    6666            const double qa = qab*cos_phi; 
    6767            const double qb = qab*sin_phi; 
    68             const double form = fcc_Zq(qa, qb, qc, dnn, d_factor); 
     68            const double form = fcc_Zq(qa, qb, qc, lattice_spacing, lattice_distortion); 
    6969            inner_sum += GAUSS_W[j] * form; 
    7070        } 
    … …  
    7676    const double Pq = sphere_form(q, radius, sld, solvent_sld); 
    7777 
    78     return fcc_volume_fraction(radius, dnn) * Pq * Zq; 
     78    return fcc_volume_fraction(radius, lattice_spacing) * Pq * Zq; 
    7979} 
    8080 
    8181static double Iqabc(double qa, double qb, double qc, 
    82     double dnn, double d_factor, double radius, 
     82    double lattice_spacing, double lattice_distortion, double radius, 
    8383    double sld, double solvent_sld) 
    8484{ 
    8585    const double q = sqrt(qa*qa + qb*qb + qc*qc); 
    8686    const double Pq = sphere_form(q, radius, sld, solvent_sld); 
    87     const double Zq = fcc_Zq(qa, qb, qc, dnn, d_factor); 
    88     return fcc_volume_fraction(radius, dnn) * Pq * Zq; 
     87    const double Zq = fcc_Zq(qa, qb, qc, lattice_spacing, lattice_distortion); 
     88    return fcc_volume_fraction(radius, lattice_spacing) * Pq * Zq; 
    8989} 

  • sasmodels/models/fcc_paracrystal.py

    rda7b26b rb3f4831  
    33#note - calculation requires double precision 
    44r""" 
    5 .. warning:: This model and this model description are under review following  
    6              concerns raised by SasView users. If you need to use this model,  
    7              please email help@sasview.org for the latest situation. *The  
     5.. warning:: This model and this model description are under review following 
     6             concerns raised by SasView users. If you need to use this model, 
     7             please email help@sasview.org for the latest situation. *The 
    88             SasView Developers. September 2018.* 
    99 
    … …  
    100100 
    101101Authorship and Verification 
    102 --------------------------- 
     102---------------------------- 
    103103 
    104104* **Author:** NIST IGOR/DANSE **Date:** pre 2010 
    105 * **Last Modified by:** Paul Butler **Date:** September 29, 2016 
    106 * **Last Reviewed by:** Richard Heenan **Date:** March 21, 2016 
     105* **Last Modified by:** Paul Butler **Date:** September 16, 2018 
     106* **Last Reviewed by:** Paul Butler **Date:** September 16, 2018 
    107107""" 
    108108 
    … …  
    123123# pylint: disable=bad-whitespace, line-too-long 
    124124#             ["name", "units", default, [lower, upper], "type","description"], 
    125 parameters = [["dnn", "Ang", 220, [-inf, inf], "", "Nearest neighbour distance"], 
    126               ["d_factor", "", 0.06, [-inf, inf], "", "Paracrystal distortion factor"], 
     125parameters = [["lattice_spacing", "Ang", 220, [-inf, inf], "", "Lattice spacing"], 
     126              ["lattice_distortion", "", 0.06, [-inf, inf], "", "Paracrystal distortion factor"], 
    127127              ["radius", "Ang", 40, [0, inf], "volume", "Particle radius"], 
    128128              ["sld", "1e-6/Ang^2", 4, [-inf, inf], "sld", "Particle scattering length density"], 
    … …  
    139139    # copied from bcc_paracrystal 
    140140    radius = 10**np.random.uniform(1.3, 4) 
    141     d_factor = 10**np.random.uniform(-2, -0.7)  # sigma_d in 0.01-0.7 
    142     dnn_fraction = np.random.beta(a=10, b=1) 
    143     dnn = radius*4/np.sqrt(2)/dnn_fraction 
     141    lattice_distortion = 10**np.random.uniform(-2, -0.7)  # sigma_d in 0.01-0.7 
     142    lattice_spacing_fraction = np.random.beta(a=10, b=1) 
     143    lattice_spacing = radius*4/np.sqrt(2)/lattice_spacing_fraction 
    144144    pars = dict( 
    145145        #sld=1, sld_solvent=0, scale=1, background=1e-32, 
    146         dnn=dnn, 
    147         d_factor=d_factor, 
     146        latice_spacing=lattice_spacing, 
     147        lattice_distortion=d_factor, 
    148148        radius=radius, 
    149149    ) 

  • sasmodels/models/sc_paracrystal.c

    r71b751d rcc8b183  
    11static double 
    2 sc_Zq(double qa, double qb, double qc, double dnn, double d_factor) 
     2sc_Zq(double qa, double qb, double qc, double lattice_spacing, double lattice_distortion) 
    33{ 
    44    // Equations from Matsuoka 9-10-11, multiplied by |q| 
    … …  
    1616    //         => exp(a)^2 - 2 cos(d ak) exp(a) + 1) 
    1717    //         => (exp(a) - 2 cos(d ak)) * exp(a) + 1 
    18     const double arg = -0.5*square(dnn*d_factor)*(a1*a1 + a2*a2 + a3*a3); 
     18    const double arg = -0.5*square(lattice_spacing*lattice_distortion)*(a1*a1 + a2*a2 + a3*a3); 
    1919    const double exp_arg = exp(arg); 
    2020    const double Zq = -cube(expm1(2.0*arg)) 
    21         / ( ((exp_arg - 2.0*cos(dnn*a1))*exp_arg + 1.0) 
    22           * ((exp_arg - 2.0*cos(dnn*a2))*exp_arg + 1.0) 
    23           * ((exp_arg - 2.0*cos(dnn*a3))*exp_arg + 1.0)); 
     21        / ( ((exp_arg - 2.0*cos(lattice_spacing*a1))*exp_arg + 1.0) 
     22          * ((exp_arg - 2.0*cos(lattice_spacing*a2))*exp_arg + 1.0) 
     23          * ((exp_arg - 2.0*cos(lattice_spacing*a3))*exp_arg + 1.0)); 
    2424 
    2525    return Zq; 
    … …  
    2828// occupied volume fraction calculated from lattice symmetry and sphere radius 
    2929static double 
    30 sc_volume_fraction(double radius, double dnn) 
     30sc_volume_fraction(double radius, double lattice_spacing) 
    3131{ 
    32     return sphere_volume(radius/dnn); 
     32    return sphere_volume(radius/lattice_spacing); 
    3333} 
    3434 
    … …  
    4141 
    4242static double 
    43 Iq(double q, double dnn, 
    44     double d_factor, double radius, 
     43Iq(double q, double lattice_spacing, 
     44    double lattice_distortion, double radius, 
    4545    double sld, double solvent_sld) 
    4646{ 
    … …  
    6767            const double qa = qab*cos_phi; 
    6868            const double qb = qab*sin_phi; 
    69             const double form = sc_Zq(qa, qb, qc, dnn, d_factor); 
     69            const double form = sc_Zq(qa, qb, qc, lattice_spacing, lattice_distortion); 
    7070            inner_sum += GAUSS_W[j] * form; 
    7171        } 
    … …  
    7777    const double Pq = sphere_form(q, radius, sld, solvent_sld); 
    7878 
    79     return sc_volume_fraction(radius, dnn) * Pq * Zq; 
     79    return sc_volume_fraction(radius, lattice_spacing) * Pq * Zq; 
    8080} 
    8181 
    8282static double 
    8383Iqabc(double qa, double qb, double qc, 
    84     double dnn, double d_factor, double radius, 
     84    double lattice_spacing, double lattice_distortion, double radius, 
    8585    double sld, double solvent_sld) 
    8686{ 
    8787    const double q = sqrt(qa*qa + qb*qb + qc*qc); 
    8888    const double Pq = sphere_form(q, radius, sld, solvent_sld); 
    89     const double Zq = sc_Zq(qa, qb, qc, dnn, d_factor); 
    90     return sc_volume_fraction(radius, dnn) * Pq * Zq; 
     89    const double Zq = sc_Zq(qa, qb, qc, lattice_spacing, lattice_distortion); 
     90    return sc_volume_fraction(radius, lattice_spacing) * Pq * Zq; 
    9191} 

  • sasmodels/models/sc_paracrystal.py

    rda7b26b rb3f4831  
    11r""" 
    2 .. warning:: This model and this model description are under review following  
    3              concerns raised by SasView users. If you need to use this model,  
    4              please email help@sasview.org for the latest situation. *The  
     2.. warning:: This model and this model description are under review following 
     3             concerns raised by SasView users. If you need to use this model, 
     4             please email help@sasview.org for the latest situation. *The 
    55             SasView Developers. September 2018.* 
    6               
     6 
    77Definition 
    88---------- 
    … …  
    104104 
    105105Authorship and Verification 
    106 --------------------------- 
     106---------------------------- 
    107107 
    108108* **Author:** NIST IGOR/DANSE **Date:** pre 2010 
    109 * **Last Modified by:** Paul Butler **Date:** September 29, 2016 
    110 * **Last Reviewed by:** Richard Heenan **Date:** March 21, 2016 
     109* **Last Modified by:** Paul Butler **Date:** September 16, 2018 
     110* **Last Reviewed by:** Paul Butler **Date:** September 16, 2018 
    111111""" 
    112112 
    … …  
    129129        scale: volume fraction of spheres 
    130130        bkg:background, R: radius of sphere 
    131         dnn: Nearest neighbor distance 
    132         d_factor: Paracrystal distortion factor 
     131        lattice_spacing: Nearest neighbor distance 
     132        lattice_distortion: Paracrystal distortion factor 
    133133        radius: radius of the spheres 
    134134        sldSph: SLD of the sphere 
    … …  
    139139# pylint: disable=bad-whitespace, line-too-long 
    140140#             ["name", "units", default, [lower, upper], "type","description"], 
    141 parameters = [["dnn",         "Ang",       220.0, [0.0, inf],  "",            "Nearest neighbor distance"], 
    142               ["d_factor",    "",           0.06, [-inf, inf], "",            "Paracrystal distortion factor"], 
     141parameters = [["lattice_spacing",         "Ang",       220.0, [0.0, inf],  "",  "Lattice spacing"], 
     142              ["lattice_distortion",    "",           0.06, [-inf, inf], "",   "Paracrystal distortion factor"], 
    143143              ["radius",      "Ang",        40.0, [0.0, inf],  "volume",      "Radius of sphere"], 
    144144              ["sld",  "1e-6/Ang^2",         3.0, [0.0, inf],  "sld",         "Sphere scattering length density"], 
    … …  
    155155    # copied from bcc_paracrystal 
    156156    radius = 10**np.random.uniform(1.3, 4) 
    157     d_factor = 10**np.random.uniform(-2, -0.7)  # sigma_d in 0.01-0.7 
    158     dnn_fraction = np.random.beta(a=10, b=1) 
    159     dnn = radius*4/np.sqrt(4)/dnn_fraction 
     157    lattice_distortion = 10**np.random.uniform(-2, -0.7)  # sigma_d in 0.01-0.7 
     158    lattice_spacing_fraction = np.random.beta(a=10, b=1) 
     159    lattice_spacing = radius*4/np.sqrt(4)/lattice_spacing_fraction 
    160160    pars = dict( 
    161161        #sld=1, sld_solvent=0, scale=1, background=1e-32, 
    162         dnn=dnn, 
    163         d_factor=d_factor, 
     162        lattice_spacing=lattice_spacing, 
     163        lattice_distortion=lattice_distortion, 
    164164        radius=radius, 
    165165    ) 
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