Changeset d368d21 in sasmodels


Ignore:
Timestamp:
Feb 6, 2016 10:56:54 AM (9 years ago)
Author:
butler
Branches:
master, core_shell_microgels, costrafo411, magnetic_model, release_v0.94, release_v0.95, ticket-1257-vesicle-product, ticket_1156, ticket_1265_superball, ticket_822_more_unit_tests
Children:
321736f
Parents:
03582f9 (diff), d5e650d (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 'master' of ​https://github.com/SasView/sasmodels.git

Conflicts:

sasmodels/convert.py

Files:
34 added
52 edited

Legend:

Unmodified
Added
Removed

  • doc/guide/index.rst

    r19dcb933 rbb6f0f3  
    1 ********************** 
    2 SAS Model Organization 
    3 ********************** 
     1********** 
     2SAS Models 
     3********** 
     4 
     5Small angle X-ray and Neutron (SAXS and SANS) scattering examines the 
     6scattering patterns produced by a beam travelling through the sample 
     7and scattering at low angles.  The scattering is computed as a function 
     8of $q_x$ and $q_y$, which for a given beam wavelength corresponds to 
     9particular scattering angles. Each pixel on the detector corresponds to 
     10a different scattering angle. If the sample is unoriented, the scattering 
     11pattern will appear as rings on the detector.  In this case, a circular 
     12average can be taken with 1-dimension data at $q = \surd (q_x^2 + q_y^2)$ 
     13compared to the orientationally averaged SAS scattering pattern. 
    414 
    515Models have certain features in common. 

  • extra/pylint.rc

    r5ef0633 r823e620  
    66# Python code to execute, usually for sys.path manipulation such as 
    77# pygtk.require(). 
    8 #init-hook= 
     8#init-hook='import os, sys; sys.path.extend([os.getcwd(),os.path.join(os.getcwd(),"extra")])' 
     9init-hook='import sys; sys.path.append('extra') 
    910 
    1011# Profiled execution. 
    11 profile=no 
     12#profile=no 
    1213 
    1314# Add files or directories to the blacklist. They should be base names, not 
    … …  
    6465    star-args, 
    6566    unbalanced-tuple-unpacking, 
     67    locally-enabled, 
    6668    locally-disabled, 
    6769 
    … …  
    9092# Add a comment according to your evaluation note. This is used by the global 
    9193# evaluation report (RP0004). 
    92 comment=no 
     94#comment=no 
    9395 
    9496# Template used to display messages. This is a python new-style format string 
    … …  
    99101 
    100102# Required attributes for module, separated by a comma 
    101 required-attributes= 
     103#required-attributes= 
    102104 
    103105# List of builtins function names that should not be used, separated by a comma 
    … …  
    106108# Good variable names which should always be accepted, separated by a comma 
    107109good-names=_, 
    108     input, 
    109     i,j,k,n,x,y,z, 
    110     q,qx,qy,qz, 
    111     dt,dx,dy,dz,id, 
    112     Iq,dIq,Qx,Qy,Qz, 
    113     p, 
     110    input, id, 
     111    h, i, j, k, n, p, v, w, x, y, z, 
     112    q, qx, qy, qz, Q, Qx, Qy, Qz, 
     113    dt, dx, dy, dz, dq, 
     114    Iq, dIq, Iqxy, Iq_calc, 
    114115    ER, call_ER, VR, call_VR, 
     116    Rmax, SElength, 
    115117 
    116118# Bad variable names which should always be refused, separated by a comma 
    … …  
    278280# (useful for modules/projects where namespaces are manipulated during runtime 
    279281# and thus existing member attributes cannot be deduced by static analysis 
    280 ignored-modules=numpy,np 
     282ignored-modules=numpy,np,numpy.random, 
     283    bumps,sas, 
    281284 
    282285# List of classes names for which member attributes should not be checked 
    … …  
    286289# When zope mode is activated, add a predefined set of Zope acquired attributes 
    287290# to generated-members. 
    288 zope=no 
     291#zope=no 
    289292 
    290293# List of members which are set dynamically and missed by pylint inference 
    … …  
    316319# List of interface methods to ignore, separated by a comma. This is used for 
    317320# instance to not check methods defines in Zope's Interface base class. 
    318 ignore-iface-methods=isImplementedBy,deferred,extends,names,namesAndDescriptions,queryDescriptionFor,getBases,getDescriptionFor,getDoc,getName,getTaggedValue,getTaggedValueTags,isEqualOrExtendedBy,setTaggedValue,isImplementedByInstancesOf,adaptWith,is_implemented_by 
     321#ignore-iface-methods=isImplementedBy,deferred,extends,names,namesAndDescriptions,queryDescriptionFor,getBases,getDescriptionFor,getDoc,getName,getTaggedValue,getTaggedValueTags,isEqualOrExtendedBy,setTaggedValue,isImplementedByInstancesOf,adaptWith,is_implemented_by 
    319322 
    320323# List of method names used to declare (i.e. assign) instance attributes. 
    … …  
    335338 
    336339# Maximum number of arguments for function / method 
    337 max-args=5 
     340max-args=15 
    338341 
    339342# Argument names that match this expression will be ignored. Default to name 
    … …  
    342345 
    343346# Maximum number of locals for function / method body 
    344 max-locals=15 
     347max-locals=25 
    345348 
    346349# Maximum number of return / yield for function / method body 

  • extra/pylint_numpy.py

    r63b32bb r823e620  
    88    #print("processing",module.name) 
    99    if module.name.startswith('numpy'): 
    10         if module.name == 'numpy':  import numpy 
    11         elif module.name == 'numpy.random': import numpy.random 
     10        if module.name == 'numpy': 
     11            import numpy 
     12        elif module.name == 'numpy.random': 
     13            import numpy.random 
     14            from numpy.random import seed, get_state, set_state 
    1215 

  • extra/pylint_pyopencl.py

    r3c56da87 r823e620  
    99    if module.name == 'pyopencl': 
    1010        import pyopencl 
     11        import pyopencl as cl 

  • extra/run-pylint.py

    rf01e53d r823e620  
    66 
    77def main(): 
     8    extra = abspath(dirname(__file__)) 
     9    root = abspath(joinpath(extra, '..')) 
     10 
    811    envpath = os.environ.get('PYTHONPATH',None) 
    912    path = [envpath] if envpath else [] 
    10     path.append(abspath(dirname(__file__))) # so we can find the plugins 
     13    path.append(extra) 
     14 
     15    #bumps = abspath(joinpath(root, "..", "bumps")) 
     16    #periodictable = abspath(joinpath(root, "..", "periodictable")) 
     17    #sasview = abspath(joinpath(root, "..", "sasview", "src")) 
     18    #path.extend((bumps, periodictable, sasview)) 
     19 
    1120    os.environ['PYTHONPATH'] = ':'.join(path) 
    12     root = abspath(joinpath(dirname(__file__), '..')) 
     21 
     22    # Run the lint command 
     23    cmd = "pylint --rcfile extra/pylint.rc -f parseable sasmodels" 
    1324    os.chdir(root) 
    14     cmd = "pylint --rcfile extra/pylint.rc -f parseable sasmodels" 
    1525    status = os.system(cmd) 
    1626    sys.exit(status) 

  • sasmodels/__init__.py

    r9890053 r37a7252  
     1""" 
     2sasmodels 
     3========= 
     4 
     5**sasmodels** is a package containing models for small angle neutron and xray 
     6scattering.  Models supported are the one dimensional circular average and 
     7two dimensional oriented patterns.  As well as the form factor calculations 
     8for the individual shapes **sasmodels** also provides automatic shape 
     9polydispersity, angular dispersion and resolution convolution.  SESANS 
     10patterns can be computed for any model. 
     11 
     12Models can be written in python or in C.  C models can run on the GPU if 
     13OpenCL drivers are available.  See :mod:`generate` for details on 
     14defining new models. 
     15""" 
     16 
    117__version__ = "0.9" 

  • sasmodels/bumps_model.py

    rec7e360 r190fc2b  
    66arguments to set the initial value for each parameter. 
    77 
    8 :class:`Experiment` combines the *Model* function with a data file loaded by the 
    9 sasview data loader.  *Experiment* takes a *cutoff* parameter controlling 
     8:class:`Experiment` combines the *Model* function with a data file loaded by 
     9the sasview data loader.  *Experiment* takes a *cutoff* parameter controlling 
    1010how far the polydispersity integral extends. 
    1111 
    … …  
    1919from .direct_model import DataMixin 
    2020 
     21__all__ = [ 
     22    "Model", "Experiment", 
     23    ] 
     24 
    2125# CRUFT: old style bumps wrapper which doesn't separate data and model 
     26# pylint: disable=invalid-name 
    2227def BumpsModel(data, model, cutoff=1e-5, **kw): 
     28    r""" 
     29    Bind a model to data, along with a polydispersity cutoff. 
     30 
     31    *data* is a :class:`data.Data1D`, :class:`data.Data2D` or 
     32    :class:`data.Sesans` object.  Use :func:`data.empty_data1D` or 
     33    :func:`data.empty_data2D` to define $q, \Delta q$ calculation 
     34    points for displaying the SANS curve when there is no measured data. 
     35 
     36    *model* is a runnable module as returned from :func:`core.load_model`. 
     37 
     38    *cutoff* is the polydispersity weight cutoff. 
     39 
     40    Any additional *key=value* pairs are model dependent parameters. 
     41 
     42    Returns an :class:`Experiment` object. 
     43 
     44    Note that the usual Bumps semantics is not fully supported, since 
     45    assigning *M.name = parameter* on the returned experiment object 
     46    does not set that parameter in the model.  Range setting will still 
     47    work as expected though. 
     48 
     49    .. deprecated:: 0.1 
     50        Use :class:`Experiment` instead. 
     51    """ 
    2352    warnings.warn("Use of BumpsModel is deprecated.  Use bumps_model.Experiment instead.") 
     53 
     54    # Create the model and experiment 
    2455    model = Model(model, **kw) 
    2556    experiment = Experiment(data=data, model=model, cutoff=cutoff) 
    26     for k in model._parameter_names: 
    27         setattr(experiment, k, getattr(model, k)) 
     57 
     58    # Copy the model parameters up to the experiment object. 
     59    for k, v in model.parameters().items(): 
     60        setattr(experiment, k, v) 
    2861    return experiment 
    2962 
     63 
    3064def create_parameters(model_info, **kwargs): 
     65    """ 
     66    Generate Bumps parameters from the model info. 
     67 
     68    *model_info* is returned from :func:`generate.model_info` on the 
     69    model definition module. 
     70 
     71    Any additional *key=value* pairs are initial values for the parameters 
     72    to the models.  Uninitialized parameters will use the model default 
     73    value. 
     74 
     75    Returns a dictionary of *{name: Parameter}* containing the bumps 
     76    parameters for each model parameter, and a dictionary of 
     77    *{name: str}* containing the polydispersity distribution types. 
     78    """ 
    3179    # lazy import; this allows the doc builder and nosetests to run even 
    3280    # when bumps is not on the path. 
    3381    from bumps.names import Parameter 
    34  
    35     partype = model_info['partype'] 
    3682 
    3783    pars = {} 
    … …  
    4086        value = kwargs.pop(name, default) 
    4187        pars[name] = Parameter.default(value, name=name, limits=limits) 
    42     for name in partype['pd-2d']: 
     88    for name in model_info['partype']['pd-2d']: 
    4389        for xpart, xdefault, xlimits in [ 
    44             ('_pd', 0., limits), 
    45             ('_pd_n', 35., (0, 1000)), 
    46             ('_pd_nsigma', 3., (0, 10)), 
    47         ]: 
     90                ('_pd', 0., limits), 
     91                ('_pd_n', 35., (0, 1000)), 
     92                ('_pd_nsigma', 3., (0, 10)), 
     93            ]: 
    4894            xname = name + xpart 
    4995            xvalue = kwargs.pop(xname, xdefault) 
    … …  
    5197 
    5298    pd_types = {} 
    53     for name in partype['pd-2d']: 
     99    for name in model_info['partype']['pd-2d']: 
    54100        xname = name + '_pd_type' 
    55101        xvalue = kwargs.pop(xname, 'gaussian') 
    … …  
    63109 
    64110class Model(object): 
     111    """ 
     112    Bumps wrapper for a SAS model. 
     113 
     114    *model* is a runnable module as returned from :func:`core.load_model`. 
     115 
     116    *cutoff* is the polydispersity weight cutoff. 
     117 
     118    Any additional *key=value* pairs are model dependent parameters. 
     119    """ 
    65120    def __init__(self, model, **kwargs): 
    66121        self._sasmodel = model 
    67122        pars, pd_types = create_parameters(model.info, **kwargs) 
    68         for k,v in pars.items(): 
     123        for k, v in pars.items(): 
    69124            setattr(self, k, v) 
    70         for k,v in pd_types.items(): 
     125        for k, v in pd_types.items(): 
    71126            setattr(self, k, v) 
    72127        self._parameter_names = list(pars.keys()) 
    … …  
    75130    def parameters(self): 
    76131        """ 
    77         Return a dictionary of parameters 
     132        Return a dictionary of parameters objects for the parameters, 
     133        excluding polydispersity distribution type. 
    78134        """ 
    79135        return dict((k, getattr(self, k)) for k in self._parameter_names) 
    80136 
    81137    def state(self): 
     138        """ 
     139        Return a dictionary of current values for all the parameters, 
     140        including polydispersity distribution type. 
     141        """ 
    82142        pars = dict((k, getattr(self, k).value) for k in self._parameter_names) 
    83143        pars.update((k, getattr(self, k)) for k in self._pd_type_names) 
    … …  
    85145 
    86146class Experiment(DataMixin): 
    87     """ 
    88     Return a bumps wrapper for a SAS model. 
    89  
    90     *data* is the data to be fitted. 
    91  
    92     *model* is the SAS model from :func:`core.load_model`. 
     147    r""" 
     148    Bumps wrapper for a SAS experiment. 
     149 
     150    *data* is a :class:`data.Data1D`, :class:`data.Data2D` or 
     151    :class:`data.Sesans` object.  Use :func:`data.empty_data1D` or 
     152    :func:`data.empty_data2D` to define $q, \Delta q$ calculation 
     153    points for displaying the SANS curve when there is no measured data. 
     154 
     155    *model* is a :class:`Model` object. 
    93156 
    94157    *cutoff* is the integration cutoff, which avoids computing the 
    95158    the SAS model where the polydispersity weight is low. 
    96159 
    97     Model parameters can be initialized with additional keyword 
    98     arguments, or by assigning to model.parameter_name.value. 
    99  
    100     The resulting bumps model can be used directly in a FitProblem call. 
     160    The resulting model can be used directly in a Bumps FitProblem call. 
    101161    """ 
    102162    def __init__(self, data, model, cutoff=1e-5): 
    … …  
    109169 
    110170    def update(self): 
     171        """ 
     172        Call when model parameters have changed and theory needs to be 
     173        recalculated. 
     174        """ 
    111175        self._cache = {} 
    112176 
    113177    def numpoints(self): 
    114178        """ 
    115             Return the number of points 
     179        Return the number of data points 
    116180        """ 
    117181        return len(self.Iq) 
    … …  
    124188 
    125189    def theory(self): 
     190        """ 
     191        Return the theory corresponding to the model parameters. 
     192 
     193        This method uses lazy evaluation, and requires model.update() to be 
     194        called when the parameters have changed. 
     195        """ 
    126196        if 'theory' not in self._cache: 
    127197            pars = self.model.state() 
    … …  
    130200 
    131201    def residuals(self): 
     202        """ 
     203        Return theory minus data normalized by uncertainty. 
     204        """ 
    132205        #if np.any(self.err ==0): print("zeros in err") 
    133206        return (self.theory() - self.Iq) / self.dIq 
    134207 
    135208    def nllf(self): 
     209        """ 
     210        Return the negative log likelihood of seeing data given the model 
     211        parameters, up to a normalizing constant which depends on the data 
     212        uncertainty. 
     213        """ 
    136214        delta = self.residuals() 
    137215        #if np.any(np.isnan(R)): print("NaN in residuals") 
    … …  
    149227 
    150228    def simulate_data(self, noise=None): 
     229        """ 
     230        Generate simulated data. 
     231        """ 
    151232        Iq = self.theory() 
    152233        self._set_data(Iq, noise) 
    153234 
    154235    def save(self, basename): 
     236        """ 
     237        Save the model parameters and data into a file. 
     238 
     239        Not Implemented. 
     240        """ 
    155241        pass 
    156242 

  • sasmodels/compare.py

    r608e31e rd5e650d  
    2828 
    2929from __future__ import print_function 
     30 
     31import sys 
     32import math 
     33from os.path import basename, dirname, join as joinpath 
     34import glob 
     35import datetime 
     36import traceback 
     37 
     38import numpy as np 
     39 
     40from . import core 
     41from . import kerneldll 
     42from . import generate 
     43from .data import plot_theory, empty_data1D, empty_data2D 
     44from .direct_model import DirectModel 
     45from .convert import revert_model, constrain_new_to_old 
    3046 
    3147USAGE = """ 
    … …  
    7288# doc string so that we can display it at run time if there is an error. 
    7389# lin 
    74 __doc__ = __doc__ + """ 
     90__doc__ = (__doc__  # pylint: disable=redefined-builtin 
     91           + """ 
    7592Program description 
    7693------------------- 
    7794 
    78 """ + USAGE 
    79  
    80  
    81  
    82 import sys 
    83 import math 
    84 from os.path import basename, dirname, join as joinpath 
    85 import glob 
    86 import datetime 
    87 import traceback 
    88  
    89 import numpy as np 
    90  
     95""" 
     96           + USAGE) 
     97 
     98kerneldll.ALLOW_SINGLE_PRECISION_DLLS = True 
     99 
     100# List of available models 
    91101ROOT = dirname(__file__) 
    92 sys.path.insert(0, ROOT)  # Make sure sasmodels is first on the path 
    93  
    94  
    95 from . import core 
    96 from . import kerneldll 
    97 from . import generate 
    98 from .data import plot_theory, empty_data1D, empty_data2D 
    99 from .direct_model import DirectModel 
    100 from .convert import revert_model, constrain_new_to_old 
    101 kerneldll.ALLOW_SINGLE_PRECISION_DLLS = True 
    102  
    103 # List of available models 
    104102MODELS = [basename(f)[:-3] 
    105103          for f in sorted(glob.glob(joinpath(ROOT, "models", "[a-zA-Z]*.py")))] 
    … …  
    115113        return dt.total_seconds() 
    116114 
     115 
     116class push_seed(object): 
     117    """ 
     118    Set the seed value for the random number generator. 
     119 
     120    When used in a with statement, the random number generator state is 
     121    restored after the with statement is complete. 
     122 
     123    :Parameters: 
     124 
     125    *seed* : int or array_like, optional 
     126        Seed for RandomState 
     127 
     128    :Example: 
     129 
     130    Seed can be used directly to set the seed:: 
     131 
     132        >>> from numpy.random import randint 
     133        >>> push_seed(24) 
     134        <...push_seed object at...> 
     135        >>> print(randint(0,1000000,3)) 
     136        [242082    899 211136] 
     137 
     138    Seed can also be used in a with statement, which sets the random 
     139    number generator state for the enclosed computations and restores 
     140    it to the previous state on completion:: 
     141 
     142        >>> with push_seed(24): 
     143        ...    print(randint(0,1000000,3)) 
     144        [242082    899 211136] 
     145 
     146    Using nested contexts, we can demonstrate that state is indeed 
     147    restored after the block completes:: 
     148 
     149        >>> with push_seed(24): 
     150        ...    print(randint(0,1000000)) 
     151        ...    with push_seed(24): 
     152        ...        print(randint(0,1000000,3)) 
     153        ...    print(randint(0,1000000)) 
     154        242082 
     155        [242082    899 211136] 
     156        899 
     157 
     158    The restore step is protected against exceptions in the block:: 
     159 
     160        >>> with push_seed(24): 
     161        ...    print(randint(0,1000000)) 
     162        ...    try: 
     163        ...        with push_seed(24): 
     164        ...            print(randint(0,1000000,3)) 
     165        ...            raise Exception() 
     166        ...    except: 
     167        ...        print("Exception raised") 
     168        ...    print(randint(0,1000000)) 
     169        242082 
     170        [242082    899 211136] 
     171        Exception raised 
     172        899 
     173    """ 
     174    def __init__(self, seed=None): 
     175        self._state = np.random.get_state() 
     176        np.random.seed(seed) 
     177 
     178    def __enter__(self): 
     179        return None 
     180 
     181    def __exit__(self, *args): 
     182        np.random.set_state(self._state) 
    117183 
    118184def tic(): 
    … …  
    177243        return [0, (2*v if v > 0 else 1)] 
    178244 
     245 
    179246def _randomize_one(p, v): 
    180247    """ 
    … …  
    186253        return np.random.uniform(*parameter_range(p, v)) 
    187254 
     255 
    188256def randomize_pars(pars, seed=None): 
    189257    """ 
    … …  
    195263    :func:`constrain_pars` needs to be called afterward.. 
    196264    """ 
    197     np.random.seed(seed) 
    198     # Note: the sort guarantees order `of calls to random number generator 
    199     pars = dict((p, _randomize_one(p, v)) 
    200                 for p, v in sorted(pars.items())) 
     265    with push_seed(seed): 
     266        # Note: the sort guarantees order `of calls to random number generator 
     267        pars = dict((p, _randomize_one(p, v)) 
     268                    for p, v in sorted(pars.items())) 
    201269    return pars 
    202270 
    … …  
    281349            theory = lambda: smearer.get_value() 
    282350        else: 
    283             theory = lambda: model.evalDistribution([data.qx_data[index], data.qy_data[index]]) 
     351            theory = lambda: model.evalDistribution([data.qx_data[index], 
     352                                                     data.qy_data[index]]) 
    284353    elif smearer is not None: 
    285354        theory = lambda: smearer(model.evalDistribution(data.x)) 
    … …  
    416485        try: 
    417486            base_value, base_time = time_calculation(base, pars, Nbase) 
    418             print("%s t=%.1f ms, intensity=%.0f"%(base.engine, base_time, sum(base_value))) 
     487            print("%s t=%.1f ms, intensity=%.0f" 
     488                  % (base.engine, base_time, sum(base_value))) 
    419489        except ImportError: 
    420490            traceback.print_exc() 
    … …  
    426496        try: 
    427497            comp_value, comp_time = time_calculation(comp, pars, Ncomp) 
    428             print("%s t=%.1f ms, intensity=%.0f"%(comp.engine, comp_time, sum(comp_value))) 
     498            print("%s t=%.1f ms, intensity=%.0f" 
     499                  % (comp.engine, comp_time, sum(comp_value))) 
    429500        except ImportError: 
    430501            traceback.print_exc() 
    … …  
    433504    # Compare, but only if computing both forms 
    434505    if Nbase > 0 and Ncomp > 0: 
    435         #print("speedup %.2g"%(comp_time/base_time)) 
    436         #print("max |base/comp|", max(abs(base_value/comp_value)), "%.15g"%max(abs(base_value)), "%.15g"%max(abs(comp_value))) 
    437         #comp *= max(base_value/comp_value) 
    438506        resid = (base_value - comp_value) 
    439507        relerr = resid/comp_value 
    440         _print_stats("|%s-%s|"%(base.engine, comp.engine) + (" "*(3+len(comp.engine))), 
     508        _print_stats("|%s-%s|" 
     509                     % (base.engine, comp.engine) + (" "*(3+len(comp.engine))), 
    441510                     resid) 
    442         _print_stats("|(%s-%s)/%s|"%(base.engine, comp.engine, comp.engine), 
     511        _print_stats("|(%s-%s)/%s|" 
     512                     % (base.engine, comp.engine, comp.engine), 
    443513                     relerr) 
    444514 
    … …  
    459529    if Nbase > 0: 
    460530        if Ncomp > 0: plt.subplot(131) 
    461         plot_theory(data, base_value, view=view, plot_data=False, limits=limits) 
     531        plot_theory(data, base_value, view=view, use_data=False, limits=limits) 
    462532        plt.title("%s t=%.1f ms"%(base.engine, base_time)) 
    463533        #cbar_title = "log I" 
    464534    if Ncomp > 0: 
    465535        if Nbase > 0: plt.subplot(132) 
    466         plot_theory(data, comp_value, view=view, plot_data=False, limits=limits) 
     536        plot_theory(data, comp_value, view=view, use_data=False, limits=limits) 
    467537        plt.title("%s t=%.1f ms"%(comp.engine, comp_time)) 
    468538        #cbar_title = "log I" 
    469539    if Ncomp > 0 and Nbase > 0: 
    470540        plt.subplot(133) 
    471         if '-abs' in opts: 
     541        if not opts['rel_err']: 
    472542            err, errstr, errview = resid, "abs err", "linear" 
    473543        else: 
    474544            err, errstr, errview = abs(relerr), "rel err", "log" 
    475545        #err,errstr = base/comp,"ratio" 
    476         plot_theory(data, None, resid=err, view=errview, plot_data=False) 
     546        plot_theory(data, None, resid=err, view=errview, use_data=False) 
     547        if view == 'linear': 
     548            plt.xscale('linear') 
    477549        plt.title("max %s = %.3g"%(errstr, max(abs(err)))) 
    478550        #cbar_title = errstr if errview=="linear" else "log "+errstr 
    … …  
    534606def columnize(L, indent="", width=79): 
    535607    """ 
    536     Format a list of strings into columns for printing. 
     608    Format a list of strings into columns. 
     609 
     610    Returns a string with carriage returns ready for printing. 
    537611    """ 
    538612    column_width = max(len(w) for w in L) + 1 
    … …  
    591665    invalid = [o[1:] for o in flags 
    592666               if o[1:] not in NAME_OPTIONS 
    593                    and not any(o.startswith('-%s='%t) for t in VALUE_OPTIONS)] 
     667               and not any(o.startswith('-%s='%t) for t in VALUE_OPTIONS)] 
    594668    if invalid: 
    595669        print("Invalid options: %s"%(", ".join(invalid))) 
    … …  
    597671 
    598672 
     673    # pylint: disable=bad-whitespace 
    599674    # Interpret the flags 
    600675    opts = { 
    … …  
    651726        elif arg == '-sasview': engines.append(arg[1:]) 
    652727        elif arg == '-edit':    opts['explore'] = True 
     728    # pylint: enable=bad-whitespace 
    653729 
    654730    if len(engines) == 0: 
    … …  
    675751    presets = {} 
    676752    for arg in values: 
    677         k,v = arg.split('=',1) 
     753        k, v = arg.split('=', 1) 
    678754        if k not in pars: 
    679755            # extract base name without polydispersity info 
    680756            s = set(p.split('_pd')[0] for p in pars) 
    681             print("%r invalid; parameters are: %s"%(k,", ".join(sorted(s)))) 
     757            print("%r invalid; parameters are: %s"%(k, ", ".join(sorted(s)))) 
    682758            sys.exit(1) 
    683759        presets[k] = float(v) if not k.endswith('type') else v 
    … …  
    697773 
    698774    # Create the computational engines 
    699     data, _index = make_data(opts) 
     775    data, _ = make_data(opts) 
    700776    if n1: 
    701777        base = make_engine(model_definition, data, engines[0], opts['cutoff']) 
    … …  
    707783        comp = None 
    708784 
     785    # pylint: disable=bad-whitespace 
    709786    # Remember it all 
    710787    opts.update({ 
    … …  
    718795        'engines'   : [base, comp], 
    719796    }) 
     797    # pylint: enable=bad-whitespace 
    720798 
    721799    return opts 
    722800 
    723 def main(): 
    724     opts = parse_opts() 
    725     if opts['explore']: 
    726         explore(opts) 
    727     else: 
    728         compare(opts) 
    729  
    730801def explore(opts): 
     802    """ 
     803    Explore the model using the Bumps GUI. 
     804    """ 
    731805    import wx 
    732806    from bumps.names import FitProblem 
    … …  
    734808 
    735809    problem = FitProblem(Explore(opts)) 
    736     isMac = "cocoa" in wx.version() 
     810    is_mac = "cocoa" in wx.version() 
    737811    app = wx.App() 
    738812    frame = AppFrame(parent=None, title="explore") 
    739     if not isMac: frame.Show() 
     813    if not is_mac: frame.Show() 
    740814    frame.panel.set_model(model=problem) 
    741815    frame.panel.Layout() 
    742816    frame.panel.aui.Split(0, wx.TOP) 
    743     if isMac: frame.Show() 
     817    if is_mac: frame.Show() 
    744818    app.MainLoop() 
    745819 
    746820class Explore(object): 
    747821    """ 
    748     Return a bumps wrapper for a SAS model comparison. 
     822    Bumps wrapper for a SAS model comparison. 
     823 
     824    The resulting object can be used as a Bumps fit problem so that 
     825    parameters can be adjusted in the GUI, with plots updated on the fly. 
    749826    """ 
    750827    def __init__(self, opts): 
    … …  
    787864        Return cost. 
    788865        """ 
     866        # pylint: disable=no-self-use 
    789867        return 0.  # No nllf 
    790868 
    … …  
    804882 
    805883 
     884def main(): 
     885    """ 
     886    Main program. 
     887    """ 
     888    opts = parse_opts() 
     889    if opts['explore']: 
     890        explore(opts) 
     891    else: 
     892        compare(opts) 
     893 
    806894if __name__ == "__main__": 
    807895    main() 

  • sasmodels/compare_many.py

    r6458608 r4f2478e  
    11#!/usr/bin/env python 
     2""" 
     3Program to compare results from many random parameter sets for a given model. 
     4 
     5The result is a comma separated value (CSV) table that can be redirected 
     6from standard output into a file and loaded into a spreadsheet. 
     7 
     8The models are compared for each parameter set and if the difference is 
     9greater than expected for that precision, the parameter set is labeled 
     10as bad and written to the output, along with the random seed used to 
     11generate that parameter value.  This seed can be used with :mod:`compare` 
     12to reload and display the details of the model. 
     13""" 
    214from __future__ import print_function 
    315 
    … …  
    1426 
    1527def calc_stats(target, value, index): 
     28    """ 
     29    Calculate statistics between the target value and the computed value. 
     30 
     31    *target* and *value* are the vectors being compared, with the 
     32    difference normalized by *target* to get relative error.  Only 
     33    the elements listed in *index* are used, though index may be 
     34    and empty slice defined by *slice(None, None)*. 
     35 
     36    Returns: 
     37 
     38        *maxrel* the maximum relative difference 
     39 
     40        *rel95* the relative difference with the 5% biggest differences ignored 
     41 
     42        *maxabs* the maximum absolute difference for the 5% biggest differences 
     43 
     44        *maxval* the maximum value for the 5% biggest differences 
     45    """ 
    1646    resid = abs(value-target)[index] 
    1747    relerr = resid/target[index] 
    18     srel = np.argsort(relerr) 
     48    sorted_rel_index = np.argsort(relerr) 
    1949    #p90 = int(len(relerr)*0.90) 
    2050    p95 = int(len(relerr)*0.95) 
    2151    maxrel = np.max(relerr) 
    22     rel95 = relerr[srel[p95]] 
    23     maxabs = np.max(resid[srel[p95:]]) 
    24     maxval = np.max(value[srel[p95:]]) 
    25     return maxrel,rel95,maxabs,maxval 
     52    rel95 = relerr[sorted_rel_index[p95]] 
     53    maxabs = np.max(resid[sorted_rel_index[p95:]]) 
     54    maxval = np.max(value[sorted_rel_index[p95:]]) 
     55    return maxrel, rel95, maxabs, maxval 
    2656 
    2757def print_column_headers(pars, parts): 
     58    """ 
     59    Generate column headers for the differences and for the parameters, 
     60    and print them to standard output. 
     61    """ 
    2862    stats = list('Max rel err|95% rel err|Max abs err above 90% rel|Max value above 90% rel'.split('|')) 
    2963    groups = [''] 
    … …  
    3670    print(','.join('"%s"'%c for c in columns)) 
    3771 
     72# Target 'good' value for various precision levels. 
    3873PRECISION = { 
    3974    'fast': 1e-3, 
    … …  
    4883def compare_instance(name, data, index, N=1, mono=True, cutoff=1e-5, 
    4984                     base='sasview', comp='double'): 
    50     is2D = hasattr(data, 'qx_data') 
     85    r""" 
     86    Compare the model under different calculation engines. 
     87 
     88    *name* is the name of the model. 
     89 
     90    *data* is the data object giving $q, \Delta q$ calculation points. 
     91 
     92    *index* is the active set of points. 
     93 
     94    *N* is the number of comparisons to make. 
     95 
     96    *cutoff* is the polydispersity weight cutoff to make the calculation 
     97    a little bit faster. 
     98 
     99    *base* and *comp* are the names of the calculation engines to compare. 
     100    """ 
     101 
     102    is_2d = hasattr(data, 'qx_data') 
    51103    model_definition = core.load_model_definition(name) 
    52104    pars = get_demo_pars(model_definition) 
    53105    header = ('\n"Model","%s","Count","%d","Dimension","%s"' 
    54               % (name, N, "2D" if is2D else "1D")) 
     106              % (name, N, "2D" if is_2d else "1D")) 
    55107    if not mono: header += ',"Cutoff",%g'%(cutoff,) 
    56108    print(header) 
    57109 
    58     if is2D: 
     110    if is_2d: 
    59111        info = generate.make_info(model_definition) 
    60112        partype = info['partype'] 
    … …  
    69121    # declarations are not available in python 2.7. 
    70122    def try_model(fn, pars): 
     123        """ 
     124        Return the model evaluated at *pars*.  If there is an exception, 
     125        print it and return NaN of the right shape. 
     126        """ 
    71127        try: 
    72128            result = fn(**pars) 
    … …  
    82138        return result 
    83139    def check_model(pars): 
     140        """ 
     141        Run the two calculators against *pars*, returning statistics 
     142        on the differences.  See :func:`calc_stats` for the list of stats. 
     143        """ 
    84144        base_value = try_model(calc_base, pars) 
    85145        comp_value = try_model(calc_comp, pars) 
    … …  
    108168        good = [True] 
    109169        columns = check_model(pars_i) 
    110         columns += [v for _,v in sorted(pars_i.items())] 
     170        columns += [v for _, v in sorted(pars_i.items())] 
    111171        if first: 
    112172            labels = [" vs. ".join((calc_base.engine, calc_comp.engine))] 
    … …  
    121181 
    122182def print_usage(): 
     183    """ 
     184    Print the command usage string. 
     185    """ 
    123186    print("usage: compare_many.py MODEL COUNT (1dNQ|2dNQ) (CUTOFF|mono) (single|double|quad)") 
    124187 
    125188 
    126189def print_models(): 
     190    """ 
     191    Print the list of available models in columns. 
     192    """ 
    127193    print(columnize(MODELS, indent="  ")) 
    128194 
    129195 
    130196def print_help(): 
     197    """ 
     198    Print usage string, the option description and the list of available models. 
     199    """ 
    131200    print_usage() 
    132201    print("""\ 
    … …  
    158227 
    159228def main(): 
    160     if len(sys.argv) not in (6,7): 
     229    """ 
     230    Main program. 
     231    """ 
     232    if len(sys.argv) not in (6, 7): 
    161233        print_help() 
    162234        sys.exit(1) 
    … …  
    182254 
    183255    data, index = make_data({'qmax':1.0, 'is2d':is2D, 'nq':Nq, 'res':0., 
    184                               'accuracy': 'Low', 'view':'log'}) 
     256                             'accuracy': 'Low', 'view':'log'}) 
    185257    model_list = [model] if model != "all" else MODELS 
    186258    for model in model_list: 
    … …  
    189261 
    190262if __name__ == "__main__": 
     263    #from .compare import push_seed 
     264    #with push_seed(1): main() 
    191265    main() 

  • sasmodels/convert.py

    r03582f9 rd368d21  
    1 """ 
    2 Convert models to and from sasview. 
    3 """ 
    4 import warnings 
    5  
    6 PD_DOT = [ 
    7     ("", ""), 
    8     ("_pd", ".width"), 
    9     ("_pd_n", ".npts"), 
    10     ("_pd_nsigma", ".nsigmas"), 
    11     ("_pd_type", ".type"), 
    12     ] 
    13 def _convert_pars(pars, mapping): 
    14     """ 
    15     Rename the parameters and any associated polydispersity attributes. 
    16     """ 
    17     newpars = pars.copy() 
    18     for new, old in mapping.items(): 
    19         if old == new: continue 
    20         for pd, dot in PD_DOT: 
    21             if old+dot in newpars: 
    22                 if new is not None: 
    23                     newpars[new+pd] = pars[old+dot] 
    24                 del newpars[old+dot] 
    25     return newpars 
    26  
    27 def _rescale_sld(pars): 
    28     """ 
    29     rescale all sld parameters in the new model definition by 1e6 so the 
    30     numbers are nicer.  Relies on the fact that all sld parameters in the 
    31     new model definition end with sld. 
    32     """ 
    33     return dict((p, (v*1e6 if p.endswith('sld') else v)) 
    34                 for p,v in pars.items()) 
    35  
    36 def convert_model(name, pars): 
    37     """ 
    38     Convert model from old style parameter names to new style. 
    39     """ 
    40     _,_ = name,pars # lint 
    41     raise NotImplementedError 
    42     # need to load all new models in order to determine old=>new 
    43     # model name mapping 
    44  
    45 def _unscale_sld(pars): 
    46     """ 
    47     rescale all sld parameters in the new model definition by 1e6 so the 
    48     numbers are nicer.  Relies on the fact that all sld parameters in the 
    49     new model definition end with sld. 
    50     """ 
    51     return dict((p, (v*1e-6 if p.endswith('sld') else v)) 
    52                 for p,v in pars.items()) 
    53  
    54 def _remove_pd(pars, key, name): 
    55     """ 
    56     Remove polydispersity from the parameter list. 
    57  
    58     Note: operates in place 
    59     """ 
    60     # Bumps style parameter names 
    61     pd = pars.pop(key+".width", 0.0) 
    62     pd_n = pars.pop(key+".npts", 0) 
    63     if pd != 0.0 and pd_n != 0: 
    64         warnings.warn("parameter %s not polydisperse in sasview %s"%(key, name)) 
    65     pars.pop(key+".nsigmas", None) 
    66     pars.pop(key+".type", None) 
    67     return pars 
    68  
    69 def _revert_pars(pars, mapping): 
    70     """ 
    71     Rename the parameters and any associated polydispersity attributes. 
    72     """ 
    73     newpars = pars.copy() 
    74  
    75     for new, old in mapping.items(): 
    76         for pd, dot in PD_DOT: 
    77             if old and old+pd == new+dot: 
    78                 continue 
    79             if new+pd in newpars: 
    80                 if old is not None: 
    81                     newpars[old+dot] = pars[new+pd] 
    82                 del newpars[new+pd] 
    83     for k in list(newpars.keys()): 
    84         for pd, dot in PD_DOT[1:]:  # skip "" => "" 
    85             if k.endswith(pd): 
    86                 newpars[k[:-len(pd)]+dot] = newpars[k] 
    87                 del newpars[k] 
    88     return newpars 
    89  
    90 def revert_model(model_definition, pars): 
    91     """ 
    92     Convert model from new style parameter names to old style. 
    93     """ 
    94     mapping = model_definition.oldpars 
    95     oldname = model_definition.oldname 
    96     oldpars = _revert_pars(_unscale_sld(pars), mapping) 
    97  
    98     # Note: update compare.constrain_pars to match 
    99     name = model_definition.name 
    100     if name in ('teubner_strey', 'broad_peak', 'two_lorentzian', 'gel_fit', 
    101                 'binary_hard_sphere'): 
    102         if oldpars.pop('scale', 1.0) != 1.0: 
    103             warnings.warn("parameter scale not used in sasview %s"%name) 
    104     elif name in ('guinier',): 
    105         if oldpars.pop('background', 0.0) != 0.0: 
    106             warnings.warn("parameter background not used in sasview %s"%name) 
    107     elif getattr(model_definition, 'category', None) == 'structure-factor': 
    108         if oldpars.pop('scale', 1.0) != 1.0: 
    109             warnings.warn("parameter scale not used in sasview %s"%name) 
    110         if oldpars.pop('background', 0.0) != 0.0: 
    111             warnings.warn("parameter background not used in sasview %s"%name) 
    112     elif name == 'pearl_necklace': 
    113         _remove_pd(oldpars, 'num_pearls', name) 
    114         _remove_pd(oldpars, 'thick_string', name) 
    115     elif name == 'rpa': 
    116         # convert scattering lengths from femtometers to centimeters 
    117         for p in "La", "Lb", "Lc", "Ld": 
    118             if p in oldpars: oldpars[p] *= 1e-13 
    119  
    120     return oldname, oldpars 
    121  
    122 def constrain_new_to_old(model_definition, pars): 
    123     """ 
    124     Restrict parameter values to those that will match sasview. 
    125     """ 
    126     # Note: update convert.revert_model to match 
    127     name = model_definition.name 
    128     if name in ('teubner_strey','broad_peak', 'two_lorentzian', 'gel_fit'): 
    129         pars['scale'] = 1 
    130     elif name in ('guinier',): 
    131         pars['background'] = 0 
    132     elif name == 'pearl_necklace': 
    133         pars['string_thickness_pd_n'] = 0 
    134         pars['number_of_pearls_pd_n'] = 0 
    135     elif name == 'rpa': 
    136         pars['case_num'] = int(pars['case_num']) 
    137     elif getattr(model_definition, 'category', None) == 'structure-factor': 
    138         pars['scale'], pars['background'] = 1, 0 
    139  
     1 
     2 
     3 
     4 
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     399      157 lines (141 sloc) 
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     401    5.04 KB 
     402  </div> 
     403</div> 
     404 
     405   
     406 
     407  <div class="blob-wrapper data type-python"> 
     408      <table class="highlight tab-size js-file-line-container" data-tab-size="8"> 
     409      <tr> 
     410        <td id="L1" class="blob-num js-line-number" data-line-number="1"></td> 
     411        <td id="LC1" class="blob-code blob-code-inner js-file-line"><span class="pl-s"><span class="pl-pds">&quot;&quot;&quot;</span></span></td> 
     412      </tr> 
     413      <tr> 
     414        <td id="L2" class="blob-num js-line-number" data-line-number="2"></td> 
     415        <td id="LC2" class="blob-code blob-code-inner js-file-line"><span class="pl-s">Convert models to and from sasview.</span></td> 
     416      </tr> 
     417      <tr> 
     418        <td id="L3" class="blob-num js-line-number" data-line-number="3"></td> 
     419        <td id="LC3" class="blob-code blob-code-inner js-file-line"><span class="pl-s"><span class="pl-pds">&quot;&quot;&quot;</span></span></td> 
     420      </tr> 
     421      <tr> 
     422        <td id="L4" class="blob-num js-line-number" data-line-number="4"></td> 
     423        <td id="LC4" class="blob-code blob-code-inner js-file-line"><span class="pl-k">import</span> warnings</td> 
     424      </tr> 
     425      <tr> 
     426        <td id="L5" class="blob-num js-line-number" data-line-number="5"></td> 
     427        <td id="LC5" class="blob-code blob-code-inner js-file-line"> 
     428</td> 
     429      </tr> 
     430      <tr> 
     431        <td id="L6" class="blob-num js-line-number" data-line-number="6"></td> 
     432        <td id="LC6" class="blob-code blob-code-inner js-file-line"><span class="pl-c"># List of models which SasView versions don&#39;t contain the explicit &#39;scale&#39; argument.</span></td> 
     433      </tr> 
     434      <tr> 
     435        <td id="L7" class="blob-num js-line-number" data-line-number="7"></td> 
     436        <td id="LC7" class="blob-code blob-code-inner js-file-line"><span class="pl-c"># When converting such a model, please update this list.</span></td> 
     437      </tr> 
     438      <tr> 
     439        <td id="L8" class="blob-num js-line-number" data-line-number="8"></td> 
     440        <td id="LC8" class="blob-code blob-code-inner js-file-line"><span class="pl-c1">MODELS_WITHOUT_SCALE</span> <span class="pl-k">=</span> [</td> 
     441      </tr> 
     442      <tr> 
     443        <td id="L9" class="blob-num js-line-number" data-line-number="9"></td> 
     444        <td id="LC9" class="blob-code blob-code-inner js-file-line">    <span class="pl-s"><span class="pl-pds">&#39;</span>teubner_strey<span class="pl-pds">&#39;</span></span>,</td> 
     445      </tr> 
     446      <tr> 
     447        <td id="L10" class="blob-num js-line-number" data-line-number="10"></td> 
     448        <td id="LC10" class="blob-code blob-code-inner js-file-line">    <span class="pl-s"><span class="pl-pds">&#39;</span>broad_peak<span class="pl-pds">&#39;</span></span>,</td> 
     449      </tr> 
     450      <tr> 
     451        <td id="L11" class="blob-num js-line-number" data-line-number="11"></td> 
     452        <td id="LC11" class="blob-code blob-code-inner js-file-line">    <span class="pl-s"><span class="pl-pds">&#39;</span>two_lorentzian<span class="pl-pds">&#39;</span></span>,</td> 
     453      </tr> 
     454      <tr> 
     455        <td id="L12" class="blob-num js-line-number" data-line-number="12"></td> 
     456        <td id="LC12" class="blob-code blob-code-inner js-file-line">    <span class="pl-s"><span class="pl-pds">&#39;</span>gel_fit<span class="pl-pds">&#39;</span></span>,</td> 
     457      </tr> 
     458      <tr> 
     459        <td id="L13" class="blob-num js-line-number" data-line-number="13"></td> 
     460        <td id="LC13" class="blob-code blob-code-inner js-file-line">    <span class="pl-s"><span class="pl-pds">&#39;</span>gauss_lorentz_gel<span class="pl-pds">&#39;</span></span>,</td> 
     461      </tr> 
     462      <tr> 
     463        <td id="L14" class="blob-num js-line-number" data-line-number="14"></td> 
     464        <td id="LC14" class="blob-code blob-code-inner js-file-line">    <span class="pl-s"><span class="pl-pds">&#39;</span>be_polyelectrolyte<span class="pl-pds">&#39;</span></span>,</td> 
     465      </tr> 
     466      <tr> 
     467        <td id="L15" class="blob-num js-line-number" data-line-number="15"></td> 
     468        <td id="LC15" class="blob-code blob-code-inner js-file-line">    <span class="pl-s"><span class="pl-pds">&#39;</span>correlation_length<span class="pl-pds">&#39;</span></span>,</td> 
     469      </tr> 
     470      <tr> 
     471        <td id="L16" class="blob-num js-line-number" data-line-number="16"></td> 
     472        <td id="LC16" class="blob-code blob-code-inner js-file-line">]</td> 
     473      </tr> 
     474      <tr> 
     475        <td id="L17" class="blob-num js-line-number" data-line-number="17"></td> 
     476        <td id="LC17" class="blob-code blob-code-inner js-file-line"> 
     477</td> 
     478      </tr> 
     479      <tr> 
     480        <td id="L18" class="blob-num js-line-number" data-line-number="18"></td> 
     481        <td id="LC18" class="blob-code blob-code-inner js-file-line"><span class="pl-c"># List of models which SasView versions don&#39;t contain the explicit &#39;background&#39; argument.</span></td> 
     482      </tr> 
     483      <tr> 
     484        <td id="L19" class="blob-num js-line-number" data-line-number="19"></td> 
     485        <td id="LC19" class="blob-code blob-code-inner js-file-line"><span class="pl-c"># When converting such a model, please update this list.</span></td> 
     486      </tr> 
     487      <tr> 
     488        <td id="L20" class="blob-num js-line-number" data-line-number="20"></td> 
     489        <td id="LC20" class="blob-code blob-code-inner js-file-line"><span class="pl-c1">MODELS_WITHOUT_BACKGROUND</span> <span class="pl-k">=</span> [</td> 
     490      </tr> 
     491      <tr> 
     492        <td id="L21" class="blob-num js-line-number" data-line-number="21"></td> 
     493        <td id="LC21" class="blob-code blob-code-inner js-file-line">    <span class="pl-s"><span class="pl-pds">&#39;</span>guinier<span class="pl-pds">&#39;</span></span>,</td> 
     494      </tr> 
     495      <tr> 
     496        <td id="L22" class="blob-num js-line-number" data-line-number="22"></td> 
     497        <td id="LC22" class="blob-code blob-code-inner js-file-line">]</td> 
     498      </tr> 
     499      <tr> 
     500        <td id="L23" class="blob-num js-line-number" data-line-number="23"></td> 
     501        <td id="LC23" class="blob-code blob-code-inner js-file-line"> 
     502</td> 
     503      </tr> 
     504      <tr> 
     505        <td id="L24" class="blob-num js-line-number" data-line-number="24"></td> 
     506        <td id="LC24" class="blob-code blob-code-inner js-file-line"><span class="pl-c1">PD_DOT</span> <span class="pl-k">=</span> [</td> 
     507      </tr> 
     508      <tr> 
     509        <td id="L25" class="blob-num js-line-number" data-line-number="25"></td> 
     510        <td id="LC25" class="blob-code blob-code-inner js-file-line">    (<span class="pl-s"><span class="pl-pds">&quot;</span><span class="pl-pds">&quot;</span></span>, <span class="pl-s"><span class="pl-pds">&quot;</span><span class="pl-pds">&quot;</span></span>),</td> 
     511      </tr> 
     512      <tr> 
     513        <td id="L26" class="blob-num js-line-number" data-line-number="26"></td> 
     514        <td id="LC26" class="blob-code blob-code-inner js-file-line">    (<span class="pl-s"><span class="pl-pds">&quot;</span>_pd<span class="pl-pds">&quot;</span></span>, <span class="pl-s"><span class="pl-pds">&quot;</span>.width<span class="pl-pds">&quot;</span></span>),</td> 
     515      </tr> 
     516      <tr> 
     517        <td id="L27" class="blob-num js-line-number" data-line-number="27"></td> 
     518        <td id="LC27" class="blob-code blob-code-inner js-file-line">    (<span class="pl-s"><span class="pl-pds">&quot;</span>_pd_n<span class="pl-pds">&quot;</span></span>, <span class="pl-s"><span class="pl-pds">&quot;</span>.npts<span class="pl-pds">&quot;</span></span>),</td> 
     519      </tr> 
     520      <tr> 
     521        <td id="L28" class="blob-num js-line-number" data-line-number="28"></td> 
     522        <td id="LC28" class="blob-code blob-code-inner js-file-line">    (<span class="pl-s"><span class="pl-pds">&quot;</span>_pd_nsigma<span class="pl-pds">&quot;</span></span>, <span class="pl-s"><span class="pl-pds">&quot;</span>.nsigmas<span class="pl-pds">&quot;</span></span>),</td> 
     523      </tr> 
     524      <tr> 
     525        <td id="L29" class="blob-num js-line-number" data-line-number="29"></td> 
     526        <td id="LC29" class="blob-code blob-code-inner js-file-line">    (<span class="pl-s"><span class="pl-pds">&quot;</span>_pd_type<span class="pl-pds">&quot;</span></span>, <span class="pl-s"><span class="pl-pds">&quot;</span>.type<span class="pl-pds">&quot;</span></span>),</td> 
     527      </tr> 
     528      <tr> 
     529        <td id="L30" class="blob-num js-line-number" data-line-number="30"></td> 
     530        <td id="LC30" class="blob-code blob-code-inner js-file-line">    ]</td> 
     531      </tr> 
     532      <tr> 
     533        <td id="L31" class="blob-num js-line-number" data-line-number="31"></td> 
     534        <td id="LC31" class="blob-code blob-code-inner js-file-line"><span class="pl-k">def</span> <span class="pl-en">_convert_pars</span>(<span class="pl-smi">pars</span>, <span class="pl-smi">mapping</span>):</td> 
     535      </tr> 
     536      <tr> 
     537        <td id="L32" class="blob-num js-line-number" data-line-number="32"></td> 
     538        <td id="LC32" class="blob-code blob-code-inner js-file-line">    <span class="pl-s"><span class="pl-pds">&quot;&quot;&quot;</span></span></td> 
     539      </tr> 
     540      <tr> 
     541        <td id="L33" class="blob-num js-line-number" data-line-number="33"></td> 
     542        <td id="LC33" class="blob-code blob-code-inner js-file-line"><span class="pl-s">    Rename the parameters and any associated polydispersity attributes.</span></td> 
     543      </tr> 
     544      <tr> 
     545        <td id="L34" class="blob-num js-line-number" data-line-number="34"></td> 
     546        <td id="LC34" class="blob-code blob-code-inner js-file-line"><span class="pl-s">    <span class="pl-pds">&quot;&quot;&quot;</span></span></td> 
     547      </tr> 
     548      <tr> 
     549        <td id="L35" class="blob-num js-line-number" data-line-number="35"></td> 
     550        <td id="LC35" class="blob-code blob-code-inner js-file-line">    newpars <span class="pl-k">=</span> pars.copy()</td> 
     551      </tr> 
     552      <tr> 
     553        <td id="L36" class="blob-num js-line-number" data-line-number="36"></td> 
     554        <td id="LC36" class="blob-code blob-code-inner js-file-line">    <span class="pl-k">for</span> new, old <span class="pl-k">in</span> mapping.items():</td> 
     555      </tr> 
     556      <tr> 
     557        <td id="L37" class="blob-num js-line-number" data-line-number="37"></td> 
     558        <td id="LC37" class="blob-code blob-code-inner js-file-line">        <span class="pl-k">if</span> old <span class="pl-k">==</span> new: <span class="pl-k">continue</span></td> 
     559      </tr> 
     560      <tr> 
     561        <td id="L38" class="blob-num js-line-number" data-line-number="38"></td> 
     562        <td id="LC38" class="blob-code blob-code-inner js-file-line">        <span class="pl-k">for</span> pd, dot <span class="pl-k">in</span> <span class="pl-c1">PD_DOT</span>:</td> 
     563      </tr> 
     564      <tr> 
     565        <td id="L39" class="blob-num js-line-number" data-line-number="39"></td> 
     566        <td id="LC39" class="blob-code blob-code-inner js-file-line">            <span class="pl-k">if</span> old<span class="pl-k">+</span>dot <span class="pl-k">in</span> newpars:</td> 
     567      </tr> 
     568      <tr> 
     569        <td id="L40" class="blob-num js-line-number" data-line-number="40"></td> 
     570        <td id="LC40" class="blob-code blob-code-inner js-file-line">                <span class="pl-k">if</span> new <span class="pl-k">is</span> <span class="pl-k">not</span> <span class="pl-c1">None</span>:</td> 
     571      </tr> 
     572      <tr> 
     573        <td id="L41" class="blob-num js-line-number" data-line-number="41"></td> 
     574        <td id="LC41" class="blob-code blob-code-inner js-file-line">                    newpars[new<span class="pl-k">+</span>pd] <span class="pl-k">=</span> pars[old<span class="pl-k">+</span>dot]</td> 
     575      </tr> 
     576      <tr> 
     577        <td id="L42" class="blob-num js-line-number" data-line-number="42"></td> 
     578        <td id="LC42" class="blob-code blob-code-inner js-file-line">                <span class="pl-k">del</span> newpars[old<span class="pl-k">+</span>dot]</td> 
     579      </tr> 
     580      <tr> 
     581        <td id="L43" class="blob-num js-line-number" data-line-number="43"></td> 
     582        <td id="LC43" class="blob-code blob-code-inner js-file-line">    <span class="pl-k">return</span> newpars</td> 
     583      </tr> 
     584      <tr> 
     585        <td id="L44" class="blob-num js-line-number" data-line-number="44"></td> 
     586        <td id="LC44" class="blob-code blob-code-inner js-file-line"> 
     587</td> 
     588      </tr> 
     589      <tr> 
     590        <td id="L45" class="blob-num js-line-number" data-line-number="45"></td> 
     591        <td id="LC45" class="blob-code blob-code-inner js-file-line"><span class="pl-k">def</span> <span class="pl-en">_rescale_sld</span>(<span class="pl-smi">pars</span>):</td> 
     592      </tr> 
     593      <tr> 
     594        <td id="L46" class="blob-num js-line-number" data-line-number="46"></td> 
     595        <td id="LC46" class="blob-code blob-code-inner js-file-line">    <span class="pl-s"><span class="pl-pds">&quot;&quot;&quot;</span></span></td> 
     596      </tr> 
     597      <tr> 
     598        <td id="L47" class="blob-num js-line-number" data-line-number="47"></td> 
     599        <td id="LC47" class="blob-code blob-code-inner js-file-line"><span class="pl-s">    rescale all sld parameters in the new model definition by 1e6 so the</span></td> 
     600      </tr> 
     601      <tr> 
     602        <td id="L48" class="blob-num js-line-number" data-line-number="48"></td> 
     603        <td id="LC48" class="blob-code blob-code-inner js-file-line"><span class="pl-s">    numbers are nicer.  Relies on the fact that all sld parameters in the</span></td> 
     604      </tr> 
     605      <tr> 
     606        <td id="L49" class="blob-num js-line-number" data-line-number="49"></td> 
     607        <td id="LC49" class="blob-code blob-code-inner js-file-line"><span class="pl-s">    new model definition end with sld.</span></td> 
     608      </tr> 
     609      <tr> 
     610        <td id="L50" class="blob-num js-line-number" data-line-number="50"></td> 
     611        <td id="LC50" class="blob-code blob-code-inner js-file-line"><span class="pl-s">    <span class="pl-pds">&quot;&quot;&quot;</span></span></td> 
     612      </tr> 
     613      <tr> 
     614        <td id="L51" class="blob-num js-line-number" data-line-number="51"></td> 
     615        <td id="LC51" class="blob-code blob-code-inner js-file-line">    <span class="pl-k">return</span> <span class="pl-c1">dict</span>((p, (v<span class="pl-k">*</span><span class="pl-c1">1e6</span> <span class="pl-k">if</span> p.endswith(<span class="pl-s"><span class="pl-pds">&#39;</span>sld<span class="pl-pds">&#39;</span></span>) <span class="pl-k">else</span> v))</td> 
     616      </tr> 
     617      <tr> 
     618        <td id="L52" class="blob-num js-line-number" data-line-number="52"></td> 
     619        <td id="LC52" class="blob-code blob-code-inner js-file-line">                <span class="pl-k">for</span> p, v <span class="pl-k">in</span> pars.items())</td> 
     620      </tr> 
     621      <tr> 
     622        <td id="L53" class="blob-num js-line-number" data-line-number="53"></td> 
     623        <td id="LC53" class="blob-code blob-code-inner js-file-line"> 
     624</td> 
     625      </tr> 
     626      <tr> 
     627        <td id="L54" class="blob-num js-line-number" data-line-number="54"></td> 
     628        <td id="LC54" class="blob-code blob-code-inner js-file-line"><span class="pl-k">def</span> <span class="pl-en">convert_model</span>(<span class="pl-smi">name</span>, <span class="pl-smi">pars</span>):</td> 
     629      </tr> 
     630      <tr> 
     631        <td id="L55" class="blob-num js-line-number" data-line-number="55"></td> 
     632        <td id="LC55" class="blob-code blob-code-inner js-file-line">    <span class="pl-s"><span class="pl-pds">&quot;&quot;&quot;</span></span></td> 
     633      </tr> 
     634      <tr> 
     635        <td id="L56" class="blob-num js-line-number" data-line-number="56"></td> 
     636        <td id="LC56" class="blob-code blob-code-inner js-file-line"><span class="pl-s">    Convert model from old style parameter names to new style.</span></td> 
     637      </tr> 
     638      <tr> 
     639        <td id="L57" class="blob-num js-line-number" data-line-number="57"></td> 
     640        <td id="LC57" class="blob-code blob-code-inner js-file-line"><span class="pl-s">    <span class="pl-pds">&quot;&quot;&quot;</span></span></td> 
     641      </tr> 
     642      <tr> 
     643        <td id="L58" class="blob-num js-line-number" data-line-number="58"></td> 
     644        <td id="LC58" class="blob-code blob-code-inner js-file-line">    _, _ <span class="pl-k">=</span> name, pars <span class="pl-c"># lint</span></td> 
     645      </tr> 
     646      <tr> 
     647        <td id="L59" class="blob-num js-line-number" data-line-number="59"></td> 
     648        <td id="LC59" class="blob-code blob-code-inner js-file-line">    <span class="pl-k">raise</span> <span class="pl-c1">NotImplementedError</span></td> 
     649      </tr> 
     650      <tr> 
     651        <td id="L60" class="blob-num js-line-number" data-line-number="60"></td> 
     652        <td id="LC60" class="blob-code blob-code-inner js-file-line">    <span class="pl-c"># need to load all new models in order to determine old=&gt;new</span></td> 
     653      </tr> 
     654      <tr> 
     655        <td id="L61" class="blob-num js-line-number" data-line-number="61"></td> 
     656        <td id="LC61" class="blob-code blob-code-inner js-file-line">    <span class="pl-c"># model name mapping</span></td> 
     657      </tr> 
     658      <tr> 
     659        <td id="L62" class="blob-num js-line-number" data-line-number="62"></td> 
     660        <td id="LC62" class="blob-code blob-code-inner js-file-line"> 
     661</td> 
     662      </tr> 
     663      <tr> 
     664        <td id="L63" class="blob-num js-line-number" data-line-number="63"></td> 
     665        <td id="LC63" class="blob-code blob-code-inner js-file-line"><span class="pl-k">def</span> <span class="pl-en">_unscale_sld</span>(<span class="pl-smi">pars</span>):</td> 
     666      </tr> 
     667      <tr> 
     668        <td id="L64" class="blob-num js-line-number" data-line-number="64"></td> 
     669        <td id="LC64" class="blob-code blob-code-inner js-file-line">    <span class="pl-s"><span class="pl-pds">&quot;&quot;&quot;</span></span></td> 
     670      </tr> 
     671      <tr> 
     672        <td id="L65" class="blob-num js-line-number" data-line-number="65"></td> 
     673        <td id="LC65" class="blob-code blob-code-inner js-file-line"><span class="pl-s">    rescale all sld parameters in the new model definition by 1e6 so the</span></td> 
     674      </tr> 
     675      <tr> 
     676        <td id="L66" class="blob-num js-line-number" data-line-number="66"></td> 
     677        <td id="LC66" class="blob-code blob-code-inner js-file-line"><span class="pl-s">    numbers are nicer.  Relies on the fact that all sld parameters in the</span></td> 
     678      </tr> 
     679      <tr> 
     680        <td id="L67" class="blob-num js-line-number" data-line-number="67"></td> 
     681        <td id="LC67" class="blob-code blob-code-inner js-file-line"><span class="pl-s">    new model definition end with sld.</span></td> 
     682      </tr> 
     683      <tr> 
     684        <td id="L68" class="blob-num js-line-number" data-line-number="68"></td> 
     685        <td id="LC68" class="blob-code blob-code-inner js-file-line"><span class="pl-s">    <span class="pl-pds">&quot;&quot;&quot;</span></span></td> 
     686      </tr> 
     687      <tr> 
     688        <td id="L69" class="blob-num js-line-number" data-line-number="69"></td> 
     689        <td id="LC69" class="blob-code blob-code-inner js-file-line">    <span class="pl-k">return</span> <span class="pl-c1">dict</span>((p, (v<span class="pl-k">*</span><span class="pl-c1">1e-6</span> <span class="pl-k">if</span> p.endswith(<span class="pl-s"><span class="pl-pds">&#39;</span>sld<span class="pl-pds">&#39;</span></span>) <span class="pl-k">else</span> v))</td> 
     690      </tr> 
     691      <tr> 
     692        <td id="L70" class="blob-num js-line-number" data-line-number="70"></td> 
     693        <td id="LC70" class="blob-code blob-code-inner js-file-line">                <span class="pl-k">for</span> p, v <span class="pl-k">in</span> pars.items())</td> 
     694      </tr> 
     695      <tr> 
     696        <td id="L71" class="blob-num js-line-number" data-line-number="71"></td> 
     697        <td id="LC71" class="blob-code blob-code-inner js-file-line"> 
     698</td> 
     699      </tr> 
     700      <tr> 
     701        <td id="L72" class="blob-num js-line-number" data-line-number="72"></td> 
     702        <td id="LC72" class="blob-code blob-code-inner js-file-line"><span class="pl-k">def</span> <span class="pl-en">_remove_pd</span>(<span class="pl-smi">pars</span>, <span class="pl-smi">key</span>, <span class="pl-smi">name</span>):</td> 
     703      </tr> 
     704      <tr> 
     705        <td id="L73" class="blob-num js-line-number" data-line-number="73"></td> 
     706        <td id="LC73" class="blob-code blob-code-inner js-file-line">    <span class="pl-s"><span class="pl-pds">&quot;&quot;&quot;</span></span></td> 
     707      </tr> 
     708      <tr> 
     709        <td id="L74" class="blob-num js-line-number" data-line-number="74"></td> 
     710        <td id="LC74" class="blob-code blob-code-inner js-file-line"><span class="pl-s">    Remove polydispersity from the parameter list.</span></td> 
     711      </tr> 
     712      <tr> 
     713        <td id="L75" class="blob-num js-line-number" data-line-number="75"></td> 
     714        <td id="LC75" class="blob-code blob-code-inner js-file-line"><span class="pl-s"></span></td> 
     715      </tr> 
     716      <tr> 
     717        <td id="L76" class="blob-num js-line-number" data-line-number="76"></td> 
     718        <td id="LC76" class="blob-code blob-code-inner js-file-line"><span class="pl-s">    Note: operates in place</span></td> 
     719      </tr> 
     720      <tr> 
     721        <td id="L77" class="blob-num js-line-number" data-line-number="77"></td> 
     722        <td id="LC77" class="blob-code blob-code-inner js-file-line"><span class="pl-s">    <span class="pl-pds">&quot;&quot;&quot;</span></span></td> 
     723      </tr> 
     724      <tr> 
     725        <td id="L78" class="blob-num js-line-number" data-line-number="78"></td> 
     726        <td id="LC78" class="blob-code blob-code-inner js-file-line">    <span class="pl-c"># Bumps style parameter names</span></td> 
     727      </tr> 
     728      <tr> 
     729        <td id="L79" class="blob-num js-line-number" data-line-number="79"></td> 
     730        <td id="LC79" class="blob-code blob-code-inner js-file-line">    pd <span class="pl-k">=</span> pars.pop(key<span class="pl-k">+</span><span class="pl-s"><span class="pl-pds">&quot;</span>.width<span class="pl-pds">&quot;</span></span>, <span class="pl-c1">0.0</span>)</td> 
     731      </tr> 
     732      <tr> 
     733        <td id="L80" class="blob-num js-line-number" data-line-number="80"></td> 
     734        <td id="LC80" class="blob-code blob-code-inner js-file-line">    pd_n <span class="pl-k">=</span> pars.pop(key<span class="pl-k">+</span><span class="pl-s"><span class="pl-pds">&quot;</span>.npts<span class="pl-pds">&quot;</span></span>, <span class="pl-c1">0</span>)</td> 
     735      </tr> 
     736      <tr> 
     737        <td id="L81" class="blob-num js-line-number" data-line-number="81"></td> 
     738        <td id="LC81" class="blob-code blob-code-inner js-file-line">    <span class="pl-k">if</span> pd <span class="pl-k">!=</span> <span class="pl-c1">0.0</span> <span class="pl-k">and</span> pd_n <span class="pl-k">!=</span> <span class="pl-c1">0</span>:</td> 
     739      </tr> 
     740      <tr> 
     741        <td id="L82" class="blob-num js-line-number" data-line-number="82"></td> 
     742        <td id="LC82" class="blob-code blob-code-inner js-file-line">        warnings.warn(<span class="pl-s"><span class="pl-pds">&quot;</span>parameter <span class="pl-c1">%s</span> not polydisperse in sasview <span class="pl-c1">%s</span><span class="pl-pds">&quot;</span></span><span class="pl-k">%</span>(key, name))</td> 
     743      </tr> 
     744      <tr> 
     745        <td id="L83" class="blob-num js-line-number" data-line-number="83"></td> 
     746        <td id="LC83" class="blob-code blob-code-inner js-file-line">    pars.pop(key<span class="pl-k">+</span><span class="pl-s"><span class="pl-pds">&quot;</span>.nsigmas<span class="pl-pds">&quot;</span></span>, <span class="pl-c1">None</span>)</td> 
     747      </tr> 
     748      <tr> 
     749        <td id="L84" class="blob-num js-line-number" data-line-number="84"></td> 
     750        <td id="LC84" class="blob-code blob-code-inner js-file-line">    pars.pop(key<span class="pl-k">+</span><span class="pl-s"><span class="pl-pds">&quot;</span>.type<span class="pl-pds">&quot;</span></span>, <span class="pl-c1">None</span>)</td> 
     751      </tr> 
     752      <tr> 
     753        <td id="L85" class="blob-num js-line-number" data-line-number="85"></td> 
     754        <td id="LC85" class="blob-code blob-code-inner js-file-line">    <span class="pl-k">return</span> pars</td> 
     755      </tr> 
     756      <tr> 
     757        <td id="L86" class="blob-num js-line-number" data-line-number="86"></td> 
     758        <td id="LC86" class="blob-code blob-code-inner js-file-line"> 
     759</td> 
     760      </tr> 
     761      <tr> 
     762        <td id="L87" class="blob-num js-line-number" data-line-number="87"></td> 
     763        <td id="LC87" class="blob-code blob-code-inner js-file-line"><span class="pl-k">def</span> <span class="pl-en">_revert_pars</span>(<span class="pl-smi">pars</span>, <span class="pl-smi">mapping</span>):</td> 
     764      </tr> 
     765      <tr> 
     766        <td id="L88" class="blob-num js-line-number" data-line-number="88"></td> 
     767        <td id="LC88" class="blob-code blob-code-inner js-file-line">    <span class="pl-s"><span class="pl-pds">&quot;&quot;&quot;</span></span></td> 
     768      </tr> 
     769      <tr> 
     770        <td id="L89" class="blob-num js-line-number" data-line-number="89"></td> 
     771        <td id="LC89" class="blob-code blob-code-inner js-file-line"><span class="pl-s">    Rename the parameters and any associated polydispersity attributes.</span></td> 
     772      </tr> 
     773      <tr> 
     774        <td id="L90" class="blob-num js-line-number" data-line-number="90"></td> 
     775        <td id="LC90" class="blob-code blob-code-inner js-file-line"><span class="pl-s">    <span class="pl-pds">&quot;&quot;&quot;</span></span></td> 
     776      </tr> 
     777      <tr> 
     778        <td id="L91" class="blob-num js-line-number" data-line-number="91"></td> 
     779        <td id="LC91" class="blob-code blob-code-inner js-file-line">    newpars <span class="pl-k">=</span> pars.copy()</td> 
     780      </tr> 
     781      <tr> 
     782        <td id="L92" class="blob-num js-line-number" data-line-number="92"></td> 
     783        <td id="LC92" class="blob-code blob-code-inner js-file-line"> 
     784</td> 
     785      </tr> 
     786      <tr> 
     787        <td id="L93" class="blob-num js-line-number" data-line-number="93"></td> 
     788        <td id="LC93" class="blob-code blob-code-inner js-file-line">    <span class="pl-k">for</span> new, old <span class="pl-k">in</span> mapping.items():</td> 
     789      </tr> 
     790      <tr> 
     791        <td id="L94" class="blob-num js-line-number" data-line-number="94"></td> 
     792        <td id="LC94" class="blob-code blob-code-inner js-file-line">        <span class="pl-k">for</span> pd, dot <span class="pl-k">in</span> <span class="pl-c1">PD_DOT</span>:</td> 
     793      </tr> 
     794      <tr> 
     795        <td id="L95" class="blob-num js-line-number" data-line-number="95"></td> 
     796        <td id="LC95" class="blob-code blob-code-inner js-file-line">            <span class="pl-k">if</span> old <span class="pl-k">and</span> old<span class="pl-k">+</span>pd <span class="pl-k">==</span> new<span class="pl-k">+</span>dot:</td> 
     797      </tr> 
     798      <tr> 
     799        <td id="L96" class="blob-num js-line-number" data-line-number="96"></td> 
     800        <td id="LC96" class="blob-code blob-code-inner js-file-line">                <span class="pl-k">continue</span></td> 
     801      </tr> 
     802      <tr> 
     803        <td id="L97" class="blob-num js-line-number" data-line-number="97"></td> 
     804        <td id="LC97" class="blob-code blob-code-inner js-file-line">            <span class="pl-k">if</span> new<span class="pl-k">+</span>pd <span class="pl-k">in</span> newpars:</td> 
     805      </tr> 
     806      <tr> 
     807        <td id="L98" class="blob-num js-line-number" data-line-number="98"></td> 
     808        <td id="LC98" class="blob-code blob-code-inner js-file-line">                <span class="pl-k">if</span> old <span class="pl-k">is</span> <span class="pl-k">not</span> <span class="pl-c1">None</span>:</td> 
     809      </tr> 
     810      <tr> 
     811        <td id="L99" class="blob-num js-line-number" data-line-number="99"></td> 
     812        <td id="LC99" class="blob-code blob-code-inner js-file-line">                    newpars[old<span class="pl-k">+</span>dot] <span class="pl-k">=</span> pars[new<span class="pl-k">+</span>pd]</td> 
     813      </tr> 
     814      <tr> 
     815        <td id="L100" class="blob-num js-line-number" data-line-number="100"></td> 
     816        <td id="LC100" class="blob-code blob-code-inner js-file-line">                <span class="pl-k">del</span> newpars[new<span class="pl-k">+</span>pd]</td> 
     817      </tr> 
     818      <tr> 
     819        <td id="L101" class="blob-num js-line-number" data-line-number="101"></td> 
     820        <td id="LC101" class="blob-code blob-code-inner js-file-line">    <span class="pl-k">for</span> k <span class="pl-k">in</span> <span class="pl-c1">list</span>(newpars.keys()):</td> 
     821      </tr> 
     822      <tr> 
     823        <td id="L102" class="blob-num js-line-number" data-line-number="102"></td> 
     824        <td id="LC102" class="blob-code blob-code-inner js-file-line">        <span class="pl-k">for</span> pd, dot <span class="pl-k">in</span> <span class="pl-c1">PD_DOT</span>[<span class="pl-c1">1</span>:]:  <span class="pl-c"># skip &quot;&quot; =&gt; &quot;&quot;</span></td> 
     825      </tr> 
     826      <tr> 
     827        <td id="L103" class="blob-num js-line-number" data-line-number="103"></td> 
     828        <td id="LC103" class="blob-code blob-code-inner js-file-line">            <span class="pl-k">if</span> k.endswith(pd):</td> 
     829      </tr> 
     830      <tr> 
     831        <td id="L104" class="blob-num js-line-number" data-line-number="104"></td> 
     832        <td id="LC104" class="blob-code blob-code-inner js-file-line">                newpars[k[:<span class="pl-k">-</span><span class="pl-c1">len</span>(pd)]<span class="pl-k">+</span>dot] <span class="pl-k">=</span> newpars[k]</td> 
     833      </tr> 
     834      <tr> 
     835        <td id="L105" class="blob-num js-line-number" data-line-number="105"></td> 
     836        <td id="LC105" class="blob-code blob-code-inner js-file-line">                <span class="pl-k">del</span> newpars[k]</td> 
     837      </tr> 
     838      <tr> 
     839        <td id="L106" class="blob-num js-line-number" data-line-number="106"></td> 
     840        <td id="LC106" class="blob-code blob-code-inner js-file-line">    <span class="pl-k">return</span> newpars</td> 
     841      </tr> 
     842      <tr> 
     843        <td id="L107" class="blob-num js-line-number" data-line-number="107"></td> 
     844        <td id="LC107" class="blob-code blob-code-inner js-file-line"> 
     845</td> 
     846      </tr> 
     847      <tr> 
     848        <td id="L108" class="blob-num js-line-number" data-line-number="108"></td> 
     849        <td id="LC108" class="blob-code blob-code-inner js-file-line"><span class="pl-k">def</span> <span class="pl-en">revert_model</span>(<span class="pl-smi">model_definition</span>, <span class="pl-smi">pars</span>):</td> 
     850      </tr> 
     851      <tr> 
     852        <td id="L109" class="blob-num js-line-number" data-line-number="109"></td> 
     853        <td id="LC109" class="blob-code blob-code-inner js-file-line">    <span class="pl-s"><span class="pl-pds">&quot;&quot;&quot;</span></span></td> 
     854      </tr> 
     855      <tr> 
     856        <td id="L110" class="blob-num js-line-number" data-line-number="110"></td> 
     857        <td id="LC110" class="blob-code blob-code-inner js-file-line"><span class="pl-s">    Convert model from new style parameter names to old style.</span></td> 
     858      </tr> 
     859      <tr> 
     860        <td id="L111" class="blob-num js-line-number" data-line-number="111"></td> 
     861        <td id="LC111" class="blob-code blob-code-inner js-file-line"><span class="pl-s">    <span class="pl-pds">&quot;&quot;&quot;</span></span></td> 
     862      </tr> 
     863      <tr> 
     864        <td id="L112" class="blob-num js-line-number" data-line-number="112"></td> 
     865        <td id="LC112" class="blob-code blob-code-inner js-file-line">    mapping <span class="pl-k">=</span> model_definition.oldpars</td> 
     866      </tr> 
     867      <tr> 
     868        <td id="L113" class="blob-num js-line-number" data-line-number="113"></td> 
     869        <td id="LC113" class="blob-code blob-code-inner js-file-line">    oldname <span class="pl-k">=</span> model_definition.oldname</td> 
     870      </tr> 
     871      <tr> 
     872        <td id="L114" class="blob-num js-line-number" data-line-number="114"></td> 
     873        <td id="LC114" class="blob-code blob-code-inner js-file-line">    oldpars <span class="pl-k">=</span> _revert_pars(_unscale_sld(pars), mapping)</td> 
     874      </tr> 
     875      <tr> 
     876        <td id="L115" class="blob-num js-line-number" data-line-number="115"></td> 
     877        <td id="LC115" class="blob-code blob-code-inner js-file-line"> 
     878</td> 
     879      </tr> 
     880      <tr> 
     881        <td id="L116" class="blob-num js-line-number" data-line-number="116"></td> 
     882        <td id="LC116" class="blob-code blob-code-inner js-file-line">    <span class="pl-c"># Note: update compare.constrain_pars to match</span></td> 
     883      </tr> 
     884      <tr> 
     885        <td id="L117" class="blob-num js-line-number" data-line-number="117"></td> 
     886        <td id="LC117" class="blob-code blob-code-inner js-file-line">    name <span class="pl-k">=</span> model_definition.name</td> 
     887      </tr> 
     888      <tr> 
     889        <td id="L118" class="blob-num js-line-number" data-line-number="118"></td> 
     890        <td id="LC118" class="blob-code blob-code-inner js-file-line">    <span class="pl-k">if</span> name <span class="pl-k">in</span> <span class="pl-c1">MODELS_WITHOUT_SCALE</span>:</td> 
     891      </tr> 
     892      <tr> 
     893        <td id="L119" class="blob-num js-line-number" data-line-number="119"></td> 
     894        <td id="LC119" class="blob-code blob-code-inner js-file-line">        <span class="pl-k">if</span> oldpars.pop(<span class="pl-s"><span class="pl-pds">&#39;</span>scale<span class="pl-pds">&#39;</span></span>, <span class="pl-c1">1.0</span>) <span class="pl-k">!=</span> <span class="pl-c1">1.0</span>:</td> 
     895      </tr> 
     896      <tr> 
     897        <td id="L120" class="blob-num js-line-number" data-line-number="120"></td> 
     898        <td id="LC120" class="blob-code blob-code-inner js-file-line">            warnings.warn(<span class="pl-s"><span class="pl-pds">&quot;</span>parameter scale not used in sasview <span class="pl-c1">%s</span><span class="pl-pds">&quot;</span></span><span class="pl-k">%</span>name)</td> 
     899      </tr> 
     900      <tr> 
     901        <td id="L121" class="blob-num js-line-number" data-line-number="121"></td> 
     902        <td id="LC121" class="blob-code blob-code-inner js-file-line">    <span class="pl-k">elif</span> name <span class="pl-k">in</span> <span class="pl-c1">MODELS_WITHOUT_BACKGROUND</span>:</td> 
     903      </tr> 
     904      <tr> 
     905        <td id="L122" class="blob-num js-line-number" data-line-number="122"></td> 
     906        <td id="LC122" class="blob-code blob-code-inner js-file-line">        <span class="pl-k">if</span> oldpars.pop(<span class="pl-s"><span class="pl-pds">&#39;</span>background<span class="pl-pds">&#39;</span></span>, <span class="pl-c1">0.0</span>) <span class="pl-k">!=</span> <span class="pl-c1">0.0</span>:</td> 
     907      </tr> 
     908      <tr> 
     909        <td id="L123" class="blob-num js-line-number" data-line-number="123"></td> 
     910        <td id="LC123" class="blob-code blob-code-inner js-file-line">            warnings.warn(<span class="pl-s"><span class="pl-pds">&quot;</span>parameter background not used in sasview <span class="pl-c1">%s</span><span class="pl-pds">&quot;</span></span><span class="pl-k">%</span>name)</td> 
     911      </tr> 
     912      <tr> 
     913        <td id="L124" class="blob-num js-line-number" data-line-number="124"></td> 
     914        <td id="LC124" class="blob-code blob-code-inner js-file-line">    <span class="pl-k">elif</span> <span class="pl-c1">getattr</span>(model_definition, <span class="pl-s"><span class="pl-pds">&#39;</span>category<span class="pl-pds">&#39;</span></span>, <span class="pl-c1">None</span>) <span class="pl-k">==</span> <span class="pl-s"><span class="pl-pds">&#39;</span>structure-factor<span class="pl-pds">&#39;</span></span>:</td> 
     915      </tr> 
     916      <tr> 
     917        <td id="L125" class="blob-num js-line-number" data-line-number="125"></td> 
     918        <td id="LC125" class="blob-code blob-code-inner js-file-line">        <span class="pl-k">if</span> oldpars.pop(<span class="pl-s"><span class="pl-pds">&#39;</span>scale<span class="pl-pds">&#39;</span></span>, <span class="pl-c1">1.0</span>) <span class="pl-k">!=</span> <span class="pl-c1">1.0</span>:</td> 
     919      </tr> 
     920      <tr> 
     921        <td id="L126" class="blob-num js-line-number" data-line-number="126"></td> 
     922        <td id="LC126" class="blob-code blob-code-inner js-file-line">            warnings.warn(<span class="pl-s"><span class="pl-pds">&quot;</span>parameter scale not used in sasview <span class="pl-c1">%s</span><span class="pl-pds">&quot;</span></span><span class="pl-k">%</span>name)</td> 
     923      </tr> 
     924      <tr> 
     925        <td id="L127" class="blob-num js-line-number" data-line-number="127"></td> 
     926        <td id="LC127" class="blob-code blob-code-inner js-file-line">        <span class="pl-k">if</span> oldpars.pop(<span class="pl-s"><span class="pl-pds">&#39;</span>background<span class="pl-pds">&#39;</span></span>, <span class="pl-c1">0.0</span>) <span class="pl-k">!=</span> <span class="pl-c1">0.0</span>:</td> 
     927      </tr> 
     928      <tr> 
     929        <td id="L128" class="blob-num js-line-number" data-line-number="128"></td> 
     930        <td id="LC128" class="blob-code blob-code-inner js-file-line">            warnings.warn(<span class="pl-s"><span class="pl-pds">&quot;</span>parameter background not used in sasview <span class="pl-c1">%s</span><span class="pl-pds">&quot;</span></span><span class="pl-k">%</span>name)</td> 
     931      </tr> 
     932      <tr> 
     933        <td id="L129" class="blob-num js-line-number" data-line-number="129"></td> 
     934        <td id="LC129" class="blob-code blob-code-inner js-file-line">    <span class="pl-k">elif</span> name <span class="pl-k">==</span> <span class="pl-s"><span class="pl-pds">&#39;</span>pearl_necklace<span class="pl-pds">&#39;</span></span>:</td> 
     935      </tr> 
     936      <tr> 
     937        <td id="L130" class="blob-num js-line-number" data-line-number="130"></td> 
     938        <td id="LC130" class="blob-code blob-code-inner js-file-line">        _remove_pd(oldpars, <span class="pl-s"><span class="pl-pds">&#39;</span>num_pearls<span class="pl-pds">&#39;</span></span>, name)</td> 
     939      </tr> 
     940      <tr> 
     941        <td id="L131" class="blob-num js-line-number" data-line-number="131"></td> 
     942        <td id="LC131" class="blob-code blob-code-inner js-file-line">        _remove_pd(oldpars, <span class="pl-s"><span class="pl-pds">&#39;</span>thick_string<span class="pl-pds">&#39;</span></span>, name)</td> 
     943      </tr> 
     944      <tr> 
     945        <td id="L132" class="blob-num js-line-number" data-line-number="132"></td> 
     946        <td id="LC132" class="blob-code blob-code-inner js-file-line">    <span class="pl-k">elif</span> name <span class="pl-k">==</span> <span class="pl-s"><span class="pl-pds">&#39;</span>rpa<span class="pl-pds">&#39;</span></span>:</td> 
     947      </tr> 
     948      <tr> 
     949        <td id="L133" class="blob-num js-line-number" data-line-number="133"></td> 
     950        <td id="LC133" class="blob-code blob-code-inner js-file-line">        <span class="pl-c"># convert scattering lengths from femtometers to centimeters</span></td> 
     951      </tr> 
     952      <tr> 
     953        <td id="L134" class="blob-num js-line-number" data-line-number="134"></td> 
     954        <td id="LC134" class="blob-code blob-code-inner js-file-line">        <span class="pl-k">for</span> p <span class="pl-k">in</span> <span class="pl-s"><span class="pl-pds">&quot;</span>La<span class="pl-pds">&quot;</span></span>, <span class="pl-s"><span class="pl-pds">&quot;</span>Lb<span class="pl-pds">&quot;</span></span>, <span class="pl-s"><span class="pl-pds">&quot;</span>Lc<span class="pl-pds">&quot;</span></span>, <span class="pl-s"><span class="pl-pds">&quot;</span>Ld<span class="pl-pds">&quot;</span></span>:</td> 
     955      </tr> 
     956      <tr> 
     957        <td id="L135" class="blob-num js-line-number" data-line-number="135"></td> 
     958        <td id="LC135" class="blob-code blob-code-inner js-file-line">            <span class="pl-k">if</span> p <span class="pl-k">in</span> oldpars: oldpars[p] <span class="pl-k">*=</span> <span class="pl-c1">1e-13</span></td> 
     959      </tr> 
     960      <tr> 
     961        <td id="L136" class="blob-num js-line-number" data-line-number="136"></td> 
     962        <td id="LC136" class="blob-code blob-code-inner js-file-line"> 
     963</td> 
     964      </tr> 
     965      <tr> 
     966        <td id="L137" class="blob-num js-line-number" data-line-number="137"></td> 
     967        <td id="LC137" class="blob-code blob-code-inner js-file-line">    <span class="pl-k">return</span> oldname, oldpars</td> 
     968      </tr> 
     969      <tr> 
     970        <td id="L138" class="blob-num js-line-number" data-line-number="138"></td> 
     971        <td id="LC138" class="blob-code blob-code-inner js-file-line"> 
     972</td> 
     973      </tr> 
     974      <tr> 
     975        <td id="L139" class="blob-num js-line-number" data-line-number="139"></td> 
     976        <td id="LC139" class="blob-code blob-code-inner js-file-line"><span class="pl-k">def</span> <span class="pl-en">constrain_new_to_old</span>(<span class="pl-smi">model_definition</span>, <span class="pl-smi">pars</span>):</td> 
     977      </tr> 
     978      <tr> 
     979        <td id="L140" class="blob-num js-line-number" data-line-number="140"></td> 
     980        <td id="LC140" class="blob-code blob-code-inner js-file-line">    <span class="pl-s"><span class="pl-pds">&quot;&quot;&quot;</span></span></td> 
     981      </tr> 
     982      <tr> 
     983        <td id="L141" class="blob-num js-line-number" data-line-number="141"></td> 
     984        <td id="LC141" class="blob-code blob-code-inner js-file-line"><span class="pl-s">    Restrict parameter values to those that will match sasview.</span></td> 
     985      </tr> 
     986      <tr> 
     987        <td id="L142" class="blob-num js-line-number" data-line-number="142"></td> 
     988        <td id="LC142" class="blob-code blob-code-inner js-file-line"><span class="pl-s">    <span class="pl-pds">&quot;&quot;&quot;</span></span></td> 
     989      </tr> 
     990      <tr> 
     991        <td id="L143" class="blob-num js-line-number" data-line-number="143"></td> 
     992        <td id="LC143" class="blob-code blob-code-inner js-file-line">    <span class="pl-c"># Note: update convert.revert_model to match</span></td> 
     993      </tr> 
     994      <tr> 
     995        <td id="L144" class="blob-num js-line-number" data-line-number="144"></td> 
     996        <td id="LC144" class="blob-code blob-code-inner js-file-line">    name <span class="pl-k">=</span> model_definition.name</td> 
     997      </tr> 
     998      <tr> 
     999        <td id="L145" class="blob-num js-line-number" data-line-number="145"></td> 
     1000        <td id="LC145" class="blob-code blob-code-inner js-file-line">    <span class="pl-k">if</span> name <span class="pl-k">in</span> <span class="pl-c1">MODELS_WITHOUT_SCALE</span>:</td> 
     1001      </tr> 
     1002      <tr> 
     1003        <td id="L146" class="blob-num js-line-number" data-line-number="146"></td> 
     1004        <td id="LC146" class="blob-code blob-code-inner js-file-line">        pars[<span class="pl-s"><span class="pl-pds">&#39;</span>scale<span class="pl-pds">&#39;</span></span>] <span class="pl-k">=</span> <span class="pl-c1">1</span></td> 
     1005      </tr> 
     1006      <tr> 
     1007        <td id="L147" class="blob-num js-line-number" data-line-number="147"></td> 
     1008        <td id="LC147" class="blob-code blob-code-inner js-file-line">    <span class="pl-k">elif</span> name <span class="pl-k">in</span> <span class="pl-c1">MODELS_WITHOUT_BACKGROUND</span>:</td> 
     1009      </tr> 
     1010      <tr> 
     1011        <td id="L148" class="blob-num js-line-number" data-line-number="148"></td> 
     1012        <td id="LC148" class="blob-code blob-code-inner js-file-line">        pars[<span class="pl-s"><span class="pl-pds">&#39;</span>background<span class="pl-pds">&#39;</span></span>] <span class="pl-k">=</span> <span class="pl-c1">0</span></td> 
     1013      </tr> 
     1014      <tr> 
     1015        <td id="L149" class="blob-num js-line-number" data-line-number="149"></td> 
     1016        <td id="LC149" class="blob-code blob-code-inner js-file-line">    <span class="pl-k">elif</span> name <span class="pl-k">==</span> <span class="pl-s"><span class="pl-pds">&#39;</span>pearl_necklace<span class="pl-pds">&#39;</span></span>:</td> 
     1017      </tr> 
     1018      <tr> 
     1019        <td id="L150" class="blob-num js-line-number" data-line-number="150"></td> 
     1020        <td id="LC150" class="blob-code blob-code-inner js-file-line">        pars[<span class="pl-s"><span class="pl-pds">&#39;</span>string_thickness_pd_n<span class="pl-pds">&#39;</span></span>] <span class="pl-k">=</span> <span class="pl-c1">0</span></td> 
     1021      </tr> 
     1022      <tr> 
     1023        <td id="L151" class="blob-num js-line-number" data-line-number="151"></td> 
     1024        <td id="LC151" class="blob-code blob-code-inner js-file-line">        pars[<span class="pl-s"><span class="pl-pds">&#39;</span>number_of_pearls_pd_n<span class="pl-pds">&#39;</span></span>] <span class="pl-k">=</span> <span class="pl-c1">0</span></td> 
     1025      </tr> 
     1026      <tr> 
     1027        <td id="L152" class="blob-num js-line-number" data-line-number="152"></td> 
     1028        <td id="LC152" class="blob-code blob-code-inner js-file-line">    <span class="pl-k">elif</span> name <span class="pl-k">==</span> <span class="pl-s"><span class="pl-pds">&#39;</span>rpa<span class="pl-pds">&#39;</span></span>:</td> 
     1029      </tr> 
     1030      <tr> 
     1031        <td id="L153" class="blob-num js-line-number" data-line-number="153"></td> 
     1032        <td id="LC153" class="blob-code blob-code-inner js-file-line">        pars[<span class="pl-s"><span class="pl-pds">&#39;</span>case_num<span class="pl-pds">&#39;</span></span>] <span class="pl-k">=</span> <span class="pl-c1">int</span>(pars[<span class="pl-s"><span class="pl-pds">&#39;</span>case_num<span class="pl-pds">&#39;</span></span>])</td> 
     1033      </tr> 
     1034      <tr> 
     1035        <td id="L154" class="blob-num js-line-number" data-line-number="154"></td> 
     1036        <td id="LC154" class="blob-code blob-code-inner js-file-line">    <span class="pl-k">elif</span> <span class="pl-c1">getattr</span>(model_definition, <span class="pl-s"><span class="pl-pds">&#39;</span>category<span class="pl-pds">&#39;</span></span>, <span class="pl-c1">None</span>) <span class="pl-k">==</span> <span class="pl-s"><span class="pl-pds">&#39;</span>structure-factor<span class="pl-pds">&#39;</span></span>:</td> 
     1037      </tr> 
     1038      <tr> 
     1039        <td id="L155" class="blob-num js-line-number" data-line-number="155"></td> 
     1040        <td id="LC155" class="blob-code blob-code-inner js-file-line">        pars[<span class="pl-s"><span class="pl-pds">&#39;</span>scale<span class="pl-pds">&#39;</span></span>], pars[<span class="pl-s"><span class="pl-pds">&#39;</span>background<span class="pl-pds">&#39;</span></span>] <span class="pl-k">=</span> <span class="pl-c1">1</span>, <span class="pl-c1">0</span></td> 
     1041      </tr> 
     1042      <tr> 
     1043        <td id="L156" class="blob-num js-line-number" data-line-number="156"></td> 
     1044        <td id="LC156" class="blob-code blob-code-inner js-file-line"> 
     1045</td> 
     1046      </tr> 
     1047</table> 
     1048 
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  • sasmodels/core.py

    rcde11f0f rd18582e  
    22Core model handling routines. 
    33""" 
    4 __all__ = ["list_models", "load_model_definition",  "precompile_dll", 
    5            "load_model", "make_kernel", "call_kernel", "call_ER", "call_VR" ] 
    64 
    75from os.path import basename, dirname, join as joinpath 
    … …  
    2220    HAVE_OPENCL = False 
    2321 
     22__all__ = [ 
     23    "list_models", "load_model_definition", "precompile_dll", 
     24    "load_model", "make_kernel", "call_kernel", "call_ER", "call_VR", 
     25] 
    2426 
    2527def list_models(): 
    … …  
    3638    """ 
    3739    Load a model definition given the model name. 
     40 
     41    This returns a handle to the module defining the model.  This can be 
     42    used with functions in generate to build the docs or extract model info. 
    3843    """ 
    3944    __import__('sasmodels.models.'+model_name) 
    … …  
    6166 
    6267def isstr(s): 
     68    """ 
     69    Return True if *s* is a string-like object. 
     70    """ 
    6371    try: s + '' 
    6472    except: return False 
    6573    return True 
    6674 
    67 def load_model(model_definition, dtype="single", platform="ocl"): 
     75def load_model(model_definition, dtype=None, platform="ocl"): 
    6876    """ 
    6977    Prepare the model for the default execution platform. 
    … …  
    7987    for the calculation. Any valid numpy single or double precision identifier 
    8088    is valid, such as 'single', 'f', 'f32', or np.float32 for single, or 
    81     'double', 'd', 'f64'  and np.float64 for double. 
     89    'double', 'd', 'f64'  and np.float64 for double.  If *None*, then use 
     90    'single' unless the model defines single=False. 
    8291 
    8392    *platform* should be "dll" to force the dll to be used for C models, 
    … …  
    8695    if isstr(model_definition): 
    8796        model_definition = load_model_definition(model_definition) 
     97    if dtype is None: 
     98        dtype = 'single' if getattr(model_definition, 'single', True) else 'double' 
    8899    source, info = generate.make(model_definition) 
    89100    if callable(info.get('Iq', None)): 
    … …  
    99110    # source = open(info['name']+'.cl','r').read() 
    100111 
    101     if (platform=="dll" 
     112    if (platform == "dll" 
    102113            or not HAVE_OPENCL 
    103114            or not kernelcl.environment().has_type(dtype)): 
    … …  
    110121    Return a computation kernel from the model definition and the q input. 
    111122    """ 
    112     model_input = model.make_input(q_vectors) 
    113     return model(model_input) 
     123    return model(q_vectors) 
    114124 
    115125def get_weights(info, pars, name): 
    … …  
    128138    width = pars.get(name+'_pd', 0.0) 
    129139    nsigma = pars.get(name+'_pd_nsigma', 3.0) 
    130     value,weight = weights.get_weights( 
     140    value, weight = weights.get_weights( 
    131141        disperser, npts, width, nsigma, value, limits, relative) 
    132142    return value, weight / np.sum(weight) 
    … …  
    195205                    for name in info['partype']['volume']] 
    196206        value, weight = dispersion_mesh(vol_pars) 
    197         whole,part = VR(*value) 
     207        whole, part = VR(*value) 
    198208        return np.sum(weight*part)/np.sum(weight*whole) 
    199209 

  • sasmodels/data.py

    r013adb7 rd18582e  
    8787 
    8888class Data1D(object): 
     89    """ 
     90    1D data object. 
     91 
     92    Note that this definition matches the attributes from sasview, with 
     93    some generic 1D data vectors and some SAS specific definitions.  Some 
     94    refactoring to allow consistent naming conventions between 1D, 2D and 
     95    SESANS data would be helpful. 
     96 
     97    **Attributes** 
     98 
     99    *x*, *dx*: $q$ vector and gaussian resolution 
     100 
     101    *y*, *dy*: $I(q)$ vector and measurement uncertainty 
     102 
     103    *mask*: values to include in plotting/analysis 
     104 
     105    *dxl*: slit widths for slit smeared data, with *dx* ignored 
     106 
     107    *qmin*, *qmax*: range of $q$ values in *x* 
     108 
     109    *filename*: label for the data line 
     110 
     111    *_xaxis*, *_xunit*: label and units for the *x* axis 
     112 
     113    *_yaxis*, *_yunit*: label and units for the *y* axis 
     114    """ 
    89115    def __init__(self, x=None, y=None, dx=None, dy=None): 
    90116        self.x, self.y, self.dx, self.dy = x, y, dx, dy 
    91117        self.dxl = None 
     118        self.filename = None 
     119        self.qmin = x.min() if x is not None else np.NaN 
     120        self.qmax = x.max() if x is not None else np.NaN 
     121        # TODO: why is 1D mask False and 2D mask True? 
     122        self.mask = (np.isnan(y) if y is not None 
     123                     else np.zeros_like(x, 'b') if x is not None 
     124                     else None) 
     125        self._xaxis, self._xunit = "x", "" 
     126        self._yaxis, self._yunit = "y", "" 
    92127 
    93128    def xaxis(self, label, unit): 
    … …  
    108143 
    109144class Data2D(object): 
    110     def __init__(self): 
     145    """ 
     146    2D data object. 
     147 
     148    Note that this definition matches the attributes from sasview. Some 
     149    refactoring to allow consistent naming conventions between 1D, 2D and 
     150    SESANS data would be helpful. 
     151 
     152    **Attributes** 
     153 
     154    *qx_data*, *dqx_data*: $q_x$ matrix and gaussian resolution 
     155 
     156    *qy_data*, *dqy_data*: $q_y$ matrix and gaussian resolution 
     157 
     158    *data*, *err_data*: $I(q)$ matrix and measurement uncertainty 
     159 
     160    *mask*: values to exclude from plotting/analysis 
     161 
     162    *qmin*, *qmax*: range of $q$ values in *x* 
     163 
     164    *filename*: label for the data line 
     165 
     166    *_xaxis*, *_xunit*: label and units for the *x* axis 
     167 
     168    *_yaxis*, *_yunit*: label and units for the *y* axis 
     169 
     170    *_zaxis*, *_zunit*: label and units for the *y* axis 
     171 
     172    *Q_unit*, *I_unit*: units for Q and intensity 
     173 
     174    *x_bins*, *y_bins*: grid steps in *x* and *y* directions 
     175    """ 
     176    def __init__(self, x=None, y=None, z=None, dx=None, dy=None, dz=None): 
     177        self.qx_data, self.dqx_data = x, dx 
     178        self.qy_data, self.dqy_data = y, dy 
     179        self.data, self.err_data = z, dz 
     180        self.mask = (~np.isnan(z) if z is not None 
     181                     else np.ones_like(x) if x is not None 
     182                     else None) 
     183        self.q_data = np.sqrt(x**2 + y**2) 
     184        self.qmin = 1e-16 
     185        self.qmax = np.inf 
    111186        self.detector = [] 
    112187        self.source = Source() 
     188        self.Q_unit = "1/A" 
     189        self.I_unit = "1/cm" 
     190        self.xaxis("Q_x", "1/A") 
     191        self.yaxis("Q_y", "1/A") 
     192        self.zaxis("Intensity", "1/cm") 
     193        self._xaxis, self._xunit = "x", "" 
     194        self._yaxis, self._yunit = "y", "" 
     195        self._zaxis, self._zunit = "z", "" 
     196        self.x_bins, self.y_bins = None, None 
    113197 
    114198    def xaxis(self, label, unit): 
    … …  
    135219 
    136220class Vector(object): 
     221    """ 
     222    3-space vector of *x*, *y*, *z* 
     223    """ 
    137224    def __init__(self, x=None, y=None, z=None): 
    138225        self.x, self.y, self.z = x, y, z 
    139226 
    140227class Detector(object): 
     228    """ 
     229    Detector attributes. 
     230    """ 
     231    def __init__(self, pixel_size=(None, None), distance=None): 
     232        self.pixel_size = Vector(*pixel_size) 
     233        self.distance = distance 
     234 
     235class Source(object): 
     236    """ 
     237    Beam attributes. 
     238    """ 
    141239    def __init__(self): 
    142         self.pixel_size = Vector() 
    143  
    144 class Source(object): 
    145     pass 
    146  
    147  
    148 def empty_data1D(q, resolution=0.05): 
     240        self.wavelength = np.NaN 
     241        self.wavelength_unit = "A" 
     242 
     243 
     244def empty_data1D(q, resolution=0.0): 
    149245    """ 
    150246    Create empty 1D data using the given *q* as the x value. 
    … …  
    156252    #dIq = np.sqrt(Iq) 
    157253    Iq, dIq = None, None 
     254    q = np.asarray(q) 
    158255    data = Data1D(q, Iq, dx=resolution * q, dy=dIq) 
    159256    data.filename = "fake data" 
    160     data.qmin, data.qmax = q.min(), q.max() 
    161     data.mask = np.zeros(len(q), dtype='bool') 
    162257    return data 
    163258 
    164259 
    165 def empty_data2D(qx, qy=None, resolution=0.05): 
     260def empty_data2D(qx, qy=None, resolution=0.0): 
    166261    """ 
    167262    Create empty 2D data using the given mesh. 
    … …  
    173268    if qy is None: 
    174269        qy = qx 
     270    qx, qy = np.asarray(qx), np.asarray(qy) 
     271    # 5% dQ/Q resolution 
    175272    Qx, Qy = np.meshgrid(qx, qy) 
    176273    Qx, Qy = Qx.flatten(), Qy.flatten() 
    177274    Iq = 100 * np.ones_like(Qx) 
    178275    dIq = np.sqrt(Iq) 
    179     mask = np.ones(len(Iq), dtype='bool') 
    180  
    181     data = Data2D() 
    182     data.filename = "fake data" 
    183     data.qx_data = Qx 
    184     data.qy_data = Qy 
    185     data.data = Iq 
    186     data.err_data = dIq 
    187     data.mask = mask 
    188     data.qmin = 1e-16 
    189     data.qmax = np.inf 
    190  
    191     # 5% dQ/Q resolution 
    192276    if resolution != 0: 
    193277        # https://www.ncnr.nist.gov/staff/hammouda/distance_learning/chapter_15.pdf 
    … …  
    197281        # radial (which instead it should be inverse). 
    198282        Q = np.sqrt(Qx**2 + Qy**2) 
    199         data.dqx_data = resolution * Q 
    200         data.dqy_data = resolution * Q 
     283        dqx = resolution * Q 
     284        dqy = resolution * Q 
    201285    else: 
    202         data.dqx_data = data.dqy_data = None 
    203  
    204     detector = Detector() 
    205     detector.pixel_size.x = 5 # mm 
    206     detector.pixel_size.y = 5 # mm 
    207     detector.distance = 4 # m 
    208     data.detector.append(detector) 
     286        dqx = dqy = None 
     287 
     288    data = Data2D(x=Qx, y=Qy, z=Iq, dx=dqx, dy=dqy, dz=dIq) 
    209289    data.x_bins = qx 
    210290    data.y_bins = qy 
     291    data.filename = "fake data" 
     292 
     293    # pixel_size in mm, distance in m 
     294    detector = Detector(pixel_size=(5, 5), distance=4) 
     295    data.detector.append(detector) 
    211296    data.source.wavelength = 5 # angstroms 
    212297    data.source.wavelength_unit = "A" 
    213     data.Q_unit = "1/A" 
    214     data.I_unit = "1/cm" 
    215     data.q_data = np.sqrt(Qx ** 2 + Qy ** 2) 
    216     data.xaxis("Q_x", "A^{-1}") 
    217     data.yaxis("Q_y", "A^{-1}") 
    218     data.zaxis("Intensity", r"\text{cm}^{-1}") 
    219298    return data 
    220299 
    … …  
    223302    """ 
    224303    Plot data loaded by the sasview loader. 
     304 
     305    *data* is a sasview data object, either 1D, 2D or SESANS. 
     306 
     307    *view* is log or linear. 
     308 
     309    *limits* sets the intensity limits on the plot; if None then the limits 
     310    are inferred from the data. 
    225311    """ 
    226312    # Note: kind of weird using the plot result functions to plot just the 
    … …  
    228314    # do not repeat. 
    229315    if hasattr(data, 'lam'): 
    230         _plot_result_sesans(data, None, None, plot_data=True, limits=limits) 
     316        _plot_result_sesans(data, None, None, use_data=True, limits=limits) 
    231317    elif hasattr(data, 'qx_data'): 
    232         _plot_result2D(data, None, None, view, plot_data=True, limits=limits) 
     318        _plot_result2D(data, None, None, view, use_data=True, limits=limits) 
    233319    else: 
    234         _plot_result1D(data, None, None, view, plot_data=True, limits=limits) 
     320        _plot_result1D(data, None, None, view, use_data=True, limits=limits) 
    235321 
    236322 
    237323def plot_theory(data, theory, resid=None, view='log', 
    238                 plot_data=True, limits=None): 
     324                use_data=True, limits=None): 
     325    """ 
     326    Plot theory calculation. 
     327 
     328    *data* is needed to define the graph properties such as labels and 
     329    units, and to define the data mask. 
     330 
     331    *theory* is a matrix of the same shape as the data. 
     332 
     333    *view* is log or linear 
     334 
     335    *use_data* is True if the data should be plotted as well as the theory. 
     336 
     337    *limits* sets the intensity limits on the plot; if None then the limits 
     338    are inferred from the data. 
     339    """ 
    239340    if hasattr(data, 'lam'): 
    240         _plot_result_sesans(data, theory, resid, plot_data=True, limits=limits) 
     341        _plot_result_sesans(data, theory, resid, use_data=True, limits=limits) 
    241342    elif hasattr(data, 'qx_data'): 
    242         _plot_result2D(data, theory, resid, view, plot_data, limits=limits) 
     343        _plot_result2D(data, theory, resid, view, use_data, limits=limits) 
    243344    else: 
    244         _plot_result1D(data, theory, resid, view, plot_data, limits=limits) 
     345        _plot_result1D(data, theory, resid, view, use_data, limits=limits) 
    245346 
    246347 
    247348def protect(fn): 
     349    """ 
     350    Decorator to wrap calls in an exception trapper which prints the 
     351    exception and continues.  Keyboard interrupts are ignored. 
     352    """ 
    248353    def wrapper(*args, **kw): 
     354        """ 
     355        Trap and print errors from function. 
     356        """ 
    249357        try: 
    250358            return fn(*args, **kw) 
     359        except KeyboardInterrupt: 
     360            raise 
    251361        except: 
    252362            traceback.print_exc() 
    253             pass 
    254363 
    255364    return wrapper 
    … …  
    257366 
    258367@protect 
    259 def _plot_result1D(data, theory, resid, view, plot_data, limits=None): 
     368def _plot_result1D(data, theory, resid, view, use_data, limits=None): 
    260369    """ 
    261370    Plot the data and residuals for 1D data. 
    … …  
    264373    from numpy.ma import masked_array, masked 
    265374 
    266     plot_theory = theory is not None 
    267     plot_resid = resid is not None 
    268  
    269     if data.y is None: 
    270         plot_data = False 
     375    use_data = use_data and data.y is not None 
     376    use_theory = theory is not None 
     377    use_resid = resid is not None 
     378    num_plots = (use_data or use_theory) + use_resid 
     379 
    271380    scale = data.x**4 if view == 'q4' else 1.0 
    272381 
    273     if plot_data or plot_theory: 
    274         if plot_resid: 
    275             plt.subplot(121) 
    276  
     382    if use_data or use_theory: 
    277383        #print(vmin, vmax) 
    278384        all_positive = True 
    279385        some_present = False 
    280         if plot_data: 
     386        if use_data: 
    281387            mdata = masked_array(data.y, data.mask.copy()) 
    282388            mdata[~np.isfinite(mdata)] = masked 
    … …  
    284390                mdata[mdata <= 0] = masked 
    285391            plt.errorbar(data.x/10, scale*mdata, yerr=data.dy, fmt='.') 
    286             all_positive = all_positive and (mdata>0).all() 
     392            all_positive = all_positive and (mdata > 0).all() 
    287393            some_present = some_present or (mdata.count() > 0) 
    288394 
    289395 
    290         if plot_theory: 
     396        if use_theory: 
    291397            mtheory = masked_array(theory, data.mask.copy()) 
    292398            mtheory[~np.isfinite(mtheory)] = masked 
    293399            if view is 'log': 
    294                 mtheory[mtheory<=0] = masked 
     400                mtheory[mtheory <= 0] = masked 
    295401            plt.plot(data.x/10, scale*mtheory, '-', hold=True) 
    296             all_positive = all_positive and (mtheory>0).all() 
     402            all_positive = all_positive and (mtheory > 0).all() 
    297403            some_present = some_present or (mtheory.count() > 0) 
    298404 
    299405        if limits is not None: 
    300406            plt.ylim(*limits) 
     407 
     408        if num_plots > 1: 
     409            plt.subplot(1, num_plots, 1) 
    301410        plt.xscale('linear' if not some_present else view) 
    302411        plt.yscale('linear' 
    … …  
    306415        plt.ylabel('$I(q)$') 
    307416 
    308     if plot_resid: 
    309         if plot_data or plot_theory: 
    310             plt.subplot(122) 
    311  
     417    if use_resid: 
    312418        mresid = masked_array(resid, data.mask.copy()) 
    313419        mresid[~np.isfinite(mresid)] = masked 
    314420        some_present = (mresid.count() > 0) 
     421 
     422        if num_plots > 1: 
     423            plt.subplot(1, num_plots, (use_data or use_theory) + 1) 
    315424        plt.plot(data.x/10, mresid, '-') 
    316425        plt.xlabel("$q$/nm$^{-1}$") 
    … …  
    320429 
    321430@protect 
    322 def _plot_result_sesans(data, theory, resid, plot_data, limits=None): 
     431def _plot_result_sesans(data, theory, resid, use_data, limits=None): 
     432    """ 
     433    Plot SESANS results. 
     434    """ 
    323435    import matplotlib.pyplot as plt 
    324     if data.y is None: 
    325         plot_data = False 
    326     plot_theory = theory is not None 
    327     plot_resid = resid is not None 
    328  
    329     if plot_data or plot_theory: 
    330         if plot_resid: 
    331             plt.subplot(121) 
    332         if plot_data: 
     436    use_data = use_data and data.y is not None 
     437    use_theory = theory is not None 
     438    use_resid = resid is not None 
     439    num_plots = (use_data or use_theory) + use_resid 
     440 
     441    if use_data or use_theory: 
     442        if num_plots > 1: 
     443            plt.subplot(1, num_plots, 1) 
     444        if use_data: 
    333445            plt.errorbar(data.x, data.y, yerr=data.dy) 
    334446        if theory is not None: 
    … …  
    340452 
    341453    if resid is not None: 
    342         if plot_data or plot_theory: 
    343             plt.subplot(122) 
    344  
     454        if num_plots > 1: 
     455            plt.subplot(1, num_plots, (use_data or use_theory) + 1) 
    345456        plt.plot(data.x, resid, 'x') 
    346457        plt.xlabel('spin echo length (nm)') 
    … …  
    349460 
    350461@protect 
    351 def _plot_result2D(data, theory, resid, view, plot_data, limits=None): 
     462def _plot_result2D(data, theory, resid, view, use_data, limits=None): 
    352463    """ 
    353464    Plot the data and residuals for 2D data. 
    354465    """ 
    355466    import matplotlib.pyplot as plt 
    356     if data.data is None: 
    357         plot_data = False 
    358     plot_theory = theory is not None 
    359     plot_resid = resid is not None 
     467    use_data = use_data and data.data is not None 
     468    use_theory = theory is not None 
     469    use_resid = resid is not None 
     470    num_plots = use_data + use_theory + use_resid 
    360471 
    361472    # Put theory and data on a common colormap scale 
    362     if limits is None: 
    363         vmin, vmax = np.inf, -np.inf 
    364         if plot_data: 
    365             target = data.data[~data.mask] 
    366             datamin = target[target>0].min() if view == 'log' else target.min() 
    367             datamax = target.max() 
    368             vmin = min(vmin, datamin) 
    369             vmax = max(vmax, datamax) 
    370         if plot_theory: 
    371             theorymin = theory[theory>0].min() if view=='log' else theory.min() 
    372             theorymax = theory.max() 
    373             vmin = min(vmin, theorymin) 
    374             vmax = max(vmax, theorymax) 
    375     else: 
     473    vmin, vmax = np.inf, -np.inf 
     474    if use_data: 
     475        target = data.data[~data.mask] 
     476        datamin = target[target > 0].min() if view == 'log' else target.min() 
     477        datamax = target.max() 
     478        vmin = min(vmin, datamin) 
     479        vmax = max(vmax, datamax) 
     480    if use_theory: 
     481        theorymin = theory[theory > 0].min() if view == 'log' else theory.min() 
     482        theorymax = theory.max() 
     483        vmin = min(vmin, theorymin) 
     484        vmax = max(vmax, theorymax) 
     485 
     486    # Override data limits from the caller 
     487    if limits is not None: 
    376488        vmin, vmax = limits 
    377489 
    378     if plot_data: 
    379         if plot_theory and plot_resid: 
    380             plt.subplot(131) 
    381         elif plot_theory or plot_resid: 
    382             plt.subplot(121) 
     490    # Plot data 
     491    if use_data: 
     492        if num_plots > 1: 
     493            plt.subplot(1, num_plots, 1) 
    383494        _plot_2d_signal(data, target, view=view, vmin=vmin, vmax=vmax) 
    384495        plt.title('data') 
    … …  
    386497        h.set_label('$I(q)$') 
    387498 
    388     if plot_theory: 
    389         if plot_data and plot_resid: 
    390             plt.subplot(132) 
    391         elif plot_data: 
    392             plt.subplot(122) 
    393         elif plot_resid: 
    394             plt.subplot(121) 
     499    # plot theory 
     500    if use_theory: 
     501        if num_plots > 1: 
     502            plt.subplot(1, num_plots, use_data+1) 
    395503        _plot_2d_signal(data, theory, view=view, vmin=vmin, vmax=vmax) 
    396504        plt.title('theory') 
    397505        h = plt.colorbar() 
    398         h.set_label(r'$\log_{10}I(q)$' if view=='log' 
     506        h.set_label(r'$\log_{10}I(q)$' if view == 'log' 
    399507                    else r'$q^4 I(q)$' if view == 'q4' 
    400508                    else '$I(q)$') 
    401509 
    402     #if plot_data or plot_theory: 
    403     #    plt.colorbar() 
    404  
    405     if plot_resid: 
    406         if plot_data and plot_theory: 
    407             plt.subplot(133) 
    408         elif plot_data or plot_theory: 
    409             plt.subplot(122) 
     510    # plot resid 
     511    if use_resid: 
     512        if num_plots > 1: 
     513            plt.subplot(1, num_plots, use_data+use_theory+1) 
    410514        _plot_2d_signal(data, resid, view='linear') 
    411515        plt.title('residuals') 
    412516        h = plt.colorbar() 
    413         h.set_label('$\Delta I(q)$') 
     517        h.set_label(r'$\Delta I(q)$') 
    414518 
    415519 
    … …  
    456560 
    457561def demo(): 
     562    """ 
     563    Load and plot a SAS dataset. 
     564    """ 
    458565    data = load_data('DEC07086.DAT') 
    459566    set_beam_stop(data, 0.004) 

  • sasmodels/direct_model.py

    r9404dd3 rd18582e  
    1 import warnings 
     1""" 
     2Class interface to the model calculator. 
     3 
     4Calling a model is somewhat non-trivial since the functions called depend 
     5on the data type.  For 1D data the *Iq* kernel needs to be called, for 
     62D data the *Iqxy* kernel needs to be called, and for SESANS data the 
     7*Iq* kernel needs to be called followed by a Hankel transform.  Before 
     8the kernel is called an appropriate *q* calculation vector needs to be 
     9constructed.  This is not the simple *q* vector where you have measured 
     10the data since the resolution calculation will require values beyond the 
     11range of the measured data.  After the calculation the resolution calculator 
     12must be called to return the predicted value for each measured data point. 
     13 
     14:class:`DirectModel` is a callable object that takes *parameter=value* 
     15keyword arguments and returns the appropriate theory values for the data. 
     16 
     17:class:`DataMixin` does the real work of interpreting the data and calling 
     18the model calculator.  This is used by :class:`DirectModel`, which uses 
     19direct parameter values and by :class:`bumps_model.Experiment` which wraps 
     20the parameter values in boxes so that the user can set fitting ranges, etc. 
     21on the individual parameters and send the model to the Bumps optimizers. 
     22""" 
     23from __future__ import print_function 
    224 
    325import numpy as np 
    … …  
    1941    currently used by :class:`sasview_model.SasviewModel` since this will 
    2042    require a number of changes to SasView before we can do it. 
     43 
     44    :meth:`_interpret_data` initializes the data structures necessary 
     45    to manage the calculations.  This sets attributes in the child class 
     46    such as *data_type* and *resolution*. 
     47 
     48    :meth:`_calc_theory` evaluates the model at the given control values. 
     49 
     50    :meth:`_set_data` sets the intensity data in the data object, 
     51    possibly with random noise added.  This is useful for simulating a 
     52    dataset with the results from :meth:`_calc_theory`. 
    2153    """ 
    2254    def _interpret_data(self, data, model): 
     55        # pylint: disable=attribute-defined-outside-init 
     56 
    2357        self._data = data 
    2458        self._model = model 
    … …  
    3670        if self.data_type == 'sesans': 
    3771            q = sesans.make_q(data.sample.zacceptance, data.Rmax) 
    38             self.index = slice(None, None) 
     72            index = slice(None, None) 
     73            res = None 
    3974            if data.y is not None: 
    40                 self.Iq = data.y 
    41                 self.dIq = data.dy 
     75                Iq, dIq = data.y, data.dy 
     76            else: 
     77                Iq, dIq = None, None 
    4278            #self._theory = np.zeros_like(q) 
    4379            q_vectors = [q] 
    … …  
    4985            qmax = getattr(data, 'qmax', np.inf) 
    5086            accuracy = getattr(data, 'accuracy', 'Low') 
    51             self.index = ~data.mask & (q >= qmin) & (q <= qmax) 
     87            index = ~data.mask & (q >= qmin) & (q <= qmax) 
    5288            if data.data is not None: 
    53                 self.index &= ~np.isnan(data.data) 
    54                 self.Iq = data.data[self.index] 
    55                 self.dIq = data.err_data[self.index] 
    56             self.resolution = resolution2d.Pinhole2D(data=data, index=self.index, 
    57                                                      nsigma=3.0, accuracy=accuracy) 
     89                index &= ~np.isnan(data.data) 
     90                Iq = data.data[index] 
     91                dIq = data.err_data[index] 
     92            else: 
     93                Iq, dIq = None, None 
     94            res = resolution2d.Pinhole2D(data=data, index=index, 
     95                                         nsigma=3.0, accuracy=accuracy) 
    5896            #self._theory = np.zeros_like(self.Iq) 
    59             q_vectors = self.resolution.q_calc 
     97            q_vectors = res.q_calc 
    6098        elif self.data_type == 'Iq': 
    61             self.index = (data.x >= data.qmin) & (data.x <= data.qmax) 
     99            index = (data.x >= data.qmin) & (data.x <= data.qmax) 
    62100            if data.y is not None: 
    63                 self.index &= ~np.isnan(data.y) 
    64                 self.Iq = data.y[self.index] 
    65                 self.dIq = data.dy[self.index] 
     101                index &= ~np.isnan(data.y) 
     102                Iq = data.y[index] 
     103                dIq = data.dy[index] 
     104            else: 
     105                Iq, dIq = None, None 
    66106            if getattr(data, 'dx', None) is not None: 
    67                 q, dq = data.x[self.index], data.dx[self.index] 
    68                 if (dq>0).any(): 
    69                     self.resolution = resolution.Pinhole1D(q, dq) 
     107                q, dq = data.x[index], data.dx[index] 
     108                if (dq > 0).any(): 
     109                    res = resolution.Pinhole1D(q, dq) 
    70110                else: 
    71                     self.resolution = resolution.Perfect1D(q) 
    72             elif (getattr(data, 'dxl', None) is not None and 
    73                           getattr(data, 'dxw', None) is not None): 
    74                 self.resolution = resolution.Slit1D(data.x[self.index], 
    75                                                     width=data.dxh[self.index], 
    76                                                     height=data.dxw[self.index]) 
    77             else: 
    78                 self.resolution = resolution.Perfect1D(data.x[self.index]) 
     111                    res = resolution.Perfect1D(q) 
     112            elif (getattr(data, 'dxl', None) is not None 
     113                  and getattr(data, 'dxw', None) is not None): 
     114                res = resolution.Slit1D(data.x[index], 
     115                                        width=data.dxh[index], 
     116                                        height=data.dxw[index]) 
     117            else: 
     118                res = resolution.Perfect1D(data.x[index]) 
    79119 
    80120            #self._theory = np.zeros_like(self.Iq) 
    81             q_vectors = [self.resolution.q_calc] 
     121            q_vectors = [res.q_calc] 
    82122        else: 
    83123            raise ValueError("Unknown data type") # never gets here 
    … …  
    87127        self._kernel_inputs = [v for v in q_vectors] 
    88128        self._kernel = None 
     129        self.Iq, self.dIq, self.index = Iq, dIq, index 
     130        self.resolution = res 
    89131 
    90132    def _set_data(self, Iq, noise=None): 
     133        # pylint: disable=attribute-defined-outside-init 
    91134        if noise is not None: 
    92135            self.dIq = Iq*noise*0.01 
    … …  
    106149    def _calc_theory(self, pars, cutoff=0.0): 
    107150        if self._kernel is None: 
    108             q_input = self._model.make_input(self._kernel_inputs) 
    109             self._kernel = self._model(q_input) 
     151            self._kernel = make_kernel(self._model, self._kernel_inputs)  # pylint: disable=attribute-defined-outside-init 
    110152 
    111153        Iq_calc = call_kernel(self._kernel, pars, cutoff=cutoff) 
    … …  
    120162 
    121163class DirectModel(DataMixin): 
     164    """ 
     165    Create a calculator object for a model. 
     166 
     167    *data* is 1D SAS, 2D SAS or SESANS data 
     168 
     169    *model* is a model calculator return from :func:`generate.load_model` 
     170 
     171    *cutoff* is the polydispersity weight cutoff. 
     172    """ 
    122173    def __init__(self, data, model, cutoff=1e-5): 
    123174        self.model = model 
    124175        self.cutoff = cutoff 
     176        # Note: _interpret_data defines the model attributes 
    125177        self._interpret_data(data, model) 
    126         self.kernel = make_kernel(self.model, self._kernel_inputs) 
     178 
    127179    def __call__(self, **pars): 
    128180        return self._calc_theory(pars, cutoff=self.cutoff) 
     181 
    129182    def ER(self, **pars): 
     183        """ 
     184        Compute the equivalent radius for the model. 
     185 
     186        Return 0. if not defined. 
     187        """ 
    130188        return call_ER(self.model.info, pars) 
     189 
    131190    def VR(self, **pars): 
     191        """ 
     192        Compute the equivalent volume for the model, including polydispersity 
     193        effects. 
     194 
     195        Return 1. if not defined. 
     196        """ 
    132197        return call_VR(self.model.info, pars) 
     198 
    133199    def simulate_data(self, noise=None, **pars): 
     200        """ 
     201        Generate simulated data for the model. 
     202        """ 
    134203        Iq = self.__call__(**pars) 
    135204        self._set_data(Iq, noise=noise) 
    136205 
    137 def demo(): 
     206def main(): 
     207    """ 
     208    Program to evaluate a particular model at a set of q values. 
     209    """ 
    138210    import sys 
    139211    from .data import empty_data1D, empty_data2D 
    … …  
    144216    model_name = sys.argv[1] 
    145217    call = sys.argv[2].upper() 
    146     if call not in ("ER","VR"): 
     218    if call not in ("ER", "VR"): 
    147219        try: 
    148220            values = [float(v) for v in call.split(',')] 
    … …  
    153225            data = empty_data1D([q]) 
    154226        elif len(values) == 2: 
    155             qx,qy = values 
    156             data = empty_data2D([qx],[qy]) 
     227            qx, qy = values 
     228            data = empty_data2D([qx], [qy]) 
    157229        else: 
    158230            print("use q or qx,qy or ER or VR") 
    … …  
    162234 
    163235    model_definition = load_model_definition(model_name) 
    164     model = load_model(model_definition, dtype='single') 
     236    model = load_model(model_definition) 
    165237    calculator = DirectModel(data, model) 
    166     pars = dict((k,float(v)) 
     238    pars = dict((k, float(v)) 
    167239                for pair in sys.argv[3:] 
    168                 for k,v in [pair.split('=')]) 
     240                for k, v in [pair.split('=')]) 
    169241    if call == "ER": 
    170242        print(calculator.ER(**pars)) 
    … …  
    176248 
    177249if __name__ == "__main__": 
    178     demo() 
     250    main() 

  • sasmodels/exception.py

    reaca9eb r823e620  
    88except NameError: 
    99    class WindowsError(Exception): 
     10        """ 
     11        Fake WindowsException when not on Windows. 
     12        """ 
    1013        pass 
    1114 
    … …  
    3134    # TODO: try to incorporate current stack trace in the raised exception 
    3235    if isinstance(exc, WindowsError): 
    33         raise OSError(str(exc)+" "+msg) 
     36        raise OSError(str(exc) + " " + msg) 
    3437 
    3538    args = exc.args 
    … …  
    3841    else: 
    3942        try: 
    40             arg0 = " ".join((args[0],msg)) 
     43            arg0 = " ".join((args[0], msg)) 
    4144            exc.args = tuple([arg0] + list(args[1:])) 
    4245        except: 
    43             exc.args = (" ".join((str(exc),msg)),) 
     46            exc.args = (" ".join((str(exc), msg)),) 

  • sasmodels/generate.py

    re66c9f9 reafc9fa  
    77    particular dimensions averaged over all orientations. 
    88 
    9     *Iqxy(qx, qy, p1, p2, ...)* returns the scattering at qx,qy for a form 
     9    *Iqxy(qx, qy, p1, p2, ...)* returns the scattering at qx, qy for a form 
    1010    with particular dimensions for a single orientation. 
    1111 
    … …  
    4747the *sin* and *cos* values are needed for a particular argument.  Since 
    4848this function does not exist in C99, all use of *sincos* should be 
    49 replaced by the macro *SINCOS(value,sn,cn)* where *sn* and *cn* are 
     49replaced by the macro *SINCOS(value, sn, cn)* where *sn* and *cn* are 
    5050previously declared *double* variables.  When compiled for systems without 
    5151OpenCL, *SINCOS* will be replaced by *sin* and *cos* calls.   If *value* is 
    … …  
    194194returns a list of files required by the model. 
    195195""" 
     196from __future__ import print_function 
    196197 
    197198# TODO: identify model files which have changed since loading and reload them. 
    198  
    199 __all__ = ["make", "doc", "sources", "convert_type"] 
    200199 
    201200import sys 
    … …  
    206205 
    207206import numpy as np 
     207 
     208__all__ = ["make", "doc", "sources", "convert_type"] 
     209 
    208210C_KERNEL_TEMPLATE_PATH = joinpath(dirname(__file__), 'kernel_template.c') 
    209211 
    … …  
    216218    F128 = None 
    217219 
    218  
    219220# Scale and background, which are parameters common to every form factor 
    220221COMMON_PARAMETERS = [ 
    … …  
    222223    ["background", "1/cm", 0, [0, np.inf], "", "Source background"], 
    223224    ] 
    224  
    225225 
    226226# Conversion from units defined in the parameter table for each model 
    … …  
    266266 
    267267def format_units(units): 
     268    """ 
     269    Convert units into ReStructured Text format. 
     270    """ 
    268271    return "string" if isinstance(units, list) else RST_UNITS.get(units, units) 
    269272 
    … …  
    310313    raise ValueError("%r not found in %s" % (filename, search_path)) 
    311314 
    312 def sources(info): 
     315def model_sources(info): 
    313316    """ 
    314317    Return a list of the sources file paths for the module. 
    … …  
    335338    """ 
    336339    Convert code from double precision to the desired type. 
     340 
     341    Floating point constants are tagged with 'f' for single precision or 'L' 
     342    for long double precision. 
    337343    """ 
    338344    if dtype == F16: 
    … …  
    350356 
    351357def _convert_type(source, type_name, constant_flag): 
     358    """ 
     359    Replace 'double' with *type_name* in *source*, tagging floating point 
     360    constants with *constant_flag*. 
     361    """ 
    352362    # Convert double keyword to float/long double/half. 
    353363    # Accept an 'n' # parameter for vector # values, where n is 2, 4, 8 or 16. 
    … …  
    363373 
    364374 
    365 def kernel_name(info, is_2D): 
     375def kernel_name(info, is_2d): 
    366376    """ 
    367377    Name of the exported kernel symbol. 
    368378    """ 
    369     return info['name'] + "_" + ("Iqxy" if is_2D else "Iq") 
     379    return info['name'] + "_" + ("Iqxy" if is_2d else "Iq") 
    370380 
    371381 
    … …  
    410420 
    411421 
    412 def build_polydispersity_loops(pd_pars): 
    413     """ 
    414     Build polydispersity loops 
    415  
    416     Returns loop opening and loop closing 
    417     """ 
    418     LOOP_OPEN = """\ 
     422LOOP_OPEN = """\ 
    419423for (int %(name)s_i=0; %(name)s_i < N%(name)s; %(name)s_i++) { 
    420424  const double %(name)s = loops[2*(%(name)s_i%(offset)s)]; 
    421425  const double %(name)s_w = loops[2*(%(name)s_i%(offset)s)+1];\ 
    422426""" 
     427def build_polydispersity_loops(pd_pars): 
     428    """ 
     429    Build polydispersity loops 
     430 
     431    Returns loop opening and loop closing 
     432    """ 
    423433    depth = 4 
    424434    offset = "" 
    … …  
    455465 
    456466    # Load additional sources 
    457     source = [open(f).read() for f in sources(info)] 
     467    source = [open(f).read() for f in model_sources(info)] 
    458468 
    459469    # Prepare defines 
    … …  
    563573 
    564574    #for d in defines: print(d) 
    565     DEFINES = '\n'.join('#define %s %s' % (k, v) for k, v in defines) 
    566     SOURCES = '\n\n'.join(source) 
     575    defines = '\n'.join('#define %s %s' % (k, v) for k, v in defines) 
     576    sources = '\n\n'.join(source) 
    567577    return C_KERNEL_TEMPLATE % { 
    568         'DEFINES':DEFINES, 
    569         'SOURCES':SOURCES, 
     578        'DEFINES': defines, 
     579        'SOURCES': sources, 
    570580        } 
    571581 
    … …  
    581591    demo_parameters = getattr(kernel_module, 'demo', None) 
    582592    if demo_parameters is None: 
    583         demo_parameters = dict((p[0],p[2]) for p in parameters) 
     593        demo_parameters = dict((p[0], p[2]) for p in parameters) 
    584594    filename = abspath(kernel_module.__file__) 
    585595    kernel_id = splitext(basename(filename))[0] 
    … …  
    588598        name = " ".join(w.capitalize() for w in kernel_id.split('_')) 
    589599    info = dict( 
    590         id = kernel_id,  # string used to load the kernel 
     600        id=kernel_id,  # string used to load the kernel 
    591601        filename=abspath(kernel_module.__file__), 
    592602        name=name, 
    … …  
    625635                            %re.escape(string.punctuation)) 
    626636def _convert_section_titles_to_boldface(lines): 
     637    """ 
     638    Do the actual work of identifying and converting section headings. 
     639    """ 
    627640    prior = None 
    628641    for line in lines: 
    … …  
    631644        elif section_marker.match(line): 
    632645            if len(line) >= len(prior): 
    633                 yield "".join( ("**",prior,"**") ) 
     646                yield "".join(("**", prior, "**")) 
    634647                prior = None 
    635648            else: 
    … …  
    642655        yield prior 
    643656 
    644 def convert_section_titles_to_boldface(string): 
    645     return "\n".join(_convert_section_titles_to_boldface(string.split('\n'))) 
     657def convert_section_titles_to_boldface(s): 
     658    """ 
     659    Use explicit bold-face rather than section headings so that the table of 
     660    contents is not polluted with section names from the model documentation. 
     661 
     662    Sections are identified as the title line followed by a line of punctuation 
     663    at least as long as the title line. 
     664    """ 
     665    return "\n".join(_convert_section_titles_to_boldface(s.split('\n'))) 
    646666 
    647667def doc(kernel_module): 
    … …  
    666686 
    667687def demo_time(): 
     688    """ 
     689    Show how long it takes to process a model. 
     690    """ 
    668691    from .models import cylinder 
    669692    import datetime 
    … …  
    674697 
    675698def main(): 
     699    """ 
     700    Program which prints the source produced by the model. 
     701    """ 
    676702    if len(sys.argv) <= 1: 
    677703        print("usage: python -m sasmodels.generate modelname") 

  • sasmodels/kernel_template.c

    r9c79c32 r840b859  
    1616     using namespace std; 
    1717     #if defined(_MSC_VER) 
    18      #   define kernel extern "C" __declspec( dllexport ) 
     18         #include <limits> 
     19         #include <float.h> 
     20         #define kernel extern "C" __declspec( dllexport ) 
    1921         inline double trunc(double x) { return x>=0?floor(x):-floor(-x); } 
    20          inline double fmin(double x, double y) { return x>y ? y : x; } 
    21          inline double fmax(double x, double y) { return x<y ? y : x; } 
     22             inline double fmin(double x, double y) { return x>y ? y : x; } 
     23             inline double fmax(double x, double y) { return x<y ? y : x; } 
     24             inline double isnan(double x) { return _isnan(x); } 
     25             #define NAN (std::numeric_limits<double>::quiet_NaN()) // non-signalling NaN 
     26             static double cephes_expm1(double x) { 
     27                 // Adapted from the cephes math library. 
     28                 // Copyright 1984 - 1992 by Stephen L. Moshier 
     29                 if (x != x || x == 0.0) { 
     30                     return x; // NaN and +/- 0 
     31                 } else if (x < -0.5 || x > 0.5) { 
     32                     return exp(x) - 1.0; 
     33                 } else { 
     34                     const double xsq = x*x; 
     35                     const double p = ((( 
     36                          +1.2617719307481059087798E-4)*xsq 
     37                      +3.0299440770744196129956E-2)*xsq 
     38                      +9.9999999999999999991025E-1); 
     39                 const double q = (((( 
     40                      +3.0019850513866445504159E-6)*xsq 
     41                      +2.5244834034968410419224E-3)*xsq 
     42                      +2.2726554820815502876593E-1)*xsq 
     43                      +2.0000000000000000000897E0); 
     44                 double r = x * p; 
     45                     r =  r / (q - r); 
     46                     return r+r; 
     47                 } 
     48             } 
     49             #define expm1 cephes_expm1 
    2250     #else 
    23      #   define kernel extern "C" 
     51         #define kernel extern "C" 
    2452     #endif 
    2553     inline void SINCOS(double angle, double &svar, double &cvar) { svar=sin(angle); cvar=cos(angle); } 

  • sasmodels/kernelcl.py

    rcde11f0f re6a5556  
    11""" 
    2 GPU support through OpenCL 
     2GPU driver for C kernels 
    33 
    44There should be a single GPU environment running on the system.  This 
    … …  
    152152 
    153153 
    154 def make_result(self, size): 
    155     self.res = np.empty(size, dtype=self.dtype) 
    156     self.res_b = cl.Buffer(self.program.context, mf.READ_WRITE, self.res.nbytes) 
    157     return self.res, self.res_b 
    158  
    159  
    160154# for now, this returns one device in the context 
    161155# TODO: create a context that contains all devices on all platforms 
    … …  
    178172        #self.data_boundary = max(d.min_data_type_align_size 
    179173        #                         for d in self.context.devices) 
    180         self.queues = [cl.CommandQueue(self.context, d) 
    181                        for d in self.context.devices] 
     174        self.queues = [cl.CommandQueue(context, context.devices[0]) 
     175                       for context in self.context] 
    182176        self.compiled = {} 
    183177 
    184178    def has_type(self, dtype): 
     179        """ 
     180        Return True if all devices support a given type. 
     181        """ 
    185182        dtype = generate.F32 if dtype == 'fast' else np.dtype(dtype) 
    186         return all(has_type(d, dtype) for d in self.context.devices) 
     183        return any(has_type(d, dtype) 
     184                   for context in self.context 
     185                   for d in context.devices) 
     186 
     187    def get_queue(self, dtype): 
     188        """ 
     189        Return a command queue for the kernels of type dtype. 
     190        """ 
     191        for context, queue in zip(self.context, self.queues): 
     192            if all(has_type(d, dtype) for d in context.devices): 
     193                return queue 
     194 
     195    def get_context(self, dtype): 
     196        """ 
     197        Return a OpenCL context for the kernels of type dtype. 
     198        """ 
     199        for context, queue in zip(self.context, self.queues): 
     200            if all(has_type(d, dtype) for d in context.devices): 
     201                return context 
    187202 
    188203    def _create_some_context(self): 
     204        """ 
     205        Protected call to cl.create_some_context without interactivity.  Use 
     206        this if PYOPENCL_CTX is set in the environment.  Sets the *context* 
     207        attribute. 
     208        """ 
    189209        try: 
    190             self.context = cl.create_some_context(interactive=False) 
     210            self.context = [cl.create_some_context(interactive=False)] 
    191211        except Exception as exc: 
    192212            warnings.warn(str(exc)) 
    … …  
    195215 
    196216    def compile_program(self, name, source, dtype, fast=False): 
     217        """ 
     218        Compile the program for the device in the given context. 
     219        """ 
    197220        key = "%s-%s-%s"%(name, dtype, fast) 
    198221        if key not in self.compiled: 
    199222            #print("compiling",name) 
    200223            dtype = np.dtype(dtype) 
    201             program = compile_model(self.context, source, dtype, fast) 
     224            program = compile_model(self.get_context(dtype), source, dtype, fast) 
    202225            self.compiled[key] = program 
    203226        return self.compiled[key] 
    204227 
    205228    def release_program(self, name): 
     229        """ 
     230        Free memory associated with the program on the device. 
     231        """ 
    206232        if name in self.compiled: 
    207233            self.compiled[name].release() 
    … …  
    209235 
    210236def _get_default_context(): 
    211     default = None 
     237    """ 
     238    Get an OpenCL context, preferring GPU over CPU, and preferring Intel 
     239    drivers over AMD drivers. 
     240    """ 
     241    # Note: on mobile devices there is automatic clock scaling if either the 
     242    # CPU or the GPU is underutilized; probably doesn't affect us, but we if 
     243    # it did, it would mean that putting a busy loop on the CPU while the GPU 
     244    # is running may increase throughput. 
     245    # 
     246    # Macbook pro, base install: 
     247    #     {'Apple': [Intel CPU, NVIDIA GPU]} 
     248    # Macbook pro, base install: 
     249    #     {'Apple': [Intel CPU, Intel GPU]} 
     250    # 2 x nvidia 295 with Intel and NVIDIA opencl drivers installed 
     251    #     {'Intel': [CPU], 'NVIDIA': [GPU, GPU, GPU, GPU]} 
     252    gpu, cpu = None, None 
    212253    for platform in cl.get_platforms(): 
     254        # AMD provides a much weaker CPU driver than Intel/Apple, so avoid it. 
     255        # If someone has bothered to install the AMD/NVIDIA drivers, prefer them over the integrated 
     256        # graphics driver that may have been supplied with the CPU chipset. 
     257        preferred_cpu = platform.vendor.startswith('Intel') or platform.vendor.startswith('Apple') 
     258        preferred_gpu = platform.vendor.startswith('Advanced') or platform.vendor.startswith('NVIDIA') 
    213259        for device in platform.get_devices(): 
    214260            if device.type == cl.device_type.GPU: 
    215                 return cl.Context([device]) 
    216             if default is None: 
    217                 default = device 
    218  
    219     if not default: 
    220         raise RuntimeError("OpenCL device not found") 
    221  
    222     return cl.Context([default]) 
     261                # If the existing type is not GPU then it will be CUSTOM or ACCELERATOR, 
     262                # so don't override it. 
     263                if gpu is None or (preferred_gpu and gpu.type == cl.device_type.GPU): 
     264                    gpu = device 
     265            elif device.type == cl.device_type.CPU: 
     266                if cpu is None or preferred_cpu: 
     267                    cpu = device 
     268            else: 
     269                # System has cl.device_type.ACCELERATOR or cl.device_type.CUSTOM 
     270                # Intel Phi for example registers as an accelerator 
     271                # Since the user installed a custom device on their system and went through the 
     272                # pain of sorting out OpenCL drivers for it, lets assume they really do want to 
     273                # use it as their primary compute device. 
     274                gpu = device 
     275 
     276    # order the devices by gpu then by cpu; when searching for an available device by data type they 
     277    # will be checked in this order, which means that if the gpu supports double then the cpu will never 
     278    # be used (though we may make it possible to explicitly request the cpu at some point). 
     279    devices = [] 
     280    if gpu is not None: 
     281        devices.append(gpu) 
     282    if cpu is not None: 
     283        devices.append(cpu) 
     284    return [cl.Context([d]) for d in devices] 
    223285 
    224286 
    … …  
    241303        self.info = info 
    242304        self.source = source 
    243         self.dtype = generate.F32 if dtype=='fast' else np.dtype(dtype) 
     305        self.dtype = generate.F32 if dtype == 'fast' else np.dtype(dtype) 
    244306        self.fast = (dtype == 'fast') 
    245307        self.program = None # delay program creation 
    246308 
    247309    def __getstate__(self): 
    248         state = self.__dict__.copy() 
    249         state['program'] = None 
    250         return state 
     310        return self.info, self.source, self.dtype, self.fast 
    251311 
    252312    def __setstate__(self, state): 
    253         self.__dict__ = state.copy() 
    254  
    255     def __call__(self, q_input): 
    256         if self.dtype != q_input.dtype: 
    257             raise TypeError("data is %s kernel is %s" 
    258                             % (q_input.dtype, self.dtype)) 
     313        self.info, self.source, self.dtype, self.fast = state 
     314        self.program = None 
     315 
     316    def __call__(self, q_vectors): 
    259317        if self.program is None: 
    260318            compiler = environment().compile_program 
    261319            self.program = compiler(self.info['name'], self.source, self.dtype, 
    262320                                    self.fast) 
    263         kernel_name = generate.kernel_name(self.info, q_input.is_2D) 
     321        is_2d = len(q_vectors) == 2 
     322        kernel_name = generate.kernel_name(self.info, is_2d) 
    264323        kernel = getattr(self.program, kernel_name) 
    265         return GpuKernel(kernel, self.info, q_input) 
     324        return GpuKernel(kernel, self.info, q_vectors, self.dtype) 
    266325 
    267326    def release(self): 
     327        """ 
     328        Free the resources associated with the model. 
     329        """ 
    268330        if self.program is not None: 
    269331            environment().release_program(self.info['name']) 
    270332            self.program = None 
    271333 
    272     def make_input(self, q_vectors): 
    273         """ 
    274         Make q input vectors available to the model. 
    275  
    276         Note that each model needs its own q vector even if the case of 
    277         mixture models because some models may be OpenCL, some may be 
    278         ctypes and some may be pure python. 
    279         """ 
    280         return GpuInput(q_vectors, dtype=self.dtype) 
     334    def __del__(self): 
     335        self.release() 
    281336 
    282337# TODO: check that we don't need a destructor for buffers which go out of scope 
    … …  
    304359        self.nq = q_vectors[0].size 
    305360        self.dtype = np.dtype(dtype) 
    306         self.is_2D = (len(q_vectors) == 2) 
     361        self.is_2d = (len(q_vectors) == 2) 
    307362        # TODO: stretch input based on get_warp() 
    308363        # not doing it now since warp depends on kernel, which is not known 
    … …  
    310365        # architectures tested so far. 
    311366        self.q_vectors = [_stretch_input(q, self.dtype, 32) for q in q_vectors] 
     367        context = env.get_context(self.dtype) 
    312368        self.q_buffers = [ 
    313             cl.Buffer(env.context, mf.READ_ONLY | mf.COPY_HOST_PTR, hostbuf=q) 
     369            cl.Buffer(context, mf.READ_ONLY | mf.COPY_HOST_PTR, hostbuf=q) 
    314370            for q in self.q_vectors 
    315371        ] 
    … …  
    317373 
    318374    def release(self): 
     375        """ 
     376        Free the memory. 
     377        """ 
    319378        for b in self.q_buffers: 
    320379            b.release() 
    321380        self.q_buffers = [] 
    322381 
     382    def __del__(self): 
     383        self.release() 
     384 
    323385class GpuKernel(object): 
    324386    """ 
    325387    Callable SAS kernel. 
    326388 
    327     *kernel* is the GpuKernel object to call. 
     389    *kernel* is the GpuKernel object to call 
    328390 
    329391    *info* is the module information 
    330392 
    331     *q_input* is the DllInput q vectors at which the kernel should be 
    332     evaluated. 
     393    *q_vectors* is the q vectors at which the kernel should be evaluated 
     394 
     395    *dtype* is the kernel precision 
    333396 
    334397    The resulting call method takes the *pars*, a list of values for 
    … …  
    340403    Call :meth:`release` when done with the kernel instance. 
    341404    """ 
    342     def __init__(self, kernel, info, q_input): 
    343         self.q_input = q_input 
     405    def __init__(self, kernel, info, q_vectors, dtype): 
     406        q_input = GpuInput(q_vectors, dtype) 
    344407        self.kernel = kernel 
    345408        self.info = info 
    346409        self.res = np.empty(q_input.nq, q_input.dtype) 
    347         dim = '2d' if q_input.is_2D else '1d' 
     410        dim = '2d' if q_input.is_2d else '1d' 
    348411        self.fixed_pars = info['partype']['fixed-' + dim] 
    349412        self.pd_pars = info['partype']['pd-' + dim] 
    … …  
    352415        # Note: res may be shorter than res_b if global_size != nq 
    353416        env = environment() 
    354         self.loops_b = [cl.Buffer(env.context, mf.READ_WRITE, 
    355                                   2 * MAX_LOOPS * q_input.dtype.itemsize) 
    356                         for _ in env.queues] 
    357         self.res_b = [cl.Buffer(env.context, mf.READ_WRITE, 
    358                                 q_input.global_size[0] * q_input.dtype.itemsize) 
    359                       for _ in env.queues] 
    360  
     417        self.queue = env.get_queue(dtype) 
     418        self.loops_b = cl.Buffer(self.queue.context, mf.READ_WRITE, 
     419                                 2 * MAX_LOOPS * q_input.dtype.itemsize) 
     420        self.res_b = cl.Buffer(self.queue.context, mf.READ_WRITE, 
     421                               q_input.global_size[0] * q_input.dtype.itemsize) 
     422        self.q_input = q_input 
     423 
     424        self._need_release = [self.loops_b, self.res_b, self.q_input] 
    361425 
    362426    def __call__(self, fixed_pars, pd_pars, cutoff=1e-5): 
    … …  
    366430                else np.float32)  # will never get here, so use np.float32 
    367431 
    368         device_num = 0 
    369         queuei = environment().queues[device_num] 
    370         res_bi = self.res_b[device_num] 
     432        res_bi = self.res_b 
    371433        nq = np.uint32(self.q_input.nq) 
    372434        if pd_pars: 
    … …  
    383445                raise ValueError("too many polydispersity points") 
    384446 
    385             loops_bi = self.loops_b[device_num] 
    386             cl.enqueue_copy(queuei, loops_bi, loops) 
     447            loops_bi = self.loops_b 
     448            cl.enqueue_copy(self.queue, loops_bi, loops) 
    387449            loops_l = cl.LocalMemory(len(loops.data)) 
    388450            #ctx = environment().context 
    … …  
    393455        fixed = [real(p) for p in fixed_pars] 
    394456        args = self.q_input.q_buffers + [res_bi, nq] + dispersed + fixed 
    395         self.kernel(queuei, self.q_input.global_size, None, *args) 
    396         cl.enqueue_copy(queuei, self.res, res_bi) 
     457        self.kernel(self.queue, self.q_input.global_size, None, *args) 
     458        cl.enqueue_copy(self.queue, self.res, res_bi) 
    397459 
    398460        return self.res 
    399461 
    400462    def release(self): 
    401         for b in self.loops_b: 
    402             b.release() 
    403         self.loops_b = [] 
    404         for b in self.res_b: 
    405             b.release() 
    406         self.res_b = [] 
     463        """ 
     464        Release resources associated with the kernel. 
     465        """ 
     466        for v in self._need_release: 
     467            v.release() 
     468        self._need_release = [] 
    407469 
    408470    def __del__(self): 

  • sasmodels/kerneldll.py

    r74667d3 reafc9fa  
    11r""" 
    2 C types wrapper for sasview models. 
     2DLL driver for C kernels 
    33 
    44The global attribute *ALLOW_SINGLE_PRECISION_DLLS* should be set to *True* if 
    … …  
    4444directory for this application, then OpenMP should be supported. 
    4545""" 
     46from __future__ import print_function 
    4647 
    4748import sys 
    … …  
    122123    models are allowed as DLLs. 
    123124    """ 
    124     if callable(info.get('Iq',None)): 
     125    if callable(info.get('Iq', None)): 
    125126        return PyModel(info) 
    126127 
    … …  
    139140 
    140141    source = generate.convert_type(source, dtype) 
    141     source_files = generate.sources(info) + [info['filename']] 
    142     dll= dll_path(info, dtype) 
     142    source_files = generate.model_sources(info) + [info['filename']] 
     143    dll = dll_path(info, dtype) 
    143144    newest = max(os.path.getmtime(f) for f in source_files) 
    144     if not os.path.exists(dll) or os.path.getmtime(dll)<newest: 
     145    if not os.path.exists(dll) or os.path.getmtime(dll) < newest: 
    145146        # Replace with a proper temp file 
    146         fid, filename = tempfile.mkstemp(suffix=".c",prefix=tempfile_prefix) 
    147         os.fdopen(fid,"w").write(source) 
     147        fid, filename = tempfile.mkstemp(suffix=".c", prefix=tempfile_prefix) 
     148        os.fdopen(fid, "w").write(source) 
    148149        command = COMPILE%{"source":filename, "output":dll} 
    149150        print("Compile command: "+command) 
    … …  
    160161def load_dll(source, info, dtype="double"): 
    161162    """ 
    162     Create and load a dll corresponding to the source,info pair returned 
     163    Create and load a dll corresponding to the source, info pair returned 
    163164    from :func:`sasmodels.generate.make` compiled for the target precision. 
    164165 
    … …  
    199200        Npd2d = len(self.info['partype']['pd-2d']) 
    200201 
    201         #print("dll",self.dllpath) 
     202        #print("dll", self.dllpath) 
    202203        try: 
    203204            self.dll = ct.CDLL(self.dllpath) 
    … …  
    210211              else c_longdouble) 
    211212        pd_args_1d = [c_void_p, fp] + [c_int]*Npd1d if Npd1d else [] 
    212         pd_args_2d= [c_void_p, fp] + [c_int]*Npd2d if Npd2d else [] 
     213        pd_args_2d = [c_void_p, fp] + [c_int]*Npd2d if Npd2d else [] 
    213214        self.Iq = self.dll[generate.kernel_name(self.info, False)] 
    214215        self.Iq.argtypes = IQ_ARGS + pd_args_1d + [fp]*Nfixed1d 
    … …  
    218219 
    219220    def __getstate__(self): 
    220         return {'info': self.info, 'dllpath': self.dllpath, 'dll': None} 
     221        return self.info, self.dllpath 
    221222 
    222223    def __setstate__(self, state): 
    223         self.__dict__ = state 
    224  
    225     def __call__(self, q_input): 
    226         if self.dtype != q_input.dtype: 
    227             raise TypeError("data is %s kernel is %s" % (q_input.dtype, self.dtype)) 
     224        self.info, self.dllpath = state 
     225        self.dll = None 
     226 
     227    def __call__(self, q_vectors): 
     228        q_input = PyInput(q_vectors, self.dtype) 
    228229        if self.dll is None: self._load_dll() 
    229         kernel = self.Iqxy if q_input.is_2D else self.Iq 
     230        kernel = self.Iqxy if q_input.is_2d else self.Iq 
    230231        return DllKernel(kernel, self.info, q_input) 
    231232 
    232     # pylint: disable=no-self-use 
    233     def make_input(self, q_vectors): 
    234         """ 
    235         Make q input vectors available to the model. 
    236  
    237         Note that each model needs its own q vector even if the case of 
    238         mixture models because some models may be OpenCL, some may be 
    239         ctypes and some may be pure python. 
    240         """ 
    241         return PyInput(q_vectors, dtype=self.dtype) 
    242  
    243233    def release(self): 
     234        """ 
     235        Release any resources associated with the model. 
     236        """ 
    244237        pass # TODO: should release the dll 
    245238 
    … …  
    257250 
    258251    The resulting call method takes the *pars*, a list of values for 
    259     the fixed parameters to the kernel, and *pd_pars*, a list of (value,weight) 
     252    the fixed parameters to the kernel, and *pd_pars*, a list of (value, weight) 
    260253    vectors for the polydisperse parameters.  *cutoff* determines the 
    261254    integration limits: any points with combined weight less than *cutoff* 
    … …  
    269262        self.kernel = kernel 
    270263        self.res = np.empty(q_input.nq, q_input.dtype) 
    271         dim = '2d' if q_input.is_2D else '1d' 
     264        dim = '2d' if q_input.is_2d else '1d' 
    272265        self.fixed_pars = info['partype']['fixed-'+dim] 
    273266        self.pd_pars = info['partype']['pd-'+dim] 
    … …  
    299292 
    300293    def release(self): 
     294        """ 
     295        Release any resources associated with the kernel. 
     296        """ 
    301297        pass 

  • sasmodels/kernelpy.py

    r4c2c535 reafc9fa  
     1""" 
     2Python driver for python kernels 
     3 
     4Calls the kernel with a vector of $q$ values for a single parameter set. 
     5Polydispersity is supported by looping over different parameter sets and 
     6summing the results.  The interface to :class:`PyModel` matches those for 
     7:class:`kernelcl.GpuModel` and :class:`kerneldll.DllModel`. 
     8""" 
    19import numpy as np 
    210from numpy import pi, cos 
    … …  
    513 
    614class PyModel(object): 
     15    """ 
     16    Wrapper for pure python models. 
     17    """ 
    718    def __init__(self, info): 
    819        self.info = info 
    920 
    10     def __call__(self, q_input): 
    11         kernel = self.info['Iqxy'] if q_input.is_2D else self.info['Iq'] 
     21    def __call__(self, q_vectors): 
     22        q_input = PyInput(q_vectors, dtype=F64) 
     23        kernel = self.info['Iqxy'] if q_input.is_2d else self.info['Iq'] 
    1224        return PyKernel(kernel, self.info, q_input) 
    1325 
    14     # pylint: disable=no-self-use 
    15     def make_input(self, q_vectors): 
    16         return PyInput(q_vectors, dtype=F64) 
    17  
    1826    def release(self): 
     27        """ 
     28        Free resources associated with the model. 
     29        """ 
    1930        pass 
    2031 
    … …  
    4152        self.nq = q_vectors[0].size 
    4253        self.dtype = dtype 
    43         self.is_2D = (len(q_vectors) == 2) 
     54        self.is_2d = (len(q_vectors) == 2) 
    4455        self.q_vectors = [np.ascontiguousarray(q, self.dtype) for q in q_vectors] 
    4556        self.q_pointers = [q.ctypes.data for q in self.q_vectors] 
    4657 
    4758    def release(self): 
     59        """ 
     60        Free resources associated with the model inputs. 
     61        """ 
    4862        self.q_vectors = [] 
    4963 
    … …  
    7185        self.q_input = q_input 
    7286        self.res = np.empty(q_input.nq, q_input.dtype) 
    73         dim = '2d' if q_input.is_2D else '1d' 
     87        dim = '2d' if q_input.is_2d else '1d' 
    7488        # Loop over q unless user promises that the kernel is vectorized by 
    7589        # taggining it with vectorized=True 
    … …  
    7791            if dim == '2d': 
    7892                def vector_kernel(qx, qy, *args): 
     93                    """ 
     94                    Vectorized 2D kernel. 
     95                    """ 
    7996                    return np.array([kernel(qxi, qyi, *args) 
    8097                                     for qxi, qyi in zip(qx, qy)]) 
    8198            else: 
    8299                def vector_kernel(q, *args): 
     100                    """ 
     101                    Vectorized 1D kernel. 
     102                    """ 
    83103                    return np.array([kernel(qi, *args) 
    84104                                     for qi in q]) 
    … …  
    122142 
    123143    def release(self): 
     144        """ 
     145        Free resources associated with the kernel. 
     146        """ 
    124147        self.q_input = None 
    125148 

  • sasmodels/model_test.py

    r9404dd3 r13ed84c  
    99    if model1 is 'all', then all except the remaining models will be tested 
    1010 
    11 Each model is tested using the default parameters at q=0.1, (qx,qy)=(0.1,0.1), 
     11Each model is tested using the default parameters at q=0.1, (qx, qy)=(0.1, 0.1), 
    1212and the ER and VR are computed.  The return values at these points are not 
    1313considered.  The test is only to verify that the models run to completion, 
    … …  
    3030        [ {parameters}, (qx, qy), I(qx, Iqy)], 
    3131        [ {parameters}, [(qx1, qy1), (qx2, qy2), ...], 
    32                         [I(qx1,qy1), I(qx2,qy2), ...]], 
     32                        [I(qx1, qy1), I(qx2, qy2), ...]], 
    3333 
    3434        [ {parameters}, 'ER', ER(pars) ], 
    … …  
    4343Precision defaults to 5 digits (relative). 
    4444""" 
     45from __future__ import print_function 
    4546 
    4647import sys 
    … …  
    5556 
    5657def make_suite(loaders, models): 
     58    """ 
     59    Construct the pyunit test suite. 
     60 
     61    *loaders* is the list of kernel drivers to use, which is one of 
     62    *["dll", "opencl"]*, *["dll"]* or *["opencl"]*.  For python models, 
     63    the python driver is always used. 
     64 
     65    *models* is the list of models to test, or *["all"]* to test all models. 
     66    """ 
    5767 
    5868    ModelTestCase = _hide_model_case_from_nosetests() 
    … …  
    7585        # don't try to call cl kernel since it will not be 
    7686        # available in some environmentes. 
    77         is_py = callable(getattr(model_definition,'Iq', None)) 
     87        is_py = callable(getattr(model_definition, 'Iq', None)) 
    7888 
    7989        if is_py:  # kernel implemented in python 
    … …  
    90100                test_name = "Model: %s, Kernel: OpenCL"%model_name 
    91101                test_method_name = "test_%s_opencl" % model_name 
     102                # Using dtype=None so that the models that are only 
     103                # correct for double precision are not tested using 
     104                # single precision.  The choice is determined by the 
     105                # presence of *single=False* in the model file. 
    92106                test = ModelTestCase(test_name, model_definition, 
    93107                                     test_method_name, 
    94                                      platform="ocl", dtype='single') 
     108                                     platform="ocl", dtype=None) 
    95109                #print("defining", test_name) 
    96110                suite.addTest(test) 
    … …  
    111125def _hide_model_case_from_nosetests(): 
    112126    class ModelTestCase(unittest.TestCase): 
     127        """ 
     128        Test suit for a particular model with a particular kernel driver. 
     129 
     130        The test suite runs a simple smoke test to make sure the model 
     131        functions, then runs the list of tests at the bottom of the model 
     132        description file. 
     133        """ 
    113134        def __init__(self, test_name, definition, test_method_name, 
    114135                     platform, dtype): 
    … …  
    123144        def _runTest(self): 
    124145            smoke_tests = [ 
    125                 [{},0.1,None], 
    126                 [{},(0.1,0.1),None], 
    127                 [{},'ER',None], 
    128                 [{},'VR',None], 
     146                [{}, 0.1, None], 
     147                [{}, (0.1, 0.1), None], 
     148                [{}, 'ER', None], 
     149                [{}, 'VR', None], 
    129150                ] 
    130151 
    … …  
    140161                    ## values an error.  Only do so for the "dll" tests 
    141162                    ## to reduce noise from both opencl and dll, and because 
    142                     ## python kernels us 
     163                    ## python kernels use platform="dll". 
    143164                    #raise Exception("No test cases provided") 
    144165                    pass 
    … …  
    163184                actual = [call_VR(model.info, pars)] 
    164185            elif isinstance(x[0], tuple): 
    165                 Qx,Qy = zip(*x) 
     186                Qx, Qy = zip(*x) 
    166187                q_vectors = [np.array(Qx), np.array(Qy)] 
    167188                kernel = make_kernel(model, q_vectors) 
    … …  
    179200                    # smoke test --- make sure it runs and produces a value 
    180201                    self.assertTrue(np.isfinite(actual_yi), 
    181                         'invalid f(%s): %s' % (xi, actual_yi)) 
     202                                    'invalid f(%s): %s' % (xi, actual_yi)) 
    182203                else: 
    183                     err = abs(yi - actual_yi) 
    184                     nrm = abs(yi) 
    185                     self.assertLess(err * 10**5, nrm, 
    186                         'f(%s); expected:%s; actual:%s' % (xi, yi, actual_yi)) 
     204                    self.assertTrue(is_near(yi, actual_yi, 5), 
     205                                    'f(%s); expected:%s; actual:%s' 
     206                                    % (xi, yi, actual_yi)) 
    187207 
    188208    return ModelTestCase 
    189209 
    190  
     210def is_near(target, actual, digits=5): 
     211    """ 
     212    Returns true if *actual* is within *digits* significant digits of *target*. 
     213    """ 
     214    import math 
     215    shift = 10**math.ceil(math.log10(abs(target))) 
     216    return abs(target-actual)/shift < 1.5*10**-digits 
    191217 
    192218def main(): 
    … …  
    199225 
    200226    models = sys.argv[1:] 
     227    if models and models[0] == '-v': 
     228        verbosity = 2 
     229        models = models[1:] 
     230    else: 
     231        verbosity = 1 
    201232    if models and models[0] == 'opencl': 
    202233        if not HAVE_OPENCL: 
    … …  
    217248        print("""\ 
    218249usage: 
    219   python -m sasmodels.model_test [opencl|dll|opencl_and_dll] model1 model2 ... 
     250  python -m sasmodels.model_test [-v] [opencl|dll] model1 model2 ... 
     251 
     252If -v is included on the 
     253If neither opencl nor dll is specified, then models will be tested with 
     254both opencl and dll; the compute target is ignored for pure python models. 
    220255 
    221256If model1 is 'all', then all except the remaining models will be tested. 
    222 If no compute target is specified, then models will be tested with both opencl 
    223 and dll; the compute target is ignored for pure python models.""") 
     257 
     258""") 
    224259 
    225260        return 1 
    226261 
    227262    #runner = unittest.TextTestRunner() 
    228     runner = xmlrunner.XMLTestRunner(output='logs') 
     263    runner = xmlrunner.XMLTestRunner(output='logs', verbosity=verbosity) 
    229264    result = runner.run(make_suite(loaders, models)) 
    230265    return 1 if result.failures or result.errors else 0 
    … …  
    237272    Run "nosetests sasmodels" on the command line to invoke it. 
    238273    """ 
    239     tests = make_suite(['opencl','dll'],['all']) 
     274    tests = make_suite(['opencl', 'dll'], ['all']) 
    240275    for test_i in tests: 
    241276        yield test_i._runTest 

  • sasmodels/models/HayterMSAsq.py

    reb69cce r13ed84c  
    5656        parameters used in P(Q). 
    5757""" 
     58single = False  # double precision only for now 
    5859#             [ "name", "units", default, [lower, upper], "type", "description" ], 
    5960parameters = [["effect_radius", "Ang", 20.75, [0, inf], "volume", 
    … …  
    9091# odd that the default st has saltconc zero 
    9192demo = dict(effect_radius = 20.75,charge=19.0,volfraction = 0.0192,temperature=318.16,saltconc=0.05,dielectconst=71.08,effect_radius_pd = 0.1,effect_radius_pd_n = 40) 
     93# 
     94# attempt to use same values as old sasview unit test 
     95tests = [ 
     96        [ {'scale': 1.0, 'background' : 0.0, 'effect_radius' : 20.75, 'charge' : 19.0, 'volfraction' : 0.0192, 'temperature' : 298.0, 
     97          'saltconc' : 0,'dielectconst' : 78.0, 'effect_radius_pd' : 0}, [0.0010], [0.0712928]] 
     98        ] 
    9299 

  • sasmodels/models/barbell.py

    reb69cce rdcdf29d  
    100100title = "Cylinder with spherical end caps" 
    101101description = """ 
    102         Calculates the scattering from a barbell-shaped cylinder. That is a sphereocylinder with spherical end caps that have a radius larger than that of the cylinder and the center of the end cap 
    103         radius lies outside of the cylinder. 
    104         Note: As the length of cylinder(bar) -->0,it becomes a dumbbell. And when rad_bar = rad_bell, it is a spherocylinder. 
    105         It must be that rad_bar <(=) rad_bell. 
     102    Calculates the scattering from a barbell-shaped cylinder. 
     103    That is a sphereocylinder with spherical end caps that have a radius larger 
     104    than that of the cylinder and the center of the end cap radius lies outside 
     105    of the cylinder. 
     106    Note: As the length of cylinder(bar) -->0,it becomes a dumbbell. And when 
     107    rad_bar = rad_bell, it is a spherocylinder. 
     108    It must be that rad_bar <(=) rad_bell. 
    106109""" 
    107110category = "shape:cylinder" 
    108  
     111# pylint: disable=bad-whitespace, line-too-long 
    109112#             ["name", "units", default, [lower, upper], "type","description"], 
    110 parameters = [["sld", "4e-6/Ang^2", 4, [-inf, inf], "", "Barbell scattering length density"], 
    111               ["solvent_sld", "1e-6/Ang^2", 1, [-inf, inf], "", "Solvent scattering length density"], 
    112               ["bell_radius", "Ang", 40, [0, inf], "volume", "Spherical bell radius"], 
    113               ["radius", "Ang", 20, [0, inf], "volume", "Cylindrical bar radius"], 
    114               ["length", "Ang", 400, [0, inf], "volume", "Cylinder bar length"], 
    115               ["theta", "degrees", 60, [-inf, inf], "orientation", "In plane angle"], 
    116               ["phi", "degrees", 60, [-inf, inf], "orientation", "Out of plane angle"], 
     113parameters = [["sld",         "4e-6/Ang^2",   4, [-inf, inf], "",            "Barbell scattering length density"], 
     114              ["solvent_sld", "1e-6/Ang^2",   1, [-inf, inf], "",            "Solvent scattering length density"], 
     115              ["bell_radius", "Ang",         40, [0, inf],    "volume",      "Spherical bell radius"], 
     116              ["radius",      "Ang",         20, [0, inf],    "volume",      "Cylindrical bar radius"], 
     117              ["length",      "Ang",        400, [0, inf],    "volume",      "Cylinder bar length"], 
     118              ["theta",       "degrees",    60, [-inf, inf], "orientation", "In plane angle"], 
     119              ["phi",         "degrees",    60, [-inf, inf], "orientation", "Out of plane angle"], 
    117120             ] 
     121# pylint: enable=bad-whitespace, line-too-long 
    118122 
    119123source = ["lib/J1.c", "lib/gauss76.c", "barbell.c"] 

  • sasmodels/models/bcc.py

    reb69cce r13ed84c  
    109109title = "Body-centred cubic lattic with paracrystalline distortion" 
    110110description = """ 
    111     Calculates the scattering from a **body-centered cubic lattice** with paracrystalline distortion. Thermal vibrations 
    112     are considered to be negligible, and the size of the paracrystal is infinitely large. Paracrystalline distortion is 
    113     assumed to be isotropic and characterized by a Gaussian distribution. 
     111    Calculates the scattering from a **body-centered cubic lattice** with 
     112    paracrystalline distortion. Thermal vibrations are considered to be 
     113    negligible, and the size of the paracrystal is infinitely large. 
     114    Paracrystalline distortion is assumed to be isotropic and characterized 
     115    by a Gaussian distribution. 
    114116    """ 
    115117category = "shape:paracrystal" 
    116118 
     119single = False 
     120 
     121# pylint: disable=bad-whitespace, line-too-long 
    117122#             ["name", "units", default, [lower, upper], "type","description" ], 
    118 parameters = [["dnn", "Ang", 220, [-inf, inf], "", "Nearest neighbour distance"], 
    119               ["d_factor", "", 0.06, [-inf, inf], "", "Paracrystal distortion factor"], 
    120               ["radius", "Ang", 40, [0, inf], "volume", "Particle radius"], 
    121               ["sld", "1e-6/Ang^2", 4, [-inf, inf], "", "Particle scattering length density"], 
    122               ["solvent_sld", "1e-6/Ang^2", 1, [-inf, inf], "", "Solvent scattering length density"], 
    123               ["theta", "degrees", 60, [-inf, inf], "orientation", "In plane angle"], 
    124               ["phi", "degrees", 60, [-inf, inf], "orientation", "Out of plane angle"], 
    125               ["psi", "degrees", 60, [-inf, inf], "orientation", "Out of plane angle"] 
     123parameters = [["dnn",         "Ang",       220,    [-inf, inf], "",            "Nearest neighbour distance"], 
     124              ["d_factor",    "",            0.06, [-inf, inf], "",            "Paracrystal distortion factor"], 
     125              ["radius",      "Ang",        40,    [0, inf],    "volume",      "Particle radius"], 
     126              ["sld",         "1e-6/Ang^2",  4,    [-inf, inf], "",            "Particle scattering length density"], 
     127              ["solvent_sld", "1e-6/Ang^2",  1,    [-inf, inf], "",            "Solvent scattering length density"], 
     128              ["theta",       "degrees",    60,    [-inf, inf], "orientation", "In plane angle"], 
     129              ["phi",         "degrees",    60,    [-inf, inf], "orientation", "Out of plane angle"], 
     130              ["psi",         "degrees",    60,    [-inf, inf], "orientation", "Out of plane angle"] 
    126131             ] 
     132# pylint: enable=bad-whitespace, line-too-long 
    127133 
    128134source = ["lib/J1.c", "lib/gauss150.c", "bcc.c"] 

  • sasmodels/models/broad_peak.py

    reb69cce rdcdf29d  
    6060category = "shape-independent" 
    6161 
     62# pylint: disable=bad-whitespace, line-too-long 
    6263#             ["name", "units", default, [lower, upper], "type", "description"], 
    63 parameters = [["porod_scale", "", 1.0e-05, [-inf, inf], "", "Power law scale factor"], 
    64               ["porod_exp", "", 3.0, [-inf, inf], "", "Exponent of power law"], 
    65               ["lorentz_scale", "", 10.0, [-inf, inf], "", "Scale factor for broad Lorentzian peak"], 
    66               ["lorentz_length", "Ang", 50.0, [-inf, inf], "", "Lorentzian screening length"], 
    67               ["peak_pos", "1/Ang", 0.1, [-inf, inf], "", "Peak postion in q"], 
    68               ["lorentz_exp", "", 2.0, [-inf, inf], "", "exponent of Lorentz function"], 
     64parameters = [["porod_scale",    "", 1.0e-05, [-inf, inf], "", "Power law scale factor"], 
     65              ["porod_exp",      "",      3.0, [-inf, inf], "", "Exponent of power law"], 
     66              ["lorentz_scale",  "",    10.0, [-inf, inf], "", "Scale factor for broad Lorentzian peak"], 
     67              ["lorentz_length", "Ang",  50.0, [-inf, inf], "", "Lorentzian screening length"], 
     68              ["peak_pos",       "1/Ang", 0.1, [-inf, inf], "", "Peak position in q"], 
     69              ["lorentz_exp",    "",      2.0, [-inf, inf], "", "Exponent of Lorentz function"], 
    6970             ] 
     71# pylint: enable=bad-whitespace, line-too-long 
    7072 
    71 def Iq(q, porod_scale, porod_exp, lorentz_scale, lorentz_length, peak_pos, lorentz_exp): 
     73def Iq(q, 
     74       porod_scale=1.0e-5, 
     75       porod_exp=3.0, 
     76       lorentz_scale=10.0, 
     77       lorentz_length=50.0, 
     78       peak_pos=0.1, 
     79       lorentz_exp=2.0): 
     80    """ 
     81    :param q:              Input q-value 
     82    :param porod_scale:    Power law scale factor 
     83    :param porod_exp:      Exponent of power law 
     84    :param lorentz_scale:  Scale factor for broad Lorentzian peak 
     85    :param lorentz_length: Lorentzian screening length 
     86    :param peak_pos:       Peak position in q 
     87    :param lorentz_exp:    Exponent of Lorentz function 
     88    :return:               Calculated intensity 
     89    """ 
     90 
    7291    inten = (porod_scale / q ** porod_exp + lorentz_scale 
    7392             / (1.0 + (abs(q - peak_pos) * lorentz_length) ** lorentz_exp)) 
    … …  
    7695 
    7796def Iqxy(qx, qy, *args): 
     97    """ 
     98    :param qx:   Input q_x-value 
     99    :param qy:   Input q_y-value 
     100    :param args: Remaining arguments 
     101    :return:     2D-Intensity 
     102    """ 
    78103    return Iq(sqrt(qx ** 2 + qy ** 2), *args) 
     104 
    79105Iqxy.vectorized = True # Iqxy accepts an array of qx, qy values 
    80  
    81106 
    82107demo = dict(scale=1, background=0, 

  • sasmodels/models/capped_cylinder.py

    reb69cce rdcdf29d  
    125125""" 
    126126category = "shape:cylinder" 
     127# pylint: disable=bad-whitespace, line-too-long 
     128#             ["name", "units", default, [lower, upper], "type", "description"], 
     129parameters = [["sld",         "1e-6/Ang^2", 4, [-inf, inf], "",       "Cylinder scattering length density"], 
     130              ["solvent_sld", "1e-6/Ang^2", 1, [-inf, inf], "",       "Solvent scattering length density"], 
     131              ["radius",      "Ang",       20, [0, inf],    "volume", "Cylinder radius"], 
    127132 
    128 #             ["name", "units", default, [lower, upper], "type", "description"], 
    129 parameters = [["sld", "1e-6/Ang^2", 4, [-inf, inf], "", 
    130                "Cylinder scattering length density"], 
    131               ["solvent_sld", "1e-6/Ang^2", 1, [-inf, inf], "", 
    132                "Solvent scattering length density"], 
    133               ["radius", "Ang", 20, [0, inf], "volume", "Cylinder radius"], 
    134133              # TODO: use an expression for cap radius with fixed bounds. 
    135134              # The current form requires cap radius R bigger than cylinder radius r. 
    … …  
    141140              # in the capped cylinder, and zero for no bar in the barbell model.  In 
    142141              # both models, one would be a pill. 
    143               ["cap_radius", "Ang", 20, [0, inf], "volume", "Cap radius"], 
    144               ["length", "Ang", 400, [0, inf], "volume", "Cylinder length"], 
    145               ["theta", "degrees", 60, [-inf, inf], "orientation", "In plane angle"], 
    146               ["phi", "degrees", 60, [-inf, inf], "orientation", "Out of plane angle"], 
     142              ["cap_radius", "Ang",     20, [0, inf],    "volume", "Cap radius"], 
     143              ["length",     "Ang",    400, [0, inf],    "volume", "Cylinder length"], 
     144              ["theta",      "degrees", 60, [-inf, inf], "orientation", "In plane angle"], 
     145              ["phi",        "degrees", 60, [-inf, inf], "orientation", "Out of plane angle"], 
    147146             ] 
     147# pylint: enable=bad-whitespace, line-too-long 
    148148 
    149149source = ["lib/J1.c", "lib/gauss76.c", "capped_cylinder.c"] 
    … …  
    159159            phi_pd=15, phi_pd_n=1) 
    160160oldname = 'CappedCylinderModel' 
    161 oldpars = dict(sld='sld_capcyl', solvent_sld='sld_solv', 
    162                length='len_cyl', radius='rad_cyl', cap_radius='rad_cap') 
     161oldpars = dict(sld='sld_capcyl', 
     162               solvent_sld='sld_solv', 
     163               length='len_cyl', 
     164               radius='rad_cyl', 
     165               cap_radius='rad_cap') 

  • sasmodels/models/fcc.c

    r82d239a reeb8bac  
    101101 
    102102  double b3_x, b3_y, b1_x, b1_y, b2_x, b2_y; //b3_z, 
    103   double q_z; 
     103  // double q_z; 
    104104  double cos_val_b3, cos_val_b2, cos_val_b1; 
    105105  double a1_dot_q, a2_dot_q,a3_dot_q; 
    … …  
    124124  const double latticescale = 2.0*(4.0/3.0)*M_PI*(radius*radius*radius)/(s1*s1*s1); 
    125125  // q vector 
    126   q_z = 0.0; // for SANS; assuming qz is negligible 
     126  // q_z = 0.0; // for SANS; assuming qz is negligible 
    127127  /// Angles here are respect to detector coordinate 
    128128  ///  instead of against q coordinate(PRB 36(46), 3(6), 1754(3854)) 

  • sasmodels/models/fcc.py

    reb69cce r13ed84c  
    112112category = "shape:paracrystal" 
    113113 
     114single = False 
     115 
    114116#             ["name", "units", default, [lower, upper], "type","description"], 
    115117parameters = [["dnn", "Ang", 220, [-inf, inf], "", "Nearest neighbour distance"], 

  • sasmodels/models/gaussian_peak.py

    reb69cce r13ed84c  
    4242category = "shape-independent" 
    4343 
     44single = False 
    4445#             ["name", "units", default, [lower, upper], "type","description"], 
    4546parameters = [["q0", "1/Ang", 0.05, [-inf, inf], "", "Peak position"], 

  • sasmodels/models/gel_fit.py

    r513efc5 r168052c  
    77in the position of the polymer chains that ensure thermodynamic equilibrium, 
    88and a longer distance (denoted here as $a2$ ) needed to account for the static 
    9 accumulations of polymer pinned down by junction points or clusters of such points. 
    10 The latter is derived from a simple Guinier function. 
     9accumulations of polymer pinned down by junction points or clusters of such 
     10points. The latter is derived from a simple Guinier function. 
    1111 
    1212 
    … …  
    4040--------- 
    4141 
    42 Mitsuhiro Shibayama, Toyoichi Tanaka, Charles C Han, J. Chem. Phys. 1992, 97 (9), 
    43 6829-6841 
     42Mitsuhiro Shibayama, Toyoichi Tanaka, Charles C Han, 
     43*J. Chem. Phys.* 1992, 97 (9), 6829-6841 
    4444 
    4545Simon Mallam, Ferenc Horkay, Anne-Marie Hecht, Adrian R Rennie, Erik Geissler, 
    46 Macromolecules 1991, 24, 543-548 
     46*Macromolecules* 1991, 24, 543-548 
    4747 
    4848""" 
    … …  
    6262category = "shape-independent" 
    6363 
     64# pylint: disable=bad-whitespace, line-too-long 
    6465#             ["name", "units", default, [lower, upper], "type","description"], 
    6566parameters = [["guinier_scale",    "cm^{-1}",   1.7, [-inf, inf], "", "Guinier length scale"], 
    … …  
    6869              ["fractal_exp",      "",          2.0, [0, inf],    "", "Fractal exponent"], 
    6970              ["cor_length",       "Ang",      16.0, [0, inf],    "", "Correlation length"] 
    70               ] 
    71  
     71             ] 
     72# pylint: enable=bad-whitespace, line-too-long 
    7273 
    7374source = ["gel_fit.c"] 
    … …  
    9293           'fractal_exp': 10.0, 
    9394           'cor_length': 20.0 
    94            }, 0.1, 0.716532], 
     95          }, 0.1, 0.716532], 
    9596 
    9697         [{'guinier_scale': 4.0, 
    … …  
    100101           'cor_length': 20.0, 
    101102           'background': 20.0, 
    102            }, 5.0, 20.1224653026], 
    103  
    104          ] 
     103          }, 5.0, 20.1224653026], 
     104        ] 

  • sasmodels/models/hardsphere.py

    r7ed702f r13ed84c  
    5555               "volume fraction of hard spheres"], 
    5656             ] 
     57single = False 
    5758 
    5859# No volume normalization despite having a volume parameter 
    … …  
    106107oldpars = dict() 
    107108 
     109tests = [ 
     110        [ {'scale': 1.0, 'background' : 0.0, 'effect_radius' : 50.0, 'volfraction' : 0.2, 
     111           'effect_radius_pd' : 0}, [0.001], [0.209128]] 
     112        ] 
     113 

  • sasmodels/models/lamellarCaille.py

    rd18f8a8 r13ed84c  
    8787category = "shape:lamellae" 
    8888 
     89single = False 
     90 
    8991#             ["name", "units", default, [lower, upper], "type","description"], 
    9092parameters = [["thickness", "Ang",  30.0, [0, inf], "volume", "sheet thickness"], 
    … …  
    122124oldpars = dict(thickness='delta', Nlayers='N_plates', Caille_parameter='caille', 
    123125               sld='sld_bi',solvent_sld='sld_sol') 
     126# 
     127tests = [ 
     128        [ {'scale': 1.0, 'background' : 0.0, 'thickness' : 30.,'Nlayers' : 20.0, 'spacing' : 400., 
     129            'Caille_parameter' : 0.1, 'sld' : 6.3, 'solvent_sld' : 1.0, 
     130            'thickness_pd' : 0.0, 'spacing_pd' : 0.0 }, [0.001], [28895.13397]] 
     131        ] 

  • sasmodels/models/lamellarCailleHG.py

    rd18f8a8 r13ed84c  
    9191category = "shape:lamellae" 
    9292 
     93single = False 
    9394parameters = [ 
    94               #   [ "name", "units", default, [lower, upper], "type", 
    95               #     "description" ], 
    96               [ "tail_length", "Ang", 10, [0, inf], "volume", 
    97                 "Tail thickness" ], 
    98               [ "head_length", "Ang", 2, [0, inf], "volume", 
    99                 "head thickness" ], 
    100               [ "Nlayers", "", 30, [0, inf], "", 
    101                 "Number of layers" ], 
    102               [ "spacing", "Ang", 40., [0.0,inf], "volume", 
    103                 "d-spacing of Caille S(Q)" ], 
    104               [ "Caille_parameter", "", 0.001, [0.0,0.8], "", 
    105                 "Caille parameter" ], 
    106               [ "sld", "1e-6/Ang^2", 0.4, [-inf,inf], "", 
    107                 "Tail scattering length density" ], 
    108               [ "head_sld", "1e-6/Ang^2", 2.0, [-inf,inf], "", 
    109                 "Head scattering length density" ], 
    110               [ "solvent_sld", "1e-6/Ang^2", 6, [-inf,inf], "", 
    111                 "Solvent scattering length density" ], 
     95    #   [ "name", "units", default, [lower, upper], "type", 
     96    #     "description" ], 
     97    ["tail_length", "Ang", 10, [0, inf], "volume", 
     98     "Tail thickness"], 
     99    ["head_length", "Ang", 2, [0, inf], "volume", 
     100     "head thickness"], 
     101    ["Nlayers", "", 30, [0, inf], "", 
     102     "Number of layers"], 
     103    ["spacing", "Ang", 40., [0.0, inf], "volume", 
     104     "d-spacing of Caille S(Q)"], 
     105    ["Caille_parameter", "", 0.001, [0.0, 0.8], "", 
     106     "Caille parameter"], 
     107    ["sld", "1e-6/Ang^2", 0.4, [-inf, inf], "", 
     108     "Tail scattering length density"], 
     109    ["head_sld", "1e-6/Ang^2", 2.0, [-inf, inf], "", 
     110     "Head scattering length density"], 
     111    ["solvent_sld", "1e-6/Ang^2", 6, [-inf, inf], "", 
     112     "Solvent scattering length density"], 
    112113    ] 
    113114 
    114 source = [ "lamellarCailleHG_kernel.c"] 
     115source = ["lamellarCailleHG_kernel.c"] 
    115116 
    116117# No volume normalization despite having a volume parameter 
    … …  
    128129 
    129130demo = dict( 
    130             scale=1, background=0, 
    131             Nlayers=20, 
    132             spacing=200., Caille_parameter=0.05, 
    133             tail_length=15,head_length=10, 
    134             #sld=-1, head_sld=4.0, solvent_sld=6.0, 
    135             sld=-1, head_sld=4.1, solvent_sld=6.0, 
    136             tail_length_pd= 0.1, tail_length_pd_n=20, 
    137             head_length_pd= 0.05, head_length_pd_n=30, 
    138             spacing_pd= 0.2, spacing_pd_n=40 
    139            ) 
     131    scale=1, background=0, 
     132    Nlayers=20, spacing=200., Caille_parameter=0.05, 
     133    tail_length=15, head_length=10, 
     134    #sld=-1, head_sld=4.0, solvent_sld=6.0, 
     135    sld=-1, head_sld=4.1, solvent_sld=6.0, 
     136    tail_length_pd=0.1, tail_length_pd_n=20, 
     137    head_length_pd=0.05, head_length_pd_n=30, 
     138    spacing_pd=0.2, spacing_pd_n=40, 
     139    ) 
    140140 
    141141oldname = 'LamellarPSHGModel' 
    142 oldpars = dict(tail_length='deltaT',head_length='deltaH',Nlayers='n_plates',Caille_parameter='caille', sld='sld_tail', head_sld='sld_head',solvent_sld='sld_solvent') 
     142 
     143oldpars = dict( 
     144    tail_length='deltaT', head_length='deltaH', Nlayers='n_plates', 
     145    Caille_parameter='caille', sld='sld_tail', head_sld='sld_head', 
     146    solvent_sld='sld_solvent') 
     147# 
     148tests = [ 
     149        [ {'scale': 1.0, 'background' : 0.0, 'tail_length' : 10.0, 'head_length' : 2.0,'Nlayers' : 30.0, 'spacing' : 40., 
     150            'Caille_parameter' : 0.001, 'sld' : 0.4, 'head_sld' : 2.0, 'solvent_sld' : 6.0, 
     151            'tail_length_pd' : 0.0, 'head_length_pd' : 0.0, 'spacing_pd' : 0.0 }, [0.001], [6838238.571488]] 
     152        ] 

  • sasmodels/models/lamellarFFHG.py

    reb69cce r7f47777  
    121121oldpars = dict(head_length='h_thickness', tail_length='t_length', 
    122122               sld='sld_tail', head_sld='sld_head', solvent_sld='sld_solvent') 
     123# 
     124tests = [ 
     125        [ {'scale': 1.0, 'background' : 0.0, 'tail_length' : 15.0, 'head_length' : 10.0,'sld' : 0.4, 
     126         'head_sld' : 3.0, 'solvent_sld' : 6.0, }, [0.001], [653143.9209]] 
     127        ] 
    123128 
     129 

  • sasmodels/models/lamellarPC.py

    rd18f8a8 r13ed84c  
    111111category = "shape:lamellae" 
    112112 
     113single = False 
     114 
    113115#             ["name", "units", default, [lower, upper], "type","description"], 
    114116parameters = [["thickness", "Ang", 33.0, [0, inf], "volume", 
    … …  
    148150oldpars = dict(spacing_polydisp='pd_spacing', sld='sld_layer', 
    149151               solvent_sld='sld_solvent') 
     152# 
     153tests = [ 
     154        [ {'scale': 1.0, 'background' : 0.0, 'thickness' : 33.,'Nlayers' : 20.0, 'spacing' : 250., 'spacing_polydisp' : 0.2, 
     155            'sld' : 1.0, 'solvent_sld' : 6.34, 
     156            'thickness_pd' : 0.0, 'thickness_pd_n' : 40 }, [0.001, 0.215268], [21829.3, 0.00487686]] 
     157        ] 

  • sasmodels/models/lib/Si.c

    r7d256c8 rf12357f  
    1313 
    1414                // Explicitly writing factorial values triples the speed of the calculation 
    15                 out_cos = 1/x - 2/pow(x,3) + 24/pow(x,5) - 720/pow(x,7) + 40320/pow(x,9); 
    16                 out_sin = 1/x - 6/pow(x,4) + 120/pow(x,6) - 5040/pow(x,8) + 362880/pow(x,10); 
     15                out_cos = 1./x - 2./pow(x,3) + 24./pow(x,5) - 720./pow(x,7); 
     16                out_sin = 1./pow(x,2) - 6./pow(x,4) + 120./pow(x,6) - 5040./pow(x,8); 
    1717 
    1818                out -= cos(x) * out_cos; 
    … …  
    2222 
    2323        // Explicitly writing factorial values triples the speed of the calculation 
    24         out = x - pow(x, 3)/18 + pow(x,5)/600 - pow(x,7)/35280 + pow(x,9)/3265920; 
     24        out = x - pow(x, 3)/18. + pow(x,5)/600. - pow(x,7)/35280. + pow(x,9)/3265920. - pow(x,11)/439084800.; 
    2525 
    26         //printf ("Si=%g %g\n", x, out); 
    2726        return out; 
    2827} 

  • sasmodels/models/lib/sph_j1c.c

    r9c461c7 ra3e78c3  
    11/** 
    2 * Spherical Bessel function j1(x)/x 
     2* Spherical Bessel function 3*j1(x)/x 
    33* 
    44* Used for low q to avoid cancellation error. 
    … …  
    88* requires the first 3 terms.  Double precision requires the 4th term. 
    99* The fifth term is not needed, and is commented out. 
     10* Taylor expansion: 
     11*      1.0 + q2*(-3./30. + q2*(3./840.))+ q2*(-3./45360. + q2*(3./3991680.)))) 
     12* Expression returned from Herbie (herbie.uwpise.org/demo): 
     13*      const double t = ((1. + 3.*q2*q2/5600.) - q2/20.); 
     14*      return t*t; 
    1015*/ 
    1116 
    … …  
    1823    SINCOS(q, sin_q, cos_q); 
    1924 
    20     const double bessel = (q < 1.e-1) ? 
    21         1.0 + q2*(-3./30. + q2*(3./840. + q2*(-3./45360.))) // + q2*(3./3991680.))) 
     25    const double bessel = (q < 0.384038453352533) 
     26        ? (1.0 + q2*(-3./30. + q2*(3./840.))) 
    2227        : 3.0*(sin_q/q - cos_q)/q2; 
    2328 
    2429    return bessel; 
     30 
     31 /* 
     32    // Code to test various expressions 
     33    if (sizeof(q2) > 4) { 
     34        return 3.0*(sin_q/q - cos_q)/q2; 
     35    } else if (q < 0.384038453352533) { 
     36        //const double t = ((1. + 3.*q2*q2/5600.) - q2/20.); return t*t; 
     37        return 1.0 + q2*q2*(3./840.) - q2*(3./30.); 
     38        //return 1.0 + q2*(-3./30. + q2*(3./840.)); 
     39        //return 1.0 + q2*(-3./30. + q2*(3./840. + q2*(-3./45360.))); 
     40        //return 1.0 + q2*(-3./30. + q2*(3./840. + q2*(-3./45360. + q2*(3./3991680.)))); 
     41    } else { 
     42        return 3.0*(sin_q/q - cos_q)/q2; 
     43    } 
     44*/ 
    2545} 

  • sasmodels/models/mass_fractal.py

    r9c461c7 r168052c  
    5252--------- 
    5353 
    54 D Mildner and P Hall, *J. Phys. D: Appl. Phys.*,  19 (1986) 1535-1545 Equation(9) 
     54D Mildner and P Hall, *J. Phys. D: Appl. Phys.*, 
     5519 (1986) 1535-1545 Equation(9) 
    5556 
    5657 
    … …  
    7980category = "shape-independent" 
    8081 
     82# pylint: disable=bad-whitespace, line-too-long 
    8183#             ["name", "units", default, [lower, upper], "type","description"], 
    8284parameters = [["radius",        "Ang",  10.0, [0.0, inf], "", "Particle radius"], 
    8385              ["mass_dim",      "",      1.9, [1.0, 6.0], "", "Mass fractal dimension"], 
    8486              ["cutoff_length", "Ang", 100.0, [0.0, inf], "", "Cut-off length"], 
    85               ] 
    86  
     87             ] 
     88# pylint: enable=bad-whitespace, line-too-long 
    8789 
    8890source = ["lib/sph_j1c.c", "lib/lanczos_gamma.c", "mass_fractal.c"] 
    … …  
    9193            radius=10.0, 
    9294            mass_dim=1.9, 
    93             cutoff_length=2.3) 
     95            cutoff_length=100.0) 
    9496 
    9597oldname = 'MassFractalModel' 
    … …  
    98100               cutoff_length='co_length') 
    99101 
    100 tests = [[{'radius':        2.0, 
    101            'mass_dim':      3.3, 
    102            'cutoff_length': 1.0, 
    103            }, 0.5, 1.29016774904], 
     102tests = [ 
    104103 
    105          [{'radius':        1.0, 
    106            'mass_dim':      1.3, 
    107            'cutoff_length': 1.0, 
    108            'background':    0.8, 
    109            }, 0.001, 1.69747015932], 
     104    # Accuracy tests based on content in test/utest_other_models.py 
     105    [{'radius':         10.0, 
     106      'mass_dim':        1.9, 
     107      'cutoff_length': 100.0, 
     108     }, 0.05, 279.59322], 
    110109 
    111          [{'radius':        1.0, 
    112            'mass_dim':      2.3, 
    113            'cutoff_length': 1.0, 
    114            'scale':        10.0, 
    115            }, 0.051, 11.6227966145], 
    116          ] 
     110    # Additional tests with larger range of parameters 
     111    [{'radius':        2.0, 
     112      'mass_dim':      3.3, 
     113      'cutoff_length': 1.0, 
     114     }, 0.5, 1.29016774904], 
     115 
     116    [{'radius':        1.0, 
     117      'mass_dim':      1.3, 
     118      'cutoff_length': 1.0, 
     119      'background':    0.8, 
     120     }, 0.001, 1.69747015932], 
     121 
     122    [{'radius':        1.0, 
     123      'mass_dim':      2.3, 
     124      'cutoff_length': 1.0, 
     125      'scale':        10.0, 
     126     }, 0.051, 11.6227966145], 
     127    ] 

  • sasmodels/models/mass_surface_fractal.py

    r9c461c7 r168052c  
    5454P Schmidt, *J Appl. Cryst.*, 24 (1991) 414-435 Equation(19) 
    5555 
    56 A J Hurd, D W Schaefer, J E Martin, *Phys. Rev. A*, 35 (1987) 2361-2364 Equation(2) 
     56A J Hurd, D W Schaefer, J E Martin, *Phys. Rev. A*, 
     5735 (1987) 2361-2364 Equation(2) 
    5758 
    5859""" 
    … …  
    7980category = "shape-independent" 
    8081 
     82# pylint: disable=bad-whitespace, line-too-long 
    8183#             ["name", "units", default, [lower, upper], "type","description"], 
    8284parameters = [["mass_dim",      "",    1.8, [1e-16, 6.0], "", 
    … …  
    8890              ["primary_rg", "Ang", 4000.,  [0.0, inf], "", 
    8991               "Primary particle radius of gyration"], 
    90               ] 
    91  
     92             ] 
     93# pylint: enable=bad-whitespace, line-too-long 
    9294 
    9395source = ["mass_surface_fractal.c"] 
    … …  
    106108               primary_rg='primary_rg') 
    107109 
    108 tests = [[{'radius':        2.0, 
    109            'mass_dim':      3.3, 
    110            'surface_dim':   1.0, 
    111            'cluster_rg':   90.0, 
    112            'primary_rg': 4000.0, 
    113            }, 0.001, 0.18462699016], 
     110tests = [ 
    114111 
    115          [{'radius':        1.0, 
    116            'mass_dim':      1.3, 
    117            'surface_dim':   1.0, 
    118            'cluster_rg':   90.0, 
    119            'primary_rg': 2000.0, 
    120            'background':    0.8, 
    121            }, 0.001, 1.16539753641], 
     112    # Accuracy tests based on content in test/utest_other_models.py 
     113    [{'mass_dim':      1.8, 
     114      'surface_dim':   2.3, 
     115      'cluster_rg':   86.7, 
     116      'primary_rg': 4000.0, 
     117     }, 0.05, 1.77537e-05], 
    122118 
    123          [{'radius':        1.0, 
    124            'mass_dim':      2.3, 
    125            'surface_dim':   1.0, 
    126            'cluster_rg':   90.0, 
    127            'primary_rg': 1000.0, 
    128            'scale':        10.0, 
    129            }, 0.051, 0.000169548800377], 
    130          ] 
     119    # Additional tests with larger range of parameters 
     120    [{'mass_dim':      3.3, 
     121      'surface_dim':   1.0, 
     122      'cluster_rg':   90.0, 
     123      'primary_rg': 4000.0, 
     124     }, 0.001, 0.18462699016], 
     125 
     126    [{'mass_dim':      1.3, 
     127      'surface_dim':   1.0, 
     128      'cluster_rg':   90.0, 
     129      'primary_rg': 2000.0, 
     130      'background':    0.8, 
     131     }, 0.001, 1.16539753641], 
     132 
     133    [{'mass_dim':      2.3, 
     134      'surface_dim':   1.0, 
     135      'cluster_rg':   90.0, 
     136      'primary_rg': 1000.0, 
     137      'scale':        10.0, 
     138     }, 0.051, 0.000169548800377], 
     139    ] 

  • sasmodels/models/pearl_necklace.c

    rcf404cb rf12357f  
    1212        double string_thickness, double number_of_pearls, double sld,  
    1313        double string_sld, double solvent_sld); 
    14          
    15 double ER(double radius, double edge_separation, 
    16         double string_thickness, double number_of_pearls); 
    17 double VR(double radius, double edge_separation, 
    18         double string_thickness, double number_of_pearls); 
    1914 
    2015// From Igor library 
    … …  
    2217        double num_pearls, double sld_pearl, double sld_string, double sld_solv) 
    2318{ 
     19        //relative slds 
    2420        double contrast_pearl = sld_pearl - sld_solv; 
    2521        double contrast_string = sld_string - sld_solv; 
     22         
     23        // number of string segments 
     24        num_pearls = floor(num_pearls + 0.5); //Force integer number of pearls 
     25        double num_strings = num_pearls - 1.0; 
     26         
     27        //Pi 
     28        double pi = 4.0*atan(1.0); 
     29         
     30        // center to center distance between the neighboring pearls 
     31        double A_s = edge_separation + 2.0 * radius; 
     32         
     33        // Repeated Calculations 
     34        double sincasq = sinc(q*A_s); 
     35        double oneminussinc = 1 - sincasq; 
     36        double q_r = q * radius; 
     37        double q_edge = q * edge_separation; 
     38         
     39        // each volume 
     40        double string_vol = edge_separation * pi * thick_string * thick_string / 4.0; 
     41        double pearl_vol = 4.0 / 3.0 * pi * radius * radius * radius; 
    2642 
    2743        //total volume 
    28         double pi = 4.0*atan(1.0); 
    29         double tot_vol = form_volume(radius, edge_separation, thick_string, num_pearls); 
    30         double string_vol = edge_separation * pi * pow((thick_string / 2.0), 2); 
    31         double pearl_vol = 4.0 /3.0 * pi * pow(radius, 3); 
    32         double num_strings = num_pearls - 1; 
    33         //each mass 
     44        double tot_vol; 
     45        //each masses 
    3446        double m_r= contrast_string * string_vol; 
    3547        double m_s= contrast_pearl * pearl_vol; 
    3648        double psi, gamma, beta; 
    3749        //form factors 
    38         double sss; //pearls 
    39         double srr; //strings 
    40         double srs; //cross 
    41         double A_s, srr_1, srr_2, srr_3; 
     50        double sss, srr, srs; //cross 
     51        double srr_1, srr_2, srr_3; 
    4252        double form_factor; 
     53        tot_vol = num_strings * string_vol; 
     54        tot_vol += num_pearls * pearl_vol; 
    4355 
    4456        //sine functions of a pearl 
    45         psi = sin(q*radius); 
    46         psi -= q * radius * cos(q * radius); 
    47         psi /= pow((q * radius), 3); 
     57        psi = sin(q_r); 
     58        psi -= q_r * cos(q_r); 
    4859        psi *= 3.0; 
     60        psi /= q_r * q_r * q_r; 
    4961 
    5062        // Note take only 20 terms in Si series: 10 terms may be enough though. 
    51         gamma = Si(q* edge_separation); 
    52         gamma /= (q* edge_separation); 
    53         beta = Si(q * (edge_separation + radius)); 
    54         beta -= Si(q * radius); 
    55         beta /= (q* edge_separation); 
    56  
    57         // center to center distance between the neighboring pearls 
    58         A_s = edge_separation + 2.0 * radius; 
     63        gamma = Si(q_edge); 
     64        gamma /= (q_edge); 
     65        beta = Si(q * (A_s - radius)); 
     66        beta -= Si(q_r); 
     67        beta /= q_edge; 
    5968 
    6069        // form factor for num_pearls 
    61         sss = 1.0 - pow(sinc(q*A_s), num_pearls ); 
    62         sss /= pow((1.0-sinc(q*A_s)), 2); 
    63         sss *= -sinc(q*A_s); 
    64         sss -= num_pearls/2.0; 
    65         sss += num_pearls/(1.0-sinc(q*A_s)); 
    66         sss *= 2.0 * pow((m_s*psi), 2); 
     70        sss = 1.0 - pow(sincasq, num_pearls); 
     71        sss /= oneminussinc * oneminussinc; 
     72        sss *= -sincasq; 
     73        sss -= num_pearls / 2.0; 
     74        sss += num_pearls / oneminussinc; 
     75        sss *= 2.0 * m_s * psi * m_s * psi; 
    6776 
    6877        // form factor for num_strings (like thin rods) 
    69         srr_1 = -pow(sinc(q*edge_separation/2.0), 2); 
     78        srr_1 = -sinc(q_edge/2.0) * sinc(q_edge/2.0); 
    7079 
    7180        srr_1 += 2.0 * gamma; 
    7281        srr_1 *= num_strings; 
    73         srr_2 = 2.0/(1.0-sinc(q*A_s)); 
    74         srr_2 *= num_strings * pow(beta, 2); 
    75         srr_3 = 1.0 - pow(sinc(q*A_s), num_strings); 
    76         srr_3 /= pow((1.0-sinc(q*A_s)), 2); 
    77         srr_3 *= -2.0 * pow(beta, 2); 
     82        srr_2 = 2.0/oneminussinc; 
     83        srr_2 *= num_strings; 
     84        srr_2 *= beta * beta; 
     85        srr_3 = 1.0 - pow(sincasq, num_strings); 
     86        srr_3 /= oneminussinc * oneminussinc; 
     87        srr_3 *= beta * beta; 
     88        srr_3 *= -2.0; 
    7889 
    7990        // total srr 
    8091        srr = srr_1 + srr_2 + srr_3; 
    81         srr *= pow(m_r, 2); 
     92        srr *= m_r * m_r; 
    8293 
    8394        // form factor for correlations 
    8495        srs = 1.0; 
    85         srs -= pow(sinc(q*A_s), num_strings); 
    86         srs /= pow((1.0-sinc(q*A_s)), 2); 
    87         srs *= -sinc(q*A_s); 
    88         srs += (num_strings/(1.0-sinc(q*A_s))); 
    89         srs *= 4.0 * (m_r * m_s * beta * psi); 
     96        srs -= pow(sincasq, num_strings); 
     97        srs /= oneminussinc * oneminussinc; 
     98        srs *= -sincasq; 
     99        srs += num_strings/oneminussinc; 
     100        srs *= 4.0; 
     101        srs *= (m_r * m_s * beta * psi); 
    90102 
    91103        form_factor = sss + srr + srs; 
    … …  
    103115        double number_of_strings = number_of_pearls - 1.0; 
    104116         
    105         double string_vol = edge_separation * pi * pow((string_thickness / 2.0), 2); 
    106         double pearl_vol = 4.0 /3.0 * pi * pow(radius, 3); 
     117        double string_vol = edge_separation * pi * string_thickness * string_thickness / 4.0; 
     118        double pearl_vol = 4.0 / 3.0 * pi * radius * radius * radius; 
    107119 
    108120        total_vol = number_of_strings * string_vol; 
    … …  
    124136        double string_sld, double solvent_sld) 
    125137{ 
    126         double value = 0.0; 
    127         double tot_vol = 0.0; 
     138        double value, tot_vol; 
     139         
     140        if (string_thickness >= radius || number_of_pearls <= 0) { 
     141                return NAN; 
     142        } 
    128143         
    129144        value = _pearl_necklace_kernel(q, radius, edge_separation, string_thickness, 
    … …  
    142157                sld, string_sld, solvent_sld)); 
    143158} 
    144  
    145  
    146 double ER(double radius, double edge_separation, 
    147         double string_thickness, double number_of_pearls) 
    148 { 
    149         double tot_vol = form_volume(radius, edge_separation, string_thickness, number_of_pearls); 
    150         double pi = 4.0*atan(1.0); 
    151  
    152     double rad_out = pow((3.0*tot_vol/4.0/pi), 0.33333); 
    153      
    154     return rad_out; 
    155 } 
    156 double VR(double radius, double edge_separation, 
    157         double string_thickness, double number_of_pearls) 
    158 { 
    159         return 1.0; 
    160 } 

  • sasmodels/models/pearl_necklace.py

    rcf404cb rd18582e  
    11r""" 
    2 This model provides the form factor for a pearl necklace composed of two  
    3 elements: *N* pearls (homogeneous spheres of radius *R*) freely jointed by *M*  
     2This model provides the form factor for a pearl necklace composed of two 
     3elements: *N* pearls (homogeneous spheres of radius *R*) freely jointed by *M* 
    44rods (like strings - with a total mass *Mw* = *M* \* *m*\ :sub:`r` + *N* \* *m*\ 
    5 :sub:`s`, and the string segment length (or edge separation) *l*  
     5:sub:`s`, and the string segment length (or edge separation) *l* 
    66(= *A* - 2\ *R*)). *A* is the center-to-center pearl separation distance. 
    77 
    … …  
    1313---------- 
    1414 
    15 The output of the scattering intensity function for the PearlNecklaceModel is  
     15The output of the scattering intensity function for the PearlNecklaceModel is 
    1616given by (Schweins, 2004) 
    1717 
    … …  
    3737    \beta(q) &= \frac{\int_{qR}^{q(A-R)}\frac{sin(t)}{t}dt}{ql} 
    3838 
    39 where the mass *m*\ :sub:`i` is (SLD\ :sub:`i` - SLD\ :sub:`solvent`) \*  
     39where the mass *m*\ :sub:`i` is (SLD\ :sub:`i` - SLD\ :sub:`solvent`) \* 
    4040(volume of the *N* pearls/rods). *V* is the total volume of the necklace. 
    4141 
    42 The 2D scattering intensity is the same as $P(q)$ above, regardless of the  
     42The 2D scattering intensity is the same as $P(q)$ above, regardless of the 
    4343orientation of the *q* vector. 
    4444 
    … …  
    5454REFERENCE 
    5555 
    56 R Schweins and K Huber, *Particle Scattering Factor of Pearl Necklace Chains*,  
     56R Schweins and K Huber, *Particle Scattering Factor of Pearl Necklace Chains*, 
    5757*Macromol. Symp.* 211 (2004) 25-42 2004 
    5858""" 
    5959 
    60 from numpy import inf 
     60from numpy import inf, pi 
    6161 
    6262name = "pearl_necklace" 
    … …  
    7979 
    8080#             ["name", "units", default, [lower, upper], "type","description"], 
    81 parameters = [["radius", "Angstrom", 80.0, [0, inf], "volume",  
     81parameters = [["radius", "Angstrom", 80.0, [0, inf], "volume", 
    8282               "Mean radius of the chained spheres"], 
    83               ["edge_separation", "Angstrom", 350.0, [0, inf], "volume",  
     83              ["edge_separation", "Angstrom", 350.0, [0, inf], "volume", 
    8484               "Mean separation of chained particles"], 
    85               ["string_thickness", "Angstrom", 2.5, [0, inf], "volume",  
     85              ["string_thickness", "Angstrom", 2.5, [0, inf], "volume", 
    8686               "Thickness of the chain linkage"], 
    87               ["number_of_pearls", "none", 3, [0, inf], "volume",  
     87              ["number_of_pearls", "none", 3, [0, inf], "volume", 
    8888               "Mean number of pearls in each necklace"], 
    89               ["sld", "Angstrom^2", 1.0, [-inf, inf], "",  
     89              ["sld", "Angstrom^2", 1.0, [-inf, inf], "", 
    9090               "Scattering length density of the chained spheres"], 
    91               ["string_sld", "Angstrom^2", 1.0, [-inf, inf], "",  
     91              ["string_sld", "Angstrom^2", 1.0, [-inf, inf], "", 
    9292               "Scattering length density of the chain linkage"], 
    93               ["solvent_sld", "Angstrom^2", 6.3, [-inf, inf], "",  
     93              ["solvent_sld", "Angstrom^2", 6.3, [-inf, inf], "", 
    9494               "Scattering length density of the solvent"], 
    95               ] 
     95             ] 
    9696 
    9797source = ["lib/Si.c", "pearl_necklace.c"] 
     98single = False  # use double precision unless told otherwise 
     99 
     100def volume(radius, edge_separation, string_thickness, number_of_pearls): 
     101    """ 
     102    Calculates the total particle volume of the necklace. 
     103    Redundant with form_volume. 
     104    """ 
     105    number_of_strings = number_of_pearls - 1.0 
     106    string_vol = edge_separation * pi * pow((string_thickness / 2.0), 2.0) 
     107    pearl_vol = 4.0 /3.0 * pi * pow(radius, 3.0) 
     108    total_vol = number_of_strings * string_vol 
     109    total_vol += number_of_pearls * pearl_vol 
     110    return total_vol 
     111 
     112def ER(radius, edge_separation, string_thickness, number_of_pearls): 
     113    """ 
     114    Calculation for effective radius. 
     115    """ 
     116    tot_vol = volume(radius, edge_separation, string_thickness, number_of_pearls) 
     117    rad_out = pow((3.0*tot_vol/4.0/pi), 0.33333) 
     118    return rad_out 
    98119 
    99120# parameters for demo 
    … …  
    110131# names and the target sasview model name. 
    111132oldname = 'PearlNecklaceModel' 
    112 oldpars = dict(scale='scale',background='background',radius='radius', 
     133oldpars = dict(scale='scale', background='background', radius='radius', 
    113134               number_of_pearls='num_pearls', solvent_sld='sld_solv', 
    114135               string_thickness='thick_string', sld='sld_pearl', 
    115136               string_sld='sld_string', edge_separation='edge_separation') 
     137 
     138tests = [[{}, 0.001, 17380.245], [{}, 'ER', 115.39502]] 

  • sasmodels/models/polymer_excl_volume.py

    r513efc5 r168052c  
    113113category = "shape-independent" 
    114114 
     115# pylint: disable=bad-whitespace, line-too-long 
    115116#             ["name", "units", default, [lower, upper], "type", "description"], 
    116117parameters = [["rg",        "Ang", 60.0, [0, inf],    "", "Radius of Gyration"], 
    117118              ["porod_exp", "",     3.0, [-inf, inf], "", "Porod exponent"], 
    118               ] 
     119             ] 
     120# pylint: enable=bad-whitespace, line-too-long 
    119121 
    120122 
    121 def Iq(q, rg, porod_exp): 
    122  
     123def Iq(q, 
     124       rg=60.0, 
     125       porod_exp=3.0): 
    123126    """ 
    124127    :param q:         Input q-value (float or [float, float]) 
    … …  
    131134    o2nu = 1.0/(2.0*nu) 
    132135 
    133     intensity = ((1.0/(nu*power(u, o2nu))) * (gamma(o2nu)*gammainc(o2nu, u) - 
     136    intensity = ((1.0/(nu*power(u, o2nu))) * 
     137                 (gamma(o2nu)*gammainc(o2nu, u) - 
    134138                  1.0/power(u, o2nu) * gamma(porod_exp) * 
    135139                  gammainc(porod_exp, u))) * (q > 0) + 1.0*(q <= 0) 
    … …  
    141145 
    142146def Iqxy(qx, qy, *args): 
    143         iq = Iq(sqrt(qx**2 + qy**2), *args) 
     147    """ 
     148    :param qx:   Input q_x-value 
     149    :param qy:   Input q_y-value 
     150    :param args: Remaining arguments 
     151    :return:     2D-Intensity 
     152    """ 
    144153 
    145         return iq 
     154    return Iq(sqrt(qx**2 + qy**2), *args) 
    146155 
    147156Iqxy.vectorized = True  # Iqxy accepts an array of qx, qy values 
    … …  
    157166               porod_exp='m') 
    158167 
    159 tests = [[{'rg': 10, 'porod_exp': 4.0}, 0.1, 0.723436675809], 
     168tests = [ 
     169    # Accuracy tests based on content in test/polyexclvol_default_igor.txt 
     170    [{'rg': 60, 'porod_exp': 3.0}, 0.001, 0.998801], 
     171    [{'rg': 60, 'porod_exp': 3.0}, 0.105363, 0.0162751], 
     172    [{'rg': 60, 'porod_exp': 3.0}, 0.665075, 6.56261e-05], 
    160173 
    161          [{'rg': 2.2, 'porod_exp': 22.0, 'background': 100.0}, 5.0, 100.0], 
    162  
    163          [{'rg': 1.1, 'porod_exp': 1, 'background': 10.0, 'scale': 1.25}, 
    164          20000., 10.0000712097] 
    165          ] 
     174    # Additional tests with larger range of parameters 
     175    [{'rg': 10, 'porod_exp': 4.0}, 0.1, 0.723436675809], 
     176    [{'rg': 2.2, 'porod_exp': 22.0, 'background': 100.0}, 5.0, 100.0], 
     177    [{'rg': 1.1, 'porod_exp': 1, 'background': 10.0, 'scale': 1.25}, 
     178     20000., 10.0000712097] 
     179    ] 

  • sasmodels/models/power_law.py

    reb69cce r841753c  
    1212    I(q) = \text{scale} \cdot q^{-\text{power}} + \text{background} 
    1313 
    14 Note the minus sign in front of the exponent. The exponent *power*  
     14Note the minus sign in front of the exponent. The exponent *power* 
    1515should therefore be entered as a **positive** number for fitting. 
    1616 
    17 Also note that unlike many other models, *scale* in this model  
    18 is NOT explicitly related to a volume fraction. Be careful if  
     17Also note that unlike many other models, *scale* in this model 
     18is NOT explicitly related to a volume fraction. Be careful if 
    1919combining this model with other models. 
    2020 
    … …  
    3131from numpy import inf, sqrt 
    3232 
    33 name =  "power_law" 
     33name = "power_law" 
    3434title = "Simple power law with a flat background" 
    3535 
    36 description = """\ 
    37         Evaluates the function 
    38         I(q) = scale * q^(-power) + background 
    39         NB: enter power as a positive number! 
    40         """ 
     36description = """ 
     37    Evaluates the function 
     38    I(q) = scale * q^(-power) + background 
     39    NB: enter power as a positive number! 
     40    """ 
    4141category = "shape-independent" 
    4242 
    … …  
    4545 
    4646# NB: Scale and Background are implicit parameters on every model 
    47 def Iq(q,power): 
     47def Iq(q, power): 
     48    # pylint: disable=missing-docstring 
    4849    inten = (q**-power) 
    4950    return inten 
    … …  
    5152 
    5253def Iqxy(qx, qy, *args): 
     54    # pylint: disable=missing-docstring 
    5355    return Iq(sqrt(qx ** 2 + qy ** 2), *args) 
    5456Iqxy.vectorized = True # Iqxy accepts an array of qx, qy values 
    … …  
    6466 
    6567tests = [ 
    66         [ {'scale': 1.0, 'power': 4.0, 'background' : 0.0}, [0.0106939, 0.469418], [7.64644e+07, 20.5949]] 
    67         ] 
     68    [{'scale': 1.0, 'power': 4.0, 'background' : 0.0}, 
     69     [0.0106939, 0.469418], [7.64644e+07, 20.5949]], 
     70    ] 

  • sasmodels/models/rpa.c

    r82c299f r13ed84c  
    205205  const double Kbb = 0.0; 
    206206  const double Kcc = 0.0; 
    207   const double Kdd = 0.0; 
     207  //const double Kdd = 0.0; 
    208208  const double Zaa = Kaa - Kad - Kad; 
    209209  const double Zab = Kab - Kad - Kbd; 
    … …  
    278278  const double Q12 = (-Mab*Mcc + Mac*Mcb)/DenQ; 
    279279  const double Q13 = ( Mab*Mbc - Mac*Mbb)/DenQ; 
    280   const double Q21 = (-Mba*Mcc + Mbc*Mca)/DenQ; 
     280  //const double Q21 = (-Mba*Mcc + Mbc*Mca)/DenQ; 
    281281  const double Q22 = ( Maa*Mcc - Mac*Mca)/DenQ; 
    282282  const double Q23 = (-Maa*Mbc + Mac*Mba)/DenQ; 
    283   const double Q31 = ( Mba*Mcb - Mbb*Mca)/DenQ; 
    284   const double Q32 = (-Maa*Mcb + Mab*Mca)/DenQ; 
     283  //const double Q31 = ( Mba*Mcb - Mbb*Mca)/DenQ; 
     284  //const double Q32 = (-Maa*Mcb + Mab*Mca)/DenQ; 
    285285  const double Q33 = ( Maa*Mbb - Mab*Mba)/DenQ; 
    286286 

  • sasmodels/models/star_polymer.py

    r55b283e8 r13ed84c  
    5555        """ 
    5656category = "shape-independent" 
    57  
     57single = False 
     58# pylint: disable=bad-whitespace, line-too-long 
    5859#             ["name", "units", default, [lower, upper], "type","description"], 
    5960parameters = [["radius2", "Ang", 100.0, [0.0, inf], "", "Ensemble radius of gyration squared of an arm"], 
    6061              ["arms",    "",      3,   [1.0, 6.0], "", "Number of arms in the model"], 
    61               ] 
    62  
     62             ] 
     63# pylint: enable=bad-whitespace, line-too-long 
    6364 
    6465source = ["star_polymer.c"] 
    … …  
    7576tests = [[{'radius2': 2.0, 
    7677           'arms':    3.3, 
    77            }, 0.5, 0.850646091108], 
     78          }, 0.5, 0.850646091108], 
    7879 
    7980         [{'radius2':    1.0, 
    8081           'arms':       2.0, 
    8182           'background': 1.8, 
    82            }, 1.0, 2.53575888234], 
    83          ] 
     83          }, 1.0, 2.53575888234], 
     84        ] 

  • sasmodels/models/stickyhardsphere.py

    rd18f8a8 r13ed84c  
    8585category = "structure-factor" 
    8686 
     87single = False 
    8788#             ["name", "units", default, [lower, upper], "type","description"], 
    8889parameters = [ 
    … …  
    182183demo = dict(effect_radius=200, volfraction=0.2, perturb=0.05, 
    183184            stickiness=0.2, effect_radius_pd=0.1, effect_radius_pd_n=40) 
     185# 
     186tests = [ 
     187        [ {'scale': 1.0, 'background' : 0.0, 'effect_radius' : 50.0, 'perturb' : 0.05, 'stickiness' : 0.2, 'volfraction' : 0.1, 
     188           'effect_radius_pd' : 0}, [0.001], [1.09718]] 
     189        ] 
    184190 
     191 

  • sasmodels/models/surface_fractal.py

    r9c461c7 r168052c  
    8080category = "shape-independent" 
    8181 
     82# pylint: disable=bad-whitespace, line-too-long 
    8283#             ["name", "units", default, [lower, upper], "type","description"], 
    8384parameters = [["radius",        "Ang", 10.0, [0, inf],   "", 
    … …  
    8788              ["cutoff_length", "Ang", 500., [0.0, inf], "", 
    8889               "Cut-off Length"], 
    89               ] 
    90  
     90             ] 
     91# pylint: enable=bad-whitespace, line-too-long 
    9192 
    9293source = ["lib/sph_j1c.c", "lib/lanczos_gamma.c", "surface_fractal.c"] 
    … …  
    100101               cutoff_length='co_length') 
    101102 
    102 tests = [[{'radius': 1.0, 'surface_dim': 1.0, 'cutoff_length': 10.0, 
    103            }, 0.332070182643, 1125.00321004], 
     103tests = [ 
     104    # Accuracy tests based on content in test/utest_other_models.py 
     105    [{'radius': 10.0, 
     106      'surface_dim': 2.0, 
     107      'cutoff_length': 500.0, 
     108     }, 0.05, 301428.65916], 
    104109 
    105          [{'radius': 3.5, 'surface_dim': 0.1, 'cutoff_length': 30.0, 
    106            'background': 0.01, 
    107            }, 5.0, 0.00999998891322], 
     110    # Additional tests with larger range of parameters 
     111    [{'radius': 1.0, 
     112      'surface_dim': 1.0, 
     113      'cutoff_length': 10.0, 
     114     }, 0.332070182643, 1125.00321004], 
    108115 
    109          [{'radius': 3.0, 'surface_dim': 1.0, 'cutoff_length': 33.0, 
    110            'scale': 0.1, 
    111            }, 0.51, 2.50020147004], 
    112          ] 
     116    [{'radius': 3.5, 
     117      'surface_dim': 0.1, 
     118      'cutoff_length': 30.0, 
     119      'background': 0.01, 
     120     }, 5.0, 0.00999998891322], 
     121 
     122    [{'radius': 3.0, 
     123      'surface_dim': 1.0, 
     124      'cutoff_length': 33.0, 
     125      'scale': 0.1, 
     126     }, 0.51, 2.50020147004], 
     127    ] 

  • sasmodels/models/two_lorentzian.py

    r513efc5 r168052c  
    5454category = "shape-independent" 
    5555 
     56# pylint: disable=bad-whitespace, line-too-long 
    5657#            ["name", "units", default, [lower, upper], "type", "description"], 
    57 parameters = [["lorentz_scale_1",  "",     10.0, [-inf, inf], "", 
    58                "First power law scale factor"], 
    59               ["lorentz_length_1", "Ang", 100.0, [-inf, inf], "", 
    60                "First Lorentzian screening length"], 
    61               ["lorentz_exp_1",    "",      3.0, [-inf, inf], "", 
    62                "First exponent of power law"], 
    63               ["lorentz_scale_2",  "",      1.0, [-inf, inf], "", 
    64                "Second scale factor for broad Lorentzian peak"], 
    65               ["lorentz_length_2", "Ang",  10.0, [-inf, inf], "", 
    66                "Second Lorentzian screening length"], 
    67               ["lorentz_exp_2",    "",      2.0, [-inf, inf], "", 
    68                "Second exponent of power law"], 
    69               ] 
     58parameters = [["lorentz_scale_1",  "",     10.0, [-inf, inf], "", "First power law scale factor"], 
     59              ["lorentz_length_1", "Ang", 100.0, [-inf, inf], "", "First Lorentzian screening length"], 
     60              ["lorentz_exp_1",    "",      3.0, [-inf, inf], "", "First exponent of power law"], 
     61              ["lorentz_scale_2",  "",      1.0, [-inf, inf], "", "Second scale factor for broad Lorentzian peak"], 
     62              ["lorentz_length_2", "Ang",  10.0, [-inf, inf], "", "Second Lorentzian screening length"], 
     63              ["lorentz_exp_2",    "",      2.0, [-inf, inf], "", "Second exponent of power law"], 
     64             ] 
     65# pylint: enable=bad-whitespace, line-too-long 
    7066 
    7167 
    7268def Iq(q, 
    73        lorentz_scale_1, 
    74        lorentz_length_1, 
    75        lorentz_exp_1, 
    76        lorentz_scale_2, 
    77        lorentz_length_2, 
    78        lorentz_exp_2): 
     69       lorentz_scale_1=10.0, 
     70       lorentz_length_1=100.0, 
     71       lorentz_exp_1=3.0, 
     72       lorentz_scale_2=1.0, 
     73       lorentz_length_2=10.0, 
     74       lorentz_exp_2=2.0): 
    7975 
    8076    """ 
    … …  
    8884    :return:                    Calculated intensity 
    8985    """ 
    90  
     86# pylint: disable=bad-whitespace 
    9187    intensity  = lorentz_scale_1/(1.0 + 
    9288                                  power(q*lorentz_length_1, lorentz_exp_1)) 
    9389    intensity += lorentz_scale_2/(1.0 + 
    9490                                  power(q*lorentz_length_2, lorentz_exp_2)) 
     91# pylint: enable=bad-whitespace 
    9592 
    9693    return intensity 
    … …  
    10097 
    10198def Iqxy(qx, qy, *args): 
    102         iq = Iq(sqrt(qx**2 + qy**2), *args) 
     99    """ 
     100    :param qx:   Input q_x-value 
     101    :param qy:   Input q_y-value 
     102    :param args: Remaining arguments 
     103    :return:     2D-Intensity 
     104    """ 
    103105 
    104         return iq 
     106    return Iq(sqrt(qx**2 + qy**2), *args) 
    105107 
    106108Iqxy.vectorized = True  # Iqxy accepts an array of qx, qy values 
    … …  
    108110 
    109111demo = dict(scale=1, background=0.1, 
    110             lorentz_scale_1=10, lorentz_length_1=100.0, lorentz_exp_1=3.0, 
    111             lorentz_scale_2=1,  lorentz_length_2=10,    lorentz_exp_2=2.0) 
     112            lorentz_scale_1=10, 
     113            lorentz_length_1=100.0, 
     114            lorentz_exp_1=3.0, 
     115            lorentz_scale_2=1, 
     116            lorentz_length_2=10, 
     117            lorentz_exp_2=2.0) 
    112118 
    113119oldname = "TwoLorentzianModel" 
    114120oldpars = dict(background='background', 
    115                lorentz_scale_1='scale_1',   lorentz_scale_2='scale_2', 
    116                lorentz_length_1='length_1', lorentz_length_2='length_2', 
    117                lorentz_exp_1='exponent_1',  lorentz_exp_2='exponent_2') 
     121               lorentz_scale_1='scale_1', 
     122               lorentz_scale_2='scale_2', 
     123               lorentz_length_1='length_1', 
     124               lorentz_length_2='length_2', 
     125               lorentz_exp_1='exponent_1', 
     126               lorentz_exp_2='exponent_2') 
    118127 
    119 tests = [[{'lorentz_scale_1':   10.0, 
    120            'lorentz_length_1': 100.0, 
    121            'lorentz_exp_1':      3.0, 
    122            'lorentz_scale_2':    1.0, 
    123            'lorentz_length_2':  10.0, 
    124            'lorentz_exp_2':      2.0, 
    125            }, 0.000332070182643, 10.9996228107], 
     128tests = [ 
    126129 
    127          [{'lorentz_scale_1':  0.0, 
    128            'lorentz_length_1': 0.0, 
    129            'lorentz_exp_1':    0.0, 
    130            'lorentz_scale_2':  0.0, 
    131            'lorentz_length_2': 0.0, 
    132            'lorentz_exp_2':    0.0, 
    133            'background':     100.0 
    134            }, 5.0, 100.0], 
     130    # Accuracy tests based on content in test/utest_extra_models.py 
     131    [{'lorentz_scale_1':   10.0, 
     132      'lorentz_length_1': 100.0, 
     133      'lorentz_exp_1':      3.0, 
     134      'lorentz_scale_2':    1.0, 
     135      'lorentz_length_2':  10.0, 
     136      'lorentz_exp_2':      2.0, 
     137      'background':         0.1, 
     138     }, 0.001, 11.08991], 
    135139 
    136          [{'lorentz_scale_1': 200.0, 
    137            'lorentz_length_1': 10.0, 
    138            'lorentz_exp_1':     0.1, 
    139            'lorentz_scale_2':   0.1, 
    140            'lorentz_length_2':  5.0, 
    141            'lorentz_exp_2':     2.0 
    142            }, 20000., 45.5659201896], 
    143          ] 
     140    [{'lorentz_scale_1':   10.0, 
     141      'lorentz_length_1': 100.0, 
     142      'lorentz_exp_1':      3.0, 
     143      'lorentz_scale_2':    1.0, 
     144      'lorentz_length_2':  10.0, 
     145      'lorentz_exp_2':      2.0, 
     146      'background':         0.1, 
     147     }, 0.150141, 0.410245], 
     148 
     149    [{'lorentz_scale_1':   10.0, 
     150      'lorentz_length_1': 100.0, 
     151      'lorentz_exp_1':      3.0, 
     152      'lorentz_scale_2':    1.0, 
     153      'lorentz_length_2':  10.0, 
     154      'lorentz_exp_2':      2.0, 
     155      'background':         0.1, 
     156     }, 0.442528, 0.148699], 
     157 
     158    # Additional tests with larger range of parameters 
     159    [{'lorentz_scale_1':   10.0, 
     160      'lorentz_length_1': 100.0, 
     161      'lorentz_exp_1':      3.0, 
     162      'lorentz_scale_2':    1.0, 
     163      'lorentz_length_2':  10.0, 
     164      'lorentz_exp_2':      2.0, 
     165     }, 0.000332070182643, 10.9996228107], 
     166 
     167    [{'lorentz_scale_1':  0.0, 
     168      'lorentz_length_1': 0.0, 
     169      'lorentz_exp_1':    0.0, 
     170      'lorentz_scale_2':  0.0, 
     171      'lorentz_length_2': 0.0, 
     172      'lorentz_exp_2':    0.0, 
     173      'background':     100.0 
     174     }, 5.0, 100.0], 
     175 
     176    [{'lorentz_scale_1': 200.0, 
     177      'lorentz_length_1': 10.0, 
     178      'lorentz_exp_1':     0.1, 
     179      'lorentz_scale_2':   0.1, 
     180      'lorentz_length_2':  5.0, 
     181      'lorentz_exp_2':     2.0 
     182     }, 20000., 45.5659201896], 
     183    ] 

  • sasmodels/resolution.py

    r5258859 r5925e90  
    55""" 
    66from __future__ import division 
     7 
     8from scipy.special import erf 
     9from numpy import sqrt, log, log10 
     10import numpy as np 
    711 
    812__all__ = ["Resolution", "Perfect1D", "Pinhole1D", "Slit1D", 
    … …  
    1014           "pinhole_extend_q", "slit_extend_q", "bin_edges", 
    1115           "interpolate", "linear_extrapolation", "geometric_extrapolation", 
    12            ] 
    13  
    14 from scipy.special import erf 
    15 from numpy import sqrt, log, log10 
    16 import numpy as np 
     16          ] 
    1717 
    1818MINIMUM_RESOLUTION = 1e-8 
    … …  
    8080        # default to Perfect1D if the pinhole geometry is not defined. 
    8181        self.q, self.q_width = q, q_width 
    82         self.q_calc = pinhole_extend_q(q, q_width, nsigma=nsigma) \ 
    83             if q_calc is None else np.sort(q_calc) 
    84         self.weight_matrix = pinhole_resolution(self.q_calc, 
    85                 self.q, np.maximum(q_width, MINIMUM_RESOLUTION)) 
     82        self.q_calc = (pinhole_extend_q(q, q_width, nsigma=nsigma) 
     83                       if q_calc is None else np.sort(q_calc)) 
     84        self.weight_matrix = pinhole_resolution( 
     85            self.q_calc, self.q, np.maximum(q_width, MINIMUM_RESOLUTION)) 
    8686 
    8787    def apply(self, theory): 
    … …  
    135135    """ 
    136136    #print("apply shapes", theory.shape, weight_matrix.shape) 
    137     Iq = np.dot(theory[None,:], weight_matrix) 
     137    Iq = np.dot(theory[None, :], weight_matrix) 
    138138    #print("result shape",Iq.shape) 
    139139    return Iq.flatten() 
    … …  
    153153    # neither trapezoid nor Simpson's rule improved the accuracy. 
    154154    edges = bin_edges(q_calc) 
    155     edges[edges<0.0] = 0.0 # clip edges below zero 
    156     G = erf( (edges[:,None] - q[None,:]) / (sqrt(2.0)*q_width)[None,:] ) 
     155    edges[edges < 0.0] = 0.0 # clip edges below zero 
     156    G = erf((edges[:, None] - q[None, :]) / (sqrt(2.0)*q_width)[None, :]) 
    157157    weights = G[1:] - G[:-1] 
    158     weights /= np.sum(weights, axis=0)[None,:] 
     158    weights /= np.sum(weights, axis=0)[None, :] 
    159159    return weights 
    160160 
    … …  
    287287    # The current algorithm is a midpoint rectangle rule. 
    288288    q_edges = bin_edges(q_calc) # Note: requires q > 0 
    289     q_edges[q_edges<0.0] = 0.0 # clip edges below zero 
     289    q_edges[q_edges < 0.0] = 0.0 # clip edges below zero 
    290290    weights = np.zeros((len(q), len(q_calc)), 'd') 
    291291 
    … …  
    306306            #print(qi - h, qi + h) 
    307307            #print(in_x + abs_x) 
    308             weights[i,:] = (in_x + abs_x) * np.diff(q_edges) / (2*h) 
     308            weights[i, :] = (in_x + abs_x) * np.diff(q_edges) / (2*h) 
    309309        else: 
    310310            L = n_height 
    311311            for k in range(-L, L+1): 
    312                 weights[i,:] += _q_perp_weights(q_edges, qi+k*h/L, w) 
    313             weights[i,:] /= 2*L + 1 
     312                weights[i, :] += _q_perp_weights(q_edges, qi+k*h/L, w) 
     313            weights[i, :] /= 2*L + 1 
    314314 
    315315    return weights.T 
    … …  
    358358    log-scaled data. 
    359359    """ 
    360     if len(x) < 2 or (np.diff(x)<0).any(): 
     360    if len(x) < 2 or (np.diff(x) < 0).any(): 
    361361        raise ValueError("Expected bins to be an increasing set") 
    362362    edges = np.hstack([ 
    … …  
    373373    """ 
    374374    step = np.diff(q) 
    375     index = step>max_step 
     375    index = step > max_step 
    376376    if np.any(index): 
    377377        inserts = [] 
    378         for q_i,step_i in zip(q[:-1][index],step[index]): 
     378        for q_i, step_i in zip(q[:-1][index], step[index]): 
    379379            n = np.ceil(step_i/max_step) 
    380             inserts.extend(q_i + np.arange(1,n)*(step_i/n)) 
     380            inserts.extend(q_i + np.arange(1, n)*(step_i/n)) 
    381381        # Extend a couple of fringes beyond the end of the data 
    382         inserts.extend(q[-1] + np.arange(1,8)*max_step) 
    383         q_calc = np.sort(np.hstack((q,inserts))) 
     382        inserts.extend(q[-1] + np.arange(1, 8)*max_step) 
     383        q_calc = np.sort(np.hstack((q, inserts))) 
    384384    else: 
    385385        q_calc = q 
    … …  
    399399    if q_min < q[0]: 
    400400        if q_min <= 0: q_min = q_min*MIN_Q_SCALE_FOR_NEGATIVE_Q_EXTRAPOLATION 
    401         n_low = np.ceil((q[0]-q_min) / (q[1]-q[0])) if q[1]>q[0] else 15 
     401        n_low = np.ceil((q[0]-q_min) / (q[1]-q[0])) if q[1] > q[0] else 15 
    402402        q_low = np.linspace(q_min, q[0], n_low+1)[:-1] 
    403403    else: 
    404404        q_low = [] 
    405405    if q_max > q[-1]: 
    406         n_high = np.ceil((q_max-q[-1]) / (q[-1]-q[-2])) if q[-1]>q[-2] else 15 
     406        n_high = np.ceil((q_max-q[-1]) / (q[-1]-q[-2])) if q[-1] > q[-2] else 15 
    407407        q_high = np.linspace(q[-1], q_max, n_high+1)[1:] 
    408408    else: 
    … …  
    452452        if q_min < 0: q_min = q[0]*MIN_Q_SCALE_FOR_NEGATIVE_Q_EXTRAPOLATION 
    453453        n_low = log_delta_q * (log(q[0])-log(q_min)) 
    454         q_low  = np.logspace(log10(q_min), log10(q[0]), np.ceil(n_low)+1)[:-1] 
     454        q_low = np.logspace(log10(q_min), log10(q[0]), np.ceil(n_low)+1)[:-1] 
    455455    else: 
    456456        q_low = [] 
    … …  
    470470 
    471471def eval_form(q, form, pars): 
     472    """ 
     473    Return the SAS model evaluated at *q*. 
     474 
     475    *form* is the SAS model returned from :fun:`core.load_model`. 
     476 
     477    *pars* are the parameter values to use when evaluating. 
     478    """ 
    472479    from sasmodels import core 
    473480    kernel = core.make_kernel(form, [q]) 
    … …  
    478485 
    479486def gaussian(q, q0, dq): 
     487    """ 
     488    Return the Gaussian resolution function. 
     489 
     490    *q0* is the center, *dq* is the width and *q* are the points to evaluate. 
     491    """ 
    480492    from numpy import exp, pi 
    481493    return exp(-0.5*((q-q0)/dq)**2)/(sqrt(2*pi)*dq) 
    … …  
    489501    that make it slow to evaluate but give it good accuracy. 
    490502    """ 
    491     from scipy.integrate import romberg, dblquad 
     503    from scipy.integrate import romberg 
    492504 
    493505    if any(k not in form.info['defaults'] for k in pars.keys()): 
    … …  
    520532            result[i] = r/(2*h) 
    521533        else: 
    522             w_grid = np.linspace(0, w, 21)[None,:] 
    523             h_grid = np.linspace(-h, h, 23)[:,None] 
     534            w_grid = np.linspace(0, w, 21)[None, :] 
     535            h_grid = np.linspace(-h, h, 23)[:, None] 
    524536            u = sqrt((qi+h_grid)**2 + w_grid**2) 
    525537            Iu = np.interp(u, q_calc, Iq) 
    526538            #print(np.trapz(Iu, w_grid, axis=1)) 
    527             Is = np.trapz(np.trapz(Iu, w_grid, axis=1), h_grid[:,0]) 
     539            Is = np.trapz(np.trapz(Iu, w_grid, axis=1), h_grid[:, 0]) 
    528540            result[i] = Is / (2*h*w) 
    529             """ 
    530             r, err = dblquad(_int_wh, -h, h, lambda h: 0., lambda h: w, 
    531                              args=(qi,)) 
    532             result[i] = r/(w*2*h) 
    533             """ 
     541            # from scipy.integrate import dblquad 
     542            # r, err = dblquad(_int_wh, -h, h, lambda h: 0., lambda h: w, 
     543            #                  args=(qi,)) 
     544            # result[i] = r/(w*2*h) 
    534545 
    535546    # r should be [float, ...], but it is [array([float]), array([float]),...] 
    … …  
    553564 
    554565    _fn = lambda q, q0, dq: eval_form(q, form, pars)*gaussian(q, q0, dq) 
    555     r = [romberg(_fn, max(qi-nsigma*dqi,1e-10*q[0]), qi+nsigma*dqi, args=(qi, dqi), 
    556                  divmax=100, vec_func=True, tol=0, rtol=1e-8) 
    557          for qi,dqi in zip(q,q_width)] 
     566    r = [romberg(_fn, max(qi-nsigma*dqi, 1e-10*q[0]), qi+nsigma*dqi, 
     567                 args=(qi, dqi), divmax=100, vec_func=True, tol=0, rtol=1e-8) 
     568         for qi, dqi in zip(q, q_width)] 
    558569    return np.asarray(r).flatten() 
    559570 
    560571 
    561572class ResolutionTest(unittest.TestCase): 
     573    """ 
     574    Test the resolution calculations. 
     575    """ 
    562576 
    563577    def setUp(self): 
    … …  
    595609        theory = self.Iq(resolution.q_calc) 
    596610        output = resolution.apply(theory) 
    597         answer = [ 9.0618, 8.6402, 8.1187, 7.1392, 6.1528, 
    598                    5.5555, 4.5584, 3.5606, 2.5623, 2.0000 ] 
     611        answer = [ 
     612            9.0618, 8.6402, 8.1187, 7.1392, 6.1528, 
     613            5.5555, 4.5584, 3.5606, 2.5623, 2.0000, 
     614            ] 
    599615        np.testing.assert_allclose(output, answer, atol=1e-4) 
    600616 
    … …  
    608624        theory = 1000*self.Iq(resolution.q_calc**4) 
    609625        output = resolution.apply(theory) 
    610         answer = [ 8.85785, 8.43012, 7.92687, 6.94566, 6.03660, 
    611                    5.40363, 4.40655, 3.40880, 2.41058, 2.00000 ] 
     626        answer = [ 
     627            8.85785, 8.43012, 7.92687, 6.94566, 6.03660, 
     628            5.40363, 4.40655, 3.40880, 2.41058, 2.00000, 
     629            ] 
    612630        np.testing.assert_allclose(output, answer, atol=1e-4) 
    613631 
    … …  
    620638        theory = self.Iq(resolution.q_calc) 
    621639        output = resolution.apply(theory) 
    622         answer = [ 11.0, 10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0 ] 
     640        answer = [ 
     641            11.0, 10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 
     642            ] 
    623643        np.testing.assert_allclose(output, answer, atol=1e-4) 
    624644 
    … …  
    632652        theory = self.Iq(resolution.q_calc) 
    633653        output = resolution.apply(theory) 
    634         answer = [ 11.0, 10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0 ] 
     654        answer = [ 
     655            11.0, 10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 
     656            ] 
    635657        np.testing.assert_allclose(output, answer, atol=1e-4) 
    636658 
    … …  
    652674        theory = 12.0-1000.0*resolution.q_calc 
    653675        output = resolution.apply(theory) 
    654         answer = [ 10.44785079, 9.84991299, 8.98101708, 
    655                   7.99906585, 6.99998311, 6.00001689, 
    656                   5.00093415, 4.01898292, 3.15008701, 2.55214921] 
     676        answer = [ 
     677            10.44785079, 9.84991299, 8.98101708, 
     678            7.99906585, 6.99998311, 6.00001689, 
     679            5.00093415, 4.01898292, 3.15008701, 2.55214921, 
     680            ] 
    657681        np.testing.assert_allclose(output, answer, atol=1e-8) 
    658682 
    659683 
    660684class IgorComparisonTest(unittest.TestCase): 
     685    """ 
     686    Test resolution calculations against those returned by Igor. 
     687    """ 
    661688 
    662689    def setUp(self): 
    … …  
    666693        self.model = core.load_model(sphere, dtype='double') 
    667694 
    668     def Iq_sphere(self, pars, resolution): 
     695    def _eval_sphere(self, pars, resolution): 
    669696        from sasmodels import core 
    670697        kernel = core.make_kernel(self.model, [resolution.q_calc]) 
    … …  
    674701        return result 
    675702 
    676     def compare(self, q, output, answer, tolerance): 
     703    def _compare(self, q, output, answer, tolerance): 
    677704        #err = (output - answer)/answer 
    678705        #idx = abs(err) >= tolerance 
    … …  
    691718        q, width, answer, _ = data 
    692719        resolution = Perfect1D(q) 
    693         output = self.Iq_sphere(pars, resolution) 
    694         self.compare(q, output, answer, 1e-6) 
     720        output = self._eval_sphere(pars, resolution) 
     721        self._compare(q, output, answer, 1e-6) 
    695722 
    696723    def test_pinhole(self): 
    … …  
    704731        q, q_width, answer = data 
    705732        resolution = Pinhole1D(q, q_width) 
    706         output = self.Iq_sphere(pars, resolution) 
     733        output = self._eval_sphere(pars, resolution) 
    707734        # TODO: relative error should be lower 
    708         self.compare(q, output, answer, 3e-4) 
     735        self._compare(q, output, answer, 3e-4) 
    709736 
    710737    def test_pinhole_romberg(self): 
    … …  
    727754        q_calc, tol = None, 0.01 
    728755        resolution = Pinhole1D(q, q_width, q_calc=q_calc) 
    729         output = self.Iq_sphere(pars, resolution) 
     756        output = self._eval_sphere(pars, resolution) 
    730757        if 0: # debug plot 
    731758            import matplotlib.pyplot as plt 
    732759            resolution = Perfect1D(q) 
    733             source = self.Iq_sphere(pars, resolution) 
     760            source = self._eval_sphere(pars, resolution) 
    734761            plt.loglog(q, source, '.') 
    735762            plt.loglog(q, answer, '-', hold=True) 
    736763            plt.loglog(q, output, '-', hold=True) 
    737764            plt.show() 
    738         self.compare(q, output, answer, tol) 
     765        self._compare(q, output, answer, tol) 
    739766 
    740767    def test_slit(self): 
    … …  
    748775        q, delta_qv, _, answer = data 
    749776        resolution = Slit1D(q, width=delta_qv, height=0) 
    750         output = self.Iq_sphere(pars, resolution) 
     777        output = self._eval_sphere(pars, resolution) 
    751778        # TODO: eliminate Igor test since it is too inaccurate to be useful. 
    752779        # This means we can eliminate the test data as well, and instead 
    753780        # use a generated q vector. 
    754         self.compare(q, output, answer, 0.5) 
     781        self._compare(q, output, answer, 0.5) 
    755782 
    756783    def test_slit_romberg(self): 
    … …  
    768795                               delta_qv[0], 0.) 
    769796        resolution = Slit1D(q, width=delta_qv, height=0, q_calc=q_calc) 
    770         output = self.Iq_sphere(pars, resolution) 
     797        output = self._eval_sphere(pars, resolution) 
    771798        # TODO: relative error should be lower 
    772         self.compare(q, output, answer, 0.025) 
     799        self._compare(q, output, answer, 0.025) 
    773800 
    774801    def test_ellipsoid(self): 
    … …  
    783810            } 
    784811        form = load_model('ellipsoid', dtype='double') 
    785         q = np.logspace(log10(4e-5),log10(2.5e-2), 68) 
     812        q = np.logspace(log10(4e-5), log10(2.5e-2), 68) 
    786813        width, height = 0.117, 0. 
    787814        resolution = Slit1D(q, width=width, height=height) 
    … …  
    790817        # TODO: 10% is too much error; use better algorithm 
    791818        #print(np.max(abs(answer-output)/answer)) 
    792         self.compare(q, output, answer, 0.1) 
     819        self._compare(q, output, answer, 0.1) 
    793820 
    794821    #TODO: can sas q spacing be too sparse for the resolution calculation? 
    … …  
    804831        q, q_width, answer = data[:, ::20] # Take every nth point 
    805832        resolution = Pinhole1D(q, q_width) 
    806         output = self.Iq_sphere(pars, resolution) 
    807         self.compare(q, output, answer, 1e-6) 
     833        output = self._eval_sphere(pars, resolution) 
     834        self._compare(q, output, answer, 1e-6) 
    808835 
    809836 
    … …  
    10581085 
    10591086def demo_pinhole_1d(): 
     1087    """ 
     1088    Show example of pinhole smearing. 
     1089    """ 
    10601090    q = np.logspace(-4, np.log10(0.2), 400) 
    10611091    q_width = 0.1*q 
    … …  
    10641094 
    10651095def demo_slit_1d(): 
     1096    """ 
     1097    Show example of slit smearing. 
     1098    """ 
    10661099    q = np.logspace(-4, np.log10(0.2), 100) 
    10671100    w = h = 0. 
    … …  
    10711104    #h = 0.0277790 
    10721105    resolution = Slit1D(q, w, h) 
    1073     _eval_demo_1d(resolution, title="(%g,%g) Slit Resolution"%(w,h)) 
     1106    _eval_demo_1d(resolution, title="(%g,%g) Slit Resolution"%(w, h)) 
    10741107 
    10751108def demo(): 
     1109    """ 
     1110    Run the resolution demos. 
     1111    """ 
    10761112    import matplotlib.pyplot as plt 
    10771113    plt.subplot(121) 

  • sasmodels/resolution2d.py

    r9404dd3 r823e620  
    22#This software was developed by the University of Tennessee as part of the 
    33#Distributed Data Analysis of Neutron Scattering Experiments (DANSE) 
    4 #project funded by the US National Science Foundation.  
     4#project funded by the US National Science Foundation. 
    55#See the license text in license.txt 
    66""" 
    … …  
    1919## Defaults 
    2020NR = {'xhigh':10, 'high':5, 'med':5, 'low':3} 
    21 NPHI ={'xhigh':20, 'high':12, 'med':6, 'low':4} 
     21NPHI = {'xhigh':20, 'high':12, 'med':6, 'low':4} 
    2222 
    2323class Pinhole2D(Resolution): 
    … …  
    2525    Gaussian Q smearing class for SAS 2d data 
    2626    """ 
    27       
     27 
    2828    def __init__(self, data=None, index=None, 
    2929                 nsigma=NSIGMA, accuracy='Low', coords='polar'): 
    3030        """ 
    3131        Assumption: equally spaced bins in dq_r, dq_phi space. 
    32          
     32 
    3333        :param data: 2d data used to set the smearing parameters 
    3434        :param index: 1d array with len(data) to define the range 
    … …  
    4242        ## number of bins in r axis for over-sampling 
    4343        self.nr = NR[accuracy.lower()] 
    44         ## number of bins in phi axis for over-sampling  
     44        ## number of bins in phi axis for over-sampling 
    4545        self.nphi = NPHI[accuracy.lower()] 
    4646        ## maximum nsigmas 
    47         self.nsigma= nsigma 
     47        self.nsigma = nsigma 
    4848        self.coords = coords 
    4949        self._init_data(data, index) 
    … …  
    8585    def _calc_res(self): 
    8686        """ 
    87         Over sampling of r_nbins times phi_nbins, calculate Gaussian weights,  
     87        Over sampling of r_nbins times phi_nbins, calculate Gaussian weights, 
    8888        then find smeared intensity 
    89         """     
     89        """ 
    9090        nr, nphi = self.nr, self.nphi 
    9191        # Total number of bins = # of bins 
    … …  
    143143        if self.coords == 'polar': 
    144144            q_r = sqrt(qx**2 + qy**2) 
    145             qx_res = ( (dqx*cos(dphi) + q_r) * cos(-q_phi) + 
    146                           dqy*sin(dphi) * sin(-q_phi)) 
    147             qy_res = (-(dqx*cos(dphi) + q_r) * sin(-q_phi) + 
    148                           dqy*sin(dphi) * cos(-q_phi)) 
     145            qx_res = ((dqx*cos(dphi) + q_r) * cos(-q_phi) 
     146                      + dqy*sin(dphi) * sin(-q_phi)) 
     147            qy_res = (-(dqx*cos(dphi) + q_r) * sin(-q_phi) 
     148                      + dqy*sin(dphi) * cos(-q_phi)) 
    149149        else: 
    150             qx_res = qx +  dqx*cos(dphi) 
    151             qy_res = qy +  dqy*sin(dphi) 
     150            qx_res = qx + dqx*cos(dphi) 
     151            qy_res = qy + dqy*sin(dphi) 
    152152 
    153153 
    … …  
    162162            theory = np.reshape(theory, (nbins, nq)) 
    163163            ## Averaging with Gaussian weighting: normalization included. 
    164             value =np.average(theory, axis=0, weights=self.q_calc_weights) 
     164            value = np.average(theory, axis=0, weights=self.q_calc_weights) 
    165165            ## Return the smeared values in the range of self.index 
    166166            return value 
    167167        else: 
    168168            return theory 
    169  
    170 """ 
    171 if __name__ == '__main__': 
    172     ## Test w/ 2D linear function 
    173     x = 0.001*np.arange(1, 11) 
    174     dx = np.ones(len(x))*0.0003 
    175     y = 0.001*np.arange(1, 11) 
    176     dy = np.ones(len(x))*0.001 
    177     z = np.ones(10) 
    178     dz = sqrt(z) 
    179  
    180     from sas.dataloader import Data2D 
    181     #for i in range(10): print(i, 0.001 + i*0.008/9.0) 
    182     #for i in range(100): print(i, int(math.floor( (i/ (100/9.0)) ))) 
    183     out = Data2D() 
    184     out.data = z 
    185     out.qx_data = x 
    186     out.qy_data = y 
    187     out.dqx_data = dx 
    188     out.dqy_data = dy 
    189     out.q_data = sqrt(dx * dx + dy * dy) 
    190     index = np.ones(len(x), dtype = bool) 
    191     out.mask = index 
    192     from sas.models.LineModel import LineModel 
    193     model = LineModel() 
    194     model.setParam("A", 0) 
    195  
    196     smear = Smearer2D(out, model, index) 
    197     #smear.set_accuracy('Xhigh') 
    198     value = smear.get_value() 
    199     ## All data are ones, so the smeared should also be ones. 
    200     print("Data length =", len(value)) 
    201     print(" 2D linear function, I = 0 + 1*qy") 
    202     text = " Gaussian weighted averaging on a 2D linear function will " 
    203     text += "provides the results same as without the averaging." 
    204     print(text) 
    205     print("qx_data", "qy_data", "I_nonsmear", "I_smeared") 
    206     for ind in range(len(value)): 
    207         print(x[ind], y[ind], model.evalDistribution([x, y])[ind], value[ind]) 
    208  
    209  
    210 if __name__ == '__main__': 
    211     ## Another Test w/ constant function 
    212     x = 0.001*np.arange(1,11) 
    213     dx = np.ones(len(x))*0.001 
    214     y = 0.001*np.arange(1,11) 
    215     dy = np.ones(len(x))*0.001 
    216     z = np.ones(10) 
    217     dz = sqrt(z) 
    218  
    219     from DataLoader import Data2D 
    220     #for i in range(10): print(i, 0.001 + i*0.008/9.0) 
    221     #for i in range(100): print(i, int(math.floor( (i/ (100/9.0)) ))) 
    222     out = Data2D() 
    223     out.data = z 
    224     out.qx_data = x 
    225     out.qy_data = y 
    226     out.dqx_data = dx 
    227     out.dqy_data = dy 
    228     index = np.ones(len(x), dtype = bool) 
    229     out.mask = index 
    230     from sas.models.Constant import Constant 
    231     model = Constant() 
    232  
    233     value = Smearer2D(out,model,index).get_value() 
    234     ## All data are ones, so the smeared values should also be ones. 
    235     print("Data length =",len(value), ", Data=",value) 
    236 """ 

  • sasmodels/sasview_model.py

    r9404dd3 reafc9fa  
    283283        """ 
    284284        q_vectors = [np.asarray(q) for q in args] 
    285         fn = self._model(self._model.make_input(q_vectors)) 
     285        fn = self._model(q_vectors) 
    286286        pars = [self.params[v] for v in fn.fixed_pars] 
    287287        pd_pars = [self._get_weights(p) for p in fn.pd_pars] 

  • sasmodels/sesans.py

    r3c56da87 r190fc2b  
    1212import numpy as np 
    1313from numpy import pi, exp 
    14  
    1514from scipy.special import jv as besselj 
    1615 
    17 def make_q(q_zmax, Rmax): 
     16def make_q(q_max, Rmax): 
     17    r""" 
     18    Return a $q$ vector suitable for SESANS covering from $2\pi/ (10 R_{\max})$ 
     19    to $q_max$. 
     20    """ 
    1821    q_min = dq = 0.1 * 2*pi / Rmax 
    19     #q_min = 0.00003 
    20     return np.arange(q_min, q_zmax, dq) 
    21  
    22 # TODO: dead code; for now the call to the hankel transform happens in BumpsModel 
    23 class SesansCalculator: 
    24     def __init__(self, kernel, q_zmax, Rmax, SElength, wavelength, thickness): 
    25         self._set_kernel(kernel, q_zmax, Rmax) 
    26         self.SElength = SElength 
    27         self.wavelength = wavelength 
    28         self.thickness = thickness 
    29  
    30     def _set_kernel(self, kernel, q_zmax, Rmax): 
    31         kernel_input = kernel.make_input([make_q(q_zmax, Rmax)]) 
    32         self.sans_calculator = kernel(kernel_input) 
    33  
    34     def __call__(self, pars, pd_pars, cutoff=1e-5): 
    35         Iq = self.sans_calculator(pars, pd_pars, cutoff) 
    36         P = hankel(self.SElength, self.wavelength, self.thickness, self.q, Iq) 
    37         self.Iq = Iq 
    38         return P 
     22    return np.arange(q_min, q_max, dq) 
    3923 
    4024def hankel(SElength, wavelength, thickness, q, Iq): 
    41     """ 
     25    r""" 
    4226    Compute the expected SESANS polarization for a given SANS pattern. 
    4327 
    44     Uses the hankel transform followed by the exponential.  The values 
    45     for zz (or spin echo length, or delta), wavelength and sample thickness 
    46     information should come from the dataset.  *q* should be chosen such 
    47     that the oscillations in *I(q)* are well sampled (e.g., 5*2*pi/d_max). 
     28    Uses the hankel transform followed by the exponential.  The values for *zz* 
     29    (or spin echo length, or delta), wavelength and sample thickness should 
     30    come from the dataset.  $q$ should be chosen such that the oscillations 
     31    in $I(q)$ are well sampled (e.g., $5 \cdot 2 \pi/d_{\max}$). 
    4832 
    49     *SElength* [A] is the set of z points at which to compute the hankel transform 
     33    *SElength* [A] is the set of $z$ points at which to compute the 
     34    Hankel transform 
    5035 
    5136    *wavelength* [m]  is the wavelength of each individual point *zz* 
    … …  
    5338    *thickness* [cm] is the sample thickness. 
    5439 
    55     *q* [A^{-1}] is the set of q points at which the model has been computed. 
    56     These should be equally spaced. 
     40    *q* [A$^{-1}$] is the set of $q$ points at which the model has been 
     41    computed. These should be equally spaced. 
    5742 
    58     *I* [cm^{-1}] is the value of the SANS model at *q* 
     43    *I* [cm$^{-1}$] is the value of the SANS model at *q* 
    5944    """ 
    60     G = np.zeros(len(SElength), 'd') 
    61     for i in range(len(SElength)): 
    62         integr = besselj(0,q*SElength[i])*Iq*q 
    63         G[i] = np.sum(integr) 
    64     dq=(q[1]-q[0])*1e10   # [m^-1] step size in q, needed for integration 
    65     G *= dq*1e10*2*pi # integr step, conver q into [m**-1] and 2 pi circle integr 
     45    G = np.zeros_like(SElength, 'd') 
     46    for i, SElength_i in enumerate(SElength): 
     47        integral = besselj(0, q*SElength_i)*Iq*q 
     48        G[i] = np.sum(integral) 
     49 
     50    # [m^-1] step size in q, needed for integration 
     51    dq = (q[1]-q[0])*1e10 
     52 
     53    # integration step, convert q into [m**-1] and 2 pi circle integration 
     54    G *= dq*1e10*2*pi 
     55 
    6656    P = exp(thickness*wavelength**2/(4*pi**2)*(G-G[0])) 
    6757 

  • sasmodels/weights.py

    r3c56da87 r5c962df  
    2222 
    2323    def get_pars(self): 
     24        """ 
     25        Return the parameters to the disperser as a dictionary. 
     26        """ 
    2427        pars = {'type': self.type} 
    2528        pars.update(self.__dict__) 
    … …  
    2831    # pylint: disable=no-self-use 
    2932    def set_weights(self, values, weights): 
     33        """ 
     34        Set the weights on the disperser if it is :class:`ArrayDispersion`. 
     35        """ 
    3036        raise RuntimeError("set_weights is only available for ArrayDispersion") 
    3137 
    … …  
    3642        *center* is the center of the distribution 
    3743 
    38         *lb*,*ub* are the min and max allowed values 
     44        *lb*, *ub* are the min and max allowed values 
    3945 
    4046        *relative* is True if the distribution width is proportional to the 
    … …  
    6369 
    6470class GaussianDispersion(Dispersion): 
     71    r""" 
     72    Gaussian dispersion, with 1-\ $\sigma$ width. 
     73 
     74    .. math:: 
     75 
     76        w = \exp\left(-\tfrac12 (x - c)^2/\sigma^2\right) 
     77    """ 
    6578    type = "gaussian" 
    6679    default = dict(npts=35, width=0, nsigmas=3) 
    … …  
    7285 
    7386class RectangleDispersion(Dispersion): 
     87    r""" 
     88    Uniform dispersion, with width $\sqrt{3}\sigma$. 
     89 
     90    .. math:: 
     91 
     92        w = 1 
     93    """ 
    7494    type = "rectangle" 
    7595    default = dict(npts=35, width=0, nsigmas=1.70325) 
    … …  
    81101 
    82102class LogNormalDispersion(Dispersion): 
     103    r""" 
     104    log Gaussian dispersion, with 1-\ $\sigma$ width. 
     105 
     106    .. math:: 
     107 
     108        w = \frac{\exp\left(-\tfrac12 (\ln x - c)^2/\sigma^2\right)}{x\sigma} 
     109    """ 
    83110    type = "lognormal" 
    84111    default = dict(npts=80, width=0, nsigmas=8) 
    85112    def _weights(self, center, sigma, lb, ub): 
    86         x = self._linspace(center, sigma, max(lb,1e-8), max(ub,1e-8)) 
    87         px = np.exp(-0.5*(np.log(x)-center)**2)/sigma**2/(x*sigma) 
     113        x = self._linspace(center, sigma, max(lb, 1e-8), max(ub, 1e-8)) 
     114        px = np.exp(-0.5*(np.log(x)-center)**2/sigma**2)/(x*sigma) 
    88115        return x, px 
    89116 
    90117 
    91118class SchulzDispersion(Dispersion): 
     119    r""" 
     120    Schultz dispersion, with 1-\ $\sigma$ width. 
     121 
     122    .. math:: 
     123 
     124        w = \frac{z^z\,R^{z-1}}{e^{Rz}\,c \Gamma(z)} 
     125 
     126    where $c$ is the center of the distribution, $R = x/c$ and $z=(c/\sigma)^2$. 
     127 
     128    This is evaluated using logarithms as 
     129 
     130    .. math:: 
     131 
     132        w = \exp\left(z \ln z + (z-1)\ln R - Rz - \ln c - \ln \Gamma(z) \right) 
     133    """ 
    92134    type = "schulz" 
    93135    default = dict(npts=80, width=0, nsigmas=8) 
    94136    def _weights(self, center, sigma, lb, ub): 
    95         x = self._linspace(center, sigma, max(lb,1e-8), max(ub,1e-8)) 
    96         R= x/center 
     137        x = self._linspace(center, sigma, max(lb, 1e-8), max(ub, 1e-8)) 
     138        R = x/center 
    97139        z = (center/sigma)**2 
    98140        arg = z*np.log(z) + (z-1)*np.log(R) - R*z - np.log(center) - gammaln(z) 
    … …  
    102144 
    103145class ArrayDispersion(Dispersion): 
     146    r""" 
     147    Empirical dispersion curve. 
     148 
     149    Use :meth:`set_weights` to set $w = f(x)$. 
     150    """ 
    104151    type = "array" 
    105152    default = dict(npts=35, width=0, nsigmas=1) 
    … …  
    110157 
    111158    def set_weights(self, values, weights): 
     159        """ 
     160        Set the weights for the given x values. 
     161        """ 
    112162        self.values = np.ascontiguousarray(values, 'd') 
    113163        self.weights = np.ascontiguousarray(weights, 'd') 
    … …  
    117167        # TODO: interpolate into the array dispersion using npts 
    118168        x = center + self.values*sigma 
    119         idx = (x>=lb)&(x<=ub) 
     169        idx = (x >= lb) & (x <= ub) 
    120170        x = x[idx] 
    121171        px = self.weights[idx] 
    … …  
    124174 
    125175# dispersion name -> disperser lookup table. 
    126 models = dict((d.type,d) for d in ( 
     176models = dict((d.type, d) for d in ( 
    127177    GaussianDispersion, RectangleDispersion, 
    128178    ArrayDispersion, SchulzDispersion, LogNormalDispersion 
    … …  
    149199    of the parameter, and false if it is an absolute width. 
    150200 
    151     Returns *(value,weight)*, where *value* and *weight* are vectors. 
     201    Returns *(value, weight)*, where *value* and *weight* are vectors. 
    152202    """ 
    153203    cls = models[disperser] 
    154204    obj = cls(n, width, nsigmas) 
    155     v,w = obj.get_weights(value, limits[0], limits[1], relative) 
    156     return v,w 
     205    v, w = obj.get_weights(value, limits[0], limits[1], relative) 
     206    return v, w 
    157207 
    158208# Hack to allow sasview dispersion objects to interoperate with sasmodels 
    159 dispersers = dict((v.__name__,k) for k,v in models.items()) 
     209dispersers = dict((v.__name__, k) for k, v in models.items()) 
    160210dispersers['DispersionModel'] = RectangleDispersion.type 
    161211 
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