Changeset ce1eed5 in sasmodels


Ignore:
Timestamp:
Oct 22, 2018 3:23:00 AM (4 weeks ago)
Author:
wojciech
Branches:
master, beta_approx, py3, ticket-1015-gpu-mem-error, ticket-1015-quick-fix, ticket-1157, ticket-608-user-defined-weights, ticket_1156
Children:
a4f1a73
Parents:
afe206d (diff), 353e899 (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:

Fixed conflict after merging master

Location:
sasmodels
Files:
12 edited

Legend:

Unmodified
Added
Removed
  • sasmodels/__init__.py

    re65c3ba ra1ec908  
    1414defining new models. 
    1515""" 
    16 __version__ = "0.97" 
     16__version__ = "0.98" 
    1717 
    1818def data_files(): 
  • sasmodels/compare.py

    rbd7630d r610ef23  
    368368 
    369369    # Limit magnetic SLDs to a smaller range, from zero to iron=5/A^2 
    370     if par.name.startswith('M0:'): 
     370    if par.name.endswith('_M0'): 
    371371        return np.random.uniform(0, 5) 
    372372 
     
    538538    magnetic_pars = [] 
    539539    for p in parameters.user_parameters(pars, is2d): 
    540         if any(p.id.startswith(x) for x in ('M0:', 'mtheta:', 'mphi:')): 
     540        if any(p.id.endswith(x) for x in ('_M0', '_mtheta', '_mphi')): 
    541541            continue 
    542542        if p.id.startswith('up:'): 
     
    550550            pdtype=pars.get(p.id+"_pd_type", 'gaussian'), 
    551551            relative_pd=p.relative_pd, 
    552             M0=pars.get('M0:'+p.id, 0.), 
    553             mphi=pars.get('mphi:'+p.id, 0.), 
    554             mtheta=pars.get('mtheta:'+p.id, 0.), 
     552            M0=pars.get(p.id+'_M0', 0.), 
     553            mphi=pars.get(p.id+'_mphi', 0.), 
     554            mtheta=pars.get(p.id+'_mtheta', 0.), 
    555555        ) 
    556556        lines.append(_format_par(p.name, **fields)) 
     
    618618    if suppress: 
    619619        for p in pars: 
    620             if p.startswith("M0:"): 
     620            if p.endswith("_M0"): 
    621621                pars[p] = 0 
    622622    else: 
     
    624624        first_mag = None 
    625625        for p in pars: 
    626             if p.startswith("M0:"): 
     626            if p.endswith("_M0"): 
    627627                any_mag |= (pars[p] != 0) 
    628628                if first_mag is None: 
  • sasmodels/convert.py

    ra69d8cd r610ef23  
    165165    if version == (3, 1, 2): 
    166166        oldpars = _hand_convert_3_1_2_to_4_1(name, oldpars) 
     167    if version < (4, 2, 0): 
     168        oldpars = _rename_magnetic_pars(oldpars) 
    167169    return oldpars 
     170 
     171def _rename_magnetic_pars(pars): 
     172    """ 
     173    Change from M0:par to par_M0, etc. 
     174    """ 
     175    keys = list(pars.items()) 
     176    for k in keys: 
     177        if k.startswith('M0:'): 
     178            pars[k[3:]+'_M0'] = pars.pop(k) 
     179        elif k.startswith('mtheta:'): 
     180            pars[k[7:]+'_mtheta'] = pars.pop(k) 
     181        elif k.startswith('mphi:'): 
     182            pars[k[5:]+'_mphi'] = pars.pop(k) 
     183        elif k.startswith('up:'): 
     184            pars['up_'+k[3:]] = pars.pop(k) 
     185    return pars 
    168186 
    169187def _hand_convert_3_1_2_to_4_1(name, oldpars): 
  • sasmodels/custom/__init__.py

    r0f48f1e rd321747  
    1212import sys 
    1313import os 
    14 from os.path import basename, splitext 
     14from os.path import basename, splitext, join as joinpath, exists, dirname 
    1515 
    1616try: 
     
    1818    from importlib.util import spec_from_file_location, module_from_spec  # type: ignore 
    1919    def load_module_from_path(fullname, path): 
     20        # type: (str, str) -> "module" 
    2021        """load module from *path* as *fullname*""" 
    2122        spec = spec_from_file_location(fullname, os.path.expanduser(path)) 
     
    2728    import imp 
    2829    def load_module_from_path(fullname, path): 
     30        # type: (str, str) -> "module" 
    2931        """load module from *path* as *fullname*""" 
    3032        # Clear out old definitions, if any 
     
    3739        return module 
    3840 
     41_MODULE_CACHE = {} # type: Dict[str, Tuple("module", int)] 
     42_MODULE_DEPENDS = {} # type: Dict[str, List[str]] 
     43_MODULE_DEPENDS_STACK = [] # type: List[str] 
    3944def load_custom_kernel_module(path): 
     45    # type: str -> "module" 
    4046    """load SAS kernel from *path* as *sasmodels.custom.modelname*""" 
    4147    # Pull off the last .ext if it exists; there may be others 
    4248    name = basename(splitext(path)[0]) 
    43     # Placing the model in the 'sasmodels.custom' name space. 
    44     kernel_module = load_module_from_path('sasmodels.custom.'+name, 
    45                                           os.path.expanduser(path)) 
    46     return kernel_module 
     49    path = os.path.expanduser(path) 
     50 
     51    # Reload module if necessary. 
     52    if need_reload(path): 
     53        # Assume the module file is the only dependency 
     54        _MODULE_DEPENDS[path] = set([path]) 
     55 
     56        # Load the module while pushing it onto the dependency stack.  If 
     57        # this triggers any submodules, then they will add their dependencies 
     58        # to this module as the "working_on" parent.  Pop the stack when the 
     59        # module is loaded. 
     60        _MODULE_DEPENDS_STACK.append(path) 
     61        module = load_module_from_path('sasmodels.custom.'+name, path) 
     62        _MODULE_DEPENDS_STACK.pop() 
     63 
     64        # Include external C code in the dependencies.  We are looking 
     65        # for module.source and assuming that it is a list of C source files 
     66        # relative to the module itself.  Any files that do not exist, 
     67        # such as those in the standard libraries, will be ignored. 
     68        # TODO: look in builtin module path for standard c sources 
     69        # TODO: share code with generate.model_sources 
     70        c_sources = getattr(module, 'source', None) 
     71        if isinstance(c_sources, (list, tuple)): 
     72            _MODULE_DEPENDS[path].update(_find_sources(path, c_sources)) 
     73 
     74        # Cache the module, and tag it with the newest timestamp 
     75        timestamp = max(os.path.getmtime(f) for f in _MODULE_DEPENDS[path]) 
     76        _MODULE_CACHE[path] = module, timestamp 
     77 
     78        #print("loading", os.path.basename(path), _MODULE_CACHE[path][1], 
     79        #    [os.path.basename(p) for p in _MODULE_DEPENDS[path]]) 
     80 
     81    # Add path and all its dependence to the parent module, if there is one. 
     82    if _MODULE_DEPENDS_STACK: 
     83        working_on = _MODULE_DEPENDS_STACK[-1] 
     84        _MODULE_DEPENDS[working_on].update(_MODULE_DEPENDS[path]) 
     85 
     86    return _MODULE_CACHE[path][0] 
     87 
     88def need_reload(path): 
     89    # type: str -> bool 
     90    """ 
     91    Return True if any path dependencies have a timestamp newer than the time 
     92    when the path was most recently loaded. 
     93    """ 
     94    # TODO: fails if a dependency has a modification time in the future 
     95    # If the newest dependency has a time stamp in the future, then this 
     96    # will be recorded as the cached time.  When a second dependency 
     97    # is updated to the current time stamp, it will still be considered 
     98    # older than the current build and the reload will not be triggered. 
     99    # Could instead treat all future times as 0 here and in the code above 
     100    # which records the newest timestamp.  This will force a reload when 
     101    # the future time is reached, but other than that should perform 
     102    # correctly.  Probably not worth the extra code... 
     103    _, cache_time = _MODULE_CACHE.get(path, (None, -1)) 
     104    depends = _MODULE_DEPENDS.get(path, [path]) 
     105    #print("reload", any(cache_time < os.path.getmtime(p) for p in depends)) 
     106    #for f in depends: print(">>>  ", f, os.path.getmtime(f)) 
     107    return any(cache_time < os.path.getmtime(p) for p in depends) 
     108 
     109def _find_sources(path, source_list): 
     110    # type: (str, List[str]) -> List[str] 
     111    """ 
     112    Return a list of the sources relative to base file; ignore any that 
     113    are not found. 
     114    """ 
     115    root = dirname(path) 
     116    found = [] 
     117    for source_name in source_list: 
     118        source_path = joinpath(root, source_name) 
     119        if exists(source_path): 
     120            found.append(source_path) 
     121    return found 
  • sasmodels/modelinfo.py

    r7b9e4dd rbd547d0  
    466466        self.is_asymmetric = any(p.name == 'psi' for p in self.kernel_parameters) 
    467467        self.magnetism_index = [k for k, p in enumerate(self.call_parameters) 
    468                                 if p.id.startswith('M0:')] 
     468                                if p.id.endswith('_M0')] 
    469469 
    470470        self.pd_1d = set(p.name for p in self.call_parameters 
     
    586586        if self.nmagnetic > 0: 
    587587            full_list.extend([ 
    588                 Parameter('up:frac_i', '', 0., [0., 1.], 
     588                Parameter('up_frac_i', '', 0., [0., 1.], 
    589589                          'magnetic', 'fraction of spin up incident'), 
    590                 Parameter('up:frac_f', '', 0., [0., 1.], 
     590                Parameter('up_frac_f', '', 0., [0., 1.], 
    591591                          'magnetic', 'fraction of spin up final'), 
    592                 Parameter('up:angle', 'degrees', 0., [0., 360.], 
     592                Parameter('up_angle', 'degrees', 0., [0., 360.], 
    593593                          'magnetic', 'spin up angle'), 
    594594            ]) 
     
    596596            for p in slds: 
    597597                full_list.extend([ 
    598                     Parameter('M0:'+p.id, '1e-6/Ang^2', 0., [-np.inf, np.inf], 
     598                    Parameter(p.id+'_M0', '1e-6/Ang^2', 0., [-np.inf, np.inf], 
    599599                              'magnetic', 'magnetic amplitude for '+p.description), 
    600                     Parameter('mtheta:'+p.id, 'degrees', 0., [-90., 90.], 
     600                    Parameter(p.id+'_mtheta', 'degrees', 0., [-90., 90.], 
    601601                              'magnetic', 'magnetic latitude for '+p.description), 
    602                     Parameter('mphi:'+p.id, 'degrees', 0., [-180., 180.], 
     602                    Parameter(p.id+'_mphi', 'degrees', 0., [-180., 180.], 
    603603                              'magnetic', 'magnetic longitude for '+p.description), 
    604604                ]) 
     
    640640 
    641641        Parameters marked as sld will automatically have a set of associated 
    642         magnetic parameters (m0:p, mtheta:p, mphi:p), as well as polarization 
    643         information (up:theta, up:frac_i, up:frac_f). 
     642        magnetic parameters (p_M0, p_mtheta, p_mphi), as well as polarization 
     643        information (up_theta, up_frac_i, up_frac_f). 
    644644        """ 
    645645        # control parameters go first 
     
    668668            result.append(expanded_pars[name]) 
    669669            if is2d: 
    670                 for tag in 'M0:', 'mtheta:', 'mphi:': 
    671                     if tag+name in expanded_pars: 
    672                         result.append(expanded_pars[tag+name]) 
     670                for tag in '_M0', '_mtheta', '_mphi': 
     671                    if name+tag in expanded_pars: 
     672                        result.append(expanded_pars[name+tag]) 
    673673 
    674674        # Gather the user parameters in order 
     
    703703                append_group(p.id) 
    704704 
    705         if is2d and 'up:angle' in expanded_pars: 
     705        if is2d and 'up_angle' in expanded_pars: 
    706706            result.extend([ 
    707                 expanded_pars['up:frac_i'], 
    708                 expanded_pars['up:frac_f'], 
    709                 expanded_pars['up:angle'], 
     707                expanded_pars['up_frac_i'], 
     708                expanded_pars['up_frac_f'], 
     709                expanded_pars['up_angle'], 
    710710            ]) 
    711711 
     
    793793    info.structure_factor = getattr(kernel_module, 'structure_factor', False) 
    794794    info.profile_axes = getattr(kernel_module, 'profile_axes', ['x', 'y']) 
     795    # Note: custom.load_custom_kernel_module assumes the C sources are defined 
     796    # by this attribute. 
    795797    info.source = getattr(kernel_module, 'source', []) 
    796798    info.c_code = getattr(kernel_module, 'c_code', None) 
     
    10141016                         for k in range(control+1, p.length+1) 
    10151017                         if p.length > 1) 
     1018            for p in self.parameters.kernel_parameters: 
     1019                if p.length > 1 and p.type == "sld": 
     1020                    for k in range(control+1, p.length+1): 
     1021                        base = p.id+str(k) 
     1022                        hidden.update((base+"_M0", base+"_mtheta", base+"_mphi")) 
    10161023        return hidden 
  • sasmodels/models/bcc_paracrystal.py

    r2d81cfe rda7b26b  
    11r""" 
     2.. warning:: This model and this model description are under review following  
     3             concerns raised by SasView users. If you need to use this model,  
     4             please email help@sasview.org for the latest situation. *The  
     5             SasView Developers. September 2018.* 
     6 
    27Definition 
    38---------- 
     
    1318 
    1419    I(q) = \frac{\text{scale}}{V_p} V_\text{lattice} P(q) Z(q) 
    15  
    1620 
    1721where *scale* is the volume fraction of spheres, $V_p$ is the volume of the 
     
    97101 
    98102Authorship and Verification 
    99 ---------------------------- 
     103--------------------------- 
    100104 
    101105* **Author:** NIST IGOR/DANSE **Date:** pre 2010 
  • sasmodels/models/be_polyelectrolyte.py

    ref07e95 rca77fc1  
    11r""" 
     2.. note:: Please read the Validation section below. 
     3 
    24Definition 
    35---------- 
     
    1113 
    1214    I(q) = K\frac{q^2+k^2}{4\pi L_b\alpha ^2} 
    13     \frac{1}{1+r_{0}^2(q^2+k^2)(q^2-12hC_a/b^2)} + background 
     15    \frac{1}{1+r_{0}^4(q^2+k^2)(q^2-12hC_a/b^2)} + background 
    1416 
    1517    k^2 = 4\pi L_b(2C_s + \alpha C_a) 
    1618 
    17     r_{0}^2 = \frac{1}{\alpha \sqrt{C_a} \left( b/\sqrt{48\pi L_b}\right)} 
     19    r_{0}^2 = \frac{b}{\alpha \sqrt{C_a 48\pi L_b}} 
    1820 
    1921where 
    2022 
    2123$K$ is the contrast factor for the polymer which is defined differently than in 
    22 other models and is given in barns where $1 barn = 10^{-24} cm^2$.  $K$ is 
     24other models and is given in barns where 1 $barn = 10^{-24}$ $cm^2$.  $K$ is 
    2325defined as: 
    2426 
     
    2931    a = b_p - (v_p/v_s) b_s 
    3032 
    31 where $b_p$ and $b_s$ are sum of the scattering lengths of the atoms 
    32 constituting the monomer of the polymer and the sum of the scattering lengths 
    33 of the atoms constituting the solvent molecules respectively, and $v_p$ and 
    34 $v_s$ are the partial molar volume of the polymer and the solvent respectively 
    35  
    36 $L_b$ is the Bjerrum length(|Ang|) - **Note:** This parameter needs to be 
    37 kept constant for a given solvent and temperature! 
    38  
    39 $h$ is the virial parameter (|Ang^3|/mol) - **Note:** See [#Borue]_ for the 
    40 correct interpretation of this parameter.  It incorporates second and third 
    41 virial coefficients and can be Negative. 
    42  
    43 $b$ is the monomer length(|Ang|), $C_s$ is the concentration of monovalent 
    44 salt(mol/L), $\alpha$ is the ionization degree (ionization degree : ratio of 
    45 charged monomers  to total number of monomers), $C_a$ is the polymer molar 
    46 concentration(mol/L), and $background$ is the incoherent background. 
     33where: 
     34 
     35- $b_p$ and $b_s$ are **sum of the scattering lengths of the atoms** 
     36  constituting the polymer monomer and the solvent molecules, respectively. 
     37 
     38- $v_p$ and $v_s$ are the partial molar volume of the polymer and the  
     39  solvent, respectively. 
     40 
     41- $L_b$ is the Bjerrum length (|Ang|) - **Note:** This parameter needs to be 
     42  kept constant for a given solvent and temperature! 
     43 
     44- $h$ is the virial parameter (|Ang^3|) - **Note:** See [#Borue]_ for the 
     45  correct interpretation of this parameter.  It incorporates second and third 
     46  virial coefficients and can be *negative*. 
     47 
     48- $b$ is the monomer length (|Ang|). 
     49 
     50- $C_s$ is the concentration of monovalent salt(1/|Ang^3| - internally converted from mol/L). 
     51 
     52- $\alpha$ is the degree of ionization (the ratio of charged monomers to the total  
     53  number of monomers) 
     54 
     55- $C_a$ is the polymer molar concentration (1/|Ang^3| - internally converted from mol/L) 
     56 
     57- $background$ is the incoherent background. 
    4758 
    4859For 2D data the scattering intensity is calculated in the same way as 1D, 
     
    5263 
    5364    q = \sqrt{q_x^2 + q_y^2} 
     65 
     66Validation 
     67---------- 
     68 
     69As of the last revision, this code is believed to be correct.  However it 
     70needs further validation and should be used with caution at this time.  The 
     71history of this code goes back to a 1998 implementation. It was recently noted 
     72that in that implementation, while both the polymer concentration and salt 
     73concentration were converted from experimental units of mol/L to more 
     74dimensionally useful units of 1/|Ang^3|, only the converted version of the 
     75polymer concentration was actually being used in the calculation while the 
     76unconverted salt concentration (still in apparent units of mol/L) was being  
     77used.  This was carried through to Sasmodels as used for SasView 4.1 (though  
     78the line of code converting the salt concentration to the new units was removed  
     79somewhere along the line). Simple dimensional analysis of the calculation shows  
     80that the converted salt concentration should be used, which the original code  
     81suggests was the intention, so this has now been corrected (for SasView 4.2).  
     82Once better validation has been performed this note will be removed. 
    5483 
    5584References 
     
    6695 
    6796* **Author:** NIST IGOR/DANSE **Date:** pre 2010 
    68 * **Last Modified by:** Paul Kienzle **Date:** July 24, 2016 
    69 * **Last Reviewed by:** Paul Butler and Richard Heenan **Date:** October 07, 2016 
     97* **Last Modified by:** Paul Butler **Date:** September 25, 2018 
     98* **Last Reviewed by:** Paul Butler **Date:** September 25, 2018 
    7099""" 
    71100 
     
    92121    ["contrast_factor",       "barns",   10.0,  [-inf, inf], "", "Contrast factor of the polymer"], 
    93122    ["bjerrum_length",        "Ang",      7.1,  [0, inf],    "", "Bjerrum length"], 
    94     ["virial_param",          "Ang^3/mol", 12.0,  [-inf, inf], "", "Virial parameter"], 
     123    ["virial_param",          "Ang^3", 12.0,  [-inf, inf], "", "Virial parameter"], 
    95124    ["monomer_length",        "Ang",     10.0,  [0, inf],    "", "Monomer length"], 
    96125    ["salt_concentration",    "mol/L",    0.0,  [-inf, inf], "", "Concentration of monovalent salt"], 
     
    102131 
    103132def Iq(q, 
    104        contrast_factor=10.0, 
    105        bjerrum_length=7.1, 
    106        virial_param=12.0, 
    107        monomer_length=10.0, 
    108        salt_concentration=0.0, 
    109        ionization_degree=0.05, 
    110        polymer_concentration=0.7): 
     133       contrast_factor, 
     134       bjerrum_length, 
     135       virial_param, 
     136       monomer_length, 
     137       salt_concentration, 
     138       ionization_degree, 
     139       polymer_concentration): 
    111140    """ 
    112     :param q:                     Input q-value 
    113     :param contrast_factor:       Contrast factor of the polymer 
    114     :param bjerrum_length:        Bjerrum length 
    115     :param virial_param:          Virial parameter 
    116     :param monomer_length:        Monomer length 
    117     :param salt_concentration:    Concentration of monovalent salt 
    118     :param ionization_degree:     Degree of ionization 
    119     :param polymer_concentration: Polymer molar concentration 
    120     :return:                      1-D intensity 
     141    :params: see parameter table 
     142    :return: 1-D form factor for polyelectrolytes in low salt 
     143     
     144    parameter names, units, default values, and behavior (volume, sld etc) are 
     145    defined in the parameter table.  The concentrations are converted from 
     146    experimental mol/L to dimensionaly useful 1/A3 in first two lines 
    121147    """ 
    122148 
    123     concentration = polymer_concentration * 6.022136e-4 
    124  
    125     k_square = 4.0 * pi * bjerrum_length * (2*salt_concentration + 
    126                                             ionization_degree * concentration) 
    127  
    128     r0_square = 1.0/ionization_degree/sqrt(concentration) * \ 
     149    concentration_pol = polymer_concentration * 6.022136e-4 
     150    concentration_salt = salt_concentration * 6.022136e-4 
     151 
     152    k_square = 4.0 * pi * bjerrum_length * (2*concentration_salt + 
     153                                            ionization_degree * concentration_pol) 
     154 
     155    r0_square = 1.0/ionization_degree/sqrt(concentration_pol) * \ 
    129156                (monomer_length/sqrt((48.0*pi*bjerrum_length))) 
    130157 
     
    133160 
    134161    term2 = 1.0 + r0_square**2 * (q**2 + k_square) * \ 
    135         (q**2 - (12.0 * virial_param * concentration/(monomer_length**2))) 
     162        (q**2 - (12.0 * virial_param * concentration_pol/(monomer_length**2))) 
    136163 
    137164    return term1/term2 
     
    174201 
    175202    # Accuracy tests based on content in test/utest_other_models.py 
     203    # Note that these should some day be validated beyond this self validation 
     204    # (circular reasoning). -- i.e. the "good value," at least for those with 
     205    # non zero salt concentrations, were obtained by running the current 
     206    # model in SasView and copying the appropriate result here. 
     207    #    PDB -- Sep 26, 2018 
    176208    [{'contrast_factor':       10.0, 
    177209      'bjerrum_length':         7.1, 
     
    184216     }, 0.001, 0.0948379], 
    185217 
    186     # Additional tests with larger range of parameters 
    187218    [{'contrast_factor':       10.0, 
    188219      'bjerrum_length':       100.0, 
    189220      'virial_param':           3.0, 
    190       'monomer_length':         1.0, 
    191       'salt_concentration':    10.0, 
    192       'ionization_degree':      2.0, 
    193       'polymer_concentration': 10.0, 
     221      'monomer_length':         5.0, 
     222      'salt_concentration':     1.0, 
     223      'ionization_degree':      0.1, 
     224      'polymer_concentration':  1.0, 
    194225      'background':             0.0, 
    195      }, 0.1, -3.75693800588], 
     226     }, 0.1, 0.253469484], 
    196227 
    197228    [{'contrast_factor':       10.0, 
    198229      'bjerrum_length':       100.0, 
    199230      'virial_param':           3.0, 
    200       'monomer_length':         1.0, 
    201       'salt_concentration':    10.0, 
    202       'ionization_degree':      2.0, 
    203       'polymer_concentration': 10.0, 
    204       'background':           100.0 
    205      }, 5.0, 100.029142149], 
     231      'monomer_length':         5.0, 
     232      'salt_concentration':     1.0, 
     233      'ionization_degree':      0.1, 
     234      'polymer_concentration':  1.0, 
     235      'background':             1.0, 
     236     }, 0.05, 1.738358122], 
    206237 
    207238    [{'contrast_factor':     100.0, 
    208239      'bjerrum_length':       10.0, 
    209       'virial_param':        180.0, 
    210       'monomer_length':        1.0, 
     240      'virial_param':         12.0, 
     241      'monomer_length':       10.0, 
    211242      'salt_concentration':    0.1, 
    212243      'ionization_degree':     0.5, 
    213244      'polymer_concentration': 0.1, 
    214       'background':             0.0, 
    215      }, 200., 1.80664667511e-06], 
     245      'background':           0.01, 
     246     }, 0.5, 0.012881893], 
    216247    ] 
  • sasmodels/models/fcc_paracrystal.py

    r2d81cfe rda7b26b  
    33#note - calculation requires double precision 
    44r""" 
     5.. warning:: This model and this model description are under review following  
     6             concerns raised by SasView users. If you need to use this model,  
     7             please email help@sasview.org for the latest situation. *The  
     8             SasView Developers. September 2018.* 
     9 
     10Definition 
     11---------- 
     12 
    513Calculates the scattering from a **face-centered cubic lattice** with 
    614paracrystalline distortion. Thermal vibrations are considered to be 
     
    816Paracrystalline distortion is assumed to be isotropic and characterized by 
    917a Gaussian distribution. 
    10  
    11 Definition 
    12 ---------- 
    1318 
    1419The scattering intensity $I(q)$ is calculated as 
     
    2328is the paracrystalline structure factor for a face-centered cubic structure. 
    2429 
    25 Equation (1) of the 1990 reference is used to calculate $Z(q)$, using 
    26 equations (23)-(25) from the 1987 paper for $Z1$, $Z2$, and $Z3$. 
     30Equation (1) of the 1990 reference\ [#CIT1990]_ is used to calculate $Z(q)$, 
     31using equations (23)-(25) from the 1987 paper\ [#CIT1987]_ for $Z1$, $Z2$, and 
     32$Z3$. 
    2733 
    2834The lattice correction (the occupied volume of the lattice) for a 
     
    8894---------- 
    8995 
    90 Hideki Matsuoka et. al. *Physical Review B*, 36 (1987) 1754-1765 
    91 (Original Paper) 
     96.. [#CIT1987] Hideki Matsuoka et. al. *Physical Review B*, 36 (1987) 1754-1765 
     97   (Original Paper) 
     98.. [#CIT1990] Hideki Matsuoka et. al. *Physical Review B*, 41 (1990) 3854 -3856 
     99   (Corrections to FCC and BCC lattice structure calculation) 
    92100 
    93 Hideki Matsuoka et. al. *Physical Review B*, 41 (1990) 3854 -3856 
    94 (Corrections to FCC and BCC lattice structure calculation) 
     101Authorship and Verification 
     102--------------------------- 
     103 
     104* **Author:** NIST IGOR/DANSE **Date:** pre 2010 
     105* **Last Modified by:** Paul Butler **Date:** September 29, 2016 
     106* **Last Reviewed by:** Richard Heenan **Date:** March 21, 2016 
    95107""" 
    96108 
  • sasmodels/models/sc_paracrystal.py

    r2d81cfe rda7b26b  
    11r""" 
     2.. warning:: This model and this model description are under review following  
     3             concerns raised by SasView users. If you need to use this model,  
     4             please email help@sasview.org for the latest situation. *The  
     5             SasView Developers. September 2018.* 
     6              
     7Definition 
     8---------- 
     9 
    210Calculates the scattering from a **simple cubic lattice** with 
    311paracrystalline distortion. Thermal vibrations are considered to be 
     
    513Paracrystalline distortion is assumed to be isotropic and characterized 
    614by a Gaussian distribution. 
    7  
    8 Definition 
    9 ---------- 
    1015 
    1116The scattering intensity $I(q)$ is calculated as 
     
    2025$Z(q)$ is the paracrystalline structure factor for a simple cubic structure. 
    2126 
    22 Equation (16) of the 1987 reference is used to calculate $Z(q)$, using 
    23 equations (13)-(15) from the 1987 paper for Z1, Z2, and Z3. 
     27Equation (16) of the 1987 reference\ [#CIT1987]_ is used to calculate $Z(q)$, 
     28using equations (13)-(15) from the 1987 paper\ [#CIT1987]_ for $Z1$, $Z2$, and 
     29$Z3$. 
    2430 
    2531The lattice correction (the occupied volume of the lattice) for a simple cubic 
     
    9197Reference 
    9298--------- 
    93 Hideki Matsuoka et. al. *Physical Review B,* 36 (1987) 1754-1765 
    94 (Original Paper) 
    9599 
    96 Hideki Matsuoka et. al. *Physical Review B,* 41 (1990) 3854 -3856 
    97 (Corrections to FCC and BCC lattice structure calculation) 
     100.. [#CIT1987] Hideki Matsuoka et. al. *Physical Review B*, 36 (1987) 1754-1765 
     101   (Original Paper) 
     102.. [#CIT1990] Hideki Matsuoka et. al. *Physical Review B*, 41 (1990) 3854 -3856 
     103   (Corrections to FCC and BCC lattice structure calculation) 
     104 
     105Authorship and Verification 
     106--------------------------- 
     107 
     108* **Author:** NIST IGOR/DANSE **Date:** pre 2010 
     109* **Last Modified by:** Paul Butler **Date:** September 29, 2016 
     110* **Last Reviewed by:** Richard Heenan **Date:** March 21, 2016 
    98111""" 
    99112 
  • sasmodels/sasview_model.py

    r12eec1e rce1eed5  
    6262#: set of defined models (standard and custom) 
    6363MODELS = {}  # type: Dict[str, SasviewModelType] 
     64# TODO: remove unused MODEL_BY_PATH cache once sasview no longer references it 
    6465#: custom model {path: model} mapping so we can check timestamps 
    6566MODEL_BY_PATH = {}  # type: Dict[str, SasviewModelType] 
     67#: Track modules that we have loaded so we can determine whether the model 
     68#: has changed since we last reloaded. 
     69_CACHED_MODULE = {}  # type: Dict[str, "module"] 
    6670 
    6771def find_model(modelname): 
     
    106110    Load a custom model given the model path. 
    107111    """ 
    108     model = MODEL_BY_PATH.get(path, None) 
    109     if model is not None and model.timestamp == getmtime(path): 
    110         #logger.info("Model already loaded %s", path) 
    111         return model 
    112  
    113112    #logger.info("Loading model %s", path) 
     113 
     114    # Load the kernel module.  This may already be cached by the loader, so 
     115    # only requires checking the timestamps of the dependents. 
    114116    kernel_module = custom.load_custom_kernel_module(path) 
    115     if hasattr(kernel_module, 'Model'): 
    116         model = kernel_module.Model 
     117 
     118    # Check if the module has changed since we last looked. 
     119    reloaded = kernel_module != _CACHED_MODULE.get(path, None) 
     120    _CACHED_MODULE[path] = kernel_module 
     121 
     122    # Turn the module into a model.  We need to do this in even if the 
     123    # model has already been loaded so that we can determine the model 
     124    # name and retrieve it from the MODELS cache. 
     125    model = getattr(kernel_module, 'Model', None) 
     126    if model is not None: 
    117127        # Old style models do not set the name in the class attributes, so 
    118128        # set it here; this name will be overridden when the object is created 
     
    127137        model_info = modelinfo.make_model_info(kernel_module) 
    128138        model = make_model_from_info(model_info) 
    129     model.timestamp = getmtime(path) 
    130139 
    131140    # If a model name already exists and we are loading a different model, 
     
    143152                    _previous_name, model.name, model.filename) 
    144153 
    145     MODELS[model.name] = model 
    146     MODEL_BY_PATH[path] = model 
    147     return model 
     154    # Only update the model if the module has changed 
     155    if reloaded or model.name not in MODELS: 
     156        MODELS[model.name] = model 
     157 
     158    return MODELS[model.name] 
    148159 
    149160 
     
    372383            hidden.add('background') 
    373384            self._model_info.parameters.defaults['background'] = 0. 
     385 
     386        # Update the parameter lists to exclude any hidden parameters 
     387        self.magnetic_params = tuple(pname for pname in self.magnetic_params 
     388                                     if pname not in hidden) 
     389        self.orientation_params = tuple(pname for pname in self.orientation_params 
     390                                        if pname not in hidden) 
    374391 
    375392        self._persistency_dict = {} 
     
    883900    Model = _make_standard_model('sphere') 
    884901    model = Model() 
    885     model.setParam('M0:sld', 8) 
     902    model.setParam('sld_M0', 8) 
    886903    q = np.linspace(-0.35, 0.35, 500) 
    887904    qx, qy = np.meshgrid(q, q) 
  • sasmodels/kernelpy.py

    r91bd550 r12eec1e  
    3737        self.info = model_info 
    3838        self.dtype = np.dtype('d') 
     39        logger.info("make python model " + self.info.name) 
    3940 
    4041    def make_kernel(self, q_vectors): 
  • sasmodels/model_test.py

    r012cd34 r12eec1e  
    4747import sys 
    4848import unittest 
     49import traceback 
    4950 
    5051try: 
     
    7475# pylint: enable=unused-import 
    7576 
    76  
    7777def make_suite(loaders, models): 
    7878    # type: (List[str], List[str]) -> unittest.TestSuite 
     
    8686    *models* is the list of models to test, or *["all"]* to test all models. 
    8787    """ 
    88     ModelTestCase = _hide_model_case_from_nose() 
    8988    suite = unittest.TestSuite() 
    9089 
     
    9594        skip = [] 
    9695    for model_name in models: 
    97         if model_name in skip: 
    98             continue 
    99         model_info = load_model_info(model_name) 
    100  
    101         #print('------') 
    102         #print('found tests in', model_name) 
    103         #print('------') 
    104  
    105         # if ispy then use the dll loader to call pykernel 
    106         # don't try to call cl kernel since it will not be 
    107         # available in some environmentes. 
    108         is_py = callable(model_info.Iq) 
    109  
    110         # Some OpenCL drivers seem to be flaky, and are not producing the 
    111         # expected result.  Since we don't have known test values yet for 
    112         # all of our models, we are instead going to compare the results 
    113         # for the 'smoke test' (that is, evaluation at q=0.1 for the default 
    114         # parameters just to see that the model runs to completion) between 
    115         # the OpenCL and the DLL.  To do this, we define a 'stash' which is 
    116         # shared between OpenCL and DLL tests.  This is just a list.  If the 
    117         # list is empty (which it will be when DLL runs, if the DLL runs 
    118         # first), then the results are appended to the list.  If the list 
    119         # is not empty (which it will be when OpenCL runs second), the results 
    120         # are compared to the results stored in the first element of the list. 
    121         # This is a horrible stateful hack which only makes sense because the 
    122         # test suite is thrown away after being run once. 
    123         stash = [] 
    124  
    125         if is_py:  # kernel implemented in python 
    126             test_name = "%s-python"%model_name 
    127             test_method_name = "test_%s_python" % model_info.id 
     96        if model_name not in skip: 
     97            model_info = load_model_info(model_name) 
     98            _add_model_to_suite(loaders, suite, model_info) 
     99 
     100    return suite 
     101 
     102def _add_model_to_suite(loaders, suite, model_info): 
     103    ModelTestCase = _hide_model_case_from_nose() 
     104 
     105    #print('------') 
     106    #print('found tests in', model_name) 
     107    #print('------') 
     108 
     109    # if ispy then use the dll loader to call pykernel 
     110    # don't try to call cl kernel since it will not be 
     111    # available in some environmentes. 
     112    is_py = callable(model_info.Iq) 
     113 
     114    # Some OpenCL drivers seem to be flaky, and are not producing the 
     115    # expected result.  Since we don't have known test values yet for 
     116    # all of our models, we are instead going to compare the results 
     117    # for the 'smoke test' (that is, evaluation at q=0.1 for the default 
     118    # parameters just to see that the model runs to completion) between 
     119    # the OpenCL and the DLL.  To do this, we define a 'stash' which is 
     120    # shared between OpenCL and DLL tests.  This is just a list.  If the 
     121    # list is empty (which it will be when DLL runs, if the DLL runs 
     122    # first), then the results are appended to the list.  If the list 
     123    # is not empty (which it will be when OpenCL runs second), the results 
     124    # are compared to the results stored in the first element of the list. 
     125    # This is a horrible stateful hack which only makes sense because the 
     126    # test suite is thrown away after being run once. 
     127    stash = [] 
     128 
     129    if is_py:  # kernel implemented in python 
     130        test_name = "%s-python"%model_info.name 
     131        test_method_name = "test_%s_python" % model_info.id 
     132        test = ModelTestCase(test_name, model_info, 
     133                                test_method_name, 
     134                                platform="dll",  # so that 
     135                                dtype="double", 
     136                                stash=stash) 
     137        suite.addTest(test) 
     138    else:   # kernel implemented in C 
     139 
     140        # test using dll if desired 
     141        if 'dll' in loaders or not use_opencl(): 
     142            test_name = "%s-dll"%model_info.name 
     143            test_method_name = "test_%s_dll" % model_info.id 
    128144            test = ModelTestCase(test_name, model_info, 
    129                                  test_method_name, 
    130                                  platform="dll",  # so that 
    131                                  dtype="double", 
    132                                  stash=stash) 
     145                                    test_method_name, 
     146                                    platform="dll", 
     147                                    dtype="double", 
     148                                    stash=stash) 
    133149            suite.addTest(test) 
    134         else:   # kernel implemented in C 
    135  
    136             # test using dll if desired 
    137             if 'dll' in loaders or not use_opencl(): 
    138                 test_name = "%s-dll"%model_name 
    139                 test_method_name = "test_%s_dll" % model_info.id 
    140                 test = ModelTestCase(test_name, model_info, 
    141                                      test_method_name, 
    142                                      platform="dll", 
    143                                      dtype="double", 
    144                                      stash=stash) 
    145                 suite.addTest(test) 
    146  
    147             # test using opencl if desired and available 
    148             if 'opencl' in loaders and use_opencl(): 
    149                 test_name = "%s-opencl"%model_name 
    150                 test_method_name = "test_%s_opencl" % model_info.id 
    151                 # Using dtype=None so that the models that are only 
    152                 # correct for double precision are not tested using 
    153                 # single precision.  The choice is determined by the 
    154                 # presence of *single=False* in the model file. 
    155                 test = ModelTestCase(test_name, model_info, 
    156                                      test_method_name, 
    157                                      platform="ocl", dtype=None, 
    158                                      stash=stash) 
    159                 #print("defining", test_name) 
    160                 suite.addTest(test) 
    161  
    162     return suite 
     150 
     151        # test using opencl if desired and available 
     152        if 'opencl' in loaders and use_opencl(): 
     153            test_name = "%s-opencl"%model_info.name 
     154            test_method_name = "test_%s_opencl" % model_info.id 
     155            # Using dtype=None so that the models that are only 
     156            # correct for double precision are not tested using 
     157            # single precision.  The choice is determined by the 
     158            # presence of *single=False* in the model file. 
     159            test = ModelTestCase(test_name, model_info, 
     160                                    test_method_name, 
     161                                    platform="ocl", dtype=None, 
     162                                    stash=stash) 
     163            #print("defining", test_name) 
     164            suite.addTest(test) 
     165 
    163166 
    164167def _hide_model_case_from_nose(): 
     
    348351    return abs(target-actual)/shift < 1.5*10**-digits 
    349352 
    350 def run_one(model): 
    351     # type: (str) -> str 
    352     """ 
    353     Run the tests for a single model, printing the results to stdout. 
    354  
    355     *model* can by a python file, which is handy for checking user defined 
    356     plugin models. 
     353# CRUFT: old interface; should be deprecated and removed 
     354def run_one(model_name): 
     355    # msg = "use check_model(model_info) rather than run_one(model_name)" 
     356    # warnings.warn(msg, category=DeprecationWarning, stacklevel=2) 
     357    try: 
     358        model_info = load_model_info(model_name) 
     359    except Exception: 
     360        output = traceback.format_exc() 
     361        return output 
     362 
     363    success, output = check_model(model_info) 
     364    return output 
     365 
     366def check_model(model_info): 
     367    # type: (ModelInfo) -> str 
     368    """ 
     369    Run the tests for a single model, capturing the output. 
     370 
     371    Returns success status and the output string. 
    357372    """ 
    358373    # Note that running main() directly did not work from within the 
     
    369384    # Build a test suite containing just the model 
    370385    loaders = ['opencl'] if use_opencl() else ['dll'] 
    371     models = [model] 
    372     try: 
    373         suite = make_suite(loaders, models) 
    374     except Exception: 
    375         import traceback 
    376         stream.writeln(traceback.format_exc()) 
    377         return 
     386    suite = unittest.TestSuite() 
     387    _add_model_to_suite(loaders, suite, model_info) 
    378388 
    379389    # Warn if there are no user defined tests. 
     
    390400    for test in suite: 
    391401        if not test.info.tests: 
    392             stream.writeln("Note: %s has no user defined tests."%model) 
     402            stream.writeln("Note: %s has no user defined tests."%model_info.name) 
    393403        break 
    394404    else: 
     
    406416    output = stream.getvalue() 
    407417    stream.close() 
    408     return output 
     418    return result.wasSuccessful(), output 
    409419 
    410420 
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