Changeset 6da1d76 in sasmodels


Ignore:
Timestamp:
Oct 3, 2018 3:00:10 PM (6 years ago)
Author:
Paul Kienzle <pkienzle@…>
Branches:
master, core_shell_microgels, magnetic_model, ticket-1257-vesicle-product, ticket_1156, ticket_1265_superball, ticket_822_more_unit_tests
Children:
17695aa
Parents:
0535624 (diff), a1ec908 (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' into ticket-1157

Files:
14 edited

Legend:

Unmodified
Added
Removed

  • doc/guide/plugin.rst

    rf796469 r2015f02  
    423423calculations, but instead rely on numerical integration to compute the 
    424424appropriately smeared pattern. 
     425 
     426Each .py file also contains a function:: 
     427 
     428        def random(): 
     429        ... 
     430         
     431This function provides a model-specific random parameter set which shows model  
     432features in the USANS to SANS range.  For example, core-shell sphere sets the  
     433outer radius of the sphere logarithmically in `[20, 20,000]`, which sets the Q  
     434value for the transition from flat to falling.  It then uses a beta distribution  
     435to set the percentage of the shape which is shell, giving a preference for very  
     436thin or very thick shells (but never 0% or 100%).  Using `-sets=10` in sascomp  
     437should show a reasonable variety of curves over the default sascomp q range.   
     438The parameter set is returned as a dictionary of `{parameter: value, ...}`.   
     439Any model parameters not included in the dictionary will default according to  
     440the code in the `_randomize_one()` function from sasmodels/compare.py. 
    425441 
    426442Python Models 

  • sasmodels/__init__.py

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

  • 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/kernelpy.py

    r108e70e r91bd550  
    3737        self.info = model_info 
    3838        self.dtype = np.dtype('d') 
    39         logger.info("load python model " + self.info.name) 
    4039 
    4140    def make_kernel(self, q_vectors): 

  • sasmodels/modelinfo.py

    r0535624 r6da1d76  
    824824    info.structure_factor = structure_factor 
    825825    info.profile_axes = getattr(kernel_module, 'profile_axes', ['x', 'y']) 
     826    # Note: custom.load_custom_kernel_module assumes the C sources are defined 
     827    # by this attribute. 
    826828    info.source = getattr(kernel_module, 'source', []) 
    827829    info.c_code = getattr(kernel_module, 'c_code', None) 

  • 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

    r0535624 r6da1d76  
    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 

  • sasmodels/compare.py

    rbd7630d rb6dab59  
    11511151        'rel_err'   : True, 
    11521152        'explore'   : False, 
    1153         'use_demo'  : True, 
     1153        'use_demo'  : False, 
    11541154        'zero'      : False, 
    11551155        'html'      : False, 

  • sasmodels/direct_model.py

    r7b9e4dd rb6d1d52  
    3131from . import resolution2d 
    3232from .details import make_kernel_args, dispersion_mesh 
    33 from .modelinfo import DEFAULT_BACKGROUND 
    3433 
    3534# pylint: disable=unused-import 
    … …  
    350349 
    351350        # Need to pull background out of resolution for multiple scattering 
    352         background = pars.get('background', DEFAULT_BACKGROUND) 
     351        default_background = self._model.info.parameters.common_parameters[1].default 
     352        background = pars.get('background', default_background) 
    353353        pars = pars.copy() 
    354354        pars['background'] = 0. 

  • sasmodels/generate.py

    r6e45516 rb6dab59  
    968968        pars = model_info.parameters.kernel_parameters 
    969969    else: 
    970         pars = model_info.parameters.COMMON + model_info.parameters.kernel_parameters 
     970        pars = (model_info.parameters.common_parameters 
     971                + model_info.parameters.kernel_parameters) 
    971972    partable = make_partable(pars) 
    972973    subst = dict(id=model_info.id.replace('_', '-'), 

  • sasmodels/models/hardsphere.py

    r2d81cfe rb6dab59  
    171171# VR defaults to 1.0 
    172172 
    173 demo = dict(radius_effective=200, volfraction=0.2, 
    174             radius_effective_pd=0.1, radius_effective_pd_n=40) 
    175173# Q=0.001 is in the Taylor series, low Q part, so add Q=0.1, 
    176174# assuming double precision sasview is correct 
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