Changeset d5b5b71 in sasmodels


Ignore:
Timestamp:
Mar 1, 2017 7:29:46 AM (3 years ago)
Author:
Paul Kienzle <pkienzle@…>
Branches:
master, core_shell_microgels, costrafo411, magnetic_model, ticket-1257-vesicle-product, ticket_1156, ticket_1265_superball, ticket_822_more_unit_tests
Children:
b216880
Parents:
e09d1e0 (diff), e1aa129 (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-814

Files:
1 added
5 edited

Legend:

Unmodified
Added
Removed
  • sasmodels/sesans.py

    rb397165 r94d13f1  
    1414import numpy as np  # type: ignore 
    1515from numpy import pi, exp  # type: ignore 
    16 from scipy.special import jv as besselj 
    17 #import direct_model.DataMixin as model 
    18          
    19 def make_q(q_max, Rmax): 
    20     r""" 
    21     Return a $q$ vector suitable for SESANS covering from $2\pi/ (10 R_{\max})$ 
    22     to $q_max$. This is the integration range of the Hankel transform; bigger range and  
    23     more points makes a better numerical integration. 
    24     Smaller q_min will increase reliable spin echo length range.  
    25     Rmax is the "radius" of the largest expected object and can be set elsewhere. 
    26     q_max is determined by the acceptance angle of the SESANS instrument. 
     16from scipy.special import j0 
     17 
     18class SesansTransform(object): 
    2719    """ 
    28     from sas.sascalc.data_util.nxsunit import Converter 
     20    Spin-Echo SANS transform calculator.  Similar to a resolution function, 
     21    the SesansTransform object takes I(q) for the set of *q_calc* values and 
     22    produces a transformed dataset 
    2923 
    30     q_min = dq = 0.1 * 2*pi / Rmax 
    31     return np.arange(q_min, 
    32                      Converter(q_max[1])(q_max[0], 
    33                                          units="1/A"), 
    34                      dq) 
    35      
    36 def make_all_q(data): 
     24    *SElength* (A) is the set of spin-echo lengths in the measured data. 
     25 
     26    *zaccept* (1/A) is the maximum acceptance of scattering vector in the spin 
     27    echo encoding dimension (for ToF: Q of min(R) and max(lam)). 
     28 
     29    *Rmax* (A) is the maximum size sensitivity; larger radius requires more 
     30    computation time. 
    3731    """ 
    38     Return a $q$ vector suitable for calculating the total scattering cross section for 
    39     calculating the effect of finite acceptance angles on Time of Flight SESANS instruments. 
    40     If no acceptance is given, or unwanted (set "unwanted" flag in paramfile), no all_q vector is needed. 
    41     If the instrument has a rectangular acceptance, 2 all_q vectors are needed. 
    42     If the instrument has a circular acceptance, 1 all_q vector is needed 
    43      
    44     """ 
    45     if not data.has_no_finite_acceptance: 
    46         return [] 
    47     elif data.has_yz_acceptance(data): 
    48         # compute qx, qy 
    49         Qx, Qy = np.meshgrid(qx, qy) 
    50         return [Qx, Qy] 
    51     else: 
    52         # else only need q 
    53         # data.has_z_acceptance 
    54         return [q] 
     32    #: SElength from the data in the original data units; not used by transform 
     33    #: but the GUI uses it, so make sure that it is present. 
     34    q = None  # type: np.ndarray 
    5535 
    56 def transform(data, q_calc, Iq_calc, qmono, Iq_mono): 
    57     """ 
    58     Decides which transform type is to be used, based on the experiment data file contents (header) 
    59     (2016-03-19: currently controlled from parameters script) 
    60     nqmono is the number of q vectors to be used for the detector integration 
    61     """ 
    62     nqmono = len(qmono) 
    63     if nqmono == 0: 
    64         result = call_hankel(data, q_calc, Iq_calc) 
    65     elif nqmono == 1: 
    66         q = qmono[0] 
    67         result = call_HankelAccept(data, q_calc, Iq_calc, q, Iq_mono) 
    68     else: 
    69         Qx, Qy = [qmono[0], qmono[1]] 
    70         Qx = np.reshape(Qx, nqx, nqy) 
    71         Qy = np.reshape(Qy, nqx, nqy) 
    72         Iq_mono = np.reshape(Iq_mono, nqx, nqy) 
    73         qx = Qx[0, :] 
    74         qy = Qy[:, 0] 
    75         result = call_Cosine2D(data, q_calc, Iq_calc, qx, qy, Iq_mono) 
     36    #: q values to calculate when computing transform 
     37    q_calc = None  # type: np.ndarray 
    7638 
    77     return result 
     39    # transform arrays 
     40    _H = None  # type: np.ndarray 
     41    _H0 = None # type: np.ndarray 
    7842 
    79 def call_hankel(data, q_calc, Iq_calc): 
    80     return hankel((data.x, data.x_unit), 
    81                   (data.lam, data.lam_unit), 
    82                   (data.sample.thickness, 
    83                    data.sample.thickness_unit), 
    84                   q_calc, Iq_calc) 
    85    
    86 def call_HankelAccept(data, q_calc, Iq_calc, q_mono, Iq_mono): 
    87     return hankel(data.x, data.lam * 1e-9, 
    88                   data.sample.thickness / 10, 
    89                   q_calc, Iq_calc) 
    90                    
    91 def call_Cosine2D(data, q_calc, Iq_calc, qx, qy, Iq_mono): 
    92     return hankel(data.x, data.y, data.lam * 1e-9, 
    93                   data.sample.thickness / 10, 
    94                   q_calc, Iq_calc) 
    95                          
    96 def TotalScatter(model, parameters):  #Work in progress!! 
    97 #    Calls a model with existing model parameters already in place, then integrate the product of q and I(q) from 0 to (4*pi/lambda) 
    98     allq = np.linspace(0,4*pi/wavelength,1000) 
    99     allIq = 1 
    100     integral = allq*allIq 
    101      
     43    def __init__(self, z, SElength, zaccept, Rmax): 
     44        # type: (np.ndarray, float, float) -> None 
     45        #import logging; logging.info("creating SESANS transform") 
     46        self.q = z 
     47        self._set_hankel(SElength, zaccept, Rmax) 
    10248 
     49    def apply(self, Iq): 
     50        # tye: (np.ndarray) -> np.ndarray 
     51        G0 = np.dot(self._H0, Iq) 
     52        G = np.dot(self._H.T, Iq) 
     53        P = G - G0 
     54        return P 
    10355 
    104 def Cosine2D(wavelength, magfield, thickness, qy, qz, Iqy, Iqz, modelname): #Work in progress!! Needs to call model still 
    105 #============================================================================== 
    106 #     2D Cosine Transform if "wavelength" is a vector 
    107 #============================================================================== 
    108 #allq is the q-space needed to create the total scattering cross-section 
     56    def _set_hankel(self, SElength, zaccept, Rmax): 
     57        # type: (np.ndarray, float, float) -> None 
     58        # Force float32 arrays, otherwise run into memory problems on some machines 
     59        SElength = np.asarray(SElength, dtype='float32') 
    10960 
    110     Gprime = np.zeros_like(wavelength, 'd') 
    111     s = np.zeros_like(wavelength, 'd') 
    112     sd = np.zeros_like(wavelength, 'd') 
    113     Gprime = np.zeros_like(wavelength, 'd') 
    114     f = np.zeros_like(wavelength, 'd') 
    115     for i, wavelength_i in enumerate(wavelength): 
    116         z = magfield*wavelength_i 
    117         allq=np.linspace() #for calculating the Q-range of the  scattering power integral 
    118         allIq=np.linspace()  # This is the model applied to the allq q-space. Needs to refference the model somehow 
    119         alldq = (allq[1]-allq[0])*1e10 
    120         sigma[i]=wavelength[i]^2*thickness/2/pi*np.sum(allIq*allq*alldq) 
    121         s[i]=1-exp(-sigma) 
    122         for j, Iqy_j, qy_j in enumerate(qy): 
    123             for k, Iqz_k, qz_k in enumerate(qz): 
    124                 Iq = np.sqrt(Iqy_j^2+Iqz_k^2) 
    125                 q = np.sqrt(qy_j^2 + qz_k^2) 
    126                 Gintegral = Iq*cos(z*Qz_k) 
    127                 Gprime[i] += Gintegral 
    128 #                sigma = wavelength^2*thickness/2/pi* allq[i]*allIq[i] 
    129 #                s[i] += 1-exp(Totalscatter(modelname)*thickness) 
    130 #                For now, work with standard 2-phase scatter 
     61        #Rmax = #value in text box somewhere in FitPage? 
     62        q_max = 2*pi / (SElength[1] - SElength[0]) 
     63        q_min = 0.1 * 2*pi / (np.size(SElength) * SElength[-1]) 
     64        q = np.arange(q_min, q_max, q_min, dtype='float32') 
     65        dq = q_min 
    13166 
     67        H0 = np.float32(dq/(2*pi)) * q 
    13268 
    133                 sd[i] += Iq 
    134         f[i] = 1-s[i]+sd[i] 
    135         P[i] = (1-sd[i]/f[i])+1/f[i]*Gprime[i] 
     69        repq = np.tile(q, (SElength.size, 1)).T 
     70        repSE = np.tile(SElength, (q.size, 1)) 
     71        H = np.float32(dq/(2*pi)) * j0(repSE*repq) * repq 
    13672 
    137  
    138  
    139  
    140 def HankelAccept(wavelength, magfield, thickness, q, Iq, theta, modelname): 
    141 #============================================================================== 
    142 #     HankelTransform with fixed circular acceptance angle (circular aperture) for Time of Flight SESANS 
    143 #============================================================================== 
    144 #acceptq is the q-space needed to create limited acceptance effect 
    145     SElength= wavelength*magfield 
    146     G = np.zeros_like(SElength, 'd') 
    147     threshold=2*pi*theta/wavelength 
    148     for i, SElength_i in enumerate(SElength): 
    149         allq=np.linspace() #for calculating the Q-range of the  scattering power integral 
    150         allIq=np.linspace()  # This is the model applied to the allq q-space. Needs to refference the model somehow 
    151         alldq = (allq[1]-allq[0])*1e10 
    152         sigma[i]=wavelength[i]^2*thickness/2/pi*np.sum(allIq*allq*alldq) 
    153         s[i]=1-exp(-sigma) 
    154  
    155         dq = (q[1]-q[0])*1e10 
    156         a = (x<threshold) 
    157         acceptq = a*q 
    158         acceptIq = a*Iq 
    159  
    160         G[i] = np.sum(besselj(0, acceptq*SElength_i)*acceptIq*acceptq*dq) 
    161  
    162 #        G[i]=np.sum(integral) 
    163  
    164     G *= dq*1e10*2*pi 
    165  
    166     P = exp(thickness*wavelength**2/(4*pi**2)*(G-G[0])) 
    167      
    168 def hankel(SElength, wavelength, thickness, q, Iq): 
    169     r""" 
    170     Compute the expected SESANS polarization for a given SANS pattern. 
    171  
    172     Uses the hankel transform followed by the exponential.  The values for *zz* 
    173     (or spin echo length, or delta), wavelength and sample thickness should 
    174     come from the dataset.  $q$ should be chosen such that the oscillations 
    175     in $I(q)$ are well sampled (e.g., $5 \cdot 2 \pi/d_{\max}$). 
    176  
    177     *SElength* [A] is the set of $z$ points at which to compute the 
    178     Hankel transform 
    179  
    180     *wavelength* [m]  is the wavelength of each individual point *zz* 
    181  
    182     *thickness* [cm] is the sample thickness. 
    183  
    184     *q* [A$^{-1}$] is the set of $q$ points at which the model has been 
    185     computed. These should be equally spaced. 
    186  
    187     *I* [cm$^{-1}$] is the value of the SANS model at *q* 
    188     """ 
    189  
    190     from sas.sascalc.data_util.nxsunit import Converter 
    191     wavelength = Converter(wavelength[1])(wavelength[0],"A") 
    192     thickness = Converter(thickness[1])(thickness[0],"A") 
    193     Iq = Converter("1/cm")(Iq,"1/A") # All models default to inverse centimeters 
    194     SElength = Converter(SElength[1])(SElength[0],"A") 
    195  
    196     G = np.zeros_like(SElength, 'd') 
    197 #============================================================================== 
    198 #     Hankel Transform method if "wavelength" is a scalar; mono-chromatic SESANS 
    199 #============================================================================== 
    200     for i, SElength_i in enumerate(SElength): 
    201         integral = besselj(0, q*SElength_i)*Iq*q 
    202         G[i] = np.sum(integral) 
    203     G0 = np.sum(Iq*q) 
    204  
    205     # [m^-1] step size in q, needed for integration 
    206     dq = (q[1]-q[0]) 
    207  
    208     # integration step, convert q into [m**-1] and 2 pi circle integration 
    209     G *= dq*2*pi 
    210     G0 = np.sum(Iq*q)*dq*2*np.pi 
    211  
    212     P = exp(thickness*wavelength**2/(4*pi**2)*(G-G0)) 
    213  
    214     return P 
     73        self.q_calc = q 
     74        self._H, self._H0 = H, H0 
  • sasmodels/compare_many.py

    r424fe00 r5124c969  
    106106    header = ('\n"Model","%s","Count","%d","Dimension","%s"' 
    107107              % (name, N, "2D" if is_2d else "1D")) 
    108     if not mono: header += ',"Cutoff",%g'%(cutoff,) 
     108    if not mono: 
     109        header += ',"Cutoff",%g'%(cutoff,) 
    109110    print(header) 
    110111 
     
    161162    max_diff = [0] 
    162163    for k in range(N): 
    163         print("%s %d"%(name, k), file=sys.stderr) 
     164        print("Model %s %d"%(name, k+1), file=sys.stderr) 
    164165        seed = np.random.randint(1e6) 
    165166        pars_i = randomize_pars(model_info, pars, seed) 
    166167        constrain_pars(model_info, pars_i) 
    167         constrain_new_to_old(model_info, pars_i) 
     168        if 'sasview' in (base, comp): 
     169            constrain_new_to_old(model_info, pars_i) 
    168170        if mono: 
    169171            pars_i = suppress_pd(pars_i) 
     
    204206    print("""\ 
    205207 
    206 MODEL is the model name of the model or "all" for all the models 
    207 in alphabetical order. 
     208MODEL is the model name of the model or one of the model types listed in 
     209sasmodels.core.list_models (all, py, c, double, single, opencl, 1d, 2d, 
     210nonmagnetic, magnetic).  Model types can be combined, such as 2d+single. 
    208211 
    209212COUNT is the number of randomly generated parameter sets to try. A value 
     
    237240        return 
    238241 
    239     model = argv[0] 
    240     if not (model in MODELS) and (model != "all"): 
    241         print('Bad model %s.  Use "all" or one of:'%model) 
     242    target = argv[0] 
     243    try: 
     244        model_list = [target] if target in MODELS else core.list_models(target) 
     245    except ValueError: 
     246        print('Bad model %s.  Use model type or one of:'%model) 
    242247        print_models() 
     248        print('model types: all, py, c, double, single, opencl, 1d, 2d, nonmagnetic, magnetic') 
    243249        return 
    244250    try: 
     
    258264    data, index = make_data({'qmax':1.0, 'is2d':is2D, 'nq':Nq, 'res':0., 
    259265                             'accuracy': 'Low', 'view':'log', 'zero': False}) 
    260     model_list = [model] if model != "all" else MODELS 
    261266    for model in model_list: 
    262267        compare_instance(model, data, index, N=count, mono=mono, 
  • sasmodels/core.py

    r52e9a45 r5124c969  
    6969        * magnetic: models with an sld 
    7070        * nommagnetic: models without an sld 
    71     """ 
    72     if kind and kind not in KINDS: 
     71 
     72    For multiple conditions, combine with plus.  For example, *c+single+2d* 
     73    would return all oriented models implemented in C which can be computed 
     74    accurately with single precision arithmetic. 
     75    """ 
     76    if kind and any(k not in KINDS for k in kind.split('+')): 
    7377        raise ValueError("kind not in " + ", ".join(KINDS)) 
    7478    files = sorted(glob(joinpath(generate.MODEL_PATH, "[a-zA-Z]*.py"))) 
    7579    available_models = [basename(f)[:-3] for f in files] 
    76     selected = [name for name in available_models if _matches(name, kind)] 
     80    if kind and '+' in kind: 
     81        all_kinds = kind.split('+') 
     82        condition = lambda name: all(_matches(name, k) for k in all_kinds) 
     83    else: 
     84        condition = lambda name: _matches(name, kind) 
     85    selected = [name for name in available_models if condition(name)] 
    7786 
    7887    return selected 
  • sasmodels/model_test.py

    r479d0f3 re09d1e0  
    9797        is_py = callable(model_info.Iq) 
    9898 
     99        # Some OpenCL drivers seem to be flaky, and are not producing the 
     100        # expected result.  Since we don't have known test values yet for 
     101        # all of our models, we are instead going to compare the results 
     102        # for the 'smoke test' (that is, evaluation at q=0.1 for the default 
     103        # parameters just to see that the model runs to completion) between 
     104        # the OpenCL and the DLL.  To do this, we define a 'stash' which is 
     105        # shared between OpenCL and DLL tests.  This is just a list.  If the 
     106        # list is empty (which it will be when DLL runs, if the DLL runs 
     107        # first), then the results are appended to the list.  If the list 
     108        # is not empty (which it will be when OpenCL runs second), the results 
     109        # are compared to the results stored in the first element of the list. 
     110        # This is a horrible stateful hack which only makes sense because the 
     111        # test suite is thrown away after being run once. 
     112        stash = [] 
     113 
    99114        if is_py:  # kernel implemented in python 
    100115            test_name = "Model: %s, Kernel: python"%model_name 
     
    103118                                 test_method_name, 
    104119                                 platform="dll",  # so that 
    105                                  dtype="double") 
     120                                 dtype="double", 
     121                                 stash=stash) 
    106122            suite.addTest(test) 
    107123        else:   # kernel implemented in C 
     124 
     125            # test using dll if desired 
     126            if 'dll' in loaders or not core.HAVE_OPENCL: 
     127                test_name = "Model: %s, Kernel: dll"%model_name 
     128                test_method_name = "test_%s_dll" % model_info.id 
     129                test = ModelTestCase(test_name, model_info, 
     130                                     test_method_name, 
     131                                     platform="dll", 
     132                                     dtype="double", 
     133                                     stash=stash) 
     134                suite.addTest(test) 
     135 
    108136            # test using opencl if desired and available 
    109137            if 'opencl' in loaders and core.HAVE_OPENCL: 
     
    116144                test = ModelTestCase(test_name, model_info, 
    117145                                     test_method_name, 
    118                                      platform="ocl", dtype=None) 
     146                                     platform="ocl", dtype=None, 
     147                                     stash=stash) 
    119148                #print("defining", test_name) 
    120                 suite.addTest(test) 
    121  
    122             # test using dll if desired 
    123             if 'dll' in loaders or not core.HAVE_OPENCL: 
    124                 test_name = "Model: %s, Kernel: dll"%model_name 
    125                 test_method_name = "test_%s_dll" % model_info.id 
    126                 test = ModelTestCase(test_name, model_info, 
    127                                      test_method_name, 
    128                                      platform="dll", 
    129                                      dtype="double") 
    130149                suite.addTest(test) 
    131150 
     
    144163        """ 
    145164        def __init__(self, test_name, model_info, test_method_name, 
    146                      platform, dtype): 
    147             # type: (str, ModelInfo, str, str, DType) -> None 
     165                     platform, dtype, stash): 
     166            # type: (str, ModelInfo, str, str, DType, List[Any]) -> None 
    148167            self.test_name = test_name 
    149168            self.info = model_info 
    150169            self.platform = platform 
    151170            self.dtype = dtype 
     171            self.stash = stash  # container for the results of the first run 
    152172 
    153173            setattr(self, test_method_name, self.run_all) 
     
    174194                ] 
    175195 
    176             tests = self.info.tests 
     196            tests = smoke_tests + self.info.tests 
    177197            try: 
    178198                model = build_model(self.info, dtype=self.dtype, 
    179199                                    platform=self.platform) 
    180                 for test in smoke_tests + tests: 
    181                     self.run_one(model, test) 
    182  
    183                 if not tests and self.platform == "dll": 
    184                     ## Uncomment the following to make forgetting the test 
    185                     ## values an error.  Only do so for the "dll" tests 
    186                     ## to reduce noise from both opencl and dll, and because 
    187                     ## python kernels use platform="dll". 
    188                     #raise Exception("No test cases provided") 
    189                     pass 
     200                results = [self.run_one(model, test) for test in tests] 
     201                if self.stash: 
     202                    for test, target, actual in zip(tests, self.stash[0], results): 
     203                        assert np.all(abs(target-actual)<2e-5*abs(actual)),\ 
     204                            "expected %s but got %s for %s"%(target, actual, test[0]) 
     205                else: 
     206                    self.stash.append(results) 
     207 
     208                # Check for missing tests.  Only do so for the "dll" tests 
     209                # to reduce noise from both opencl and dll, and because 
     210                # python kernels use platform="dll". 
     211                if self.platform == "dll": 
     212                    missing = [] 
     213                    ## Uncomment the following to require test cases 
     214                    #missing = self._find_missing_tests() 
     215                    if missing: 
     216                        raise ValueError("Missing tests for "+", ".join(missing)) 
    190217 
    191218            except: 
    192219                annotate_exception(self.test_name) 
    193220                raise 
     221 
     222        def _find_missing_tests(self): 
     223            # type: () -> None 
     224            """make sure there are 1D, 2D, ER and VR tests as appropriate""" 
     225            model_has_VR = callable(self.info.VR) 
     226            model_has_ER = callable(self.info.ER) 
     227            model_has_1D = True 
     228            model_has_2D = any(p.type == 'orientation' 
     229                               for p in self.info.parameters.kernel_parameters) 
     230 
     231            # Lists of tests that have a result that is not None 
     232            single = [test for test in self.info.tests 
     233                      if not isinstance(test[2], list) and test[2] is not None] 
     234            tests_has_VR = any(test[1] == 'VR' for test in single) 
     235            tests_has_ER = any(test[1] == 'ER' for test in single) 
     236            tests_has_1D_single = any(isinstance(test[1], float) for test in single) 
     237            tests_has_2D_single = any(isinstance(test[1], tuple) for test in single) 
     238 
     239            multiple = [test for test in self.info.tests 
     240                        if isinstance(test[2], list) 
     241                            and not all(result is None for result in test[2])] 
     242            tests_has_1D_multiple = any(isinstance(test[1][0], float) 
     243                                        for test in multiple) 
     244            tests_has_2D_multiple = any(isinstance(test[1][0], tuple) 
     245                                        for test in multiple) 
     246 
     247            missing = [] 
     248            if model_has_VR and not tests_has_VR: 
     249                missing.append("VR") 
     250            if model_has_ER and not tests_has_ER: 
     251                missing.append("ER") 
     252            if model_has_1D and not (tests_has_1D_single or tests_has_1D_multiple): 
     253                missing.append("1D") 
     254            if model_has_2D and not (tests_has_2D_single or tests_has_2D_multiple): 
     255                missing.append("2D") 
     256 
     257            return missing 
    194258 
    195259        def run_one(self, model, test): 
     
    207271 
    208272            if x[0] == 'ER': 
    209                 actual = [call_ER(model.info, pars)] 
     273                actual = np.array([call_ER(model.info, pars)]) 
    210274            elif x[0] == 'VR': 
    211                 actual = [call_VR(model.info, pars)] 
     275                actual = np.array([call_VR(model.info, pars)]) 
    212276            elif isinstance(x[0], tuple): 
    213277                qx, qy = zip(*x) 
     
    238302                                    'f(%s); expected:%s; actual:%s' 
    239303                                    % (xi, yi, actual_yi)) 
     304            return actual 
    240305 
    241306    return ModelTestCase 
  • sasmodels/modelinfo.py

    r85fe7f8 r5124c969  
    730730    info.docs = kernel_module.__doc__ 
    731731    info.category = getattr(kernel_module, 'category', None) 
    732     info.single = getattr(kernel_module, 'single', True) 
    733     info.opencl = getattr(kernel_module, 'opencl', True) 
    734732    info.structure_factor = getattr(kernel_module, 'structure_factor', False) 
    735733    info.profile_axes = getattr(kernel_module, 'profile_axes', ['x', 'y']) 
     
    745743    info.profile = getattr(kernel_module, 'profile', None) # type: ignore 
    746744    info.sesans = getattr(kernel_module, 'sesans', None) # type: ignore 
     745    # Default single and opencl to True for C models.  Python models have callable Iq. 
     746    info.opencl = getattr(kernel_module, 'opencl', not callable(info.Iq)) 
     747    info.single = getattr(kernel_module, 'single', not callable(info.Iq)) 
    747748 
    748749    # multiplicity info 
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