Changes in / [2a12d8d8:599993b9] in sasmodels


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  • doc/guide/plugin.rst

    r2015f02 r57c609b  
    428428        def random(): 
    429429        ... 
    430          
    431 This function provides a model-specific random parameter set which shows model  
    432 features in the USANS to SANS range.  For example, core-shell sphere sets the  
    433 outer radius of the sphere logarithmically in `[20, 20,000]`, which sets the Q  
    434 value for the transition from flat to falling.  It then uses a beta distribution  
    435 to set the percentage of the shape which is shell, giving a preference for very  
    436 thin or very thick shells (but never 0% or 100%).  Using `-sets=10` in sascomp  
    437 should show a reasonable variety of curves over the default sascomp q range.   
    438 The parameter set is returned as a dictionary of `{parameter: value, ...}`.   
    439 Any model parameters not included in the dictionary will default according to  
     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 
    440440the code in the `_randomize_one()` function from sasmodels/compare.py. 
    441441 
    … …  
    701701    erf, erfc, tgamma, lgamma:  **do not use** 
    702702        Special functions that should be part of the standard, but are missing 
    703         or inaccurate on some platforms. Use sas_erf, sas_erfc and sas_gamma 
    704         instead (see below). Note: lgamma(x) has not yet been tested. 
     703        or inaccurate on some platforms. Use sas_erf, sas_erfc, sas_gamma 
     704        and sas_lgamma instead (see below). 
    705705 
    706706Some non-standard constants and functions are also provided: 
    … …  
    769769        Gamma function sas_gamma\ $(x) = \Gamma(x)$. 
    770770 
    771         The standard math function, tgamma(x) is unstable for $x < 1$ 
     771        The standard math function, tgamma(x), is unstable for $x < 1$ 
    772772        on some platforms. 
    773773 
    774774        :code:`source = ["lib/sas_gamma.c", ...]` 
    775775        (`sas_gamma.c <https://github.com/SasView/sasmodels/tree/master/sasmodels/models/lib/sas_gamma.c>`_) 
     776 
     777    sas_gammaln(x): 
     778        log gamma function sas_gammaln\ $(x) = \log \Gamma(|x|)$. 
     779 
     780        The standard math function, lgamma(x), is incorrect for single 
     781        precision on some platforms. 
     782 
     783        :code:`source = ["lib/sas_gammainc.c", ...]` 
     784        (`sas_gammainc.c <https://github.com/SasView/sasmodels/tree/master/sasmodels/models/lib/sas_gammainc.c>`_) 
     785 
     786    sas_gammainc(a, x), sas_gammaincc(a, x): 
     787        Incomplete gamma function 
     788        sas_gammainc\ $(a, x) = \int_0^x t^{a-1}e^{-t}\,dt / \Gamma(a)$ 
     789        and complementary incomplete gamma function 
     790        sas_gammaincc\ $(a, x) = \int_x^\infty t^{a-1}e^{-t}\,dt / \Gamma(a)$ 
     791 
     792        :code:`source = ["lib/sas_gammainc.c", ...]` 
     793        (`sas_gammainc.c <https://github.com/SasView/sasmodels/tree/master/sasmodels/models/lib/sas_gammainc.c>`_) 
    776794 
    777795    sas_erf(x), sas_erfc(x): 
    … …  
    811829        If $n$ = 0 or 1, it uses sas_J0($x$) or sas_J1($x$), respectively. 
    812830 
     831        Warning: JN(n,x) can be very inaccurate (0.1%) for x not in [0.1, 100]. 
     832 
    813833        The standard math function jn(n, x) is not available on all platforms. 
    814834 
    … …  
    819839        Sine integral Si\ $(x) = \int_0^x \tfrac{\sin t}{t}\,dt$. 
    820840 
     841        Warning: Si(x) can be very inaccurate (0.1%) for x in [0.1, 100]. 
     842 
    821843        This function uses Taylor series for small and large arguments: 
    822844 
    823         For large arguments, 
     845        For large arguments use the following Taylor series, 
    824846 
    825847        .. math:: 
    … …  
    829851             - \frac{\sin(x)}{x}\left(\frac{1}{x} - \frac{3!}{x^3} + \frac{5!}{x^5} - \frac{7!}{x^7}\right) 
    830852 
    831         For small arguments, 
     853        For small arguments , 
    832854 
    833855        .. math:: 

  • explore/precision.py

    r2a7e20e rfba9ca0  
    9595            neg:    [-100,100] 
    9696 
     97        For arbitrary range use "start:stop:steps:scale" where scale is 
     98        one of log, lin, or linear. 
     99 
    97100        *diff* is "relative", "absolute" or "none" 
    98101 
    … …  
    102105        linear = not xrange.startswith("log") 
    103106        if xrange == "zoom": 
    104             lin_min, lin_max, lin_steps = 1000, 1010, 2000 
     107            start, stop, steps = 1000, 1010, 2000 
    105108        elif xrange == "neg": 
    106             lin_min, lin_max, lin_steps = -100.1, 100.1, 2000 
     109            start, stop, steps = -100.1, 100.1, 2000 
    107110        elif xrange == "linear": 
    108             lin_min, lin_max, lin_steps = 1, 1000, 2000 
    109             lin_min, lin_max, lin_steps = 0.001, 2, 2000 
     111            start, stop, steps = 1, 1000, 2000 
     112            start, stop, steps = 0.001, 2, 2000 
    110113        elif xrange == "log": 
    111             log_min, log_max, log_steps = -3, 5, 400 
     114            start, stop, steps = -3, 5, 400 
    112115        elif xrange == "logq": 
    113             log_min, log_max, log_steps = -4, 1, 400 
     116            start, stop, steps = -4, 1, 400 
     117        elif ':' in xrange: 
     118            parts = xrange.split(':') 
     119            linear = parts[3] != "log" if len(parts) == 4 else True 
     120            steps = int(parts[2]) if len(parts) > 2 else 400 
     121            start = float(parts[0]) 
     122            stop = float(parts[1]) 
     123 
    114124        else: 
    115125            raise ValueError("unknown range "+xrange) 
    … …  
    121131            # value to x in the given precision. 
    122132            if linear: 
    123                 lin_min = max(lin_min, self.limits[0]) 
    124                 lin_max = min(lin_max, self.limits[1]) 
    125                 qrf = np.linspace(lin_min, lin_max, lin_steps, dtype='single') 
    126                 #qrf = np.linspace(lin_min, lin_max, lin_steps, dtype='double') 
     133                start = max(start, self.limits[0]) 
     134                stop = min(stop, self.limits[1]) 
     135                qrf = np.linspace(start, stop, steps, dtype='single') 
     136                #qrf = np.linspace(start, stop, steps, dtype='double') 
    127137                qr = [mp.mpf(float(v)) for v in qrf] 
    128                 #qr = mp.linspace(lin_min, lin_max, lin_steps) 
     138                #qr = mp.linspace(start, stop, steps) 
    129139            else: 
    130                 log_min = np.log10(max(10**log_min, self.limits[0])) 
    131                 log_max = np.log10(min(10**log_max, self.limits[1])) 
    132                 qrf = np.logspace(log_min, log_max, log_steps, dtype='single') 
    133                 #qrf = np.logspace(log_min, log_max, log_steps, dtype='double') 
     140                start = np.log10(max(10**start, self.limits[0])) 
     141                stop = np.log10(min(10**stop, self.limits[1])) 
     142                qrf = np.logspace(start, stop, steps, dtype='single') 
     143                #qrf = np.logspace(start, stop, steps, dtype='double') 
    134144                qr = [mp.mpf(float(v)) for v in qrf] 
    135                 #qr = [10**v for v in mp.linspace(log_min, log_max, log_steps)] 
     145                #qr = [10**v for v in mp.linspace(start, stop, steps)] 
    136146 
    137147        target = self.call_mpmath(qr, bits=500) 
    … …  
    176186    """ 
    177187    if diff == "relative": 
    178         err = np.array([abs((t-a)/t) for t, a in zip(target, actual)], 'd') 
     188        err = np.array([(abs((t-a)/t) if t != 0 else a) for t, a in zip(target, actual)], 'd') 
    179189        #err = np.clip(err, 0, 1) 
    180190        pylab.loglog(x, err, '-', label=label) 
    … …  
    197207    return model_info 
    198208 
     209# Hack to allow second parameter A in two parameter functions 
     210A = 1 
     211def parse_extra_pars(): 
     212    global A 
     213 
     214    A_str = str(A) 
     215    pop = [] 
     216    for k, v in enumerate(sys.argv[1:]): 
     217        if v.startswith("A="): 
     218            A_str = v[2:] 
     219            pop.append(k+1) 
     220    if pop: 
     221        sys.argv = [v for k, v in enumerate(sys.argv) if k not in pop] 
     222        A = float(A_str) 
     223 
     224parse_extra_pars() 
     225 
    199226 
    200227# =============== FUNCTION DEFINITIONS ================ 
    … …  
    297324    ocl_function=make_ocl("return sas_gamma(q);", "sas_gamma", ["lib/sas_gamma.c"]), 
    298325    limits=(-3.1, 10), 
     326) 
     327add_function( 
     328    name="gammaln(x)", 
     329    mp_function=mp.loggamma, 
     330    np_function=scipy.special.gammaln, 
     331    ocl_function=make_ocl("return sas_gammaln(q);", "sas_gammaln", ["lib/sas_gammainc.c"]), 
     332    #ocl_function=make_ocl("return lgamma(q);", "sas_gammaln"), 
     333) 
     334add_function( 
     335    name="gammainc(x)", 
     336    mp_function=lambda x, a=A: mp.gammainc(a, a=0, b=x)/mp.gamma(a), 
     337    np_function=lambda x, a=A: scipy.special.gammainc(a, x), 
     338    ocl_function=make_ocl("return sas_gammainc(%.15g,q);"%A, "sas_gammainc", ["lib/sas_gammainc.c"]), 
     339) 
     340add_function( 
     341    name="gammaincc(x)", 
     342    mp_function=lambda x, a=A: mp.gammainc(a, a=x, b=mp.inf)/mp.gamma(a), 
     343    np_function=lambda x, a=A: scipy.special.gammaincc(a, x), 
     344    ocl_function=make_ocl("return sas_gammaincc(%.15g,q);"%A, "sas_gammaincc", ["lib/sas_gammainc.c"]), 
    299345) 
    300346add_function( 
    … …  
    463509lanczos_gamma = """\ 
    464510    const double coeff[] = { 
    465             76.18009172947146,     -86.50532032941677, 
    466             24.01409824083091,     -1.231739572450155, 
     511            76.18009172947146, -86.50532032941677, 
     512            24.01409824083091, -1.231739572450155, 
    467513            0.1208650973866179e-2,-0.5395239384953e-5 
    468514            }; 
    … …  
    475521""" 
    476522add_function( 
    477     name="log gamma(x)", 
     523    name="loggamma(x)", 
    478524    mp_function=mp.loggamma, 
    479525    np_function=scipy.special.gammaln, 
    … …  
    599645 
    600646ALL_FUNCTIONS = set(FUNCTIONS.keys()) 
    601 ALL_FUNCTIONS.discard("loggamma")  # OCL version not ready yet 
     647ALL_FUNCTIONS.discard("loggamma")  # use cephes-based gammaln instead 
    602648ALL_FUNCTIONS.discard("3j1/x:taylor") 
    603649ALL_FUNCTIONS.discard("3j1/x:trig") 
    … …  
    615661    -r indicates that the relative error should be plotted (default), 
    616662    -x<range> indicates the steps in x, where <range> is one of the following 
    617       log indicates log stepping in [10^-3, 10^5] (default) 
    618       logq indicates log stepping in [10^-4, 10^1] 
    619       linear indicates linear stepping in [1, 1000] 
    620       zoom indicates linear stepping in [1000, 1010] 
    621       neg indicates linear stepping in [-100.1, 100.1] 
    622 and name is "all" or one of: 
     663        log indicates log stepping in [10^-3, 10^5] (default) 
     664        logq indicates log stepping in [10^-4, 10^1] 
     665        linear indicates linear stepping in [1, 1000] 
     666        zoom indicates linear stepping in [1000, 1010] 
     667        neg indicates linear stepping in [-100.1, 100.1] 
     668        start:stop:n[:stepping] indicates an n-step plot in [start, stop] 
     669            or [10^start, 10^stop] if stepping is "log" (default n=400) 
     670Some functions (notably gammainc/gammaincc) have an additional parameter A 
     671which can be set from the command line as A=value.  Default is A=1. 
     672 
     673Name is one of: 
    623674    """+names) 
    624675    sys.exit(1) 

  • sasmodels/kernelpy.py

    r91bd550 r91bd550  
    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 

  • sasmodels/sasview_model.py

    rbd547d0 rbd547d0  
    803803            return value, [value], [1.0] 
    804804 
     805    @classmethod 
     806    def runTests(cls): 
     807        """ 
     808        Run any tests built into the model and captures the test output. 
     809 
     810        Returns success flag and output 
     811        """ 
     812        from .model_test import check_model 
     813        return check_model(cls._model_info) 
     814 
    805815def test_cylinder(): 
    806816    # type: () -> float 

  • sasmodels/special.py

    rdf69efa rfba9ca0  
    113113        The standard math function, tgamma(x) is unstable for $x < 1$ 
    114114        on some platforms. 
     115 
     116    sas_gammaln(x): 
     117        log gamma function sas_gammaln\ $(x) = \log \Gamma(|x|)$. 
     118 
     119        The standard math function, lgamma(x), is incorrect for single 
     120        precision on some platforms. 
     121 
     122    sas_gammainc(a, x), sas_gammaincc(a, x): 
     123        Incomplete gamma function 
     124        sas_gammainc\ $(a, x) = \int_0^x t^{a-1}e^{-t}\,dt / \Gamma(a)$ 
     125        and complementary incomplete gamma function 
     126        sas_gammaincc\ $(a, x) = \int_x^\infty t^{a-1}e^{-t}\,dt / \Gamma(a)$ 
    115127 
    116128    sas_erf(x), sas_erfc(x): 
    … …  
    207219from numpy import pi, nan, inf 
    208220from scipy.special import gamma as sas_gamma 
     221from scipy.special import gammaln as sas_gammaln 
     222from scipy.special import gammainc as sas_gammainc 
     223from scipy.special import gammaincc as sas_gammaincc 
    209224from scipy.special import erf as sas_erf 
    210225from scipy.special import erfc as sas_erfc 
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