[d1fe925] | 1 | """ |
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| 2 | Core model handling routines. |
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| 3 | """ |
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| 4 | __all__ = ["list_models", "load_model_definition", "precompile_dll", |
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| 5 | "load_model", "make_kernel", "call_kernel", "call_ER", "call_VR" ] |
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| 6 | |
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| 7 | from os.path import basename, dirname, join as joinpath |
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| 8 | from glob import glob |
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| 9 | |
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| 10 | import numpy as np |
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| 11 | |
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| 12 | from . import models |
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| 13 | from . import weights |
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| 14 | from . import generate |
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| 15 | |
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| 16 | from . import kernelpy |
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| 17 | from . import kerneldll |
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| 18 | try: |
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| 19 | from . import kernelcl |
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| 20 | HAVE_OPENCL = True |
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| 21 | except: |
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| 22 | HAVE_OPENCL = False |
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| 23 | |
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| 24 | |
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| 25 | def list_models(): |
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| 26 | """ |
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| 27 | Return the list of available models on the model path. |
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| 28 | """ |
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| 29 | root = dirname(__file__) |
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| 30 | files = sorted(glob(joinpath(root, 'models', "[a-zA-Z]*.py"))) |
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| 31 | available_models = [basename(f)[:-3] for f in files] |
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| 32 | return available_models |
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| 33 | |
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| 34 | |
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| 35 | def load_model_definition(model_name): |
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| 36 | """ |
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| 37 | Load a model definition given the model name. |
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| 38 | """ |
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| 39 | __import__('sasmodels.models.'+model_name) |
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| 40 | model_definition = getattr(models, model_name, None) |
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| 41 | return model_definition |
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| 42 | |
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| 43 | |
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| 44 | def precompile_dll(model_name, dtype="double"): |
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| 45 | """ |
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| 46 | Precompile the dll for a model. |
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| 47 | |
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| 48 | Returns the path to the compiled model. |
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| 49 | |
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| 50 | This can be used when build the windows distribution of sasmodels |
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| 51 | (which may be missing the OpenCL driver and the dll compiler), or |
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| 52 | otherwise sharing models with windows users who do not have a compiler. |
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| 53 | |
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| 54 | See :func:`sasmodels.kerneldll.make_dll` for details on controlling the |
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| 55 | dll path and the allowed floating point precision. |
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| 56 | """ |
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| 57 | model_definition = load_model_definition(model_name) |
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| 58 | source, info = generate.make(model_definition) |
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| 59 | return kerneldll.make_dll(source, info, dtype=dtype) |
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| 60 | |
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| 61 | |
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| 62 | def isstr(s): |
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| 63 | try: s + '' |
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| 64 | except: return False |
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| 65 | return True |
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| 66 | |
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| 67 | def load_model(model_definition, dtype="single", platform="ocl"): |
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| 68 | """ |
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| 69 | Prepare the model for the default execution platform. |
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| 70 | |
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| 71 | This will return an OpenCL model, a DLL model or a python model depending |
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| 72 | on the model and the computing platform. |
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| 73 | |
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| 74 | *model_definition* is the python module which defines the model. If the |
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| 75 | model name is given instead, then :func:`load_model_definition` will be |
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| 76 | called with the model name. |
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| 77 | |
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| 78 | *dtype* indicates whether the model should use single or double precision |
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| 79 | for the calculation. Any valid numpy single or double precision identifier |
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| 80 | is valid, such as 'single', 'f', 'f32', or np.float32 for single, or |
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| 81 | 'double', 'd', 'f64' and np.float64 for double. |
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| 82 | |
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| 83 | *platform* should be "dll" to force the dll to be used for C models, |
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| 84 | otherwise it uses the default "ocl". |
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| 85 | """ |
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| 86 | if isstr(model_definition): |
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| 87 | model_definition = load_model_definition(model_definition) |
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| 88 | source, info = generate.make(model_definition) |
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| 89 | if callable(info.get('Iq', None)): |
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| 90 | return kernelpy.PyModel(info) |
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| 91 | |
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| 92 | ## for debugging: |
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| 93 | ## 1. uncomment open().write so that the source will be saved next time |
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| 94 | ## 2. run "python -m sasmodels.direct_model $MODELNAME" to save the source |
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| 95 | ## 3. recomment the open.write() and uncomment open().read() |
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| 96 | ## 4. rerun "python -m sasmodels.direct_model $MODELNAME" |
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| 97 | ## 5. uncomment open().read() so that source will be regenerated from model |
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| 98 | # open(info['name']+'.c','w').write(source) |
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| 99 | # source = open(info['name']+'.cl','r').read() |
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| 100 | |
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| 101 | dtype = np.dtype(dtype) |
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| 102 | if (platform=="dll" |
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| 103 | or not HAVE_OPENCL |
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| 104 | or (dtype == np.float64 and not kernelcl.environment().has_double)): |
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| 105 | return kerneldll.load_dll(source, info, dtype) |
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| 106 | else: |
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| 107 | return kernelcl.GpuModel(source, info, dtype) |
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| 108 | |
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| 109 | def make_kernel(model, q_vectors): |
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| 110 | """ |
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| 111 | Return a computation kernel from the model definition and the q input. |
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| 112 | """ |
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| 113 | model_input = model.make_input(q_vectors) |
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| 114 | return model(model_input) |
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| 115 | |
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| 116 | def get_weights(info, pars, name): |
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| 117 | """ |
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| 118 | Generate the distribution for parameter *name* given the parameter values |
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| 119 | in *pars*. |
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| 120 | |
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| 121 | Uses "name", "name_pd", "name_pd_type", "name_pd_n", "name_pd_sigma" |
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| 122 | from the *pars* dictionary for parameter value and parameter dispersion. |
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| 123 | """ |
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| 124 | relative = name in info['partype']['pd-rel'] |
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| 125 | limits = info['limits'] |
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| 126 | disperser = pars.get(name+'_pd_type', 'gaussian') |
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| 127 | value = pars.get(name, info['defaults'][name]) |
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| 128 | npts = pars.get(name+'_pd_n', 0) |
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| 129 | width = pars.get(name+'_pd', 0.0) |
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| 130 | nsigma = pars.get(name+'_pd_nsigma', 3.0) |
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| 131 | value,weight = weights.get_weights( |
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| 132 | disperser, npts, width, nsigma, |
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| 133 | value, limits[name], relative) |
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| 134 | return value,weight/np.sum(weight) |
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| 135 | |
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| 136 | def dispersion_mesh(pars): |
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| 137 | """ |
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| 138 | Create a mesh grid of dispersion parameters and weights. |
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| 139 | |
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| 140 | Returns [p1,p2,...],w where pj is a vector of values for parameter j |
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| 141 | and w is a vector containing the products for weights for each |
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| 142 | parameter set in the vector. |
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| 143 | """ |
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| 144 | value, weight = zip(*pars) |
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| 145 | if len(value) > 1: |
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| 146 | value = [v.flatten() for v in np.meshgrid(*value)] |
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| 147 | weight = np.vstack([v.flatten() for v in np.meshgrid(*weight)]) |
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| 148 | weight = np.prod(weight, axis=0) |
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| 149 | return value, weight |
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| 150 | |
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| 151 | def call_kernel(kernel, pars, cutoff=0): |
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| 152 | """ |
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| 153 | Call *kernel* returned from :func:`make_kernel` with parameters *pars*. |
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| 154 | |
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| 155 | *cutoff* is the limiting value for the product of dispersion weights used |
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| 156 | to perform the multidimensional dispersion calculation more quickly at a |
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| 157 | slight cost to accuracy. The default value of *cutoff=0* integrates over |
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| 158 | the entire dispersion cube. Using *cutoff=1e-5* can be 50% faster, but |
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| 159 | with an error of about 1%, which is usually less than the measurement |
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| 160 | uncertainty. |
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| 161 | """ |
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| 162 | fixed_pars = [pars.get(name, kernel.info['defaults'][name]) |
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| 163 | for name in kernel.fixed_pars] |
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| 164 | pd_pars = [get_weights(kernel.info, pars, name) for name in kernel.pd_pars] |
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| 165 | return kernel(fixed_pars, pd_pars, cutoff=cutoff) |
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| 166 | |
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| 167 | def call_ER(info, pars): |
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| 168 | """ |
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| 169 | Call the model ER function using *pars*. |
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| 170 | |
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| 171 | *info* is either *model.info* if you have a loaded model, or *kernel.info* |
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| 172 | if you have a model kernel prepared for evaluation. |
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| 173 | """ |
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| 174 | ER = info.get('ER', None) |
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| 175 | if ER is None: |
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| 176 | return 1.0 |
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| 177 | else: |
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| 178 | vol_pars = [get_weights(info, pars, name) |
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| 179 | for name in info['partype']['volume']] |
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| 180 | value, weight = dispersion_mesh(vol_pars) |
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| 181 | individual_radii = ER(*value) |
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| 182 | #print values[0].shape, weights.shape, fv.shape |
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| 183 | return np.sum(weight*individual_radii) / np.sum(weight) |
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| 184 | |
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| 185 | def call_VR(info, pars): |
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| 186 | """ |
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| 187 | Call the model VR function using *pars*. |
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| 188 | |
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| 189 | *info* is either *model.info* if you have a loaded model, or *kernel.info* |
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| 190 | if you have a model kernel prepared for evaluation. |
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| 191 | """ |
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| 192 | VR = info.get('VR', None) |
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| 193 | if VR is None: |
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| 194 | return 1.0 |
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| 195 | else: |
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| 196 | vol_pars = [get_weights(info, pars, name) |
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| 197 | for name in info['partype']['volume']] |
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| 198 | value, weight = dispersion_mesh(vol_pars) |
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| 199 | whole,part = VR(*value) |
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| 200 | return np.sum(weight*part)/np.sum(weight*whole) |
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| 201 | |
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