[aa4946b] | 1 | """ |
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| 2 | Core model handling routines. |
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| 3 | """ |
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[f734e7d] | 4 | |
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| 5 | from os.path import basename, dirname, join as joinpath |
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| 6 | from glob import glob |
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| 7 | |
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| 8 | import numpy as np |
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| 9 | |
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| 10 | from . import models |
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| 11 | from . import weights |
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[aa4946b] | 12 | from . import generate |
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[f734e7d] | 13 | |
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[aa4946b] | 14 | from . import kernelpy |
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| 15 | from . import kerneldll |
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[f734e7d] | 16 | try: |
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[aa4946b] | 17 | from . import kernelcl |
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| 18 | HAVE_OPENCL = True |
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[3c56da87] | 19 | except: |
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[aa4946b] | 20 | HAVE_OPENCL = False |
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| 21 | |
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[190fc2b] | 22 | __all__ = [ |
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[17bbadd] | 23 | "list_models", "load_model_info", "precompile_dll", |
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| 24 | "build_model", "make_kernel", "call_kernel", "call_ER_VR", |
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[190fc2b] | 25 | ] |
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[f734e7d] | 26 | |
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| 27 | def list_models(): |
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[aa4946b] | 28 | """ |
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| 29 | Return the list of available models on the model path. |
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| 30 | """ |
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[f734e7d] | 31 | root = dirname(__file__) |
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| 32 | files = sorted(glob(joinpath(root, 'models', "[a-zA-Z]*.py"))) |
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| 33 | available_models = [basename(f)[:-3] for f in files] |
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| 34 | return available_models |
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| 35 | |
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[b8e5e21] | 36 | def isstr(s): |
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| 37 | """ |
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| 38 | Return True if *s* is a string-like object. |
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| 39 | """ |
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| 40 | try: s + '' |
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| 41 | except: return False |
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| 42 | return True |
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| 43 | |
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| 44 | def load_model(model_name, **kw): |
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| 45 | """ |
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| 46 | Load model info and build model. |
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| 47 | """ |
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[7b3e62c] | 48 | parts = model_name.split('+') |
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| 49 | if len(parts) > 1: |
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| 50 | from .mixture import MixtureModel |
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| 51 | models = [load_model(p, **kw) for p in parts] |
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| 52 | return MixtureModel(models) |
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| 53 | |
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| 54 | parts = model_name.split('*') |
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| 55 | if len(parts) > 1: |
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| 56 | # Note: currently have circular reference |
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| 57 | from .product import ProductModel |
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| 58 | if len(parts) > 2: |
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| 59 | raise ValueError("use P*S to apply structure factor S to model P") |
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| 60 | P, Q = [load_model(p, **kw) for p in parts] |
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| 61 | return ProductModel(P, Q) |
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| 62 | |
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[b8e5e21] | 63 | return build_model(load_model_info(model_name), **kw) |
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[aa4946b] | 64 | |
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[17bbadd] | 65 | def load_model_info(model_name): |
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[aa4946b] | 66 | """ |
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| 67 | Load a model definition given the model name. |
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[1d4017a] | 68 | |
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| 69 | This returns a handle to the module defining the model. This can be |
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| 70 | used with functions in generate to build the docs or extract model info. |
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[aa4946b] | 71 | """ |
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[7b3e62c] | 72 | #import sys; print "\n".join(sys.path) |
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[f734e7d] | 73 | __import__('sasmodels.models.'+model_name) |
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[17bbadd] | 74 | kernel_module = getattr(models, model_name, None) |
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| 75 | return generate.make_model_info(kernel_module) |
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[f734e7d] | 76 | |
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[aa4946b] | 77 | |
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[17bbadd] | 78 | def build_model(model_info, dtype=None, platform="ocl"): |
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[aa4946b] | 79 | """ |
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| 80 | Prepare the model for the default execution platform. |
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| 81 | |
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| 82 | This will return an OpenCL model, a DLL model or a python model depending |
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| 83 | on the model and the computing platform. |
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| 84 | |
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[17bbadd] | 85 | *model_info* is the model definition structure returned from |
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| 86 | :func:`load_model_info`. |
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[bcd3aa3] | 87 | |
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[aa4946b] | 88 | *dtype* indicates whether the model should use single or double precision |
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| 89 | for the calculation. Any valid numpy single or double precision identifier |
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| 90 | is valid, such as 'single', 'f', 'f32', or np.float32 for single, or |
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[d18582e] | 91 | 'double', 'd', 'f64' and np.float64 for double. If *None*, then use |
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| 92 | 'single' unless the model defines single=False. |
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[aa4946b] | 93 | |
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| 94 | *platform* should be "dll" to force the dll to be used for C models, |
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| 95 | otherwise it uses the default "ocl". |
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| 96 | """ |
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[17bbadd] | 97 | source = generate.make_source(model_info) |
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[d18582e] | 98 | if dtype is None: |
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[17bbadd] | 99 | dtype = 'single' if model_info['single'] else 'double' |
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| 100 | if callable(model_info.get('Iq', None)): |
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| 101 | return kernelpy.PyModel(model_info) |
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[aa4946b] | 102 | |
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| 103 | ## for debugging: |
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| 104 | ## 1. uncomment open().write so that the source will be saved next time |
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| 105 | ## 2. run "python -m sasmodels.direct_model $MODELNAME" to save the source |
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| 106 | ## 3. recomment the open.write() and uncomment open().read() |
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| 107 | ## 4. rerun "python -m sasmodels.direct_model $MODELNAME" |
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| 108 | ## 5. uncomment open().read() so that source will be regenerated from model |
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[17bbadd] | 109 | # open(model_info['name']+'.c','w').write(source) |
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| 110 | # source = open(model_info['name']+'.cl','r').read() |
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[aa4946b] | 111 | |
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[d15a908] | 112 | if (platform == "dll" |
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[aa4946b] | 113 | or not HAVE_OPENCL |
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[5d316e9] | 114 | or not kernelcl.environment().has_type(dtype)): |
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[17bbadd] | 115 | return kerneldll.load_dll(source, model_info, dtype) |
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[aa4946b] | 116 | else: |
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[17bbadd] | 117 | return kernelcl.GpuModel(source, model_info, dtype) |
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[aa4946b] | 118 | |
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[b8e5e21] | 119 | def precompile_dll(model_name, dtype="double"): |
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| 120 | """ |
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| 121 | Precompile the dll for a model. |
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| 122 | |
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| 123 | Returns the path to the compiled model, or None if the model is a pure |
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| 124 | python model. |
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| 125 | |
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| 126 | This can be used when build the windows distribution of sasmodels |
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| 127 | (which may be missing the OpenCL driver and the dll compiler), or |
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| 128 | otherwise sharing models with windows users who do not have a compiler. |
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| 129 | |
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| 130 | See :func:`sasmodels.kerneldll.make_dll` for details on controlling the |
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| 131 | dll path and the allowed floating point precision. |
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| 132 | """ |
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| 133 | model_info = load_model_info(model_name) |
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| 134 | source = generate.make_source(model_info) |
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| 135 | return kerneldll.make_dll(source, model_info, dtype=dtype) if source else None |
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| 136 | |
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| 137 | |
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[f734e7d] | 138 | def make_kernel(model, q_vectors): |
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| 139 | """ |
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| 140 | Return a computation kernel from the model definition and the q input. |
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| 141 | """ |
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[eafc9fa] | 142 | return model(q_vectors) |
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[f734e7d] | 143 | |
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[17bbadd] | 144 | def get_weights(model_info, pars, name): |
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[f734e7d] | 145 | """ |
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| 146 | Generate the distribution for parameter *name* given the parameter values |
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| 147 | in *pars*. |
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| 148 | |
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[aa4946b] | 149 | Uses "name", "name_pd", "name_pd_type", "name_pd_n", "name_pd_sigma" |
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| 150 | from the *pars* dictionary for parameter value and parameter dispersion. |
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[f734e7d] | 151 | """ |
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[17bbadd] | 152 | relative = name in model_info['partype']['pd-rel'] |
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| 153 | limits = model_info['limits'][name] |
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[f734e7d] | 154 | disperser = pars.get(name+'_pd_type', 'gaussian') |
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[17bbadd] | 155 | value = pars.get(name, model_info['defaults'][name]) |
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[f734e7d] | 156 | npts = pars.get(name+'_pd_n', 0) |
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| 157 | width = pars.get(name+'_pd', 0.0) |
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| 158 | nsigma = pars.get(name+'_pd_nsigma', 3.0) |
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[d15a908] | 159 | value, weight = weights.get_weights( |
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[7cf2cfd] | 160 | disperser, npts, width, nsigma, value, limits, relative) |
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| 161 | return value, weight / np.sum(weight) |
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[f734e7d] | 162 | |
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[9890053] | 163 | def dispersion_mesh(pars): |
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| 164 | """ |
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| 165 | Create a mesh grid of dispersion parameters and weights. |
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| 166 | |
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| 167 | Returns [p1,p2,...],w where pj is a vector of values for parameter j |
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| 168 | and w is a vector containing the products for weights for each |
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| 169 | parameter set in the vector. |
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| 170 | """ |
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[3c56da87] | 171 | value, weight = zip(*pars) |
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| 172 | if len(value) > 1: |
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| 173 | value = [v.flatten() for v in np.meshgrid(*value)] |
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| 174 | weight = np.vstack([v.flatten() for v in np.meshgrid(*weight)]) |
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| 175 | weight = np.prod(weight, axis=0) |
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| 176 | return value, weight |
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[9890053] | 177 | |
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[aa4946b] | 178 | def call_kernel(kernel, pars, cutoff=0): |
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| 179 | """ |
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| 180 | Call *kernel* returned from :func:`make_kernel` with parameters *pars*. |
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| 181 | |
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| 182 | *cutoff* is the limiting value for the product of dispersion weights used |
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| 183 | to perform the multidimensional dispersion calculation more quickly at a |
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| 184 | slight cost to accuracy. The default value of *cutoff=0* integrates over |
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| 185 | the entire dispersion cube. Using *cutoff=1e-5* can be 50% faster, but |
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| 186 | with an error of about 1%, which is usually less than the measurement |
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| 187 | uncertainty. |
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| 188 | """ |
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[f734e7d] | 189 | fixed_pars = [pars.get(name, kernel.info['defaults'][name]) |
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| 190 | for name in kernel.fixed_pars] |
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[3c56da87] | 191 | pd_pars = [get_weights(kernel.info, pars, name) for name in kernel.pd_pars] |
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[f734e7d] | 192 | return kernel(fixed_pars, pd_pars, cutoff=cutoff) |
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| 193 | |
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[17bbadd] | 194 | def call_ER_VR(model_info, vol_pars): |
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| 195 | """ |
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| 196 | Return effect radius and volume ratio for the model. |
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| 197 | |
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| 198 | *info* is either *kernel.info* for *kernel=make_kernel(model,q)* |
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| 199 | or *model.info*. |
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| 200 | |
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| 201 | *pars* are the parameters as expected by :func:`call_kernel`. |
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| 202 | """ |
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| 203 | ER = model_info.get('ER', None) |
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| 204 | VR = model_info.get('VR', None) |
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| 205 | value, weight = dispersion_mesh(vol_pars) |
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| 206 | |
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| 207 | individual_radii = ER(*value) if ER else 1.0 |
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| 208 | whole, part = VR(*value) if VR else (1.0, 1.0) |
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| 209 | |
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| 210 | effect_radius = np.sum(weight*individual_radii) / np.sum(weight) |
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| 211 | volume_ratio = np.sum(weight*part)/np.sum(weight*whole) |
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| 212 | return effect_radius, volume_ratio |
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| 213 | |
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| 214 | |
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[3c56da87] | 215 | def call_ER(info, pars): |
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[aa4946b] | 216 | """ |
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| 217 | Call the model ER function using *pars*. |
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| 218 | *info* is either *model.info* if you have a loaded model, or *kernel.info* |
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| 219 | if you have a model kernel prepared for evaluation. |
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| 220 | """ |
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[3c56da87] | 221 | ER = info.get('ER', None) |
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[9890053] | 222 | if ER is None: |
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| 223 | return 1.0 |
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| 224 | else: |
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[3c56da87] | 225 | vol_pars = [get_weights(info, pars, name) |
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| 226 | for name in info['partype']['volume']] |
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| 227 | value, weight = dispersion_mesh(vol_pars) |
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| 228 | individual_radii = ER(*value) |
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[9404dd3] | 229 | #print(values[0].shape, weights.shape, fv.shape) |
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[3c56da87] | 230 | return np.sum(weight*individual_radii) / np.sum(weight) |
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[9890053] | 231 | |
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[3c56da87] | 232 | def call_VR(info, pars): |
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[aa4946b] | 233 | """ |
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| 234 | Call the model VR function using *pars*. |
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| 235 | *info* is either *model.info* if you have a loaded model, or *kernel.info* |
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| 236 | if you have a model kernel prepared for evaluation. |
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| 237 | """ |
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[3c56da87] | 238 | VR = info.get('VR', None) |
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[9890053] | 239 | if VR is None: |
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| 240 | return 1.0 |
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| 241 | else: |
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[3c56da87] | 242 | vol_pars = [get_weights(info, pars, name) |
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| 243 | for name in info['partype']['volume']] |
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| 244 | value, weight = dispersion_mesh(vol_pars) |
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[d15a908] | 245 | whole, part = VR(*value) |
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[3c56da87] | 246 | return np.sum(weight*part)/np.sum(weight*whole) |
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[9890053] | 247 | |
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[17bbadd] | 248 | # TODO: remove call_ER, call_VR |
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| 249 | |
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