[87985ca] | 1 | """ |
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| 2 | Sasview model constructor. |
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| 3 | |
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| 4 | Given a module defining an OpenCL kernel such as sasmodels.models.cylinder, |
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| 5 | create a sasview model class to run that kernel as follows:: |
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| 6 | |
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[92d38285] | 7 | from sasmodels.sasview_model import load_custom_model |
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| 8 | CylinderModel = load_custom_model('sasmodels/models/cylinder.py') |
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[87985ca] | 9 | """ |
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[4d76711] | 10 | from __future__ import print_function |
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[87985ca] | 11 | |
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[ce27e21] | 12 | import math |
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| 13 | from copy import deepcopy |
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[2622b3f] | 14 | import collections |
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[4d76711] | 15 | import traceback |
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| 16 | import logging |
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[724257c] | 17 | from os.path import basename, splitext, abspath, getmtime |
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[9f8ade1] | 18 | try: |
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| 19 | import _thread as thread |
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| 20 | except ImportError: |
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| 21 | import thread |
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[ce27e21] | 22 | |
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[7ae2b7f] | 23 | import numpy as np # type: ignore |
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[ce27e21] | 24 | |
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[aa4946b] | 25 | from . import core |
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[4d76711] | 26 | from . import custom |
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[a4f1a73] | 27 | from . import kernelcl |
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[a80e64c] | 28 | from . import product |
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[72a081d] | 29 | from . import generate |
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[fb5914f] | 30 | from . import weights |
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[6d6508e] | 31 | from . import modelinfo |
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[bde38b5] | 32 | from .details import make_kernel_args, dispersion_mesh |
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[a4f1a73] | 33 | from .kernelcl import reset_environment |
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[ff7119b] | 34 | |
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[2d81cfe] | 35 | # pylint: disable=unused-import |
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[fa5fd8d] | 36 | try: |
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[2d81cfe] | 37 | from typing import (Dict, Mapping, Any, Sequence, Tuple, NamedTuple, |
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| 38 | List, Optional, Union, Callable) |
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[fa5fd8d] | 39 | from .modelinfo import ModelInfo, Parameter |
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| 40 | from .kernel import KernelModel |
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| 41 | MultiplicityInfoType = NamedTuple( |
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[a9bc435] | 42 | 'MultiplicityInfo', |
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[fa5fd8d] | 43 | [("number", int), ("control", str), ("choices", List[str]), |
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| 44 | ("x_axis_label", str)]) |
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[60f03de] | 45 | SasviewModelType = Callable[[int], "SasviewModel"] |
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[fa5fd8d] | 46 | except ImportError: |
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| 47 | pass |
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[2d81cfe] | 48 | # pylint: enable=unused-import |
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[fa5fd8d] | 49 | |
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[724257c] | 50 | logger = logging.getLogger(__name__) |
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| 51 | |
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[a38b065] | 52 | calculation_lock = thread.allocate_lock() |
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| 53 | |
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[724257c] | 54 | #: True if pre-existing plugins, with the old names and parameters, should |
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| 55 | #: continue to be supported. |
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[c95dfc63] | 56 | SUPPORT_OLD_STYLE_PLUGINS = True |
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| 57 | |
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[fa5fd8d] | 58 | # TODO: separate x_axis_label from multiplicity info |
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| 59 | MultiplicityInfo = collections.namedtuple( |
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| 60 | 'MultiplicityInfo', |
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| 61 | ["number", "control", "choices", "x_axis_label"], |
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| 62 | ) |
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| 63 | |
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[724257c] | 64 | #: set of defined models (standard and custom) |
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| 65 | MODELS = {} # type: Dict[str, SasviewModelType] |
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[839fd68] | 66 | # TODO: remove unused MODEL_BY_PATH cache once sasview no longer references it |
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[724257c] | 67 | #: custom model {path: model} mapping so we can check timestamps |
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| 68 | MODEL_BY_PATH = {} # type: Dict[str, SasviewModelType] |
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[d321747] | 69 | #: Track modules that we have loaded so we can determine whether the model |
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| 70 | #: has changed since we last reloaded. |
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| 71 | _CACHED_MODULE = {} # type: Dict[str, "module"] |
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[724257c] | 72 | |
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[a4f1a73] | 73 | def reset_environment(): |
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| 74 | # type: () -> None |
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| 75 | """ |
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| 76 | Clear the compute engine context so that the GUI can change devices. |
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| 77 | |
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| 78 | This removes all compiled kernels, even those that are active on fit |
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| 79 | pages, but they will be restored the next time they are needed. |
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| 80 | """ |
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| 81 | kernelcl.reset_environment() |
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| 82 | for model in MODELS.values(): |
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| 83 | model._model = None |
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| 84 | |
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[92d38285] | 85 | def find_model(modelname): |
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[b32dafd] | 86 | # type: (str) -> SasviewModelType |
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| 87 | """ |
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| 88 | Find a model by name. If the model name ends in py, try loading it from |
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| 89 | custom models, otherwise look for it in the list of builtin models. |
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| 90 | """ |
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[92d38285] | 91 | # TODO: used by sum/product model to load an existing model |
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| 92 | # TODO: doesn't handle custom models properly |
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| 93 | if modelname.endswith('.py'): |
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| 94 | return load_custom_model(modelname) |
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| 95 | elif modelname in MODELS: |
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| 96 | return MODELS[modelname] |
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| 97 | else: |
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| 98 | raise ValueError("unknown model %r"%modelname) |
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| 99 | |
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[56b2687] | 100 | |
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[fa5fd8d] | 101 | # TODO: figure out how to say that the return type is a subclass |
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[4d76711] | 102 | def load_standard_models(): |
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[60f03de] | 103 | # type: () -> List[SasviewModelType] |
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[4d76711] | 104 | """ |
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| 105 | Load and return the list of predefined models. |
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| 106 | |
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| 107 | If there is an error loading a model, then a traceback is logged and the |
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| 108 | model is not returned. |
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| 109 | """ |
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| 110 | for name in core.list_models(): |
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| 111 | try: |
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[92d38285] | 112 | MODELS[name] = _make_standard_model(name) |
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[ee8f734] | 113 | except Exception: |
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[724257c] | 114 | logger.error(traceback.format_exc()) |
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[c95dfc63] | 115 | if SUPPORT_OLD_STYLE_PLUGINS: |
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| 116 | _register_old_models() |
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| 117 | |
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[724257c] | 118 | return list(MODELS.values()) |
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[de97440] | 119 | |
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[4d76711] | 120 | |
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| 121 | def load_custom_model(path): |
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[60f03de] | 122 | # type: (str) -> SasviewModelType |
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[4d76711] | 123 | """ |
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| 124 | Load a custom model given the model path. |
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[ff7119b] | 125 | """ |
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[724257c] | 126 | #logger.info("Loading model %s", path) |
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[d321747] | 127 | |
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| 128 | # Load the kernel module. This may already be cached by the loader, so |
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| 129 | # only requires checking the timestamps of the dependents. |
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[4d76711] | 130 | kernel_module = custom.load_custom_kernel_module(path) |
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[d321747] | 131 | |
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| 132 | # Check if the module has changed since we last looked. |
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| 133 | reloaded = kernel_module != _CACHED_MODULE.get(path, None) |
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| 134 | _CACHED_MODULE[path] = kernel_module |
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| 135 | |
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| 136 | # Turn the module into a model. We need to do this in even if the |
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| 137 | # model has already been loaded so that we can determine the model |
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| 138 | # name and retrieve it from the MODELS cache. |
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| 139 | model = getattr(kernel_module, 'Model', None) |
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| 140 | if model is not None: |
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[9457498] | 141 | # Old style models do not set the name in the class attributes, so |
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| 142 | # set it here; this name will be overridden when the object is created |
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| 143 | # with an instance variable that has the same value. |
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| 144 | if model.name == "": |
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| 145 | model.name = splitext(basename(path))[0] |
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[20a70bc] | 146 | if not hasattr(model, 'filename'): |
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[724257c] | 147 | model.filename = abspath(kernel_module.__file__).replace('.pyc', '.py') |
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[e4bf271] | 148 | if not hasattr(model, 'id'): |
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| 149 | model.id = splitext(basename(model.filename))[0] |
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[724257c] | 150 | else: |
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[56b2687] | 151 | model_info = modelinfo.make_model_info(kernel_module) |
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[bcdd6c9] | 152 | model = make_model_from_info(model_info) |
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[ed10b57] | 153 | |
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[2f2c70c] | 154 | # If a model name already exists and we are loading a different model, |
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| 155 | # use the model file name as the model name. |
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| 156 | if model.name in MODELS and not model.filename == MODELS[model.name].filename: |
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| 157 | _previous_name = model.name |
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| 158 | model.name = model.id |
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[bf8c271] | 159 | |
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[2f2c70c] | 160 | # If the new model name is still in the model list (for instance, |
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| 161 | # if we put a cylinder.py in our plug-in directory), then append |
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| 162 | # an identifier. |
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| 163 | if model.name in MODELS and not model.filename == MODELS[model.name].filename: |
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| 164 | model.name = model.id + '_user' |
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[724257c] | 165 | logger.info("Model %s already exists: using %s [%s]", |
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| 166 | _previous_name, model.name, model.filename) |
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[ed10b57] | 167 | |
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[d321747] | 168 | # Only update the model if the module has changed |
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| 169 | if reloaded or model.name not in MODELS: |
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| 170 | MODELS[model.name] = model |
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| 171 | |
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| 172 | return MODELS[model.name] |
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[4d76711] | 173 | |
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[87985ca] | 174 | |
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[bcdd6c9] | 175 | def make_model_from_info(model_info): |
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| 176 | # type: (ModelInfo) -> SasviewModelType |
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| 177 | """ |
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| 178 | Convert *model_info* into a SasView model wrapper. |
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| 179 | """ |
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| 180 | def __init__(self, multiplicity=None): |
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| 181 | SasviewModel.__init__(self, multiplicity=multiplicity) |
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| 182 | attrs = _generate_model_attributes(model_info) |
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| 183 | attrs['__init__'] = __init__ |
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| 184 | attrs['filename'] = model_info.filename |
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| 185 | ConstructedModel = type(model_info.name, (SasviewModel,), attrs) # type: SasviewModelType |
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| 186 | return ConstructedModel |
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| 187 | |
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| 188 | |
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[4d76711] | 189 | def _make_standard_model(name): |
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[60f03de] | 190 | # type: (str) -> SasviewModelType |
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[ff7119b] | 191 | """ |
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[4d76711] | 192 | Load the sasview model defined by *name*. |
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[72a081d] | 193 | |
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[4d76711] | 194 | *name* can be a standard model name or a path to a custom model. |
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[87985ca] | 195 | |
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[4d76711] | 196 | Returns a class that can be used directly as a sasview model. |
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[ff7119b] | 197 | """ |
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[4d76711] | 198 | kernel_module = generate.load_kernel_module(name) |
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[fa5fd8d] | 199 | model_info = modelinfo.make_model_info(kernel_module) |
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[bcdd6c9] | 200 | return make_model_from_info(model_info) |
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[72a081d] | 201 | |
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| 202 | |
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[724257c] | 203 | def _register_old_models(): |
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| 204 | # type: () -> None |
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| 205 | """ |
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| 206 | Place the new models into sasview under the old names. |
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| 207 | |
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| 208 | Monkey patch sas.sascalc.fit as sas.models so that sas.models.pluginmodel |
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| 209 | is available to the plugin modules. |
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| 210 | """ |
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| 211 | import sys |
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| 212 | import sas # needed in order to set sas.models |
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| 213 | import sas.sascalc.fit |
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| 214 | sys.modules['sas.models'] = sas.sascalc.fit |
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| 215 | sas.models = sas.sascalc.fit |
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| 216 | import sas.models |
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| 217 | from sasmodels.conversion_table import CONVERSION_TABLE |
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[e65c3ba] | 218 | |
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[724257c] | 219 | for new_name, conversion in CONVERSION_TABLE.get((3, 1, 2), {}).items(): |
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| 220 | # CoreShellEllipsoidModel => core_shell_ellipsoid:1 |
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| 221 | new_name = new_name.split(':')[0] |
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| 222 | old_name = conversion[0] if len(conversion) < 3 else conversion[2] |
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| 223 | module_attrs = {old_name: find_model(new_name)} |
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| 224 | ConstructedModule = type(old_name, (), module_attrs) |
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| 225 | old_path = 'sas.models.' + old_name |
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| 226 | setattr(sas.models, old_path, ConstructedModule) |
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| 227 | sys.modules[old_path] = ConstructedModule |
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| 228 | |
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| 229 | |
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[a80e64c] | 230 | def MultiplicationModel(form_factor, structure_factor): |
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| 231 | # type: ("SasviewModel", "SasviewModel") -> "SasviewModel" |
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[e65c3ba] | 232 | """ |
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| 233 | Returns a constructed product model from form_factor and structure_factor. |
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| 234 | """ |
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[a80e64c] | 235 | model_info = product.make_product_info(form_factor._model_info, |
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| 236 | structure_factor._model_info) |
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[bcdd6c9] | 237 | ConstructedModel = make_model_from_info(model_info) |
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[a06af5d] | 238 | return ConstructedModel(form_factor.multiplicity) |
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[a80e64c] | 239 | |
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[ce27e21] | 240 | |
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[fa5fd8d] | 241 | def _generate_model_attributes(model_info): |
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| 242 | # type: (ModelInfo) -> Dict[str, Any] |
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| 243 | """ |
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| 244 | Generate the class attributes for the model. |
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| 245 | |
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| 246 | This should include all the information necessary to query the model |
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| 247 | details so that you do not need to instantiate a model to query it. |
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| 248 | |
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| 249 | All the attributes should be immutable to avoid accidents. |
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| 250 | """ |
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| 251 | |
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| 252 | # TODO: allow model to override axis labels input/output name/unit |
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| 253 | |
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[a18c5b3] | 254 | # Process multiplicity |
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[fa5fd8d] | 255 | non_fittable = [] # type: List[str] |
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[04045f4] | 256 | xlabel = model_info.profile_axes[0] if model_info.profile is not None else "" |
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| 257 | variants = MultiplicityInfo(0, "", [], xlabel) |
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[a18c5b3] | 258 | for p in model_info.parameters.kernel_parameters: |
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[04045f4] | 259 | if p.name == model_info.control: |
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[fa5fd8d] | 260 | non_fittable.append(p.name) |
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[04045f4] | 261 | variants = MultiplicityInfo( |
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[ce176ca] | 262 | len(p.choices) if p.choices else int(p.limits[1]), |
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| 263 | p.name, p.choices, xlabel |
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[fa5fd8d] | 264 | ) |
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| 265 | break |
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| 266 | |
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[50ec515] | 267 | # Only a single drop-down list parameter available |
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| 268 | fun_list = [] |
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| 269 | for p in model_info.parameters.kernel_parameters: |
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| 270 | if p.choices: |
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| 271 | fun_list = p.choices |
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| 272 | if p.length > 1: |
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| 273 | non_fittable.extend(p.id+str(k) for k in range(1, p.length+1)) |
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| 274 | break |
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| 275 | |
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[a18c5b3] | 276 | # Organize parameter sets |
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[fa5fd8d] | 277 | orientation_params = [] |
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| 278 | magnetic_params = [] |
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| 279 | fixed = [] |
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[85fe7f8] | 280 | for p in model_info.parameters.user_parameters({}, is2d=True): |
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[fa5fd8d] | 281 | if p.type == 'orientation': |
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| 282 | orientation_params.append(p.name) |
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| 283 | orientation_params.append(p.name+".width") |
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| 284 | fixed.append(p.name+".width") |
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[32e3c9b] | 285 | elif p.type == 'magnetic': |
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[fa5fd8d] | 286 | orientation_params.append(p.name) |
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| 287 | magnetic_params.append(p.name) |
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| 288 | fixed.append(p.name+".width") |
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[a18c5b3] | 289 | |
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[32e3c9b] | 290 | |
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[a18c5b3] | 291 | # Build class dictionary |
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| 292 | attrs = {} # type: Dict[str, Any] |
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| 293 | attrs['_model_info'] = model_info |
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| 294 | attrs['name'] = model_info.name |
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| 295 | attrs['id'] = model_info.id |
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| 296 | attrs['description'] = model_info.description |
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| 297 | attrs['category'] = model_info.category |
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| 298 | attrs['is_structure_factor'] = model_info.structure_factor |
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| 299 | attrs['is_form_factor'] = model_info.ER is not None |
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| 300 | attrs['is_multiplicity_model'] = variants[0] > 1 |
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| 301 | attrs['multiplicity_info'] = variants |
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[fa5fd8d] | 302 | attrs['orientation_params'] = tuple(orientation_params) |
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| 303 | attrs['magnetic_params'] = tuple(magnetic_params) |
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| 304 | attrs['fixed'] = tuple(fixed) |
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| 305 | attrs['non_fittable'] = tuple(non_fittable) |
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[50ec515] | 306 | attrs['fun_list'] = tuple(fun_list) |
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[fa5fd8d] | 307 | |
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| 308 | return attrs |
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[4d76711] | 309 | |
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[ce27e21] | 310 | class SasviewModel(object): |
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| 311 | """ |
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| 312 | Sasview wrapper for opencl/ctypes model. |
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| 313 | """ |
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[fa5fd8d] | 314 | # Model parameters for the specific model are set in the class constructor |
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| 315 | # via the _generate_model_attributes function, which subclasses |
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| 316 | # SasviewModel. They are included here for typing and documentation |
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| 317 | # purposes. |
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| 318 | _model = None # type: KernelModel |
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| 319 | _model_info = None # type: ModelInfo |
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| 320 | #: load/save name for the model |
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| 321 | id = None # type: str |
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| 322 | #: display name for the model |
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| 323 | name = None # type: str |
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| 324 | #: short model description |
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| 325 | description = None # type: str |
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| 326 | #: default model category |
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| 327 | category = None # type: str |
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| 328 | |
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| 329 | #: names of the orientation parameters in the order they appear |
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[724257c] | 330 | orientation_params = None # type: List[str] |
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[fa5fd8d] | 331 | #: names of the magnetic parameters in the order they appear |
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[724257c] | 332 | magnetic_params = None # type: List[str] |
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[fa5fd8d] | 333 | #: names of the fittable parameters |
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[724257c] | 334 | fixed = None # type: List[str] |
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[fa5fd8d] | 335 | # TODO: the attribute fixed is ill-named |
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| 336 | |
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| 337 | # Axis labels |
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| 338 | input_name = "Q" |
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| 339 | input_unit = "A^{-1}" |
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| 340 | output_name = "Intensity" |
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| 341 | output_unit = "cm^{-1}" |
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| 342 | |
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| 343 | #: default cutoff for polydispersity |
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| 344 | cutoff = 1e-5 |
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| 345 | |
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| 346 | # Note: Use non-mutable values for class attributes to avoid errors |
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| 347 | #: parameters that are not fitted |
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| 348 | non_fittable = () # type: Sequence[str] |
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| 349 | |
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| 350 | #: True if model should appear as a structure factor |
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| 351 | is_structure_factor = False |
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| 352 | #: True if model should appear as a form factor |
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| 353 | is_form_factor = False |
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| 354 | #: True if model has multiplicity |
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| 355 | is_multiplicity_model = False |
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[1f35235] | 356 | #: Multiplicity information |
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[fa5fd8d] | 357 | multiplicity_info = None # type: MultiplicityInfoType |
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| 358 | |
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| 359 | # Per-instance variables |
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| 360 | #: parameter {name: value} mapping |
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| 361 | params = None # type: Dict[str, float] |
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| 362 | #: values for dispersion width, npts, nsigmas and type |
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| 363 | dispersion = None # type: Dict[str, Any] |
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| 364 | #: units and limits for each parameter |
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[60f03de] | 365 | details = None # type: Dict[str, Sequence[Any]] |
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| 366 | # # actual type is Dict[str, List[str, float, float]] |
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[04dc697] | 367 | #: multiplicity value, or None if no multiplicity on the model |
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[fa5fd8d] | 368 | multiplicity = None # type: Optional[int] |
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[04dc697] | 369 | #: memory for polydispersity array if using ArrayDispersion (used by sasview). |
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| 370 | _persistency_dict = None # type: Dict[str, Tuple[np.ndarray, np.ndarray]] |
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[fa5fd8d] | 371 | |
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| 372 | def __init__(self, multiplicity=None): |
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[04dc697] | 373 | # type: (Optional[int]) -> None |
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[2622b3f] | 374 | |
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[04045f4] | 375 | # TODO: _persistency_dict to persistency_dict throughout sasview |
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| 376 | # TODO: refactor multiplicity to encompass variants |
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| 377 | # TODO: dispersion should be a class |
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[fa5fd8d] | 378 | # TODO: refactor multiplicity info |
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| 379 | # TODO: separate profile view from multiplicity |
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| 380 | # The button label, x and y axis labels and scale need to be under |
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| 381 | # the control of the model, not the fit page. Maximum flexibility, |
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| 382 | # the fit page would supply the canvas and the profile could plot |
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| 383 | # how it wants, but this assumes matplotlib. Next level is that |
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| 384 | # we provide some sort of data description including title, labels |
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| 385 | # and lines to plot. |
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| 386 | |
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[1f35235] | 387 | # Get the list of hidden parameters given the multiplicity |
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[04045f4] | 388 | # Don't include multiplicity in the list of parameters |
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[fa5fd8d] | 389 | self.multiplicity = multiplicity |
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[04045f4] | 390 | if multiplicity is not None: |
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| 391 | hidden = self._model_info.get_hidden_parameters(multiplicity) |
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| 392 | hidden |= set([self.multiplicity_info.control]) |
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| 393 | else: |
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| 394 | hidden = set() |
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[8f93522] | 395 | if self._model_info.structure_factor: |
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| 396 | hidden.add('scale') |
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| 397 | hidden.add('background') |
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| 398 | self._model_info.parameters.defaults['background'] = 0. |
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[04045f4] | 399 | |
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[bd547d0] | 400 | # Update the parameter lists to exclude any hidden parameters |
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| 401 | self.magnetic_params = tuple(pname for pname in self.magnetic_params |
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| 402 | if pname not in hidden) |
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| 403 | self.orientation_params = tuple(pname for pname in self.orientation_params |
---|
| 404 | if pname not in hidden) |
---|
| 405 | |
---|
[04dc697] | 406 | self._persistency_dict = {} |
---|
[fa5fd8d] | 407 | self.params = collections.OrderedDict() |
---|
[b3a85cd] | 408 | self.dispersion = collections.OrderedDict() |
---|
[fa5fd8d] | 409 | self.details = {} |
---|
[8977226] | 410 | for p in self._model_info.parameters.user_parameters({}, is2d=True): |
---|
[04045f4] | 411 | if p.name in hidden: |
---|
[fa5fd8d] | 412 | continue |
---|
[fcd7bbd] | 413 | self.params[p.name] = p.default |
---|
[fa5fd8d] | 414 | self.details[p.id] = [p.units, p.limits[0], p.limits[1]] |
---|
[fb5914f] | 415 | if p.polydisperse: |
---|
[fa5fd8d] | 416 | self.details[p.id+".width"] = [ |
---|
| 417 | "", 0.0, 1.0 if p.relative_pd else np.inf |
---|
| 418 | ] |
---|
[fb5914f] | 419 | self.dispersion[p.name] = { |
---|
| 420 | 'width': 0, |
---|
| 421 | 'npts': 35, |
---|
| 422 | 'nsigmas': 3, |
---|
| 423 | 'type': 'gaussian', |
---|
| 424 | } |
---|
[ce27e21] | 425 | |
---|
[de97440] | 426 | def __get_state__(self): |
---|
[fa5fd8d] | 427 | # type: () -> Dict[str, Any] |
---|
[de97440] | 428 | state = self.__dict__.copy() |
---|
[4d76711] | 429 | state.pop('_model') |
---|
[de97440] | 430 | # May need to reload model info on set state since it has pointers |
---|
| 431 | # to python implementations of Iq, etc. |
---|
| 432 | #state.pop('_model_info') |
---|
| 433 | return state |
---|
| 434 | |
---|
| 435 | def __set_state__(self, state): |
---|
[fa5fd8d] | 436 | # type: (Dict[str, Any]) -> None |
---|
[de97440] | 437 | self.__dict__ = state |
---|
[fb5914f] | 438 | self._model = None |
---|
[de97440] | 439 | |
---|
[ce27e21] | 440 | def __str__(self): |
---|
[fa5fd8d] | 441 | # type: () -> str |
---|
[ce27e21] | 442 | """ |
---|
| 443 | :return: string representation |
---|
| 444 | """ |
---|
| 445 | return self.name |
---|
| 446 | |
---|
| 447 | def is_fittable(self, par_name): |
---|
[fa5fd8d] | 448 | # type: (str) -> bool |
---|
[ce27e21] | 449 | """ |
---|
| 450 | Check if a given parameter is fittable or not |
---|
| 451 | |
---|
| 452 | :param par_name: the parameter name to check |
---|
| 453 | """ |
---|
[e758662] | 454 | return par_name in self.fixed |
---|
[ce27e21] | 455 | #For the future |
---|
| 456 | #return self.params[str(par_name)].is_fittable() |
---|
| 457 | |
---|
| 458 | |
---|
| 459 | def getProfile(self): |
---|
[fa5fd8d] | 460 | # type: () -> (np.ndarray, np.ndarray) |
---|
[ce27e21] | 461 | """ |
---|
| 462 | Get SLD profile |
---|
| 463 | |
---|
| 464 | : return: (z, beta) where z is a list of depth of the transition points |
---|
| 465 | beta is a list of the corresponding SLD values |
---|
| 466 | """ |
---|
[745b7bb] | 467 | args = {} # type: Dict[str, Any] |
---|
[fa5fd8d] | 468 | for p in self._model_info.parameters.kernel_parameters: |
---|
| 469 | if p.id == self.multiplicity_info.control: |
---|
[745b7bb] | 470 | value = float(self.multiplicity) |
---|
[fa5fd8d] | 471 | elif p.length == 1: |
---|
[745b7bb] | 472 | value = self.params.get(p.id, np.NaN) |
---|
[fa5fd8d] | 473 | else: |
---|
[745b7bb] | 474 | value = np.array([self.params.get(p.id+str(k), np.NaN) |
---|
[b32dafd] | 475 | for k in range(1, p.length+1)]) |
---|
[745b7bb] | 476 | args[p.id] = value |
---|
| 477 | |
---|
[e7fe459] | 478 | x, y = self._model_info.profile(**args) |
---|
| 479 | return x, 1e-6*y |
---|
[ce27e21] | 480 | |
---|
| 481 | def setParam(self, name, value): |
---|
[fa5fd8d] | 482 | # type: (str, float) -> None |
---|
[ce27e21] | 483 | """ |
---|
| 484 | Set the value of a model parameter |
---|
| 485 | |
---|
| 486 | :param name: name of the parameter |
---|
| 487 | :param value: value of the parameter |
---|
| 488 | |
---|
| 489 | """ |
---|
| 490 | # Look for dispersion parameters |
---|
| 491 | toks = name.split('.') |
---|
[de0c4ba] | 492 | if len(toks) == 2: |
---|
[ce27e21] | 493 | for item in self.dispersion.keys(): |
---|
[e758662] | 494 | if item == toks[0]: |
---|
[ce27e21] | 495 | for par in self.dispersion[item]: |
---|
[e758662] | 496 | if par == toks[1]: |
---|
[ce27e21] | 497 | self.dispersion[item][par] = value |
---|
| 498 | return |
---|
| 499 | else: |
---|
| 500 | # Look for standard parameter |
---|
| 501 | for item in self.params.keys(): |
---|
[e758662] | 502 | if item == name: |
---|
[ce27e21] | 503 | self.params[item] = value |
---|
| 504 | return |
---|
| 505 | |
---|
[63b32bb] | 506 | raise ValueError("Model does not contain parameter %s" % name) |
---|
[ce27e21] | 507 | |
---|
| 508 | def getParam(self, name): |
---|
[fa5fd8d] | 509 | # type: (str) -> float |
---|
[ce27e21] | 510 | """ |
---|
| 511 | Set the value of a model parameter |
---|
| 512 | |
---|
| 513 | :param name: name of the parameter |
---|
| 514 | |
---|
| 515 | """ |
---|
| 516 | # Look for dispersion parameters |
---|
| 517 | toks = name.split('.') |
---|
[de0c4ba] | 518 | if len(toks) == 2: |
---|
[ce27e21] | 519 | for item in self.dispersion.keys(): |
---|
[e758662] | 520 | if item == toks[0]: |
---|
[ce27e21] | 521 | for par in self.dispersion[item]: |
---|
[e758662] | 522 | if par == toks[1]: |
---|
[ce27e21] | 523 | return self.dispersion[item][par] |
---|
| 524 | else: |
---|
| 525 | # Look for standard parameter |
---|
| 526 | for item in self.params.keys(): |
---|
[e758662] | 527 | if item == name: |
---|
[ce27e21] | 528 | return self.params[item] |
---|
| 529 | |
---|
[63b32bb] | 530 | raise ValueError("Model does not contain parameter %s" % name) |
---|
[ce27e21] | 531 | |
---|
| 532 | def getParamList(self): |
---|
[04dc697] | 533 | # type: () -> Sequence[str] |
---|
[ce27e21] | 534 | """ |
---|
| 535 | Return a list of all available parameters for the model |
---|
| 536 | """ |
---|
[04dc697] | 537 | param_list = list(self.params.keys()) |
---|
[ce27e21] | 538 | # WARNING: Extending the list with the dispersion parameters |
---|
[de0c4ba] | 539 | param_list.extend(self.getDispParamList()) |
---|
| 540 | return param_list |
---|
[ce27e21] | 541 | |
---|
| 542 | def getDispParamList(self): |
---|
[04dc697] | 543 | # type: () -> Sequence[str] |
---|
[ce27e21] | 544 | """ |
---|
[fb5914f] | 545 | Return a list of polydispersity parameters for the model |
---|
[ce27e21] | 546 | """ |
---|
[1780d59] | 547 | # TODO: fix test so that parameter order doesn't matter |
---|
[3bcb88c] | 548 | ret = ['%s.%s' % (p_name, ext) |
---|
| 549 | for p_name in self.dispersion.keys() |
---|
| 550 | for ext in ('npts', 'nsigmas', 'width')] |
---|
[9404dd3] | 551 | #print(ret) |
---|
[1780d59] | 552 | return ret |
---|
[ce27e21] | 553 | |
---|
| 554 | def clone(self): |
---|
[04dc697] | 555 | # type: () -> "SasviewModel" |
---|
[ce27e21] | 556 | """ Return a identical copy of self """ |
---|
| 557 | return deepcopy(self) |
---|
| 558 | |
---|
| 559 | def run(self, x=0.0): |
---|
[fa5fd8d] | 560 | # type: (Union[float, (float, float), List[float]]) -> float |
---|
[ce27e21] | 561 | """ |
---|
| 562 | Evaluate the model |
---|
| 563 | |
---|
| 564 | :param x: input q, or [q,phi] |
---|
| 565 | |
---|
| 566 | :return: scattering function P(q) |
---|
| 567 | |
---|
| 568 | **DEPRECATED**: use calculate_Iq instead |
---|
| 569 | """ |
---|
[de0c4ba] | 570 | if isinstance(x, (list, tuple)): |
---|
[3c56da87] | 571 | # pylint: disable=unpacking-non-sequence |
---|
[ce27e21] | 572 | q, phi = x |
---|
[60f03de] | 573 | return self.calculate_Iq([q*math.cos(phi)], [q*math.sin(phi)])[0] |
---|
[ce27e21] | 574 | else: |
---|
[60f03de] | 575 | return self.calculate_Iq([x])[0] |
---|
[ce27e21] | 576 | |
---|
| 577 | |
---|
| 578 | def runXY(self, x=0.0): |
---|
[fa5fd8d] | 579 | # type: (Union[float, (float, float), List[float]]) -> float |
---|
[ce27e21] | 580 | """ |
---|
| 581 | Evaluate the model in cartesian coordinates |
---|
| 582 | |
---|
| 583 | :param x: input q, or [qx, qy] |
---|
| 584 | |
---|
| 585 | :return: scattering function P(q) |
---|
| 586 | |
---|
| 587 | **DEPRECATED**: use calculate_Iq instead |
---|
| 588 | """ |
---|
[de0c4ba] | 589 | if isinstance(x, (list, tuple)): |
---|
[60f03de] | 590 | return self.calculate_Iq([x[0]], [x[1]])[0] |
---|
[ce27e21] | 591 | else: |
---|
[60f03de] | 592 | return self.calculate_Iq([x])[0] |
---|
[ce27e21] | 593 | |
---|
| 594 | def evalDistribution(self, qdist): |
---|
[04dc697] | 595 | # type: (Union[np.ndarray, Tuple[np.ndarray, np.ndarray], List[np.ndarray]]) -> np.ndarray |
---|
[d138d43] | 596 | r""" |
---|
[ce27e21] | 597 | Evaluate a distribution of q-values. |
---|
| 598 | |
---|
[d138d43] | 599 | :param qdist: array of q or a list of arrays [qx,qy] |
---|
[ce27e21] | 600 | |
---|
[d138d43] | 601 | * For 1D, a numpy array is expected as input |
---|
[ce27e21] | 602 | |
---|
[d138d43] | 603 | :: |
---|
[ce27e21] | 604 | |
---|
[d138d43] | 605 | evalDistribution(q) |
---|
[ce27e21] | 606 | |
---|
[d138d43] | 607 | where *q* is a numpy array. |
---|
[ce27e21] | 608 | |
---|
[d138d43] | 609 | * For 2D, a list of *[qx,qy]* is expected with 1D arrays as input |
---|
[ce27e21] | 610 | |
---|
[d138d43] | 611 | :: |
---|
[ce27e21] | 612 | |
---|
[d138d43] | 613 | qx = [ qx[0], qx[1], qx[2], ....] |
---|
| 614 | qy = [ qy[0], qy[1], qy[2], ....] |
---|
[ce27e21] | 615 | |
---|
[d138d43] | 616 | If the model is 1D only, then |
---|
[ce27e21] | 617 | |
---|
[d138d43] | 618 | .. math:: |
---|
[ce27e21] | 619 | |
---|
[d138d43] | 620 | q = \sqrt{q_x^2+q_y^2} |
---|
[ce27e21] | 621 | |
---|
| 622 | """ |
---|
[de0c4ba] | 623 | if isinstance(qdist, (list, tuple)): |
---|
[ce27e21] | 624 | # Check whether we have a list of ndarrays [qx,qy] |
---|
| 625 | qx, qy = qdist |
---|
[05df1de] | 626 | return self.calculate_Iq(qx, qy) |
---|
[ce27e21] | 627 | |
---|
| 628 | elif isinstance(qdist, np.ndarray): |
---|
| 629 | # We have a simple 1D distribution of q-values |
---|
| 630 | return self.calculate_Iq(qdist) |
---|
| 631 | |
---|
| 632 | else: |
---|
[3c56da87] | 633 | raise TypeError("evalDistribution expects q or [qx, qy], not %r" |
---|
| 634 | % type(qdist)) |
---|
[ce27e21] | 635 | |
---|
[9dcb21d] | 636 | def calc_composition_models(self, qx): |
---|
[64614ad] | 637 | """ |
---|
[9dcb21d] | 638 | returns parts of the composition model or None if not a composition |
---|
| 639 | model. |
---|
[64614ad] | 640 | """ |
---|
[946c8d27] | 641 | # TODO: have calculate_Iq return the intermediates. |
---|
| 642 | # |
---|
| 643 | # The current interface causes calculate_Iq() to be called twice, |
---|
| 644 | # once to get the combined result and again to get the intermediate |
---|
| 645 | # results. This is necessary for now. |
---|
| 646 | # Long term, the solution is to change the interface to calculate_Iq |
---|
| 647 | # so that it returns a results object containing all the bits: |
---|
[9644b5a] | 648 | # the A, B, C, ... of the composition model (and any subcomponents?) |
---|
[946c8d27] | 649 | # the P and S of the product model, |
---|
| 650 | # the combined model before resolution smearing, |
---|
| 651 | # the sasmodel before sesans conversion, |
---|
| 652 | # the oriented 2D model used to fit oriented usans data, |
---|
| 653 | # the final I(q), |
---|
| 654 | # ... |
---|
[9644b5a] | 655 | # |
---|
[946c8d27] | 656 | # Have the model calculator add all of these blindly to the data |
---|
| 657 | # tree, and update the graphs which contain them. The fitter |
---|
| 658 | # needs to be updated to use the I(q) value only, ignoring the rest. |
---|
| 659 | # |
---|
| 660 | # The simple fix of returning the existing intermediate results |
---|
| 661 | # will not work for a couple of reasons: (1) another thread may |
---|
| 662 | # sneak in to compute its own results before calc_composition_models |
---|
| 663 | # is called, and (2) calculate_Iq is currently called three times: |
---|
| 664 | # once with q, once with q values before qmin and once with q values |
---|
| 665 | # after q max. Both of these should be addressed before |
---|
| 666 | # replacing this code. |
---|
[9644b5a] | 667 | composition = self._model_info.composition |
---|
| 668 | if composition and composition[0] == 'product': # only P*S for now |
---|
| 669 | with calculation_lock: |
---|
| 670 | self._calculate_Iq(qx) |
---|
| 671 | return self._intermediate_results |
---|
| 672 | else: |
---|
| 673 | return None |
---|
[bf8c271] | 674 | |
---|
[fa5fd8d] | 675 | def calculate_Iq(self, qx, qy=None): |
---|
| 676 | # type: (Sequence[float], Optional[Sequence[float]]) -> np.ndarray |
---|
[ff7119b] | 677 | """ |
---|
| 678 | Calculate Iq for one set of q with the current parameters. |
---|
| 679 | |
---|
| 680 | If the model is 1D, use *q*. If 2D, use *qx*, *qy*. |
---|
| 681 | |
---|
| 682 | This should NOT be used for fitting since it copies the *q* vectors |
---|
| 683 | to the card for each evaluation. |
---|
| 684 | """ |
---|
[a38b065] | 685 | ## uncomment the following when trying to debug the uncoordinated calls |
---|
| 686 | ## to calculate_Iq |
---|
| 687 | #if calculation_lock.locked(): |
---|
[724257c] | 688 | # logger.info("calculation waiting for another thread to complete") |
---|
| 689 | # logger.info("\n".join(traceback.format_stack())) |
---|
[a38b065] | 690 | |
---|
| 691 | with calculation_lock: |
---|
| 692 | return self._calculate_Iq(qx, qy) |
---|
| 693 | |
---|
| 694 | def _calculate_Iq(self, qx, qy=None): |
---|
[fb5914f] | 695 | if self._model is None: |
---|
[a4f1a73] | 696 | # Only need one copy of the compiled kernel regardless of how many |
---|
| 697 | # times it is used, so store it in the class. Also, to reset the |
---|
| 698 | # compute engine, need to clear out all existing compiled kernels, |
---|
| 699 | # which is much easier to do if we store them in the class. |
---|
| 700 | self.__class__._model = core.build_model(self._model_info) |
---|
[fa5fd8d] | 701 | if qy is not None: |
---|
| 702 | q_vectors = [np.asarray(qx), np.asarray(qy)] |
---|
| 703 | else: |
---|
| 704 | q_vectors = [np.asarray(qx)] |
---|
[a738209] | 705 | calculator = self._model.make_kernel(q_vectors) |
---|
[6a0d6aa] | 706 | parameters = self._model_info.parameters |
---|
| 707 | pairs = [self._get_weights(p) for p in parameters.call_parameters] |
---|
[9c1a59c] | 708 | #weights.plot_weights(self._model_info, pairs) |
---|
[bde38b5] | 709 | call_details, values, is_magnetic = make_kernel_args(calculator, pairs) |
---|
[4edec6f] | 710 | #call_details.show() |
---|
[05df1de] | 711 | #print("================ parameters ==================") |
---|
| 712 | #for p, v in zip(parameters.call_parameters, pairs): print(p.name, v[0]) |
---|
[ce99754] | 713 | #for k, p in enumerate(self._model_info.parameters.call_parameters): |
---|
| 714 | # print(k, p.name, *pairs[k]) |
---|
[4edec6f] | 715 | #print("params", self.params) |
---|
| 716 | #print("values", values) |
---|
| 717 | #print("is_mag", is_magnetic) |
---|
[6a0d6aa] | 718 | result = calculator(call_details, values, cutoff=self.cutoff, |
---|
[9eb3632] | 719 | magnetic=is_magnetic) |
---|
[ce99754] | 720 | #print("result", result) |
---|
[bf8c271] | 721 | self._intermediate_results = getattr(calculator, 'results', None) |
---|
[a738209] | 722 | calculator.release() |
---|
[d533590] | 723 | #self._model.release() |
---|
[ce27e21] | 724 | return result |
---|
| 725 | |
---|
| 726 | def calculate_ER(self): |
---|
[fa5fd8d] | 727 | # type: () -> float |
---|
[ce27e21] | 728 | """ |
---|
| 729 | Calculate the effective radius for P(q)*S(q) |
---|
| 730 | |
---|
| 731 | :return: the value of the effective radius |
---|
| 732 | """ |
---|
[4bfd277] | 733 | if self._model_info.ER is None: |
---|
[ce27e21] | 734 | return 1.0 |
---|
| 735 | else: |
---|
[4bfd277] | 736 | value, weight = self._dispersion_mesh() |
---|
| 737 | fv = self._model_info.ER(*value) |
---|
[9404dd3] | 738 | #print(values[0].shape, weights.shape, fv.shape) |
---|
[4bfd277] | 739 | return np.sum(weight * fv) / np.sum(weight) |
---|
[ce27e21] | 740 | |
---|
| 741 | def calculate_VR(self): |
---|
[fa5fd8d] | 742 | # type: () -> float |
---|
[ce27e21] | 743 | """ |
---|
| 744 | Calculate the volf ratio for P(q)*S(q) |
---|
| 745 | |
---|
| 746 | :return: the value of the volf ratio |
---|
| 747 | """ |
---|
[4bfd277] | 748 | if self._model_info.VR is None: |
---|
[ce27e21] | 749 | return 1.0 |
---|
| 750 | else: |
---|
[4bfd277] | 751 | value, weight = self._dispersion_mesh() |
---|
| 752 | whole, part = self._model_info.VR(*value) |
---|
| 753 | return np.sum(weight * part) / np.sum(weight * whole) |
---|
[ce27e21] | 754 | |
---|
| 755 | def set_dispersion(self, parameter, dispersion): |
---|
[7c3fb15] | 756 | # type: (str, weights.Dispersion) -> None |
---|
[ce27e21] | 757 | """ |
---|
| 758 | Set the dispersion object for a model parameter |
---|
| 759 | |
---|
| 760 | :param parameter: name of the parameter [string] |
---|
| 761 | :param dispersion: dispersion object of type Dispersion |
---|
| 762 | """ |
---|
[fa800e72] | 763 | if parameter in self.params: |
---|
[1780d59] | 764 | # TODO: Store the disperser object directly in the model. |
---|
[56b2687] | 765 | # The current method of relying on the sasview GUI to |
---|
[fa800e72] | 766 | # remember them is kind of funky. |
---|
[1780d59] | 767 | # Note: can't seem to get disperser parameters from sasview |
---|
[9c1a59c] | 768 | # (1) Could create a sasview model that has not yet been |
---|
[1780d59] | 769 | # converted, assign the disperser to one of its polydisperse |
---|
| 770 | # parameters, then retrieve the disperser parameters from the |
---|
[9c1a59c] | 771 | # sasview model. |
---|
| 772 | # (2) Could write a disperser parameter retriever in sasview. |
---|
| 773 | # (3) Could modify sasview to use sasmodels.weights dispersers. |
---|
[1780d59] | 774 | # For now, rely on the fact that the sasview only ever uses |
---|
| 775 | # new dispersers in the set_dispersion call and create a new |
---|
| 776 | # one instead of trying to assign parameters. |
---|
[ce27e21] | 777 | self.dispersion[parameter] = dispersion.get_pars() |
---|
| 778 | else: |
---|
[7c3fb15] | 779 | raise ValueError("%r is not a dispersity or orientation parameter" |
---|
| 780 | % parameter) |
---|
[ce27e21] | 781 | |
---|
[aa4946b] | 782 | def _dispersion_mesh(self): |
---|
[fa5fd8d] | 783 | # type: () -> List[Tuple[np.ndarray, np.ndarray]] |
---|
[ce27e21] | 784 | """ |
---|
| 785 | Create a mesh grid of dispersion parameters and weights. |
---|
| 786 | |
---|
| 787 | Returns [p1,p2,...],w where pj is a vector of values for parameter j |
---|
| 788 | and w is a vector containing the products for weights for each |
---|
| 789 | parameter set in the vector. |
---|
| 790 | """ |
---|
[4bfd277] | 791 | pars = [self._get_weights(p) |
---|
| 792 | for p in self._model_info.parameters.call_parameters |
---|
| 793 | if p.type == 'volume'] |
---|
[9eb3632] | 794 | return dispersion_mesh(self._model_info, pars) |
---|
[ce27e21] | 795 | |
---|
| 796 | def _get_weights(self, par): |
---|
[fa5fd8d] | 797 | # type: (Parameter) -> Tuple[np.ndarray, np.ndarray] |
---|
[de0c4ba] | 798 | """ |
---|
[fb5914f] | 799 | Return dispersion weights for parameter |
---|
[de0c4ba] | 800 | """ |
---|
[fa5fd8d] | 801 | if par.name not in self.params: |
---|
| 802 | if par.name == self.multiplicity_info.control: |
---|
[32f87a5] | 803 | return self.multiplicity, [self.multiplicity], [1.0] |
---|
[fa5fd8d] | 804 | else: |
---|
[17db833] | 805 | # For hidden parameters use default values. This sets |
---|
| 806 | # scale=1 and background=0 for structure factors |
---|
| 807 | default = self._model_info.parameters.defaults.get(par.name, np.NaN) |
---|
| 808 | return default, [default], [1.0] |
---|
[fa5fd8d] | 809 | elif par.polydisperse: |
---|
[32f87a5] | 810 | value = self.params[par.name] |
---|
[fb5914f] | 811 | dis = self.dispersion[par.name] |
---|
[9c1a59c] | 812 | if dis['type'] == 'array': |
---|
[32f87a5] | 813 | dispersity, weight = dis['values'], dis['weights'] |
---|
[9c1a59c] | 814 | else: |
---|
[32f87a5] | 815 | dispersity, weight = weights.get_weights( |
---|
[9c1a59c] | 816 | dis['type'], dis['npts'], dis['width'], dis['nsigmas'], |
---|
[32f87a5] | 817 | value, par.limits, par.relative_pd) |
---|
| 818 | return value, dispersity, weight |
---|
[fb5914f] | 819 | else: |
---|
[32f87a5] | 820 | value = self.params[par.name] |
---|
[ce99754] | 821 | return value, [value], [1.0] |
---|
[ce27e21] | 822 | |
---|
[12eec1e] | 823 | @classmethod |
---|
| 824 | def runTests(cls): |
---|
| 825 | """ |
---|
| 826 | Run any tests built into the model and captures the test output. |
---|
| 827 | |
---|
| 828 | Returns success flag and output |
---|
| 829 | """ |
---|
| 830 | from .model_test import check_model |
---|
| 831 | return check_model(cls._model_info) |
---|
| 832 | |
---|
[749a7d4] | 833 | def test_cylinder(): |
---|
[fa5fd8d] | 834 | # type: () -> float |
---|
[4d76711] | 835 | """ |
---|
[749a7d4] | 836 | Test that the cylinder model runs, returning the value at [0.1,0.1]. |
---|
[4d76711] | 837 | """ |
---|
| 838 | Cylinder = _make_standard_model('cylinder') |
---|
[fb5914f] | 839 | cylinder = Cylinder() |
---|
[b32dafd] | 840 | return cylinder.evalDistribution([0.1, 0.1]) |
---|
[de97440] | 841 | |
---|
[8f93522] | 842 | def test_structure_factor(): |
---|
| 843 | # type: () -> float |
---|
| 844 | """ |
---|
[749a7d4] | 845 | Test that 2-D hardsphere model runs and doesn't produce NaN. |
---|
[8f93522] | 846 | """ |
---|
| 847 | Model = _make_standard_model('hardsphere') |
---|
| 848 | model = Model() |
---|
[17db833] | 849 | value2d = model.evalDistribution([0.1, 0.1]) |
---|
| 850 | value1d = model.evalDistribution(np.array([0.1*np.sqrt(2)])) |
---|
| 851 | #print("hardsphere", value1d, value2d) |
---|
| 852 | if np.isnan(value1d) or np.isnan(value2d): |
---|
| 853 | raise ValueError("hardsphere returns nan") |
---|
[8f93522] | 854 | |
---|
[ce99754] | 855 | def test_product(): |
---|
| 856 | # type: () -> float |
---|
| 857 | """ |
---|
| 858 | Test that 2-D hardsphere model runs and doesn't produce NaN. |
---|
| 859 | """ |
---|
| 860 | S = _make_standard_model('hayter_msa')() |
---|
| 861 | P = _make_standard_model('cylinder')() |
---|
| 862 | model = MultiplicationModel(P, S) |
---|
| 863 | value = model.evalDistribution([0.1, 0.1]) |
---|
| 864 | if np.isnan(value): |
---|
| 865 | raise ValueError("cylinder*hatyer_msa returns null") |
---|
| 866 | |
---|
[04045f4] | 867 | def test_rpa(): |
---|
| 868 | # type: () -> float |
---|
| 869 | """ |
---|
[749a7d4] | 870 | Test that the 2-D RPA model runs |
---|
[04045f4] | 871 | """ |
---|
| 872 | RPA = _make_standard_model('rpa') |
---|
| 873 | rpa = RPA(3) |
---|
[b32dafd] | 874 | return rpa.evalDistribution([0.1, 0.1]) |
---|
[04045f4] | 875 | |
---|
[749a7d4] | 876 | def test_empty_distribution(): |
---|
| 877 | # type: () -> None |
---|
| 878 | """ |
---|
| 879 | Make sure that sasmodels returns NaN when there are no polydispersity points |
---|
| 880 | """ |
---|
| 881 | Cylinder = _make_standard_model('cylinder') |
---|
| 882 | cylinder = Cylinder() |
---|
| 883 | cylinder.setParam('radius', -1.0) |
---|
| 884 | cylinder.setParam('background', 0.) |
---|
| 885 | Iq = cylinder.evalDistribution(np.asarray([0.1])) |
---|
[2d81cfe] | 886 | assert Iq[0] == 0., "empty distribution fails" |
---|
[4d76711] | 887 | |
---|
| 888 | def test_model_list(): |
---|
[fa5fd8d] | 889 | # type: () -> None |
---|
[4d76711] | 890 | """ |
---|
[749a7d4] | 891 | Make sure that all models build as sasview models |
---|
[4d76711] | 892 | """ |
---|
| 893 | from .exception import annotate_exception |
---|
| 894 | for name in core.list_models(): |
---|
| 895 | try: |
---|
| 896 | _make_standard_model(name) |
---|
| 897 | except: |
---|
| 898 | annotate_exception("when loading "+name) |
---|
| 899 | raise |
---|
| 900 | |
---|
[c95dfc63] | 901 | def test_old_name(): |
---|
| 902 | # type: () -> None |
---|
| 903 | """ |
---|
[a69d8cd] | 904 | Load and run cylinder model as sas-models-CylinderModel |
---|
[c95dfc63] | 905 | """ |
---|
| 906 | if not SUPPORT_OLD_STYLE_PLUGINS: |
---|
| 907 | return |
---|
| 908 | try: |
---|
| 909 | # if sasview is not on the path then don't try to test it |
---|
| 910 | import sas |
---|
| 911 | except ImportError: |
---|
| 912 | return |
---|
| 913 | load_standard_models() |
---|
| 914 | from sas.models.CylinderModel import CylinderModel |
---|
| 915 | CylinderModel().evalDistribution([0.1, 0.1]) |
---|
| 916 | |
---|
[05df1de] | 917 | def magnetic_demo(): |
---|
| 918 | Model = _make_standard_model('sphere') |
---|
| 919 | model = Model() |
---|
[610ef23] | 920 | model.setParam('sld_M0', 8) |
---|
[05df1de] | 921 | q = np.linspace(-0.35, 0.35, 500) |
---|
| 922 | qx, qy = np.meshgrid(q, q) |
---|
| 923 | result = model.calculate_Iq(qx.flatten(), qy.flatten()) |
---|
| 924 | result = result.reshape(qx.shape) |
---|
| 925 | |
---|
| 926 | import pylab |
---|
| 927 | pylab.imshow(np.log(result + 0.001)) |
---|
| 928 | pylab.show() |
---|
| 929 | |
---|
[fb5914f] | 930 | if __name__ == "__main__": |
---|
[749a7d4] | 931 | print("cylinder(0.1,0.1)=%g"%test_cylinder()) |
---|
[05df1de] | 932 | #magnetic_demo() |
---|
[ce99754] | 933 | #test_product() |
---|
[17db833] | 934 | #test_structure_factor() |
---|
| 935 | #print("rpa:", test_rpa()) |
---|
[749a7d4] | 936 | #test_empty_distribution() |
---|