Source code for sasmodels.core

Core model handling routines.
from __future__ import print_function

__all__ = [
    "list_models", "load_model", "load_model_info",
    "build_model", "precompile_dlls", "reparameterize",

import os
from os.path import basename, join as joinpath
from glob import glob
import re
import copy

import numpy as np # type: ignore

# NOTE: delay loading of kernelcl, kernelcuda, kerneldll and kernelpy
# cl and cuda in particular take awhile since they try to establish a
# connection with the card to verify that the environment works.
from . import generate
from . import modelinfo
from . import product
from . import mixture
from . import custom

# pylint: disable=unused-import
    from typing import List, Union, Optional, Any, Tuple
    from .kernel import KernelModel
    from .modelinfo import ModelInfo
except ImportError:
# pylint: enable=unused-import

CUSTOM_MODEL_PATH = os.environ.get('SAS_MODELPATH', "")
    CUSTOM_MODEL_PATH = joinpath(os.path.expanduser("~"), ".sasmodels", "custom_models")
    #if not os.path.isdir(CUSTOM_MODEL_PATH):
    #    os.makedirs(CUSTOM_MODEL_PATH)

# TODO: refactor composite model support
# The current load_model_info/build_model does not reuse existing model
# definitions when loading a composite model, instead reloading and
# rebuilding the kernel for each component model in the expression.  This
# is fine in a scripting environment where the model is built when the script
# starts and is thrown away when the script ends, but may not be the best
# solution in a long-lived application.  This affects the following functions:
#    load_model
#    load_model_info
#    build_model

KINDS = ("all", "py", "c", "double", "single", "opencl", "1d", "2d",
         "nonmagnetic", "magnetic")
[docs]def list_models(kind=None): # type: (str) -> List[str] """ Return the list of available models on the model path. *kind* can be one of the following: * all: all models * py: python models only * c: c models only * single: c models which support single precision * double: c models which require double precision * opencl: c models which run in opencl * dll: c models which do not run in opencl * 1d: models without orientation * 2d: models with orientation * magnetic: models supporting magnetic sld * nommagnetic: models without magnetic parameter For multiple conditions, combine with plus. For example, *c+single+2d* would return all oriented models implemented in C which can be computed accurately with single precision arithmetic. """ if kind and any(k not in KINDS for k in kind.split('+')): raise ValueError("kind not in " + ", ".join(KINDS)) files = sorted(glob(joinpath(generate.MODEL_PATH, "[a-zA-Z]*.py"))) available_models = [basename(f)[:-3] for f in files] if kind and '+' in kind: all_kinds = kind.split('+') condition = lambda name: all(_matches(name, k) for k in all_kinds) else: condition = lambda name: _matches(name, kind) selected = [name for name in available_models if condition(name)] return selected
def _matches(name, kind): if kind is None or kind == "all": return True info = load_model_info(name) pars = info.parameters.kernel_parameters # TODO: may be adding Fq to the list at some point is_pure_py = callable(info.Iq) if kind == "py": return is_pure_py elif kind == "c": return not is_pure_py elif kind == "double": return not info.single and not is_pure_py elif kind == "single": return info.single and not is_pure_py elif kind == "opencl": return info.opencl elif kind == "dll": return not info.opencl and not is_pure_py elif kind == "2d": return any(p.type == 'orientation' for p in pars) elif kind == "1d": return all(p.type != 'orientation' for p in pars) elif kind == "magnetic": return any(p.type == 'sld' for p in pars) elif kind == "nonmagnetic": return not any(p.type == 'sld' for p in pars) return False
[docs]def load_model(model_name, dtype=None, platform='ocl'): # type: (str, str, str) -> KernelModel """ Load model info and build model. *model_name* is the name of the model, or perhaps a model expression such as sphere*hardsphere or sphere+cylinder. *dtype* and *platform* are given by :func:`build_model`. """ return build_model(load_model_info(model_name), dtype=dtype, platform=platform)
[docs]def load_model_info(model_string): # type: (str) -> modelinfo.ModelInfo """ Load a model definition given the model name. *model_string* is the name of the model, or perhaps a model expression such as sphere*cylinder or sphere+cylinder. Use '@' for a structure factor product, e.g. sphere@hardsphere. Custom models can be specified by prefixing the model name with 'custom.', e.g. 'custom.MyModel+sphere'. This returns a handle to the module defining the model. This can be used with functions in generate to build the docs or extract model info. """ if "+" in model_string: parts = [load_model_info(part) for part in model_string.split("+")] return mixture.make_mixture_info(parts, operation='+') elif "*" in model_string: parts = [load_model_info(part) for part in model_string.split("*")] return mixture.make_mixture_info(parts, operation='*') elif "@" in model_string: p_info, q_info = [load_model_info(part) for part in model_string.split("@")] return product.make_product_info(p_info, q_info) # We are now dealing with a pure model elif "custom." in model_string: pattern = "custom.([A-Za-z0-9_-]+)" result = re.match(pattern, model_string) if result is None: raise ValueError("Model name in invalid format: " + model_string) model_name = # Use ModelName to find the path to the custom model file model_path = joinpath(CUSTOM_MODEL_PATH, model_name + ".py") if not os.path.isfile(model_path): raise ValueError("The model file {} doesn't exist".format(model_path)) kernel_module = custom.load_custom_kernel_module(model_path) return modelinfo.make_model_info(kernel_module) kernel_module = generate.load_kernel_module(model_string) return modelinfo.make_model_info(kernel_module)
_REPARAMETERIZE_DOCS = """\ Definition ---------- Constrain :ref:`%(base)s` according to the following:: %(translation)s """ _LHS_RE = re.compile(r"^ *(?<![.0-9])([A-Za-z_][A-Za-z0-9_]+) *=", flags=re.MULTILINE)
[docs]def reparameterize( base, parameters, translation, filename=None, title=None, insert_after=None, docs=None, name=None, source=None, ): """ Reparameterize an existing model. *base* is the original modelinfo. This cannot be a reparameterized model; only one level of reparameterization is supported. *parameters* are the new parameter definitions that will be included in the model info. *translation* is a string each line containing *var = expr*. The variable *var* can be a new intermediate value, or it can be a parameter from the base model that will be replace by the expression. The expression *expr* can be any C99 expression, including C-style if-expressions *condition ? value1 : value2*. Expressions can use any new or existing parameter that is not being replaced including intermediate values that are previously defined. Parameters can only be assigned once, never updated. C99 math functions are available, as well as any functions defined in the base model or included in *source* (see below). *filename* is the filename for the replacement model. This is usually *__file__*, giving the path to the model file, but it could also be a nominal filename for translations defined on-the-fly. *title* is the model title, which defaults to *base.title* plus " (reparameterized)". *insert_after* controls parameter placement. By default, the new parameters replace the old parameters in their original position. Instead, you can provide a dictionary *{'par': 'newpar1,newpar2'}* indicating that new parameters named *newpar1* and *newpar2* should be included in the table after the existing parameter *par*, or at the beginning if *par* is the empty string. *docs* constains the doc string for the translated model, which by default references the base model and gives the *translation* text. *name* is the model name (default = :code:`"constrained_" +`). *source* is a list any additional C source files that should be included to define functions and constants used in the translation expressions. This will be included after all sources for the base model. Sources will only be included once, even if they are listed in both places, so feel free to list all dependencies for the helper function, such as "lib/polevl.c". """ if not isinstance(base, modelinfo.ModelInfo): base = load_model_info(base) if name is None: name = filename if filename is not None else "constrained_" + name = os.path.basename(name).split('.')[0] if title is None: title = base.title + " (reparameterized)" if docs is None: lines = "\n ".join(s.lstrip() for s in translation.split('\n')) docs = _REPARAMETERIZE_DOCS%{'base':, 'translation': lines} #source = merge_deps(base.source, source) source = (base.source + [f for f in source if f not in base.source] if source else base.source) # TODO: don't repeat code from generate._build_translation base_pars = [ for par in base.parameters.kernel_parameters] old_pars = [ for match in _LHS_RE.finditer(translation) if in base_pars] new_pars = [modelinfo.parse_parameter(*p) for p in parameters] table = modelinfo.derive_table(base.parameters, remove=old_pars, insert=new_pars, insert_after=insert_after) caller = copy.copy(base) caller.translation = translation = = name = docs caller.filename = filename caller.parameters = table caller.source = source return caller
# Note: not used at the moment. def merge_deps(old, new): """ Merge two dependency lists. The lists are partially ordered, with all dependents coming after the items they depend on, but otherwise order doesn't matter. The merged list preserves the partial ordering. So if old and new both include the item "c", then all items that come before "c" in old and new will come before "c" in the result, and all items that come after "c" in old and new will come after "c" in the result. """ if new is None: return old result = [] for item in new: try: index = old.index(item) #print(item,"found in",old,"at",index,"giving",old[:index]) result.extend(old[:index]) old = old[index+1:] except ValueError: #print(item, "not found in", old) pass result.append(item) #print("after", item, "old", old, "result", result) result.extend(old) return result
[docs]def build_model(model_info, dtype=None, platform="ocl"): # type: (ModelInfo, str, str) -> KernelModel """ Prepare the model for the default execution platform. This will return an OpenCL model, a DLL model or a python model depending on the model and the computing platform. *model_info* is the model definition structure returned from :func:`load_model_info`. *dtype* indicates whether the model should use single or double precision for the calculation. Choices are 'single', 'double', 'quad', 'half', or 'fast'. If *dtype* ends with '!', then force the use of the DLL rather than OpenCL for the calculation. *platform* should be "dll" to force the dll to be used for C models, otherwise it uses the default "ocl". """ composition = model_info.composition if composition is not None: composition_type, parts = composition models = [build_model(p, dtype=dtype, platform=platform) for p in parts] if composition_type == 'mixture': return mixture.MixtureModel(model_info, models) elif composition_type == 'product': P, S = models return product.ProductModel(model_info, P, S) else: raise ValueError('unknown mixture type %s'%composition_type) # If it is a python model, return it immediately if callable(model_info.Iq): from . import kernelpy return kernelpy.PyModel(model_info) numpy_dtype, fast, platform = parse_dtype(model_info, dtype, platform) source = generate.make_source(model_info) if platform == "dll": from . import kerneldll #print("building dll", numpy_dtype) return kerneldll.load_dll(source['dll'], model_info, numpy_dtype) elif platform == "cuda": from . import kernelcuda return kernelcuda.GpuModel(source, model_info, numpy_dtype, fast=fast) else: from . import kernelcl #print("building ocl", numpy_dtype) return kernelcl.GpuModel(source, model_info, numpy_dtype, fast=fast)
[docs]def precompile_dlls(path, dtype="double"): # type: (str, str) -> List[str] """ Precompile the dlls for all builtin models, returning a list of dll paths. *path* is the directory in which to save the dlls. It will be created if it does not already exist. This can be used when build the windows distribution of sasmodels which may be missing the OpenCL driver and the dll compiler. """ from . import kerneldll numpy_dtype = np.dtype(dtype) if not os.path.exists(path): os.makedirs(path) compiled_dlls = [] for model_name in list_models(): model_info = load_model_info(model_name) if not callable(model_info.Iq): source = generate.make_source(model_info)['dll'] old_path = kerneldll.SAS_DLL_PATH try: kerneldll.SAS_DLL_PATH = path dll = kerneldll.make_dll(source, model_info, dtype=numpy_dtype) finally: kerneldll.SAS_DLL_PATH = old_path compiled_dlls.append(dll) return compiled_dlls
def parse_dtype(model_info, dtype=None, platform=None): # type: (ModelInfo, str, str) -> Tuple[np.dtype, bool, str] """ Interpret dtype string, returning np.dtype, fast flag and platform. Possible types include 'half', 'single', 'double' and 'quad'. If the type is 'fast', then this is equivalent to dtype 'single' but using fast native functions rather than those with the precision level guaranteed by the OpenCL standard. 'default' will choose the appropriate default for the model and platform. Platform preference can be specfied ("ocl", "cuda", "dll"), with the default being OpenCL or CUDA if available, otherwise DLL. If the dtype name ends with '!' then platform is forced to be DLL rather than GPU. The default platform is set by the environment variable SAS_OPENCL, SAS_OPENCL=driver:device for OpenCL, SAS_OPENCL=cuda:device for CUDA or SAS_OPENCL=none for DLL. This routine ignores the preferences within the model definition. This is by design. It allows us to test models in single precision even when we have flagged them as requiring double precision so we can easily check the performance on different platforms without having to change the model definition. """ # Assign default platform, overriding ocl with dll if OpenCL is unavailable # If opencl=False OpenCL is switched off if platform is None: platform = "ocl" # Check if type indicates dll regardless of which platform is given if dtype is not None and dtype.endswith('!'): platform = "dll" dtype = dtype[:-1] # Make sure model allows opencl/gpu if not model_info.opencl: platform = "dll" # Make sure opencl is available, or fallback to cuda then to dll if platform == "ocl": from . import kernelcl if not kernelcl.use_opencl(): from . import kernelcuda platform = "cuda" if kernelcuda.use_cuda() else "dll" # Convert special type names "half", "fast", and "quad" fast = (dtype == "fast") if fast: dtype = "single" elif dtype == "quad": dtype = "longdouble" elif dtype == "half": dtype = "float16" # Convert dtype string to numpy dtype. Use single precision for GPU # if model allows it, otherwise use double precision. if dtype is None or dtype == "default": numpy_dtype = (generate.F32 if model_info.single and platform in ("ocl", "cuda") else generate.F64) else: numpy_dtype = np.dtype(dtype) # Make sure that the type is supported by GPU, otherwise use dll if platform == "ocl": from . import kernelcl env = kernelcl.environment() elif platform == "cuda": from . import kernelcuda env = kernelcuda.environment() else: env = None if env is not None and not env.has_type(numpy_dtype): platform = "dll" if dtype is None: numpy_dtype = generate.F64 return numpy_dtype, fast, platform def test_composite_order(): """ Check that mixture models produce the same result independent of ordder. """ def test_models(fst, snd): """Confirm that two models produce the same parameters""" fst = load_model(fst) snd = load_model(snd) # Un-disambiguate parameter names so that we can check if the same # parameters are in a pair of composite models. Since each parameter in # the mixture model is tagged as e.g., A_sld, we ought to use a # regex subsitution s/^[A-Z]+_/_/, but removing all uppercase letters # is good enough. fst = [[x for x in if x == x.lower()] for p in] snd = [[x for x in if x == x.lower()] for p in] assert sorted(fst) == sorted(snd), "{} != {}".format(fst, snd) test_models( "cylinder+sphere", "sphere+cylinder") test_models( "cylinder*sphere", "sphere*cylinder") test_models( "cylinder@hardsphere*sphere", "sphere*cylinder@hardsphere") test_models( "barbell+sphere*cylinder@hardsphere", "sphere*cylinder@hardsphere+barbell") test_models( "barbell+cylinder@hardsphere*sphere", "cylinder@hardsphere*sphere+barbell") test_models( "barbell+sphere*cylinder@hardsphere", "barbell+cylinder@hardsphere*sphere") test_models( "sphere*cylinder@hardsphere+barbell", "cylinder@hardsphere*sphere+barbell") test_models( "barbell+sphere*cylinder@hardsphere", "cylinder@hardsphere*sphere+barbell") test_models( "barbell+cylinder@hardsphere*sphere", "sphere*cylinder@hardsphere+barbell") def test_composite(): # type: () -> None """Check that model load works""" from .product import RADIUS_ID, VOLFRAC_ID, STRUCTURE_MODE_ID, RADIUS_MODE_ID #Test the the model produces the parameters that we would expect model = load_model("cylinder@hardsphere*sphere") actual = [ for p in] target = ["sld", "sld_solvent", "radius", "length", "theta", "phi", RADIUS_ID, VOLFRAC_ID, STRUCTURE_MODE_ID, RADIUS_MODE_ID, "A_sld", "A_sld_solvent", "A_radius"] assert target == actual, "%s != %s"%(target, actual) def list_models_main(): # type: () -> int """ Run list_models as a main program. See :func:`list_models` for the kinds of models that can be requested on the command line. """ import sys kind = sys.argv[1] if len(sys.argv) > 1 else "all" try: models = list_models(kind) print("\n".join(models)) except Exception: print(list_models.__doc__) return 1 return 0 if __name__ == "__main__": list_models_main()