[f619de7] | 1 | """ |
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| 2 | Execution kernel interface |
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| 3 | ========================== |
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| 4 | |
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| 5 | :class:`KernelModel` defines the interface to all kernel models. |
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| 6 | In particular, each model should provide a :meth:`KernelModel.make_kernel` |
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| 7 | call which returns an executable kernel, :class:`Kernel`, that operates |
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| 8 | on the given set of *q_vector* inputs. On completion of the computation, |
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| 9 | the kernel should be released, which also releases the inputs. |
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| 10 | """ |
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| 11 | |
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[a738209] | 12 | from __future__ import division, print_function |
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| 13 | |
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[0ff62d4] | 14 | import numpy as np |
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| 15 | from .details import mono_details, poly_details |
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| 16 | |
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[f619de7] | 17 | try: |
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| 18 | from typing import List |
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[0ff62d4] | 19 | except ImportError: |
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| 20 | pass |
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| 21 | else: |
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[f619de7] | 22 | from .details import CallDetails |
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| 23 | from .modelinfo import ModelInfo |
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[7ae2b7f] | 24 | import numpy as np # type: ignore |
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[f619de7] | 25 | |
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| 26 | class KernelModel(object): |
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[04dc697] | 27 | info = None # type: ModelInfo |
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[a5b8477] | 28 | dtype = None # type: np.dtype |
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[f619de7] | 29 | def make_kernel(self, q_vectors): |
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| 30 | # type: (List[np.ndarray]) -> "Kernel" |
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| 31 | raise NotImplementedError("need to implement make_kernel") |
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| 32 | |
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| 33 | def release(self): |
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| 34 | # type: () -> None |
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| 35 | pass |
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| 36 | |
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| 37 | class Kernel(object): |
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| 38 | #: kernel dimension, either "1d" or "2d" |
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| 39 | dim = None # type: str |
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| 40 | info = None # type: ModelInfo |
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| 41 | results = None # type: List[np.ndarray] |
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| 42 | |
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[def2c1b] | 43 | def __call__(self, call_details, values, cutoff): |
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[f619de7] | 44 | # type: (CallDetails, np.ndarray, np.ndarray, float) -> np.ndarray |
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| 45 | raise NotImplementedError("need to implement __call__") |
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| 46 | |
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| 47 | def release(self): |
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| 48 | # type: () -> None |
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| 49 | pass |
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[0ff62d4] | 50 | |
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| 51 | try: |
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| 52 | np.meshgrid([]) |
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| 53 | meshgrid = np.meshgrid |
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| 54 | except ValueError: |
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| 55 | # CRUFT: np.meshgrid requires multiple vectors |
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| 56 | def meshgrid(*args): |
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| 57 | if len(args) > 1: |
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| 58 | return np.meshgrid(*args) |
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| 59 | else: |
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| 60 | return [np.asarray(v) for v in args] |
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| 61 | |
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| 62 | def dispersion_mesh(model_info, pars): |
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| 63 | """ |
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| 64 | Create a mesh grid of dispersion parameters and weights. |
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| 65 | |
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| 66 | Returns [p1,p2,...],w where pj is a vector of values for parameter j |
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| 67 | and w is a vector containing the products for weights for each |
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| 68 | parameter set in the vector. |
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| 69 | """ |
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| 70 | value, weight = zip(*pars) |
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| 71 | weight = [w if w else [1.] for w in weight] |
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| 72 | weight = np.vstack([v.flatten() for v in meshgrid(*weight)]) |
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| 73 | weight = np.prod(weight, axis=0) |
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| 74 | value = [v.flatten() for v in meshgrid(*value)] |
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| 75 | lengths = [par.length for par in model_info.parameters.kernel_parameters |
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| 76 | if par.type == 'volume'] |
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| 77 | if any(n > 1 for n in lengths): |
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| 78 | pars = [] |
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| 79 | offset = 0 |
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| 80 | for n in lengths: |
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| 81 | pars.append(np.vstack(value[offset:offset+n]) if n > 1 else value[offset]) |
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| 82 | offset += n |
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| 83 | value = pars |
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| 84 | return value, weight |
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| 85 | |
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| 86 | |
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| 87 | |
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| 88 | def build_details(kernel, pairs): |
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| 89 | # type: (Kernel, Tuple[List[np.ndarray], List[np.ndarray]]) -> Tuple[CallDetails, np.ndarray, np.ndarray] |
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| 90 | """ |
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| 91 | Construct the kernel call details object for calling the particular kernel. |
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| 92 | """ |
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| 93 | values, weights = zip(*pairs) |
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[a738209] | 94 | scalars = [v[0] for v in values] |
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| 95 | if all(len(w)==1 for w in weights): |
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[0ff62d4] | 96 | call_details = mono_details(kernel.info) |
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[a738209] | 97 | data = np.array(scalars, dtype=kernel.dtype) |
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| 98 | else: |
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| 99 | call_details = poly_details(kernel.info, weights) |
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| 100 | data = np.hstack(scalars+list(values)+list(weights)).astype(kernel.dtype) |
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| 101 | return call_details, data |
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