[eafc9fa] | 1 | """ |
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| 2 | Python driver for python kernels |
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| 3 | |
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| 4 | Calls the kernel with a vector of $q$ values for a single parameter set. |
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| 5 | Polydispersity is supported by looping over different parameter sets and |
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| 6 | summing the results. The interface to :class:`PyModel` matches those for |
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| 7 | :class:`kernelcl.GpuModel` and :class:`kerneldll.DllModel`. |
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| 8 | """ |
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[a738209] | 9 | from __future__ import division, print_function |
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| 10 | |
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[b139ee6] | 11 | import logging |
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| 12 | |
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[7ae2b7f] | 13 | import numpy as np # type: ignore |
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[0d6e865] | 14 | from numpy import pi, sin, cos #type: ignore |
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[b3f6bc3] | 15 | |
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[4bfd277] | 16 | from . import details |
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[c85db69] | 17 | from .generate import F64 |
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[f619de7] | 18 | from .kernel import KernelModel, Kernel |
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[b3f6bc3] | 19 | |
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[e62a134] | 20 | try: |
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[c1a888b] | 21 | from typing import Union, Callable |
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[b139ee6] | 22 | except ImportError: |
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[e62a134] | 23 | pass |
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| 24 | else: |
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| 25 | DType = Union[None, str, np.dtype] |
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| 26 | |
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[f619de7] | 27 | class PyModel(KernelModel): |
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[eafc9fa] | 28 | """ |
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| 29 | Wrapper for pure python models. |
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| 30 | """ |
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[17bbadd] | 31 | def __init__(self, model_info): |
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[6d6508e] | 32 | # Make sure Iq and Iqxy are available and vectorized |
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| 33 | _create_default_functions(model_info) |
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[17bbadd] | 34 | self.info = model_info |
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[883ecf4] | 35 | self.dtype = np.dtype('d') |
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[3b32f8d] | 36 | logging.info("load python model " + self.info.name) |
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[3c56da87] | 37 | |
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[1e2a1ba] | 38 | def make_kernel(self, q_vectors): |
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[eafc9fa] | 39 | q_input = PyInput(q_vectors, dtype=F64) |
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[6d6508e] | 40 | kernel = self.info.Iqxy if q_input.is_2d else self.info.Iq |
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[3c56da87] | 41 | return PyKernel(kernel, self.info, q_input) |
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| 42 | |
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[f734e7d] | 43 | def release(self): |
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[eafc9fa] | 44 | """ |
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| 45 | Free resources associated with the model. |
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| 46 | """ |
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[f734e7d] | 47 | pass |
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| 48 | |
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[b3f6bc3] | 49 | class PyInput(object): |
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| 50 | """ |
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| 51 | Make q data available to the gpu. |
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| 52 | |
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| 53 | *q_vectors* is a list of q vectors, which will be *[q]* for 1-D data, |
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| 54 | and *[qx, qy]* for 2-D data. Internally, the vectors will be reallocated |
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| 55 | to get the best performance on OpenCL, which may involve shifting and |
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| 56 | stretching the array to better match the memory architecture. Additional |
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| 57 | points will be evaluated with *q=1e-3*. |
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| 58 | |
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| 59 | *dtype* is the data type for the q vectors. The data type should be |
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| 60 | set to match that of the kernel, which is an attribute of |
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| 61 | :class:`GpuProgram`. Note that not all kernels support double |
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| 62 | precision, so even if the program was created for double precision, |
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| 63 | the *GpuProgram.dtype* may be single precision. |
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| 64 | |
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| 65 | Call :meth:`release` when complete. Even if not called directly, the |
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| 66 | buffer will be released when the data object is freed. |
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| 67 | """ |
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| 68 | def __init__(self, q_vectors, dtype): |
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| 69 | self.nq = q_vectors[0].size |
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| 70 | self.dtype = dtype |
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[eafc9fa] | 71 | self.is_2d = (len(q_vectors) == 2) |
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[48fbd50] | 72 | if self.is_2d: |
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| 73 | self.q = np.empty((self.nq, 2), dtype=dtype) |
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| 74 | self.q[:, 0] = q_vectors[0] |
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| 75 | self.q[:, 1] = q_vectors[1] |
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| 76 | else: |
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| 77 | self.q = np.empty(self.nq, dtype=dtype) |
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| 78 | self.q[:self.nq] = q_vectors[0] |
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[b3f6bc3] | 79 | |
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| 80 | def release(self): |
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[eafc9fa] | 81 | """ |
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| 82 | Free resources associated with the model inputs. |
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| 83 | """ |
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[48fbd50] | 84 | self.q = None |
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[b3f6bc3] | 85 | |
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[f619de7] | 86 | class PyKernel(Kernel): |
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[b3f6bc3] | 87 | """ |
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| 88 | Callable SAS kernel. |
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| 89 | |
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| 90 | *kernel* is the DllKernel object to call. |
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| 91 | |
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[17bbadd] | 92 | *model_info* is the module information |
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[b3f6bc3] | 93 | |
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[3c56da87] | 94 | *q_input* is the DllInput q vectors at which the kernel should be |
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[b3f6bc3] | 95 | evaluated. |
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| 96 | |
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| 97 | The resulting call method takes the *pars*, a list of values for |
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| 98 | the fixed parameters to the kernel, and *pd_pars*, a list of (value,weight) |
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| 99 | vectors for the polydisperse parameters. *cutoff* determines the |
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| 100 | integration limits: any points with combined weight less than *cutoff* |
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| 101 | will not be calculated. |
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| 102 | |
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| 103 | Call :meth:`release` when done with the kernel instance. |
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| 104 | """ |
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[17bbadd] | 105 | def __init__(self, kernel, model_info, q_input): |
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[bde38b5] | 106 | # type: (callable, ModelInfo, List[np.ndarray]) -> None |
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[6e7ff6d] | 107 | self.dtype = np.dtype('d') |
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[17bbadd] | 108 | self.info = model_info |
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[3c56da87] | 109 | self.q_input = q_input |
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| 110 | self.res = np.empty(q_input.nq, q_input.dtype) |
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[6e7ff6d] | 111 | self.kernel = kernel |
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| 112 | self.dim = '2d' if q_input.is_2d else '1d' |
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| 113 | |
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[6d6508e] | 114 | partable = model_info.parameters |
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[6e7ff6d] | 115 | kernel_parameters = (partable.iqxy_parameters if q_input.is_2d |
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| 116 | else partable.iq_parameters) |
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| 117 | volume_parameters = partable.form_volume_parameters |
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| 118 | |
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| 119 | # Create an array to hold the parameter values. There will be a |
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| 120 | # single array whose values are updated as the calculator goes |
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| 121 | # through the loop. Arguments to the kernel and volume functions |
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| 122 | # will use views into this vector, relying on the fact that a |
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| 123 | # an array of no dimensions acts like a scalar. |
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[a8a7f08] | 124 | parameter_vector = np.empty(len(partable.call_parameters)-2, 'd') |
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[6e7ff6d] | 125 | |
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| 126 | # Create views into the array to hold the arguments |
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[a8a7f08] | 127 | offset = 0 |
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[6e7ff6d] | 128 | kernel_args, volume_args = [], [] |
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| 129 | for p in partable.kernel_parameters: |
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| 130 | if p.length == 1: |
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| 131 | # Scalar values are length 1 vectors with no dimensions. |
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| 132 | v = parameter_vector[offset:offset+1].reshape(()) |
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[b3f6bc3] | 133 | else: |
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[6e7ff6d] | 134 | # Vector values are simple views. |
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| 135 | v = parameter_vector[offset:offset+p.length] |
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| 136 | offset += p.length |
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| 137 | if p in kernel_parameters: |
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| 138 | kernel_args.append(v) |
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| 139 | if p in volume_parameters: |
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[a8a7f08] | 140 | volume_args.append(v) |
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[6e7ff6d] | 141 | |
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| 142 | # Hold on to the parameter vector so we can use it to call kernel later. |
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| 143 | # This may also be required to preserve the views into the vector. |
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| 144 | self._parameter_vector = parameter_vector |
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| 145 | |
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| 146 | # Generate a closure which calls the kernel with the views into the |
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| 147 | # parameter array. |
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| 148 | if q_input.is_2d: |
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[6d6508e] | 149 | form = model_info.Iqxy |
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[40a87fa] | 150 | qx, qy = q_input.q[:, 0], q_input.q[:, 1] |
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[6e7ff6d] | 151 | self._form = lambda: form(qx, qy, *kernel_args) |
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[b3f6bc3] | 152 | else: |
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[6d6508e] | 153 | form = model_info.Iq |
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[6e7ff6d] | 154 | q = q_input.q |
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| 155 | self._form = lambda: form(q, *kernel_args) |
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| 156 | |
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| 157 | # Generate a closure which calls the form_volume if it exists. |
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[6d6508e] | 158 | form_volume = model_info.form_volume |
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[6e7ff6d] | 159 | self._volume = ((lambda: form_volume(*volume_args)) if form_volume |
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| 160 | else (lambda: 1.0)) |
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| 161 | |
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[32e3c9b] | 162 | def __call__(self, call_details, values, cutoff, magnetic): |
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[bde38b5] | 163 | # type: (CallDetails, np.ndarray, np.ndarray, float, bool) -> np.ndarray |
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| 164 | if magnetic: |
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| 165 | raise NotImplementedError("Magnetism not implemented for pure python models") |
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| 166 | #print("Calling python kernel") |
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| 167 | #call_details.show(values) |
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[6e7ff6d] | 168 | res = _loops(self._parameter_vector, self._form, self._volume, |
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[a738209] | 169 | self.q_input.nq, call_details, values, cutoff) |
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[b3f6bc3] | 170 | return res |
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| 171 | |
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| 172 | def release(self): |
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[bde38b5] | 173 | # type: () -> None |
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[eafc9fa] | 174 | """ |
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| 175 | Free resources associated with the kernel. |
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| 176 | """ |
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[f619de7] | 177 | self.q_input.release() |
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[3c56da87] | 178 | self.q_input = None |
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[b3f6bc3] | 179 | |
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[40a87fa] | 180 | def _loops(parameters, form, form_volume, nq, call_details, values, cutoff): |
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[9a943d0] | 181 | # type: (np.ndarray, Callable[[], np.ndarray], Callable[[], float], int, details.CallDetails, np.ndarray, np.ndarray, float) -> None |
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[f734e7d] | 182 | ################################################################ |
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| 183 | # # |
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| 184 | # !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! # |
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| 185 | # !! !! # |
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| 186 | # !! KEEP THIS CODE CONSISTENT WITH KERNEL_TEMPLATE.C !! # |
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| 187 | # !! !! # |
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| 188 | # !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! # |
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| 189 | # # |
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| 190 | ################################################################ |
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[40a87fa] | 191 | n_pars = len(parameters) |
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| 192 | parameters[:] = values[2:n_pars+2] |
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| 193 | if call_details.num_active == 0: |
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[14a15a3] | 194 | pd_norm = float(form_volume()) |
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| 195 | scale = values[0]/(pd_norm if pd_norm != 0.0 else 1.0) |
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| 196 | background = values[1] |
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| 197 | return scale*form() + background |
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[6e7ff6d] | 198 | |
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[bde38b5] | 199 | pd_value = values[2+n_pars:2+n_pars + call_details.num_weights] |
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| 200 | pd_weight = values[2+n_pars + call_details.num_weights:] |
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[a738209] | 201 | |
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| 202 | pd_norm = 0.0 |
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| 203 | partial_weight = np.NaN |
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[40a87fa] | 204 | weight = np.NaN |
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[a738209] | 205 | |
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[40a87fa] | 206 | p0_par = call_details.pd_par[0] |
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| 207 | p0_length = call_details.pd_length[0] |
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[a738209] | 208 | p0_index = p0_length |
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[40a87fa] | 209 | p0_offset = call_details.pd_offset[0] |
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[a738209] | 210 | |
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[40a87fa] | 211 | pd_par = call_details.pd_par[:call_details.num_active] |
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| 212 | pd_offset = call_details.pd_offset[:call_details.num_active] |
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| 213 | pd_stride = call_details.pd_stride[:call_details.num_active] |
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| 214 | pd_length = call_details.pd_length[:call_details.num_active] |
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[a738209] | 215 | |
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[6e7ff6d] | 216 | total = np.zeros(nq, 'd') |
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[bde38b5] | 217 | for loop_index in range(call_details.num_eval): |
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[6e7ff6d] | 218 | # update polydispersity parameter values |
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[a738209] | 219 | if p0_index == p0_length: |
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| 220 | pd_index = (loop_index//pd_stride)%pd_length |
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| 221 | parameters[pd_par] = pd_value[pd_offset+pd_index] |
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| 222 | partial_weight = np.prod(pd_weight[pd_offset+pd_index][1:]) |
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| 223 | p0_index = loop_index%p0_length |
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| 224 | |
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| 225 | weight = partial_weight * pd_weight[p0_offset + p0_index] |
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| 226 | parameters[p0_par] = pd_value[p0_offset + p0_index] |
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| 227 | p0_index += 1 |
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[6e7ff6d] | 228 | if weight > cutoff: |
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| 229 | # Call the scattering function |
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| 230 | # Assume that NaNs are only generated if the parameters are bad; |
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| 231 | # exclude all q for that NaN. Even better would be to have an |
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| 232 | # INVALID expression like the C models, but that is too expensive. |
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[dbfd471] | 233 | Iq = np.asarray(form(), 'd') |
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[b139ee6] | 234 | if np.isnan(Iq).any(): |
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| 235 | continue |
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[b3f6bc3] | 236 | |
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[6e7ff6d] | 237 | # update value and norm |
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[40a87fa] | 238 | total += weight * Iq |
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[a738209] | 239 | pd_norm += weight * form_volume() |
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[b3f6bc3] | 240 | |
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[14a15a3] | 241 | scale = values[0]/(pd_norm if pd_norm != 0.0 else 1.0) |
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| 242 | background = values[1] |
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| 243 | return scale*total + background |
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[6d6508e] | 244 | |
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| 245 | |
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[4bfd277] | 246 | def _create_default_functions(model_info): |
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| 247 | """ |
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| 248 | Autogenerate missing functions, such as Iqxy from Iq. |
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| 249 | |
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| 250 | This only works for Iqxy when Iq is written in python. :func:`make_source` |
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| 251 | performs a similar role for Iq written in C. This also vectorizes |
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| 252 | any functions that are not already marked as vectorized. |
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| 253 | """ |
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| 254 | _create_vector_Iq(model_info) |
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| 255 | _create_vector_Iqxy(model_info) # call create_vector_Iq() first |
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| 256 | |
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[6d6508e] | 257 | |
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| 258 | def _create_vector_Iq(model_info): |
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| 259 | """ |
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| 260 | Define Iq as a vector function if it exists. |
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| 261 | """ |
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| 262 | Iq = model_info.Iq |
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| 263 | if callable(Iq) and not getattr(Iq, 'vectorized', False): |
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[4bfd277] | 264 | #print("vectorizing Iq") |
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[6d6508e] | 265 | def vector_Iq(q, *args): |
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| 266 | """ |
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| 267 | Vectorized 1D kernel. |
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| 268 | """ |
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| 269 | return np.array([Iq(qi, *args) for qi in q]) |
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| 270 | vector_Iq.vectorized = True |
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| 271 | model_info.Iq = vector_Iq |
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| 272 | |
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| 273 | def _create_vector_Iqxy(model_info): |
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| 274 | """ |
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| 275 | Define Iqxy as a vector function if it exists, or default it from Iq(). |
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| 276 | """ |
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| 277 | Iq, Iqxy = model_info.Iq, model_info.Iqxy |
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[4bfd277] | 278 | if callable(Iqxy): |
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| 279 | if not getattr(Iqxy, 'vectorized', False): |
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| 280 | #print("vectorizing Iqxy") |
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| 281 | def vector_Iqxy(qx, qy, *args): |
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| 282 | """ |
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| 283 | Vectorized 2D kernel. |
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| 284 | """ |
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| 285 | return np.array([Iqxy(qxi, qyi, *args) for qxi, qyi in zip(qx, qy)]) |
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| 286 | vector_Iqxy.vectorized = True |
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| 287 | model_info.Iqxy = vector_Iqxy |
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[6d6508e] | 288 | elif callable(Iq): |
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[4bfd277] | 289 | #print("defaulting Iqxy") |
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[6d6508e] | 290 | # Iq is vectorized because create_vector_Iq was already called. |
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| 291 | def default_Iqxy(qx, qy, *args): |
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| 292 | """ |
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| 293 | Default 2D kernel. |
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| 294 | """ |
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| 295 | return Iq(np.sqrt(qx**2 + qy**2), *args) |
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| 296 | default_Iqxy.vectorized = True |
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| 297 | model_info.Iqxy = default_Iqxy |
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