[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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[b3f6bc3] | 9 | import numpy as np |
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[c85db69] | 10 | from numpy import pi, cos |
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[b3f6bc3] | 11 | |
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[c85db69] | 12 | from .generate import F64 |
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[b3f6bc3] | 13 | |
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[f734e7d] | 14 | class PyModel(object): |
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[eafc9fa] | 15 | """ |
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| 16 | Wrapper for pure python models. |
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| 17 | """ |
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[17bbadd] | 18 | def __init__(self, model_info): |
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[6d6508e] | 19 | # Make sure Iq and Iqxy are available and vectorized |
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| 20 | _create_default_functions(model_info) |
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[17bbadd] | 21 | self.info = model_info |
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[3c56da87] | 22 | |
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[1e2a1ba] | 23 | def make_kernel(self, q_vectors): |
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[eafc9fa] | 24 | q_input = PyInput(q_vectors, dtype=F64) |
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[6d6508e] | 25 | kernel = self.info.Iqxy if q_input.is_2d else self.info.Iq |
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[3c56da87] | 26 | return PyKernel(kernel, self.info, q_input) |
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| 27 | |
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[f734e7d] | 28 | def release(self): |
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[eafc9fa] | 29 | """ |
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| 30 | Free resources associated with the model. |
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| 31 | """ |
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[f734e7d] | 32 | pass |
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| 33 | |
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[b3f6bc3] | 34 | class PyInput(object): |
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| 35 | """ |
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| 36 | Make q data available to the gpu. |
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| 37 | |
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| 38 | *q_vectors* is a list of q vectors, which will be *[q]* for 1-D data, |
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| 39 | and *[qx, qy]* for 2-D data. Internally, the vectors will be reallocated |
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| 40 | to get the best performance on OpenCL, which may involve shifting and |
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| 41 | stretching the array to better match the memory architecture. Additional |
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| 42 | points will be evaluated with *q=1e-3*. |
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| 43 | |
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| 44 | *dtype* is the data type for the q vectors. The data type should be |
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| 45 | set to match that of the kernel, which is an attribute of |
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| 46 | :class:`GpuProgram`. Note that not all kernels support double |
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| 47 | precision, so even if the program was created for double precision, |
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| 48 | the *GpuProgram.dtype* may be single precision. |
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| 49 | |
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| 50 | Call :meth:`release` when complete. Even if not called directly, the |
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| 51 | buffer will be released when the data object is freed. |
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| 52 | """ |
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| 53 | def __init__(self, q_vectors, dtype): |
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| 54 | self.nq = q_vectors[0].size |
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| 55 | self.dtype = dtype |
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[eafc9fa] | 56 | self.is_2d = (len(q_vectors) == 2) |
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[48fbd50] | 57 | if self.is_2d: |
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| 58 | self.q = np.empty((self.nq, 2), dtype=dtype) |
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| 59 | self.q[:, 0] = q_vectors[0] |
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| 60 | self.q[:, 1] = q_vectors[1] |
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| 61 | else: |
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| 62 | self.q = np.empty(self.nq, dtype=dtype) |
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| 63 | self.q[:self.nq] = q_vectors[0] |
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[b3f6bc3] | 64 | |
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| 65 | def release(self): |
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[eafc9fa] | 66 | """ |
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| 67 | Free resources associated with the model inputs. |
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| 68 | """ |
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[48fbd50] | 69 | self.q = None |
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[b3f6bc3] | 70 | |
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| 71 | class PyKernel(object): |
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| 72 | """ |
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| 73 | Callable SAS kernel. |
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| 74 | |
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| 75 | *kernel* is the DllKernel object to call. |
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| 76 | |
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[17bbadd] | 77 | *model_info* is the module information |
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[b3f6bc3] | 78 | |
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[3c56da87] | 79 | *q_input* is the DllInput q vectors at which the kernel should be |
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[b3f6bc3] | 80 | evaluated. |
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| 81 | |
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| 82 | The resulting call method takes the *pars*, a list of values for |
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| 83 | the fixed parameters to the kernel, and *pd_pars*, a list of (value,weight) |
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| 84 | vectors for the polydisperse parameters. *cutoff* determines the |
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| 85 | integration limits: any points with combined weight less than *cutoff* |
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| 86 | will not be calculated. |
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| 87 | |
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| 88 | Call :meth:`release` when done with the kernel instance. |
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| 89 | """ |
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[17bbadd] | 90 | def __init__(self, kernel, model_info, q_input): |
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[6e7ff6d] | 91 | self.dtype = np.dtype('d') |
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[17bbadd] | 92 | self.info = model_info |
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[3c56da87] | 93 | self.q_input = q_input |
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| 94 | self.res = np.empty(q_input.nq, q_input.dtype) |
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[6e7ff6d] | 95 | self.kernel = kernel |
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| 96 | self.dim = '2d' if q_input.is_2d else '1d' |
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| 97 | |
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[6d6508e] | 98 | partable = model_info.parameters |
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[6e7ff6d] | 99 | kernel_parameters = (partable.iqxy_parameters if q_input.is_2d |
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| 100 | else partable.iq_parameters) |
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| 101 | volume_parameters = partable.form_volume_parameters |
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| 102 | |
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| 103 | # Create an array to hold the parameter values. There will be a |
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| 104 | # single array whose values are updated as the calculator goes |
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| 105 | # through the loop. Arguments to the kernel and volume functions |
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| 106 | # will use views into this vector, relying on the fact that a |
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| 107 | # an array of no dimensions acts like a scalar. |
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[a8a7f08] | 108 | parameter_vector = np.empty(len(partable.call_parameters)-2, 'd') |
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[6e7ff6d] | 109 | |
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| 110 | # Create views into the array to hold the arguments |
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[a8a7f08] | 111 | offset = 0 |
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[6e7ff6d] | 112 | kernel_args, volume_args = [], [] |
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| 113 | for p in partable.kernel_parameters: |
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| 114 | if p.length == 1: |
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| 115 | # Scalar values are length 1 vectors with no dimensions. |
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| 116 | v = parameter_vector[offset:offset+1].reshape(()) |
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[b3f6bc3] | 117 | else: |
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[6e7ff6d] | 118 | # Vector values are simple views. |
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| 119 | v = parameter_vector[offset:offset+p.length] |
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| 120 | offset += p.length |
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| 121 | if p in kernel_parameters: |
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| 122 | kernel_args.append(v) |
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| 123 | if p in volume_parameters: |
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[a8a7f08] | 124 | volume_args.append(v) |
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[6e7ff6d] | 125 | |
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| 126 | # Hold on to the parameter vector so we can use it to call kernel later. |
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| 127 | # This may also be required to preserve the views into the vector. |
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| 128 | self._parameter_vector = parameter_vector |
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| 129 | |
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| 130 | # Generate a closure which calls the kernel with the views into the |
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| 131 | # parameter array. |
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| 132 | if q_input.is_2d: |
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[6d6508e] | 133 | form = model_info.Iqxy |
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[6e7ff6d] | 134 | qx, qy = q_input.q[:,0], q_input.q[:,1] |
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| 135 | self._form = lambda: form(qx, qy, *kernel_args) |
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[b3f6bc3] | 136 | else: |
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[6d6508e] | 137 | form = model_info.Iq |
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[6e7ff6d] | 138 | q = q_input.q |
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| 139 | self._form = lambda: form(q, *kernel_args) |
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| 140 | |
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| 141 | # Generate a closure which calls the form_volume if it exists. |
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[6d6508e] | 142 | form_volume = model_info.form_volume |
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[6e7ff6d] | 143 | self._volume = ((lambda: form_volume(*volume_args)) if form_volume |
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| 144 | else (lambda: 1.0)) |
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| 145 | |
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[6d6508e] | 146 | def __call__(self, call_details, weights, values, cutoff): |
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| 147 | # type: (.generate.CallDetails, np.ndarray, np.ndarray, float) -> np.ndarray |
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[6e7ff6d] | 148 | res = _loops(self._parameter_vector, self._form, self._volume, |
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[6d6508e] | 149 | self.q_input.nq, call_details, weights, values, cutoff) |
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[b3f6bc3] | 150 | return res |
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| 151 | |
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| 152 | def release(self): |
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[eafc9fa] | 153 | """ |
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| 154 | Free resources associated with the kernel. |
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| 155 | """ |
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[3c56da87] | 156 | self.q_input = None |
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[b3f6bc3] | 157 | |
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[6e7ff6d] | 158 | def _loops(parameters, # type: np.ndarray |
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| 159 | form, # type: Callable[[], np.ndarray] |
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| 160 | form_volume, # type: Callable[[], float] |
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| 161 | nq, # type: int |
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[6d6508e] | 162 | call_details,# type: .generate.CallDetails |
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[6e7ff6d] | 163 | weights, # type: np.ndarray |
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| 164 | values, # type: np.ndarray |
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| 165 | cutoff, # type: float |
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| 166 | ): # type: (...) -> None |
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[f734e7d] | 167 | ################################################################ |
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| 168 | # # |
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| 169 | # !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! # |
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| 170 | # !! !! # |
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| 171 | # !! KEEP THIS CODE CONSISTENT WITH KERNEL_TEMPLATE.C !! # |
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| 172 | # !! !! # |
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| 173 | # !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! # |
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| 174 | # # |
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| 175 | ################################################################ |
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[6d6508e] | 176 | parameters[:] = values[call_details.par_offset] |
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[6e7ff6d] | 177 | scale, background = values[0], values[1] |
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[6d6508e] | 178 | if call_details.num_active == 0: |
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[6e7ff6d] | 179 | norm = float(form_volume()) |
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| 180 | if norm > 0.0: |
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| 181 | return (scale/norm)*form() + background |
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[b3f6bc3] | 182 | else: |
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[6e7ff6d] | 183 | return np.ones(nq, 'd')*background |
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| 184 | |
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| 185 | partial_weight = np.NaN |
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| 186 | spherical_correction = 1.0 |
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[6d6508e] | 187 | pd_stride = call_details.pd_stride[:call_details.num_active] |
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| 188 | pd_length = call_details.pd_length[:call_details.num_active] |
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| 189 | pd_offset = call_details.pd_offset[:call_details.num_active] |
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[6e7ff6d] | 190 | pd_index = np.empty_like(pd_offset) |
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[6d6508e] | 191 | offset = np.empty_like(call_details.par_offset) |
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| 192 | theta = call_details.theta_par |
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[6e7ff6d] | 193 | fast_length = pd_length[0] |
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| 194 | pd_index[0] = fast_length |
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| 195 | total = np.zeros(nq, 'd') |
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| 196 | norm = 0.0 |
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[6d6508e] | 197 | for loop_index in range(call_details.total_pd): |
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[6e7ff6d] | 198 | # update polydispersity parameter values |
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| 199 | if pd_index[0] == fast_length: |
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| 200 | pd_index[:] = (loop_index/pd_stride)%pd_length |
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| 201 | partial_weight = np.prod(weights[pd_offset+pd_index][1:]) |
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[6d6508e] | 202 | for k in range(call_details.num_coord): |
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| 203 | par = call_details.par_coord[k] |
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| 204 | coord = call_details.pd_coord[k] |
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| 205 | this_offset = call_details.par_offset[par] |
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[6e7ff6d] | 206 | block_size = 1 |
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| 207 | for bit in xrange(32): |
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| 208 | if coord&1: |
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| 209 | this_offset += block_size * pd_index[bit] |
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| 210 | block_size *= pd_length[bit] |
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| 211 | coord >>= 1 |
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| 212 | if coord == 0: break |
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| 213 | offset[par] = this_offset |
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| 214 | parameters[par] = values[this_offset] |
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[6d6508e] | 215 | if par == theta and not (call_details.par_coord[k]&1): |
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[6e7ff6d] | 216 | spherical_correction = max(abs(cos(pi/180 * parameters[theta])), 1e-6) |
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[6d6508e] | 217 | for k in range(call_details.num_coord): |
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| 218 | if call_details.pd_coord[k]&1: |
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| 219 | #par = call_details.par_coord[k] |
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[6e7ff6d] | 220 | parameters[par] = values[offset[par]] |
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[a8a7f08] | 221 | #print "par",par,offset[par],parameters[par+2] |
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[6e7ff6d] | 222 | offset[par] += 1 |
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| 223 | if par == theta: |
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| 224 | spherical_correction = max(abs(cos(pi/180 * parameters[theta])), 1e-6) |
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| 225 | |
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| 226 | weight = partial_weight * weights[pd_offset[0] + pd_index[0]] |
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| 227 | pd_index[0] += 1 |
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| 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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| 233 | I = form() |
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[4c2c535] | 234 | if np.isnan(I).any(): continue |
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[b3f6bc3] | 235 | |
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[6e7ff6d] | 236 | # update value and norm |
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| 237 | weight *= spherical_correction |
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| 238 | total += weight * I |
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| 239 | norm += weight * form_volume() |
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[b3f6bc3] | 240 | |
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[6e7ff6d] | 241 | if norm > 0.0: |
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| 242 | return (scale/norm)*total + background |
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| 243 | else: |
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| 244 | return np.ones(nq, 'd')*background |
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[6d6508e] | 245 | |
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| 246 | |
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| 247 | |
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| 248 | def _create_vector_Iq(model_info): |
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| 249 | """ |
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| 250 | Define Iq as a vector function if it exists. |
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| 251 | """ |
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| 252 | Iq = model_info.Iq |
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| 253 | if callable(Iq) and not getattr(Iq, 'vectorized', False): |
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| 254 | def vector_Iq(q, *args): |
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| 255 | """ |
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| 256 | Vectorized 1D kernel. |
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| 257 | """ |
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| 258 | return np.array([Iq(qi, *args) for qi in q]) |
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| 259 | vector_Iq.vectorized = True |
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| 260 | model_info.Iq = vector_Iq |
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| 261 | |
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| 262 | def _create_vector_Iqxy(model_info): |
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| 263 | """ |
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| 264 | Define Iqxy as a vector function if it exists, or default it from Iq(). |
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| 265 | """ |
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| 266 | Iq, Iqxy = model_info.Iq, model_info.Iqxy |
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| 267 | if callable(Iqxy) and not getattr(Iqxy, 'vectorized', False): |
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| 268 | def vector_Iqxy(qx, qy, *args): |
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| 269 | """ |
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| 270 | Vectorized 2D kernel. |
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| 271 | """ |
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| 272 | return np.array([Iqxy(qxi, qyi, *args) for qxi, qyi in zip(qx, qy)]) |
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| 273 | vector_Iqxy.vectorized = True |
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| 274 | model_info.Iqxy = vector_Iqxy |
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| 275 | elif callable(Iq): |
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| 276 | # Iq is vectorized because create_vector_Iq was already called. |
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| 277 | def default_Iqxy(qx, qy, *args): |
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| 278 | """ |
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| 279 | Default 2D kernel. |
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| 280 | """ |
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| 281 | return Iq(np.sqrt(qx**2 + qy**2), *args) |
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| 282 | default_Iqxy.vectorized = True |
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| 283 | model_info.Iqxy = default_Iqxy |
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| 284 | |
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| 285 | def _create_default_functions(model_info): |
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| 286 | """ |
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| 287 | Autogenerate missing functions, such as Iqxy from Iq. |
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| 288 | |
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| 289 | This only works for Iqxy when Iq is written in python. :func:`make_source` |
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| 290 | performs a similar role for Iq written in C. This also vectorizes |
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| 291 | any functions that are not already marked as vectorized. |
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| 292 | """ |
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| 293 | _create_vector_Iq(model_info) |
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| 294 | _create_vector_Iqxy(model_info) # call create_vector_Iq() first |
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| 295 | |
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