/usr/lib/python3/dist-packages/sasmodels/kernelpy.py is in python3-sasmodels 0.97~git20171104-2.
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Python driver for python kernels
Calls the kernel with a vector of $q$ values for a single parameter set.
Polydispersity is supported by looping over different parameter sets and
summing the results. The interface to :class:`PyModel` matches those for
:class:`kernelcl.GpuModel` and :class:`kerneldll.DllModel`.
"""
from __future__ import division, print_function
import logging
import numpy as np # type: ignore
from numpy import pi, sin, cos #type: ignore
from . import details
from .generate import F64
from .kernel import KernelModel, Kernel
try:
from typing import Union, Callable
except ImportError:
pass
else:
DType = Union[None, str, np.dtype]
class PyModel(KernelModel):
"""
Wrapper for pure python models.
"""
def __init__(self, model_info):
# Make sure Iq and Iqxy are available and vectorized
_create_default_functions(model_info)
self.info = model_info
self.dtype = np.dtype('d')
logging.info("load python model " + self.info.name)
def make_kernel(self, q_vectors):
q_input = PyInput(q_vectors, dtype=F64)
kernel = self.info.Iqxy if q_input.is_2d else self.info.Iq
return PyKernel(kernel, self.info, q_input)
def release(self):
"""
Free resources associated with the model.
"""
pass
class PyInput(object):
"""
Make q data available to the gpu.
*q_vectors* is a list of q vectors, which will be *[q]* for 1-D data,
and *[qx, qy]* for 2-D data. Internally, the vectors will be reallocated
to get the best performance on OpenCL, which may involve shifting and
stretching the array to better match the memory architecture. Additional
points will be evaluated with *q=1e-3*.
*dtype* is the data type for the q vectors. The data type should be
set to match that of the kernel, which is an attribute of
:class:`GpuProgram`. Note that not all kernels support double
precision, so even if the program was created for double precision,
the *GpuProgram.dtype* may be single precision.
Call :meth:`release` when complete. Even if not called directly, the
buffer will be released when the data object is freed.
"""
def __init__(self, q_vectors, dtype):
self.nq = q_vectors[0].size
self.dtype = dtype
self.is_2d = (len(q_vectors) == 2)
if self.is_2d:
self.q = np.empty((self.nq, 2), dtype=dtype)
self.q[:, 0] = q_vectors[0]
self.q[:, 1] = q_vectors[1]
else:
self.q = np.empty(self.nq, dtype=dtype)
self.q[:self.nq] = q_vectors[0]
def release(self):
"""
Free resources associated with the model inputs.
"""
self.q = None
class PyKernel(Kernel):
"""
Callable SAS kernel.
*kernel* is the DllKernel object to call.
*model_info* is the module information
*q_input* is the DllInput q vectors at which the kernel should be
evaluated.
The resulting call method takes the *pars*, a list of values for
the fixed parameters to the kernel, and *pd_pars*, a list of (value,weight)
vectors for the polydisperse parameters. *cutoff* determines the
integration limits: any points with combined weight less than *cutoff*
will not be calculated.
Call :meth:`release` when done with the kernel instance.
"""
def __init__(self, kernel, model_info, q_input):
# type: (callable, ModelInfo, List[np.ndarray]) -> None
self.dtype = np.dtype('d')
self.info = model_info
self.q_input = q_input
self.res = np.empty(q_input.nq, q_input.dtype)
self.kernel = kernel
self.dim = '2d' if q_input.is_2d else '1d'
partable = model_info.parameters
kernel_parameters = (partable.iqxy_parameters if q_input.is_2d
else partable.iq_parameters)
volume_parameters = partable.form_volume_parameters
# Create an array to hold the parameter values. There will be a
# single array whose values are updated as the calculator goes
# through the loop. Arguments to the kernel and volume functions
# will use views into this vector, relying on the fact that a
# an array of no dimensions acts like a scalar.
parameter_vector = np.empty(len(partable.call_parameters)-2, 'd')
# Create views into the array to hold the arguments
offset = 0
kernel_args, volume_args = [], []
for p in partable.kernel_parameters:
if p.length == 1:
# Scalar values are length 1 vectors with no dimensions.
v = parameter_vector[offset:offset+1].reshape(())
else:
# Vector values are simple views.
v = parameter_vector[offset:offset+p.length]
offset += p.length
if p in kernel_parameters:
kernel_args.append(v)
if p in volume_parameters:
volume_args.append(v)
# Hold on to the parameter vector so we can use it to call kernel later.
# This may also be required to preserve the views into the vector.
self._parameter_vector = parameter_vector
# Generate a closure which calls the kernel with the views into the
# parameter array.
if q_input.is_2d:
form = model_info.Iqxy
qx, qy = q_input.q[:, 0], q_input.q[:, 1]
self._form = lambda: form(qx, qy, *kernel_args)
else:
form = model_info.Iq
q = q_input.q
self._form = lambda: form(q, *kernel_args)
# Generate a closure which calls the form_volume if it exists.
form_volume = model_info.form_volume
self._volume = ((lambda: form_volume(*volume_args)) if form_volume
else (lambda: 1.0))
def __call__(self, call_details, values, cutoff, magnetic):
# type: (CallDetails, np.ndarray, np.ndarray, float, bool) -> np.ndarray
if magnetic:
raise NotImplementedError("Magnetism not implemented for pure python models")
#print("Calling python kernel")
#call_details.show(values)
res = _loops(self._parameter_vector, self._form, self._volume,
self.q_input.nq, call_details, values, cutoff)
return res
def release(self):
# type: () -> None
"""
Free resources associated with the kernel.
"""
self.q_input.release()
self.q_input = None
def _loops(parameters, form, form_volume, nq, call_details, values, cutoff):
# type: (np.ndarray, Callable[[], np.ndarray], Callable[[], float], int, details.CallDetails, np.ndarray, np.ndarray, float) -> None
################################################################
# #
# !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! #
# !! !! #
# !! KEEP THIS CODE CONSISTENT WITH KERNEL_TEMPLATE.C !! #
# !! !! #
# !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! #
# #
################################################################
n_pars = len(parameters)
parameters[:] = values[2:n_pars+2]
if call_details.num_active == 0:
pd_norm = float(form_volume())
scale = values[0]/(pd_norm if pd_norm != 0.0 else 1.0)
background = values[1]
return scale*form() + background
pd_value = values[2+n_pars:2+n_pars + call_details.num_weights]
pd_weight = values[2+n_pars + call_details.num_weights:]
pd_norm = 0.0
spherical_correction = 1.0
partial_weight = np.NaN
weight = np.NaN
p0_par = call_details.pd_par[0]
p0_is_theta = (p0_par == call_details.theta_par)
p0_length = call_details.pd_length[0]
p0_index = p0_length
p0_offset = call_details.pd_offset[0]
pd_par = call_details.pd_par[:call_details.num_active]
pd_offset = call_details.pd_offset[:call_details.num_active]
pd_stride = call_details.pd_stride[:call_details.num_active]
pd_length = call_details.pd_length[:call_details.num_active]
total = np.zeros(nq, 'd')
for loop_index in range(call_details.num_eval):
# update polydispersity parameter values
if p0_index == p0_length:
pd_index = (loop_index//pd_stride)%pd_length
parameters[pd_par] = pd_value[pd_offset+pd_index]
partial_weight = np.prod(pd_weight[pd_offset+pd_index][1:])
if call_details.theta_par >= 0:
cor = sin(pi / 180 * parameters[call_details.theta_par])
spherical_correction = max(abs(cor), 1e-6)
p0_index = loop_index%p0_length
weight = partial_weight * pd_weight[p0_offset + p0_index]
parameters[p0_par] = pd_value[p0_offset + p0_index]
if p0_is_theta:
cor = cos(pi/180 * parameters[p0_par])
spherical_correction = max(abs(cor), 1e-6)
p0_index += 1
if weight > cutoff:
# Call the scattering function
# Assume that NaNs are only generated if the parameters are bad;
# exclude all q for that NaN. Even better would be to have an
# INVALID expression like the C models, but that is too expensive.
Iq = np.asarray(form(), 'd')
if np.isnan(Iq).any():
continue
# update value and norm
weight *= spherical_correction
total += weight * Iq
pd_norm += weight * form_volume()
scale = values[0]/(pd_norm if pd_norm != 0.0 else 1.0)
background = values[1]
return scale*total + background
def _create_default_functions(model_info):
"""
Autogenerate missing functions, such as Iqxy from Iq.
This only works for Iqxy when Iq is written in python. :func:`make_source`
performs a similar role for Iq written in C. This also vectorizes
any functions that are not already marked as vectorized.
"""
_create_vector_Iq(model_info)
_create_vector_Iqxy(model_info) # call create_vector_Iq() first
def _create_vector_Iq(model_info):
"""
Define Iq as a vector function if it exists.
"""
Iq = model_info.Iq
if callable(Iq) and not getattr(Iq, 'vectorized', False):
#print("vectorizing Iq")
def vector_Iq(q, *args):
"""
Vectorized 1D kernel.
"""
return np.array([Iq(qi, *args) for qi in q])
vector_Iq.vectorized = True
model_info.Iq = vector_Iq
def _create_vector_Iqxy(model_info):
"""
Define Iqxy as a vector function if it exists, or default it from Iq().
"""
Iq, Iqxy = model_info.Iq, model_info.Iqxy
if callable(Iqxy):
if not getattr(Iqxy, 'vectorized', False):
#print("vectorizing Iqxy")
def vector_Iqxy(qx, qy, *args):
"""
Vectorized 2D kernel.
"""
return np.array([Iqxy(qxi, qyi, *args) for qxi, qyi in zip(qx, qy)])
vector_Iqxy.vectorized = True
model_info.Iqxy = vector_Iqxy
elif callable(Iq):
#print("defaulting Iqxy")
# Iq is vectorized because create_vector_Iq was already called.
def default_Iqxy(qx, qy, *args):
"""
Default 2D kernel.
"""
return Iq(np.sqrt(qx**2 + qy**2), *args)
default_Iqxy.vectorized = True
model_info.Iqxy = default_Iqxy
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