def sasview_rpa(q, pars):
from sasmodels.models import rpa
from sasmodels.compare import eval_sasview
from sasmodels.data import empty_data1D
data = empty_data1D(q, resolution=0.0)
M = eval_sasview(rpa, data)
return M(**pars)
def sasview_rpa(q, pars):
from sasmodels.models import rpa
from sasmodels.compare import eval_sasview
from sasmodels.data import empty_data1D
data = empty_data1D(q, resolution=0.0)
M = eval_sasview(rpa, data)
return M(**pars)
def _sasmodels_Iq(kernel, q, pars):
from sasmodels.data import empty_data1D
from sasmodels.direct_model import DirectModel
data = empty_data1D(q)
calculator = DirectModel(data, kernel)
Iq = calculator(**pars)
return Iq
def call_ocl(self, x, dtype, platform='ocl'):
"""
Calculation using sasmodels ocl libraries.
"""
x = np.asarray(x, dtype)
model = core.build_model(self.ocl_function, dtype=dtype)
calculator = direct_model.DirectModel(data.empty_data1D(x), model)
return calculator(background=0)
def sasmodels_fn(x, dtype, platform='ocl'):
"""
Calculation using pade approximant.
"""
from sasmodels import core, data, direct_model
model = core.load_model('bessel', dtype=dtype)
calculator = direct_model.DirectModel(data.empty_data1D(x), model)
return calculator(background=0)
def sasmodels_rpa(q, pars):
from sasmodels.models import rpa
from sasmodels.core import load_model
from sasmodels.direct_model import DirectModel
from sasmodels.data import empty_data1D
data = empty_data1D(q, resolution=0.0)
model = load_model(rpa, dtype="double", platform="dll")
#model = load_model(rpa, dtype="single", platform="ocl")
M = DirectModel(data, model)
return M(**pars)
def sasmodels_rpa(q, pars):
from sasmodels.models import rpa
from sasmodels.core import load_model
from sasmodels.direct_model import DirectModel
from sasmodels.data import empty_data1D
data = empty_data1D(q, resolution=0.0)
model = load_model(rpa, dtype="double", platform="dll")
#model = load_model(rpa, dtype="single", platform="ocl")
M = DirectModel(data, model)
return M(**pars)
def build_model(model_name, q, **pars):
from sasmodels.core import load_model_info, build_model as build_sasmodel
from sasmodels.data import empty_data1D
from sasmodels.direct_model import DirectModel
model_info = load_model_info(model_name)
model = build_sasmodel(model_info, dtype='double!')
data = empty_data1D(q)
calculator = DirectModel(data, model, cutoff=0)
calculator.pars = pars.copy()
calculator.pars.setdefault('background', 0)
return calculator
def plot_1d(model, opts, ax):
q_min, q_max, nq = opts['q_min'], opts['q_max'], opts['nq']
q_min = math.log10(q_min)
q_max = math.log10(q_max)
q = np.logspace(q_min, q_max, nq)
data = empty_data1D(q)
calculator = DirectModel(data, model)
Iq1D = calculator()
ax.plot(q, Iq1D, color='blue', lw=2, label=model_info['name'])
ax.set_xlabel(r'$Q \/(\AA^{-1})$')
ax.set_ylabel(r'$I(Q) \/(\mathrm{cm}^{-1})$')
ax.set_xscale(opts['xscale'])
ax.set_yscale(opts['yscale'])
def fitFunction(x, *tmp_params):
model = load_model(equation)
data = empty_data1D(x)
param_dict = dict(zip(param_names, tmp_params))
model_wrapper = Model(model, **param_dict)
if value_ranges is not None:
for name, values in value_ranges.items():
model_wrapper.__dict__[name].range(values[0], values[1])
func_wrapper = Experiment(data=data, model=model_wrapper)
return func_wrapper.theory()
def plot_1d(model, opts, ax):
# type: (KernelModel, Dict[str, Any], Axes) -> None
"""
Create a 1-D image.
"""
q_min, q_max, nq = opts['q_min'], opts['q_max'], opts['nq']
q_min = math.log10(q_min)
q_max = math.log10(q_max)
q = np.logspace(q_min, q_max, nq)
data = empty_data1D(q)
calculator = DirectModel(data, model)
Iq1D = calculator()
ax.plot(q, Iq1D, color='blue', lw=2, label=model.info.name)
ax.set_xlabel(r'$Q \/(\AA^{-1})$')
ax.set_ylabel(r'$I(Q) \/(\mathrm{cm}^{-1})$')
ax.set_xscale(opts['xscale'])
ax.set_yscale(opts['yscale'])
def plot_1d(model, opts, ax):
# type: (KernelModel, Dict[str, Any], Axes) -> None
"""
Create a 1-D image.
"""
q_min, q_max, nq = opts['q_min'], opts['q_max'], opts['nq']
q_min = math.log10(q_min)
q_max = math.log10(q_max)
q = np.logspace(q_min, q_max, nq)
data = empty_data1D(q)
calculator = DirectModel(data, model)
Iq1D = calculator()
ax.plot(q, Iq1D, color='blue', lw=2, label=model.info.name)
ax.set_xlabel(r'$Q \/(\AA^{-1})$')
ax.set_ylabel(r'$I(Q) \/(\mathrm{cm}^{-1})$')
ax.set_xscale(opts['xscale'])
ax.set_yscale(opts['yscale'])