def load_data(f):
"""
Loads a dataset
Parameters
----------
f: {file-like, str}
f can be a string or file-like object referring to a File to
load the dataset from.
Returns
-------
data: Data1D-like
data object
"""
try:
data = OrsoDataset(f)
return data
except Exception:
# not an ORSO file
pass
try:
d = ReflectDataset(f)
return d
except Exception:
pass
d = Data1D(f)
return d
from __future__ import print_function, division
from refnx.dataset.reflectdataset import ReflectDataset
import refnx.analysis.reflect as reflect
import refnx.analysis.curvefitter as curvefitter
import numpy as np
DATASET_NAME = 'c_PLP0011859_q.txt'
USE_DIFFERENTIAL_EVOLUTION = True
#load the data
dataset = ReflectDataset()
with open(DATASET_NAME) as f:
dataset.load(f)
xdata, ydata, dydata, dxdata = dataset.data
# create an array to hold the parameters, also create limits
layers = np.array([[0, 2.07, 0, 0], # fronting medium
[30, 3.47, 0, 3], # 1st layer
[250, 2.00, 0, 3], # 2nd layer
[0, 6.36, 0, 3]]) # backing medium
lowlim = np.array([[0, 2.07, 0, 0], # fronting medium
[15, 3.47, 0, 1], # 1st layer
[200, 0.10, 0, 1], # 2nd layer
[0, 6.36, 0, 1]]) # backing medium
hilim = np.array([[0, 2.07, 0, 0], # fronting medium
[50, 3.47, 0, 9], # 1st layer
[300, 3.00, 0, 9], # 2nd layer
[0, 6.36, 0, 9]]) # backing medium
def run():
DATASET_NAME = 'c_PLP0011859_q.txt'
USE_DIFFERENTIAL_EVOLUTION = True
#load the data
dataset = ReflectDataset()
with open(DATASET_NAME) as f:
dataset.load(f)
xdata, ydata, dydata, dxdata = dataset.data
# create an array to hold the parameters, also create limits
layers = np.array([[0, 2.07, 0, 0], # fronting medium
[30, 3.47, 0, 3], # 1st layer
[250, 2.00, 0, 3], # 2nd layer
[0, 6.36, 0, 3]]) # backing medium
lowlim = np.array([[0, 2.07, 0, 0], # fronting medium
[15, 3.47, 0, 1], # 1st layer
[200, 0.10, 0, 1], # 2nd layer
[0, 6.36, 0, 1]]) # backing medium
hilim = np.array([[0, 2.07, 0, 0], # fronting medium
[50, 3.47, 0, 9], # 1st layer
[300, 3.00, 0, 9], # 2nd layer
[0, 6.36, 0, 9]]) # backing medium
# create a linear array of the parameters
# coefs[1] is the scale factor
# coefs[6] is the background
# these will both be 1 and 0 respectively to start off with
coefs = reflect.layer_to_coefs(layers)
lowlim = reflect.layer_to_coefs(lowlim)
hilim = reflect.layer_to_coefs(hilim)
coefs[1] = 1.0
coefs[6] = 3.e-6
lowlim[1] = 0.6
hilim[1] = 1.2
lowlim[6] = 0.
hilim[6] = 9e-6
bounds = zip(lowlim, hilim)
# create a parameter instance
parameters = curvefitter.to_parameters(coefs, bounds=bounds, varies=[False] * 16)
# which parameters do you want to allow to vary
fitted_parameters = np.array([1, 6, 7, 8, 11, 12, 13, 15])
for fit in fitted_parameters:
parameters['p%d' % fit].vary = True
# use resolution smearing and fit on a logR scale (transform the data as well)
t = reflect.Transform('logY').transform
ydata, dydata = t(xdata, ydata, dydata)
fcn_kws = {'dqvals': dxdata, 'transform': t}
# create the fit instance
fitter = reflect.ReflectivityFitFunction(xdata, ydata, parameters, edata=dydata,
fcn_kws=fcn_kws)
#do the fit
np.random.seed(1)
if USE_DIFFERENTIAL_EVOLUTION:
fitter.fit(method='differential_evolution')
