import warnings
import numpy as np
warnings.filterwarnings('ignore', module='numpy')
warnings.filterwarnings('ignore')
import mantid.simpleapi as api
from lmfit.models import GaussianModel
[docs]
def process_data(workspace, summed=True, tof_step=200):
r"""
Process a Mantid workspace to extract counts vs pixel.
:param workspace: Mantid workspace
:param summed: if True, the x pixels with be summed
:param tof_step: TOF bin size
"""
tof_min = workspace.getTofMin()
tof_max = workspace.getTofMax()
_ws = api.Rebin(InputWorkspace=workspace, Params="%s,%s,%s" % (tof_min, tof_step, tof_max))
y=_ws.extractY()
y = np.reshape(y, (256, 304, y.shape[1]))
tof=_ws.extractX()[0]
tof = (tof[:-1]+tof[1:])/2.0
if summed:
y = np.sum(y, axis=2)
_y = np.sum(y, axis=0)
_x = np.arange(304)
return tof, _x, _y
[docs]
def fit_signal_flat_bck(x, y, x_min=110, x_max=170, center=None, sigma=None,
background=None):
r"""
Fit a Gaussian peak.
:param x: list of x values
:param y: list of y values
:param x_min: start index of the list of points
:param x_max: end index of the list of points
:param center: estimated center position
:param sigma: if provided, the sigma will be fixed to the given value
:param background: if provided, the value will be subtracted from y
"""
gauss = GaussianModel(prefix='g_')
amplitude_guess = np.max(y[x_min:x_max])
if background is None:
background = np.min(y[x_min:x_max])
_center = 140
_sigma = 2
if center is not None:
_center = center
if sigma is not None:
_sigma = sigma
pars = gauss.make_params(amplitude=amplitude_guess, center=_center, sigma=_sigma)
if sigma is not None:
pars['g_sigma'].vary=False
pars['g_amplitude'].min=0
pars['g_center'].min=_center-2
pars['g_center'].max=_center+2
weights=1/np.sqrt(y)
weights[y<1]=1
fit = gauss.fit(y[x_min:x_max]-background, pars, method='leastsq',
x=x[x_min:x_max],
weights=1/weights[x_min:x_max])
fit.params['g_amplitude']
c=fit.params['g_center']
width=fit.params['g_sigma']
return c, width, fit