def initialize_data(self, data):
if isinstance(data, NXdata):
if len(data.nxsignal.shape) > 1:
raise NeXusError("Fitting only possible on one-dimensional arrays")
fit_data = NXdata(data.nxsignal, data.nxaxes, title=data.nxtitle)
if data.nxerrors:
fit_data.errors = data.nxerrors
return fit_data
else:
raise NeXusError("Must be an NXdata group")
def initialize_data(self, data):
if isinstance(data, NXdata):
if len(data.nxsignal.shape) > 1:
raise NeXusError(
"Fitting only possible on one-dimensional arrays")
fit_data = NXdata(data.nxsignal, data.nxaxes, title=data.nxtitle)
if data.nxerrors:
fit_data.errors = data.nxerrors
return fit_data
else:
raise NeXusError("Must be an NXdata group")
def convert_QE(self):
"""Convert S(phi,eps) to S(Q,eps)"""
self.read_parameters()
entry = self.root['entry']
Ei = self.Ei
dQ = self.dQ
dE = self.dE
pol, tof = centers(entry.data.nxsignal, entry.data.nxaxes)
en = self.convert_tof(tof)
idx_max = min(np.where(np.abs(en-0.75*Ei)<0.1)[0])
en = en[:idx_max]
data = entry.data.nxsignal.nxdata[:,:idx_max]
if entry.data.nxerrors:
errors = entry.data.nxerrors.nxdata[:]
Q = np.zeros((len(pol), len(en)))
E = np.zeros((len(pol), len(en)))
for i in range(0,len(pol)):
for j in range(0,len(en)):
Q[i,j] = np.sqrt((2*Ei - en[j] - 2*np.sqrt(Ei*(Ei-en[j]))
* np.cos(pol[i]*np.pi/180.0))/2.0721)
E[i,j]=en[j]
s = Q.shape
Qin = Q.reshape(s[0]*s[1])
Ein = E.reshape(s[0]*s[1])
datain = data.reshape(s[0]*s[1])
if entry.data.nxerrors:
errorsin = errors.reshape(s[0]*s[1])
qmin = Q.min()
qmax = Q.max()
emin = E.min()
emax = E.max()
NQ = int((qmax-qmin)/dQ) + 1
NE = int((emax-emin)/dE) + 1
Qb = np.linspace(qmin, qmax, NQ)
Eb = np.linspace(emin, emax, NE)
#histogram and normalize
norm, nbin = np.histogramdd((Ein,Qin), bins=(Eb,Qb))
hist, hbin = np.histogramdd((Ein,Qin), bins=(Eb,Qb), weights=datain)
if entry.data.nxerrors:
histe, hbin = np.histogramdd((Ein,Qin), bins=(Eb,Qb), weights=errorsin*errorsin)
histe = histe**0.5
err = histe/norm
I = NXfield(hist/norm, name='S(Q,E)')
Qb = NXfield(Qb[:-1]+dQ/2., name='Q')
Eb = NXfield(Eb[:-1]+dE/2., name='E')
result = NXdata(I, (Eb, Qb))
if entry.data.nxerrors:
result.errors = NXfield(err)
return result
def convert_QE(self):
"""Convert S(phi,eps) to S(Q,eps)"""
self.read_parameters()
Ei = self.Ei
dQ = self.dQ
dE = self.dE
signal = self.entry['data'].nxsignal
pol = centers(self.entry['data/polar_angle'], signal.shape[0])
tof = centers(self.entry['data/time_of_flight'], signal.shape[1])
en = self.convert_tof(tof)
idx_max = min(np.where(np.abs(en - 0.75 * Ei) < 0.1)[0])
en = en[:idx_max]
data = signal.nxdata[:, :idx_max]
if self.entry['data'].nxerrors:
errors = self.entry['data'].nxerrors.nxdata[:]
Q = np.zeros((len(pol), len(en)))
E = np.zeros((len(pol), len(en)))
for i in range(0, len(pol)):
p = pol[i]
Q[i, :] = np.array(
np.sqrt(
(2 * Ei - en -
2 * np.sqrt(Ei * (Ei - en)) * np.cos(p * np.pi / 180.0)) /
2.0721))
E[i, :] = np.array(en)
s = Q.shape
Qin = Q.reshape(s[0] * s[1])
Ein = E.reshape(s[0] * s[1])
datain = data.reshape(s[0] * s[1])
if self.entry['data'].nxerrors:
errorsin = errors.reshape(s[0] * s[1])
qmin = Q.min()
qmax = Q.max()
emin = E.min()
emax = E.max()
NQ = int((qmax - qmin) / dQ) + 1
NE = int((emax - emin) / dE) + 1
Qb = np.linspace(qmin, qmax, NQ)
Eb = np.linspace(emin, emax, NE)
# histogram and normalize
norm, nbin = np.histogramdd((Ein, Qin), bins=(Eb, Qb))
hist, hbin = np.histogramdd((Ein, Qin), bins=(Eb, Qb), weights=datain)
if self.entry['data'].nxerrors:
histe, hbin = np.histogramdd((Ein, Qin),
bins=(Eb, Qb),
weights=errorsin * errorsin)
histe = histe**0.5
err = histe / norm
Ib = NXfield(hist / norm, name='S(Q,E)')
Qb = NXfield(Qb[:-1] + dQ / 2., name='Q')
Eb = NXfield(Eb[:-1] + dE / 2., name='E')
result = NXdata(Ib, (Eb, Qb))
if self.entry.data.nxerrors:
result.errors = NXfield(err)
return result