def Smear(q, C, dC, extrapname, sFinal, slitlength, quiet = False, weighted_transition=True):
'''
Smear the data of C(q) into S(q) using the slit-length
weighting function :func:`~jldesmear.api.smear.Plengt()` and an extrapolation
of the data to avoid truncation errors. Assume that
:func:`~jldesmear.api.smear.Plengt()` goes to zero for ``l > l_o`` (the slit length).
Also assume that the slit length function is symmetrical
about ``l = zero``.
.. math::
S(q) = 2 \int_0^{l_o} P_l(l) \ C(\sqrt{q^2+l^2}) \ dl
This routine is written so that if :func:`~jldesmear.api.smear.Plengt()` is changed
(for example) to a Gaussian, that no further modification
is necessary to the integration procedure. That is,
this routine will integrate the data out to "slitlength" (``l_o``).
:param numpy.ndarray q: magnitude of scattering vector
:param numpy.ndarray C: unsmeared data is C(q) +/- dC(q)
:param numpy.ndarray dC: estimated uncertainties of C
:param str extrapname: one of ``constant | linear | powerlaw | Porod``
:param float sFinal: fit extrapolation to I(q) for q >= sFinal
:param float slitlength: l_o, same units as q
:param bool quiet: if True, then no printed output from this routine
:param bool weighted_transition: if True, make a weighted transition between sFinal <= q < qMax
:return: tuple of (S, extrap)
:rtype: (numpy.ndarray, object)
:var numpy.ndarray S: smeared version of C
'''
# make the slit-length weighting function
NumPts = len(q)
q0 = q[0]
qMax = q[-1]
qRange = qMax - q0
x = slitlength * (q - q0) / qRange # use "x" rather than "l" to avoid typos
w = Plengt(x, slitlength) # w = P_l(l) or P_l(x)
# prepare for interpolation of existing data, log(I)
interp = interp1d(q, numpy.log(C))
# select and fit the extrapolation
if extrapolation.functions is None:
extrapolation.discover_extrapolations()
try:
extrap = prepare_extrapolation(q, C, dC, extrapname, sFinal)
except Exception:
message = "prepare_extrapolation had a problem: " + str(sys.exc_info)
raise Exception, message
S = numpy.ndarray((NumPts,)) # slit-smeared intensity (to be the result)
for i, qNow in enumerate(q):
if not quiet: toolbox.Spinner(i)
Ic = w * get_Ic(qNow, sFinal, qMax, x, interp, extrap, weighted_transition)
S[i] = 2 * numpy.trapz(Ic, x) # symmetrical about zero
return S, extrap
def prepare_extrapolation(q, C, dC, extrapname, sFinal):
'''
Pick the extrapolation function for smearing
:param numpy.ndarray q: magnitude of scattering vector
:param numpy.ndarray C: array (list) such that data is C(q) +/- dC(q)
:param numpy.ndarray dC: estimated uncertainties of C
:param str extrapname: one of ``constant``, ``linear``, ``powerlaw``, or ``Porod``
:param float sFinal: fit extrapolation to I(q) for q >= sFinal
:return: function object of selected extrapolation
:rtype: object
'''
if sFinal > q[-1]:
raise Exception, "no data to fit extrapolation"
for i in range(len(q)):
start = i
if q[i] > sFinal:
break
if len(q) - start < 2:
raise Exception, "not enough data to fit"
functions = extrapolation.discover_extrapolations()
if extrapname not in functions.keys():
msg = "did not identify extrapolation function: " + extrapname
raise RuntimeError, msg
extrap = functions[extrapname]()
extrap.fit(q[start:-1], C[start:-1], dC[start:-1])
return extrap
def _init_Adjustables_Panel(self, parent):
'''contains adjustable parameters'''
box = QGroupBox('Adjustable parameters', parent)
layout = QGridLayout()
box.setLayout(layout)
row = 0
tip = 'desmearing slit length, l_o'
self.slitlength = QLineEdit()
self.slitlength.setToolTip(tip)
self.slitlength.setStatusTip(tip)
layout.addWidget(QLabel('l_o'), row, 0)
layout.addWidget(self.slitlength, row, 1)
self.slitlength.setText('0.1')
row += 1
tip = 'functional form of extrapolation for desmearing'
functions = extrapolation.discover_extrapolations()
self.extrapolation = QComboBox()
self.extrapolation.insertItems(999, sorted(functions.keys()))
self.extrapolation.setToolTip(tip)
self.extrapolation.setStatusTip(tip)
layout.addWidget(QLabel('extrap'), row, 0)
layout.addWidget(self.extrapolation, row, 1)
self.setExtrapolationMethod('constant')
row += 1
tip = 'evaluate extrapolation constants based on data for q > q_F'
self.qFinal = QLineEdit()
self.qFinal.setToolTip(tip)
self.qFinal.setStatusTip(tip)
layout.addWidget(QLabel('q_F'), row, 0)
layout.addWidget(self.qFinal, row, 1)
self.qFinal.setText('0.1')
row += 1
tip = 'functional form of desmearing feedback, always use "fast"'
self.feedback = QComboBox()
self.feedback.insertItems(999, sorted(desmear.Weighting_Methods.keys()))
self.feedback.setCurrentIndex(2)
self.feedback.setToolTip(tip)
self.feedback.setStatusTip(tip)
layout.addWidget(QLabel('feedback'), row, 0)
layout.addWidget(self.feedback, row, 1)
self.setFeedbackMethod('fast')
row += 1
tip = 'specifies number of desmearing iterations, N_i'
self.num_iterations = QSpinBox()
self.num_iterations.setRange(2, 1000)
self.num_iterations.setToolTip(tip)
self.num_iterations.setStatusTip(tip)
layout.addWidget(QLabel('N_i'), row, 0)
layout.addWidget(self.num_iterations, row, 1)
self.num_iterations.setValue(10)
return box
def read(self, filename):
'''
read desmearing parameters from a command input file
:param str filename: full path to the command input file
:returns: instance of :class:`jldesmear.api.info.Info`
'''
def get_buf_item(key, item=0):
return buf[key].split()[item]
ext = '.inp'
if not filename.endswith(ext): return None
if not os.path.exists(filename): return None
self.info = info.Info()
# read a .inp file
self.info.parameterfile = filename
path = os.path.dirname(filename)
owd = os.getcwd()
os.chdir(path)
buf = open(os.path.abspath(filename), 'r').readlines()
if len(buf) < 7:
msg = "not enough information in command input file: " + filename
raise RuntimeError, msg
functions = extrapolation.discover_extrapolations()
self.info.fileio_class = self
self.info.filename = filename
self.info.quiet = True
self.info.callback = None
self.info.infile = os.path.abspath(os.path.join(path, get_buf_item(0)))
self.info.outfile = os.path.abspath(os.path.join(path, get_buf_item(1)))
self.info.slitlength = float(get_buf_item(2))
self.info.extrapname = get_buf_item(3)
self.info.sFinal = float(get_buf_item(4))
self.info.NumItr = int(get_buf_item(5))
self.info.LakeWeighting = get_buf_item(6)
self.info.extrap = functions[self.info.extrapname]
os.chdir(owd)
return self.info
