def getIq2DError(self, qx, qy):
"""
Return the simulated value along with its estimated
error for a given q-value
Propagation of errors is used to evaluate the
uncertainty.
@param qx: qx-value [float]
@param qy: qy-value [float]
@return: mean, error [float, float]
"""
self._create_modelObject()
norm = 1.0e8 / self.params['lores_density'] * self.params['scale']
val = norm*pointsmodelpy.get_complex_iq_2D(self.complex_model, self.points, qx, qy)\
+ self.params['background']
# Simulation error (statistical)
norm = 1.0e8/self.params['lores_density']*self.params['scale'] \
* math.pow(self.npts/self.params['lores_density'], 1.0/3.0)/self.npts
err = norm * pointsmodelpy.get_complex_iq_2D_err(
self.complex_model, self.points, qx, qy)
# Error on V/N
simerr = 2 * val / self.npts
# The error used for the position is over-simplified.
# The actual error was empirically found to be about
# an order of magnitude larger.
return val, 10.0 * err + simerr
def getIq2DError(self, qx, qy):
"""
Return the simulated value along with its estimated
error for a given q-value
Propagation of errors is used to evaluate the
uncertainty.
@param qx: qx-value [float]
@param qy: qy-value [float]
@return: mean, error [float, float]
"""
self._create_modelObject()
norm = 1.0e8/self.params['lores_density']*self.params['scale']
val = norm*pointsmodelpy.get_complex_iq_2D(self.complex_model, self.points, qx, qy)\
+ self.params['background']
# Simulation error (statistical)
norm = 1.0e8/self.params['lores_density']*self.params['scale'] \
* math.pow(self.npts/self.params['lores_density'], 1.0/3.0)/self.npts
err = norm*pointsmodelpy.get_complex_iq_2D_err(self.complex_model, self.points, qx, qy)
# Error on V/N
simerr = 2*val/self.npts
# The error used for the position is over-simplified.
# The actual error was empirically found to be about
# an order of magnitude larger.
return val, 10.0*err+simerr
def getIq2D(self, qx, qy):
"""
Returns simulate I(q) for given q_x and q_y values.
@param qx: q_x [A-1]
@param qy: q_y [A-1]
@return: I(q) [cm-1]
"""
# If this is the first simulation call, we need to generate the
# space points
if self.points is None:
self._create_modelObject()
# Protect against empty model
if self.points is None:
return 0
# Evalute I(q)
norm = 1.0e8 / self.params['lores_density'] * self.params['scale']
return norm*pointsmodelpy.get_complex_iq_2D(self.complex_model, self.points, qx, qy)\
+ self.params['background']
def getIq2D(self, qx, qy):
"""
Returns simulate I(q) for given q_x and q_y values.
@param qx: q_x [A-1]
@param qy: q_y [A-1]
@return: I(q) [cm-1]
"""
# If this is the first simulation call, we need to generate the
# space points
if self.points is None:
self._create_modelObject()
# Protect against empty model
if self.points is None:
return 0
# Evalute I(q)
norm = 1.0e8/self.params['lores_density']*self.params['scale']
return norm*pointsmodelpy.get_complex_iq_2D(self.complex_model, self.points, qx, qy)\
+ self.params['background']