def addMagAtom(self, magAtom, symmetry):
# add a secondary magnetic atom object to the model
if self.magAtoms != None:
self.magAtoms.append(magAtom)
else:
self.magAtoms = [magAtom]
self.matom_index += 1
matom = self.magAtoms[self.matom_index]
matom.setLabel(rstrip(matom.label()))
self.symmetry = symmetry
self.magnetic = True
self.numVectors = self.symmetry.numBasisFunc()[self.magAtoms[self.matom_index].irrepNum()[0]-1]
if self.matom_index != 0:
param_label = " kvect: " + str(self.matom_index+1)
else:
param_label = ""
if self.coeffs == None:
self.coeffs = [None] * self.numVectors
j = 0
else:
self.coeffs.extend([None] * self.numVectors)
j = len(self.coeffs)
for i in xrange(self.numVectors):
self.coeffs[i+j] = Parameter(matom.basis()[0][i], name=self.atom.label() + " C" + str(i) + param_label)
if self.phases == None:
self.phases = [Parameter(matom.phase[0], name=self.atom.label() + " phase"+param_label)]
else:
self.phases.append(Parameter(matom.phase[0], name=self.atom.label() + " phase"+param_label))
self.magAtoms[self.matom_index] = matom
def load_model(model, name=None, **kw):
sans = __import__('sans.models.' + model)
ModelClass = getattr(getattr(sans.models, model, None), model, None)
if ModelClass is None:
raise ValueError("could not find model %r in sans.models" % model)
M = ModelClass()
prefix = (name if name else _model_name(M)) + " "
M._bumps_pars = {}
valid_pars = M.getParamList()
for k, v in kw.items():
# dispersion parameters initialized with _field instead of .field
if k.endswith('_width'): k = k[:-6] + '.width'
elif k.endswith('_npts'): k = k[:-5] + '.npts'
elif k.endswith('_nsigmas'): k = k[:-7] + '.nsigmas'
elif k.endswith('_type'): k = k[:-5] + '.type'
if k not in valid_pars:
formatted_pars = ", ".join(valid_pars)
raise KeyError("invalid parameter %r for %s--use one of: %s" %
(k, model, formatted_pars))
if '.' in k and not k.endswith('.width'):
M.setParam(k, v)
elif isinstance(v, BaseParameter):
M._bumps_pars[k] = v
elif isinstance(v, (tuple, list)):
low, high = v
P = Parameter((low + high) / 2, bounds=v, name=prefix + k)
M._bumps_pars[k] = P
else:
P = Parameter(v, name=prefix + k)
M._bumps_pars[k] = P
return M
def __init__(self, a, b, c, beta):
self.cell = CrystalCell()
self.a = Parameter(a, name='a')
self.b = Parameter(b, name='b')
self.c = Parameter(c, name='c')
self.beta = Parameter(beta, name='beta')
self.update()
def __init__(self, data, model, cutoff=1e-5, **kw):
from bumps.names import Parameter
# remember inputs so we can inspect from outside
self.data = data
self.model = model
self.cutoff = cutoff
partype = model.info['partype']
# interpret data
if hasattr(data, 'qx_data'):
self.index = (data.mask==0) & (~np.isnan(data.data))
self.iq = data.data[self.index]
self.diq = data.err_data[self.index]
self._theory = np.zeros_like(data.data)
if not partype['orientation'] and not partype['magnetic']:
q_vectors = [np.sqrt(data.qx_data**2+data.qy_data**2)]
else:
q_vectors = [data.qx_data, data.qy_data]
else:
self.index = (data.x>=data.qmin) & (data.x<=data.qmax) & ~np.isnan(data.y)
self.iq = data.y[self.index]
self.diq = data.dy[self.index]
self._theory = np.zeros_like(data.y)
q_vectors = [data.x]
# Remember function inputs so we can delay loading the function and
# so we can save/restore state
self._fn_inputs = [v[self.index] for v in q_vectors]
self._fn = None
# define bumps parameters
pars = []
for p in model.info['parameters']:
name, default, limits, ptype = p[0], p[2], p[3], p[4]
value = kw.pop(name, default)
setattr(self, name, Parameter.default(value, name=name, limits=limits))
pars.append(name)
for name in partype['pd-2d']:
for xpart,xdefault,xlimits in [
('_pd', 0, limits),
('_pd_n', 35, (0,1000)),
('_pd_nsigma', 3, (0, 10)),
('_pd_type', 'gaussian', None),
]:
xname = name+xpart
xvalue = kw.pop(xname, xdefault)
if xlimits is not None:
xvalue = Parameter.default(xvalue, name=xname, limits=xlimits)
pars.append(xname)
setattr(self, xname, xvalue)
self._parameter_names = pars
if kw:
raise TypeError("unexpected parameters: %s"%(", ".join(sorted(kw.keys()))))
self.update()
def __init__(self, data, model, dtype='float32', **kw):
self.__dict__['_parameters'] = {}
#self.name = data.filename
self.is2D = hasattr(data,'qx_data')
self.data = data
if self.is2D:
self.index = (data.mask==0) & (~np.isnan(data.data))
self.iq = data.data[self.index]
self.diq = data.err_data[self.index]
self.qx = data.qx_data
self.qy = data.qy_data
self.gpu = model(self.qx, self.qy, dtype=dtype)
else:
self.index = (data.mask==0) & (~np.isnan(data.y))
self.iq = data.y[self.index]
self.diq = data.dy[self.index]
self.q = data.x
self.gpu = model(self.q, dtype=dtype)
pd_pars = set(base+attr for base in model.PD_PARS for attr in ('_pd','_pd_n','_pd_nsigma'))
total_pars = set(model.PARS.keys()) | pd_pars
extra_pars = set(kw.keys()) - total_pars
if extra_pars:
raise TypeError("unexpected parameters %s"%(str(extra_pars,)))
pars = model.PARS.copy()
pars.update((base+'_pd', 0) for base in model.PD_PARS)
pars.update((base+'_pd_n', 35) for base in model.PD_PARS)
pars.update((base+'_pd_nsigma', 3) for base in model.PD_PARS)
pars.update(kw)
for k,v in pars.items():
setattr(self, k, Parameter.default(v, name=k))
self._parameter_names = set(pars.keys())
self.update()
def __init__(self, data, model, dtype='float32', **kw):
self.__dict__['_parameters'] = {}
#self.name = data.filename
self.is2D = hasattr(data, 'qx_data')
self.data = data
if self.is2D:
self.index = (data.mask == 0) & (~np.isnan(data.data))
self.iq = data.data[self.index]
self.diq = data.err_data[self.index]
self.qx = data.qx_data
self.qy = data.qy_data
self.gpu = model(self.qx, self.qy, dtype=dtype)
else:
self.index = (data.mask == 0) & (~np.isnan(data.y))
self.iq = data.y[self.index]
self.diq = data.dy[self.index]
self.q = data.x
self.gpu = model(self.q, dtype=dtype)
pd_pars = set(base + attr for base in model.PD_PARS
for attr in ('_pd', '_pd_n', '_pd_nsigma'))
total_pars = set(model.PARS.keys()) | pd_pars
extra_pars = set(kw.keys()) - total_pars
if extra_pars:
raise TypeError("unexpected parameters %s" % (str(extra_pars, )))
pars = model.PARS.copy()
pars.update((base + '_pd', 0) for base in model.PD_PARS)
pars.update((base + '_pd_n', 35) for base in model.PD_PARS)
pars.update((base + '_pd_nsigma', 3) for base in model.PD_PARS)
pars.update(kw)
for k, v in pars.items():
setattr(self, k, Parameter.default(v, name=k))
self._parameter_names = set(pars.keys())
self.update()
def _sas_parameter(model, pid, prefix):
par = getattr(model, '_bumps_pars', {}).get(pid, None)
if par is None:
## Don't have bounds on dispersion parameters with model details
#bounds = model.details.get(pid,[None,None,None])[1:3]
value = model.getParam(pid)
par = Parameter(value, name=prefix + pid)
return par
def create_parameters(model_info, **kwargs):
"""
Generate Bumps parameters from the model info.
*model_info* is returned from :func:`generate.model_info` on the
model definition module.
Any additional *key=value* pairs are initial values for the parameters
to the models. Uninitialized parameters will use the model default
value.
Returns a dictionary of *{name: Parameter}* containing the bumps
parameters for each model parameter, and a dictionary of
*{name: str}* containing the polydispersity distribution types.
"""
# lazy import; this allows the doc builder and nosetests to run even
# when bumps is not on the path.
from bumps.names import Parameter
pars = {}
for p in model_info['parameters']:
value = kwargs.pop(p.name, p.default)
pars[p.name] = Parameter.default(value, name=p.name, limits=p.limits)
for name in model_info['partype']['pd-2d']:
for xpart, xdefault, xlimits in [
('_pd', 0., pars[name].limits),
('_pd_n', 35., (0, 1000)),
('_pd_nsigma', 3., (0, 10)),
]:
xname = name + xpart
xvalue = kwargs.pop(xname, xdefault)
pars[xname] = Parameter.default(xvalue, name=xname, limits=xlimits)
pd_types = {}
for name in model_info['partype']['pd-2d']:
xname = name + '_pd_type'
xvalue = kwargs.pop(xname, 'gaussian')
pd_types[xname] = xvalue
if kwargs: # args not corresponding to parameters
raise TypeError("unexpected parameters: %s" %
(", ".join(sorted(kwargs.keys()))))
return pars, pd_types
def create_parameters(model_info, **kwargs):
"""
Generate Bumps parameters from the model info.
*model_info* is returned from :func:`generate.model_info` on the
model definition module.
Any additional *key=value* pairs are initial values for the parameters
to the models. Uninitialized parameters will use the model default
value.
Returns a dictionary of *{name: Parameter}* containing the bumps
parameters for each model parameter, and a dictionary of
*{name: str}* containing the polydispersity distribution types.
"""
# lazy import; this allows the doc builder and nosetests to run even
# when bumps is not on the path.
from bumps.names import Parameter
pars = {}
for p in model_info['parameters']:
value = kwargs.pop(p.name, p.default)
pars[p.name] = Parameter.default(value, name=p.name, limits=p.limits)
for name in model_info['partype']['pd-2d']:
for xpart, xdefault, xlimits in [
('_pd', 0., pars[name].limits),
('_pd_n', 35., (0, 1000)),
('_pd_nsigma', 3., (0, 10)),
]:
xname = name + xpart
xvalue = kwargs.pop(xname, xdefault)
pars[xname] = Parameter.default(xvalue, name=xname, limits=xlimits)
pd_types = {}
for name in model_info['partype']['pd-2d']:
xname = name + '_pd_type'
xvalue = kwargs.pop(xname, 'gaussian')
pd_types[xname] = xvalue
if kwargs: # args not corresponding to parameters
raise TypeError("unexpected parameters: %s"
% (", ".join(sorted(kwargs.keys()))))
return pars, pd_types
def create_parameters(
model_info, # type: ModelInfo
**kwargs # type: Union[float, str, Parameter]
):
# type: (...) -> Tuple[Dict[str, Parameter], Dict[str, str]]
"""
Generate Bumps parameters from the model info.
*model_info* is returned from :func:`generate.model_info` on the
model definition module.
Any additional *key=value* pairs are initial values for the parameters
to the models. Uninitialized parameters will use the model default
value. The value can be a float, a bumps parameter, or in the case
of the distribution type parameter, a string.
Returns a dictionary of *{name: Parameter}* containing the bumps
parameters for each model parameter, and a dictionary of
*{name: str}* containing the polydispersity distribution types.
"""
pars = {} # type: Dict[str, Parameter]
pd_types = {} # type: Dict[str, str]
for p in model_info.parameters.call_parameters:
value = kwargs.pop(p.name, p.default)
pars[p.name] = Parameter.default(value, name=p.name, limits=p.limits)
if p.polydisperse:
for part, default, limits in [
('_pd', 0., pars[p.name].limits),
('_pd_n', 35., (0, 1000)),
('_pd_nsigma', 3., (0, 10)),
]:
name = p.name + part
value = kwargs.pop(name, default)
pars[name] = Parameter.default(value, name=name, limits=limits)
name = p.name + '_pd_type'
pd_types[name] = str(kwargs.pop(name, 'gaussian'))
if kwargs: # args not corresponding to parameters
raise TypeError("unexpected parameters: %s" %
(", ".join(sorted(kwargs.keys()))))
return pars, pd_types
def create_parameters(model_info, **kwargs):
# type: (ModelInfo, **Union[float, str, Parameter]) -> Tuple[Dict[str, Parameter], Dict[str, str]]
"""
Generate Bumps parameters from the model info.
*model_info* is returned from :func:`generate.model_info` on the
model definition module.
Any additional *key=value* pairs are initial values for the parameters
to the models. Uninitialized parameters will use the model default
value. The value can be a float, a bumps parameter, or in the case
of the distribution type parameter, a string.
Returns a dictionary of *{name: Parameter}* containing the bumps
parameters for each model parameter, and a dictionary of
*{name: str}* containing the polydispersity distribution types.
"""
pars = {} # type: Dict[str, Parameter]
pd_types = {} # type: Dict[str, str]
for p in model_info.parameters.call_parameters:
value = kwargs.pop(p.name, p.default)
pars[p.name] = Parameter.default(value, name=p.name, limits=p.limits)
if p.polydisperse:
for part, default, limits in [
('_pd', 0., pars[p.name].limits),
('_pd_n', 35., (0, 1000)),
('_pd_nsigma', 3., (0, 10)),
]:
name = p.name + part
value = kwargs.pop(name, default)
pars[name] = Parameter.default(value, name=name, limits=limits)
name = p.name + '_pd_type'
pd_types[name] = str(kwargs.pop(name, 'gaussian'))
if kwargs: # args not corresponding to parameters
raise TypeError("unexpected parameters: %s"
% (", ".join(sorted(kwargs.keys()))))
return pars, pd_types
def __init__(self, atom, sgmultip):
# if isSequence(atoms):
# changes properties of both a regular atom and a magnetic atom
# (they should correspond to the same atomic position)
# self.atom = atoms[0]
# self.magAtom = atoms[1]
# self.magAtom.label = rstrip(self.magAtom.label)
# self.magnetic = True
# else:
self.atom = atom
self.magnetic = False
self.magAtoms = None
self.matom_index = -1
self.coeffs = None
self.phases = None
self.sgmultip = sgmultip
self.atom.set_atom_lab(rstrip(self.atom.label()))
self.B = Parameter(self.atom.BIso(), name=self.atom.label() + " B")
occ = self.atom.occupancy() / self.atom.multip() * self.sgmultip
self.occ = Parameter(occ, name=getAtom_lab(self.atom) + " occ")
self.x = Parameter(self.atom.coords()[0], name=self.atom.label() + " x")
self.y = Parameter(self.atom.coords()[1], name=self.atom.label() + " y")
self.z = Parameter(self.atom.coords()[2], name=self.atom.label() + " z")
def __init__(self, a, b, c, alpha, beta, gamma):
self.cell = CrystalCell()
self.a = Parameter(a, name='a')
self.b = Parameter(b, name='b')
self.c = Parameter(c, name='c')
self.alpha = Parameter(alpha, name='alpha')
self.beta = Parameter(beta, name='beta')
self.gamma = Parameter(gamma, name='gamma')
self.update()
def __init__(self, tt, observed, background, u, v, w,
wavelength, spaceGroupName, cell, atoms, exclusions=None,
magnetic=False, symmetry=None, newSymmetry=None, base=None, scale=1, eta=0,zero=None, error=None, hkls=None, muR=None):
if (isinstance(spaceGroupName, SpaceGroup)):
self.spaceGroup = spaceGroupName
else:
self.spaceGroup = SpaceGroup(spaceGroupName)
self.tt = np.array(tt)
self.observed = observed
self.background = background
self.u = Parameter(u, name='u')
self.v = Parameter(v, name='v')
self.w = Parameter(w, name='w')
self.scale = Parameter(scale, name='scale')
self.eta = Parameter(eta, name='eta')
self.error = error
self.muR = muR
# hkls for single crystal only
if hkls != None:
self.refList = hkls
if base != None:
self.base = Parameter(base, name='base')
self.has_base = True
else:
self.base=0
self.has_base = False
if zero != None:
self.zero = Parameter(zero, name='zero')
self.has_zero = True
else:
self.zero = 0
self.has_zero = False
self.wavelength = wavelength
self.cell = cell
self.exclusions = exclusions
self.ttMin = min(self.tt)
self.ttMax = max(self.tt)
self.sMin = getS(self.ttMin, self.wavelength)
self.sMax = getS(self.ttMax, self.wavelength)
self.magnetic = magnetic
if magnetic:
self.symmetry = symmetry
# used for basis vector structure factor generation
self.newSymmetry = newSymmetry
self.atomListModel = AtomListModel(atoms, self.spaceGroup.get_space_group_multip(),
True, self.newSymmetry)
else:
self.atomListModel = AtomListModel(atoms, self.spaceGroup.get_space_group_multip(), False)
self._set_reflections()
self.update()
def load_fit(filename):
data = FitReader(call_back=lambda **kw: None).read('FitPage2.fitv')
data = data[0] # no support for multiset files
fit = data.meta_data['fitstate']
model_name = fit.formfactorcombobox
pars = dict((p[1], float(p[2])) for p in fit.parameters)
for k, v in pars.items():
if abs(v) < 1e-5 and v != 0:
pars[k] = 1e-6 * Parameter(v * 1e6, name=model_name + " " + k)
model = load_model(model_name, **pars)
experiment = Experiment(model=model, data=data)
for p in fit.parameters:
if p[0]:
low = float(p[5][1]) if p[5][1] else -np.inf
high = float(p[6][1]) if p[6][1] else np.inf
try:
experiment[p[1]].range(low, high)
except KeyError:
print("%s not in experiment" % p[1])
return experiment
def build_problem(mydirectory, myfilebase, myend, cost="poisson"):
monitor_efficiency = 0.1
resolution = 0.001
scale = resolution**2 / monitor_efficiency
scale = 1e-6
M = Peaks(
[
Gaussian(name="G1-"),
Gaussian(name="G2-"),
#Gaussian(name="G3-"),
#Gaussian(name="G4-"),
Background()
],
scale,
data=read_data(mydirectory, myfilebase, myend),
#cost='poisson',
cost=cost,
)
# Peak intensity and background
background = 1
M.parts[-1].C.value = background
M.parts[-1].C.range(0, 1000)
for peak in M.parts[:-1]:
peak.A.value = 1. / (len(M.parts) - 1) # Equal size peaks
peak.A.range(0, 1)
peak1 = M.parts[0]
if 0:
# Peak centers are independent
for peak in M.parts[:-1]:
peak.xc.range(0.45, 0.55)
peak.yc.range(-0.55, -0.4)
else:
# Peak centers lie on a line
alpha = Parameter(45.0, name="alpha")
alpha.range(-90., 90.)
peak1.xc.range(0.40, 0.55)
peak1.yc.range(0.40, 0.55)
#peak1.yc.range(-0.55,-0.4)
for i, peak in enumerate(M.parts[1:-1]):
delta = Parameter(.0045, name="delta-%d" % (i + 1))
delta.range(0.00, 0.03)
peak.xc = peak1.xc + delta * pmath.cosd(alpha)
peak.yc = peak1.yc + delta * pmath.sind(alpha)
# Initial values
cx, cy = 0.4996 - 0.4957, -0.4849 + 0.4917
alpha.value = np.degrees(np.arctan2(cy, cx))
delta.value = np.sqrt(cx**2 + cy**2)
peak1.xc.value, peak1.yc.value = 0.4957, 0.4917
# Peak location and shape
dx, dy = 0.4997 - 0.4903, -0.4969 + 0.4851
dxm, dym = 0.4951 - 0.4960, -0.4941 + 0.4879
peak1.s1.value = np.sqrt(dx**2 + dy**2) / 2.35 / 2
peak1.s2.value = np.sqrt(dxm**2 + dym**2) / 2.35 / 2
peak1.theta.value = np.degrees(np.arctan2(dy, dx))
# Peak shape is the same across all peaks
peak1.s1.range(0.001, 0.010)
peak1.s2.range(0.001, 0.010)
peak1.theta.range(-90, 90)
for peak in M.parts[1:-1]:
peak.s1 = peak1.s1
peak.s2 = peak1.s2
peak.theta = peak1.theta
if 1:
print "shape", peak1.s1.value, peak1.s2.value, peak1.theta.value
print "centers alpha,delta", alpha.value, delta.value
print "centers",(peak1.xc.value,peak1.yc.value),\
(M.parts[1].xc.value,M.parts[1].yc.value)
return FitProblem(M)
def __init__(self, a, c):
self.cell = CrystalCell()
self.a = Parameter(a, name='a')
self.c = Parameter(c, name='c')
self.update()
def build_problem():
M = Peaks(
[
Gaussian(name="G1-"),
Gaussian(name="G2-"),
#Gaussian(name="G3-"),
#Gaussian(name="G4-"),
Background()
],
*read_data())
background = np.min(M.data)
background += np.sqrt(background)
signal = np.sum(M.data) - M.data.size * background
M.parts[-1].C.value = background
peak1 = M.parts[0]
peak1.xc.range(0.45, 0.55)
peak1.yc.range(-0.55, -0.4)
peak1.xc.value = 0.500
peak1.yc.value = -0.485
if 0:
# Peak centers are independent
for peak in M.parts[1:-1]:
peak.xc.range(0.45, 0.55)
peak.yc.range(-0.55, -0.4)
M.parts[1].xc.value = 0.495
M.parts[1].yc.value = -0.495
else:
# Peak centers lie on a line
theta = Parameter(45, name="theta")
theta.range(0, 90)
for i, peak in enumerate(M.parts[1:-1]):
delta = Parameter(0.0045, name="delta-%d" % (i + 1))
delta.range(0.0, 0.015)
peak.xc = peak1.xc + delta * cosd(theta)
peak.yc = peak1.yc + delta * sind(theta)
# Initial values
cx, cy = 0.4996 - 0.4957, -0.4849 + 0.4917
theta.value = np.degrees(np.arctan2(cy, cx))
delta.value = np.sqrt(cx**2 + cy**2)
# Initial values
for peak in M.parts[:-1]:
peak.A.value = signal / (len(M.parts) - 1) # Equal size peaks
dx, dy = 0.4997 - 0.4903, -0.4969 + 0.4851
dxm, dym = 0.4951 - 0.4960, -0.4941 + 0.4879
peak1.s1.value = np.sqrt(dx**2 + dy**2) / 2.35 / 2
peak1.s2.value = np.sqrt(dxm**2 + dym**2) / 2.35 / 2
peak1.theta.value = np.degrees(np.arctan2(dy, dx))
# Peak intensity varies
for peak in M.parts[:-1]:
peak.A.range(0.1 * signal, 1.1 * signal)
peak1.s1.range(0.002, 0.02)
peak1.s2.range(0.001, 0.02)
peak1.theta.range(-90, -0)
if 1:
# Peak shape is the same across all peaks
for peak in M.parts[1:-1]:
peak.s1 = peak1.s1
peak.s2 = peak1.s2
peak.theta = peak1.theta
else:
for peak in M.parts[1:-1]:
peak.s1.range(*peak1.s1.bounds.limits)
peak.s2.range(*peak1.s2.bounds.limits)
peak.theta.range(*peak1.theta.bounds.limits)
if 1:
M.parts[-1].C.pmp(100.0)
if 1:
for peak in M.parts[:-1]:
peak.s1.value = 0.006
peak.s2.value = 0.002
peak.theta.value = -60.0
peak.A.value = signal / 2
if 0:
print("shape", peak1.s1.value, peak1.s2.value, peak1.theta.value)
print("centers theta,delta", theta.value, delta.value)
print("centers", (peak1.xc.value, peak1.yc.value),
(M.parts[1].xc.value, M.parts[1].yc.value))
return FitProblem(M)
def build_problem():
M = Peaks([Gaussian(name="G1-"),
Gaussian(name="G2-"),
#Gaussian(name="G3-"),
#Gaussian(name="G4-"),
Background()],
*read_data())
background = np.min(M.data)
background += np.sqrt(background)
signal = np.sum(M.data) - M.data.size*background
M.parts[-1].C.value = background
peak1 = M.parts[0]
if 0:
# Peak centers are independent
for peak in M.parts[:-1]:
peak.xc.range(0.45,0.55)
peak.yc.range(-0.55,-0.4)
else:
# Peak centers lie on a line
theta=Parameter(45, name="theta")
theta.range(0,90)
peak1.xc.range(0.45,0.55)
peak1.yc.range(-0.55,-0.4)
for i,peak in enumerate(M.parts[1:-1]):
delta=Parameter(.0045, name="delta-%d"%(i+1))
delta.range(0.0,0.015)
peak.xc = peak1.xc + delta*pmath.cosd(theta)
peak.yc = peak1.yc + delta*pmath.sind(theta)
# Initial values
cx, cy = 0.4996-0.4957, -0.4849+0.4917
theta.value = np.degrees(np.arctan2(cy,cx))
delta.value = np.sqrt(cx**2+cy**2)
peak1.xc.value,peak1.yc.value = 0.4957,-0.4917
# Initial values
for peak in M.parts[:-1]:
peak.A.value = signal/(len(M.parts)-1) # Equal size peaks
dx, dy = 0.4997-0.4903, -0.4969+0.4851
dxm, dym = 0.4951-0.4960, -0.4941+0.4879
peak1.s1.value = np.sqrt(dx**2+dy**2)/2.35/2
peak1.s2.value = np.sqrt(dxm**2+dym**2)/2.35/2
peak1.theta.value = np.degrees(np.arctan2(dy,dx))
# Peak intensity varies
for peak in M.parts[:-1]:
peak.A.range(0.25*signal,1.1*signal)
# Peak shape is the same across all peaks
peak1.s1.range(0.002,0.02)
peak1.s2.range(0.001,0.02)
peak1.theta.range(-90, -0)
for peak in M.parts[1:-1]:
peak.s1 = peak1.s1
peak.s2 = peak1.s2
peak.theta = peak1.theta
if 1:
print "shape",peak1.s1.value,peak1.s2.value,peak1.theta.value
print "centers theta,delta",theta.value,delta.value
print "centers",(peak1.xc.value,peak1.yc.value),\
(M.parts[1].xc.value,M.parts[1].yc.value)
return FitProblem(M)
def build_problem(mydirectory, myfilebase, myend):
M = Peaks(
[
Gaussian(name="G1-"),
Gaussian(name="G2-"),
#Gaussian(name="G3-"),
#Gaussian(name="G4-"),
Background()
],
*read_data(mydirectory, myfilebase, myend))
background = np.min(M.data)
background += np.sqrt(background)
signal = np.sum(M.data) - M.data.size * background
M.parts[-1].C.value = background
M.parts[-1].C.range(0, 200)
peak1 = M.parts[0]
if 0:
# Peak centers are independent
for peak in M.parts[:-1]:
peak.xc.range(0.45, 0.55)
peak.yc.range(-0.55, -0.4)
else:
# Peak centers lie on a line
alpha = Parameter(45.0, name="alpha")
alpha.range(-90., 90.)
peak1.xc.range(0.40, 0.55)
peak1.yc.range(0.40, 0.55)
#peak1.yc.range(-0.55,-0.4)
for i, peak in enumerate(M.parts[1:-1]):
delta = Parameter(.0045, name="delta-%d" % (i + 1))
delta.range(0.00, 0.03)
peak.xc = peak1.xc + delta * pmath.cosd(alpha)
peak.yc = peak1.yc + delta * pmath.sind(alpha)
# Initial values
cx, cy = 0.4996 - 0.4957, -0.4849 + 0.4917
alpha.value = np.degrees(np.arctan2(cy, cx))
delta.value = np.sqrt(cx**2 + cy**2)
peak1.xc.value, peak1.yc.value = 0.4957, -0.4917
# Initial values
for peak in M.parts[:-1]:
peak.A.value = signal / (len(M.parts) - 1) # Equal size peaks
dx, dy = 0.4997 - 0.4903, -0.4969 + 0.4851
dxm, dym = 0.4951 - 0.4960, -0.4941 + 0.4879
peak1.s1.value = np.sqrt(dx**2 + dy**2) / 2.35 / 2
peak1.s2.value = np.sqrt(dxm**2 + dym**2) / 2.35 / 2
peak1.theta.value = np.degrees(np.arctan2(dy, dx))
# Peak intensity varies
for peak in M.parts[:-1]:
peak.A.range(0.10 * signal, 1.1 * signal)
# Peak shape is the same across all peaks
peak1.s1.range(0.001, 0.010)
peak1.s2.range(0.001, 0.004)
peak1.theta.range(0, 90)
for peak in M.parts[1:-1]:
peak.s1 = peak1.s1
peak.s2 = peak1.s2
peak.theta = peak1.theta
if 1:
print "shape", peak1.s1.value, peak1.s2.value, peak1.theta.value
print "centers alpha,delta", alpha.value, delta.value
print "centers",(peak1.xc.value,peak1.yc.value),\
(M.parts[1].xc.value,M.parts[1].yc.value)
return FitProblem(M)
def __init__(self,
tt,
observed,
background,
wavelength,
spaceGroupName,
cell,
atoms,
exclusions=None,
magnetic=False,
symmetry=None,
newSymmetry=None,
scale=1,
zero=None,
error=None,
hkls=None,
extinction=[0]):
if (isinstance(spaceGroupName, SpaceGroup)):
self.spaceGroup = spaceGroupName
else:
self.spaceGroup = SpaceGroup(spaceGroupName)
self.tt = np.array(tt)
self.obspeaks = makeXtalPeaks(
observed, [getS(ttval, wavelength) for ttval in self.tt],
refList=hkls,
error=error)
self.sList = np.array([peak.svalue for peak in self.obspeaks])
self.observed = np.array([peak.sfs2 for peak in self.obspeaks])
self.background = background
self.scale = Parameter(scale, name='scale')
self.extinctions = [
Parameter(extinction[i], name="Extinction" + str(i))
for i in range(len(extinction))
]
self.error = error
self.refList = hkls
self.base = 0
if zero != None:
self.zero = Parameter(zero, name='zero')
self.has_zero = True
else:
self.zero = 0
self.has_zero = False
self.wavelength = wavelength
self.cell = cell
self.exclusions = exclusions
if len(tt) != 0:
self.ttMin = min(self.tt)
self.ttMax = max(self.tt)
self.sMin = getS(self.ttMin, self.wavelength)
self.sMax = getS(self.ttMax, self.wavelength)
self.magnetic = magnetic
if magnetic:
self.symmetry = symmetry
# used for basis vector structure factor generation
self.newSymmetry = newSymmetry
self.atomListModel = AtomListModel(
atoms, self.spaceGroup.get_space_group_multip(), True,
self.newSymmetry)
else:
self.atomListModel = AtomListModel(
atoms, self.spaceGroup.get_space_group_multip(), False)
self._set_reflections()
self._set_observations(observed)
self.update()