def __init__(self, *args, **kwargs):
my_kwargs = self.pop_kwargs(
kwargs, *[
prop.label
for prop in self.Meta.get_local_persistent_properties()
])
super(_BaseR0Model, self).__init__(R=0, *args, **kwargs)
with self.data_changed.hold():
if self.G > 1 and "W1" in my_kwargs: # old-style model
for i in range(self.G - 1):
name = "W%d" % (i + 1)
self.mW[i] = not_none(my_kwargs.get(name, None), 0.8)
name = "F%d" % (i + 1)
setattr(
self, name,
self.mW[i] / (np.sum(np.diag(self._W)[i:]) or 1.0))
else:
for i in range(self.G - 1):
name = "inherit_F%d" % (i + 1)
setattr(self, name, my_kwargs.get(name, False))
name = "F%d" % (i + 1)
setattr(self, name,
not_none(my_kwargs.get(name, None), 0.8))
self.update()
def do_async_evaluation(self, iter_func, eval_func=None, data_func=None, result_func=None):
assert self.refiner is not None, "RefineAsyncHelper can only work when a refiner is set!"
eval_func = not_none(eval_func, self.refiner.residual_callback)
data_func = not_none(data_func, self.refiner.get_data_object)
result_func = not_none(result_func, self.refiner.update)
return super(RefineAsyncHelper, self).do_async_evaluation(
iter_func, eval_func, data_func, result_func
)
def __init__(self, W1=0.85, P1111_or_P2112=0.75, *args, **kwargs):
super(R3G2Model, self).__init__(R=3, *args, **kwargs)
with self.data_changed.hold():
self.W1 = not_none(W1, 0.85)
self.P1111_or_P2112 = not_none(P1111_or_P2112, 0.75)
self.update()
def label(self):
if self.display_stats_in_lbl and (self.project is not None and self.project.layout_mode == "FULL"):
label = self.sample_name
label += "\nRp = %.1f%%" % not_none(self.statistics.Rp, 0.0)
label += "\nRwp = %.1f%%" % not_none(self.statistics.Rwp, 0.0)
return label
else:
return self.sample_name
def get_label(self):
label = self.sample_name
if (self.project is not None and self.project.layout_mode == "FULL"):
if self.display_stats_in_lbl:
label += "\nR$_p$ = %.1f%%" % not_none(self.statistics.Rp, 0.0)
label += "\nR$_{wp}$ = %.1f%%" % not_none(self.statistics.Rwp, 0.0)
if self.display_residual_scale != 1.0:
label += "\n\nResidual x%0.1f " % not_none(self.display_residual_scale, 1.0)
return label
def label(self):
if self.display_stats_in_lbl and (self.project is not None and
self.project.layout_mode == "FULL"):
label = self.sample_name
label += "\nRp = %.1f%%" % not_none(self.statistics.Rp, 0.0)
label += "\nRwp = %.1f%%" % not_none(self.statistics.Rwp, 0.0)
return label
else:
return self.sample_name
def __init__(self,
treemodel_property_name=None,
treemodel_class_type=None,
*args,
**kwargs):
super(TreeModelMixin, self).__init__(*args, **kwargs)
self.treemodel_property_name = not_none(treemodel_property_name,
self.treemodel_property_name)
self.treemodel_class_type = not_none(treemodel_class_type,
self.treemodel_class_type)
def setup(self,
W1=0.75,
P11_or_P22=0.5,
inherit_W1=False,
inherit_P11_or_P22=False,
**kwargs):
_AbstractProbability.setup(self, R=1)
self.W1 = not_none(W1, 0.75)
self.inherit_W1 = inherit_W1
self.P11_or_P22 = not_none(P11_or_P22, 0.5)
self.inherit_P11_or_P22 = inherit_P11_or_P22
def __init__(self, minimum, maximum, refine, *args, **kwargs):
"""
Valid *positional* arguments for a RefinementInfo are:
refine: whether or not the linked parameter is selected for refinement
minimum: the minimum allowable value for the linked parameter
maximum: the maximum allowable value for the linked parameter
"""
super(RefinementInfo, self).__init__()
self.refine = refine
self.minimum = not_none(minimum, 0.0)
self.maximum = not_none(maximum, 1.0)
def get_label(self):
label = self.sample_name
if (self.project is not None and self.project.layout_mode == "FULL"):
if self.display_stats_in_lbl:
label += "\nR$_p$ = %.1f%%" % not_none(self.statistics.Rp, 0.0)
label += "\nR$_{wp}$ = %.1f%%" % not_none(
self.statistics.Rwp, 0.0)
if self.display_residual_scale != 1.0:
label += "\n\nResidual x%0.1f " % not_none(
self.display_residual_scale, 1.0)
return label
def setup(self, W1=0.75, P112_or_P211=0.75, P21=0.75, P122_or_P221=0.75,
inherit_W1=False, inherit_P112_or_P211=False,
inherit_P21=False, inherit_P122_or_P221=False, **kwargs):
_AbstractProbability.setup(self, R=2)
with self.data_changed.hold():
self.W1 = not_none(W1, 0.75)
self.inherit_W1 = inherit_W1
self.P112_or_P211 = not_none(P112_or_P211, 0.75)
self.inherit_P112_or_P211 = inherit_P112_or_P211
self.P21 = not_none(P21, 0.75)
self.inherit_P21 = inherit_P21
self.P122_or_P221 = not_none(P122_or_P221, 0.75)
self.inherit_P122_or_P221 = inherit_P122_or_P221
def create_gon_file(self):
output = """ {
"type": "Goniometer",
"properties": {
"radius": %(radius)f,
"divergence": %(divergence)f,
"soller1": %(soller1)f,
"soller2": %(soller2)f,
"min_2theta": %(twotheta_min)f,
"max_2theta": %(twotheta_max)f,
"steps": %(twotheta_count)f,
"wavelength": %(alpha_average)f,
"has_ads": false,
"ads_fact": 1.0,
"ads_phase_fact": 1.0,
"ads_phase_shift": 0.0,
"ads_const": 0.0
}
}""" % dict(
radius=float(not_none(self.radius, 25)),
divergence=float(not_none(self.divergence, 0.5)),
soller1=float(not_none(self.soller1, 2.5)),
soller2=float(not_none(self.soller2, 2.5)),
twotheta_min=float(not_none(self.twotheta_min, 3.0)),
twotheta_max=float(not_none(self.twotheta_max, 45.0)),
twotheta_count=float(not_none(self.twotheta_count, 2500)),
alpha_average=float(not_none(self.alpha_average, 0.154056)),
)
f = StringIO(output)
f.flush()
return f
def create_gon_file(self):
output = """ {
"type": "Goniometer",
"properties": {
"radius": %(radius)f,
"divergence": %(divergence)f,
"soller1": %(soller1)f,
"soller2": %(soller2)f,
"min_2theta": %(twotheta_min)f,
"max_2theta": %(twotheta_max)f,
"steps": %(twotheta_count)f,
"wavelength": %(alpha_average)f,
"has_ads": false,
"ads_fact": 1.0,
"ads_phase_fact": 1.0,
"ads_phase_shift": 0.0,
"ads_const": 0.0
}
}""" % dict(
radius=float(not_none(self.radius, 25)),
divergence=float(not_none(self.divergence, 0.5)),
soller1=float(not_none(self.soller1, 2.5)),
soller2=float(not_none(self.soller2, 2.5)),
twotheta_min=float(not_none(self.twotheta_min, 3.0)),
twotheta_max=float(not_none(self.twotheta_max, 45.0)),
twotheta_count=float(not_none(self.twotheta_count, 2500)),
alpha_average=float(not_none(self.alpha_average, 0.154056)),
)
return output
def setup(self, **kwargs):
_AbstractProbability.setup(self, R=0)
if self.G > 1 and "W1" in kwargs: # old-style model
for i in range(self.G - 1):
name = "W%d" % (i + 1)
self.mW[i] = not_none(kwargs.get(name, None), 0.8)
name = "F%d" % (i + 1)
setattr(self, name, self.mW[i] / (np.sum(np.diag(self._W)[i:]) or 1.0))
else:
for i in range(self.G - 1):
name = "inherit_F%d" % (i + 1)
setattr(self, name, kwargs.get(name, False))
name = "F%d" % (i + 1)
setattr(self, name, not_none(kwargs.get(name, None), 0.8))
def __init__(self, W1=0.75, P112_or_P211=0.75, P21=0.75, P122_or_P221=0.75,
inherit_W1=False, inherit_P112_or_P211=False,
inherit_P21=False, inherit_P122_or_P221=False, *args, **kwargs):
super(R2G2Model, self).__init__(R=2, *args, **kwargs)
with self.data_changed.hold():
self.W1 = not_none(W1, 0.75)
self.inherit_W1 = inherit_W1
self.P112_or_P211 = not_none(P112_or_P211, 0.75)
self.inherit_P112_or_P211 = inherit_P112_or_P211
self.P21 = not_none(P21, 0.75)
self.inherit_P21 = inherit_P21
self.P122_or_P221 = not_none(P122_or_P221, 0.75)
self.inherit_P122_or_P221 = inherit_P122_or_P221
self.update()
def setup(self, **kwargs):
_AbstractProbability.setup(self, R=0)
if self.G > 1 and "W1" in kwargs: # old-style model
for i in range(self.G - 1):
name = "W%d" % (i + 1)
self.mW[i] = not_none(kwargs.get(name, None), 0.8)
name = "F%d" % (i + 1)
setattr(self, name,
self.mW[i] / (np.sum(np.diag(self._W)[i:]) or 1.0))
else:
for i in range(self.G - 1):
name = "inherit_F%d" % (i + 1)
setattr(self, name, kwargs.get(name, False))
name = "F%d" % (i + 1)
setattr(self, name, not_none(kwargs.get(name, None), 0.8))
def __init__(self,
W1=0.75,
P11_or_P22=0.5,
inherit_W1=False,
inherit_P11_or_P22=False,
*args,
**kwargs):
super(R1G2Model, self).__init__(R=1, *args, **kwargs)
with self.data_changed.hold():
self.W1 = not_none(W1, 0.75)
self.inherit_W1 = inherit_W1
self.P11_or_P22 = not_none(P11_or_P22, 0.5)
self.inherit_P11_or_P22 = inherit_P11_or_P22
self.update()
def update_refinement_info(self,
current_rp=None,
message=None,
server_status=None):
if not isnan(current_rp):
self["current_residual"].set_text("%.2f" % current_rp)
self["message"].set_text(not_none(message, ""))
self.update_server_status(server_status)
def toggle_renderer(column, cell, model, itr, data=None):
try:
col = column.get_col_attr("active")
value = model.get_value(itr, col)
cell.set_property('active', not_none(value, False))
except TypeError:
if settings.DEBUG: raise
pass
def toggle_renderer(column, cell, model, itr, data=None):
try:
col = column.get_col_attr("active")
value = model.get_value(itr, col)
cell.set_property('active', not_none(value, False))
except TypeError:
if settings.DEBUG: raise
pass
def on_accept(phase_type, G, R):
index = int(not_none(self.get_selected_index(), -1)) + 1
if phase_type == "empty":
self.add_object(Phase(G=int(G), R=int(R)))
elif phase_type == "raw":
self.add_object(RawPatternPhase())
else:
filename = phase_type
self.model.load_phases(filename, insert_index=index)
def __init__(self, *args, **kwargs):
my_kwargs = self.pop_kwargs(kwargs, *[prop.label for prop in self.Meta.get_local_persistent_properties()])
super(_BaseR0Model, self).__init__(R=0, *args, **kwargs)
with self.data_changed.hold():
if self.G > 1 and "W1" in my_kwargs: # old-style model
for i in range(self.G - 1):
name = "W%d" % (i + 1)
self.mW[i] = not_none(my_kwargs.get(name, None), 0.8)
name = "F%d" % (i + 1)
setattr(self, name, self.mW[i] / (np.sum(np.diag(self._W)[i:]) or 1.0))
else:
for i in range(self.G - 1):
name = "inherit_F%d" % (i + 1)
setattr(self, name, my_kwargs.get(name, False))
name = "F%d" % (i + 1)
setattr(self, name, not_none(my_kwargs.get(name, None), 0.8))
self.update()
def load_phases(self, filename, insert_index=None):
"""
Loads all :class:`~pyxrd.phase.models.Phase` objects from the file
'filename'. An optional index can be given where the phases need to be
inserted at.
"""
insert_index = not_none(insert_index, 0)
for phase in Phase.load_phases(filename, parent=self):
self.phases.insert(insert_index, phase)
insert_index += 1
def on_accept(phase_type, G, R):
index = int(not_none(self.get_selected_index(), -1)) + 1
if phase_type == "empty":
self.add_object(Phase(G=int(G), R=int(R)))
elif phase_type == "raw":
self.add_object(RawPatternPhase())
else:
filename = phase_type
if filename != None:
self.model.load_phases(filename,
parser=JSONParser,
insert_index=index)
def setup(self,
W1=0.6,
P11_or_P22=0.3,
G1=0.5,
G2=0.4,
G3=0.5,
G4=0.2,
inherit_W1=False,
inherit_P11_or_P22=False,
inherit_G1=False,
inherit_G2=False,
inherit_G3=False,
inherit_G4=False,
**kwargs):
_AbstractProbability.setup(self, R=1)
self.W1 = not_none(W1, 0.8)
self.inherit_W1 = bool(inherit_W1)
self.P11_or_P22 = not_none(P11_or_P22, 0.7)
self.inherit_P11_or_P22 = bool(inherit_P11_or_P22)
self.G1 = not_none(G1, 0.7)
self.inherit_G1 = bool(inherit_G1)
self.G2 = not_none(G2, 0.7)
self.inherit_G2 = bool(inherit_G2)
self.G3 = not_none(G3, 0.7)
self.inherit_G3 = bool(inherit_G3)
self.G4 = not_none(G4, 0.7)
self.inherit_G4 = bool(inherit_G4)
def __init__(self,
W1=0.8,
P11_or_P22=0.7,
G1=0.7,
G2=0.7,
G3=0.7,
G4=0.7,
inherit_W1=False,
inherit_P11_or_P22=False,
inherit_G1=False,
inherit_G2=False,
inherit_G3=False,
inherit_G4=False,
*args,
**kwargs):
super(R1G3Model, self).__init__(R=1, *args, **kwargs)
with self.data_changed.hold():
self.W1 = not_none(W1, 0.8)
self.inherit_W1 = bool(inherit_W1)
self.P11_or_P22 = not_none(P11_or_P22, 0.7)
self.inherit_P11_or_P22 = bool(inherit_P11_or_P22)
self.G1 = not_none(G1, 0.7)
self.inherit_G1 = bool(inherit_G1)
self.G2 = not_none(G2, 0.7)
self.inherit_G2 = bool(inherit_G2)
self.G3 = not_none(G3, 0.7)
self.inherit_G3 = bool(inherit_G3)
self.G4 = not_none(G4, 0.7)
self.inherit_G4 = bool(inherit_G4)
self.update()
def __init__(self,
W1=0.8,
P111_or_P212=0.9,
G1=0.9,
G2=0.9,
G3=0.9,
G4=0.9,
inherit_W1=False,
inherit_P111_or_P212=False,
inherit_G1=False,
inherit_G2=False,
inherit_G3=False,
inherit_G4=False,
*args,
**kwargs):
super(R2G3Model, self).__init__(R=2, *args, **kwargs)
with self.data_changed.hold():
self.W1 = not_none(W1, 0.8)
self.inherit_W1 = inherit_W1
self.P111_or_P212 = not_none(P111_or_P212, 0.9)
self.inherit_P111_or_P212 = inherit_P111_or_P212
self.G1 = not_none(G1, 0.9)
self.inherit_G1 = inherit_G1
self.G2 = not_none(G2, 0.9)
self.inherit_G2 = inherit_G2
self.G3 = not_none(G3, 0.9)
self.inherit_G3 = inherit_G3
self.G4 = not_none(G4, 0.9)
self.inherit_G4 = inherit_G4
self.update()
def load_phases(self, filename, parser, insert_index=0):
"""
Loads all :class:`~pyxrd.phase.models.Phase` objects from the file
'filename'. An optional index can be given where the phases need to be
inserted at.
"""
# make sure we have no duplicate UUID's
insert_index = not_none(insert_index, 0)
type(Project).object_pool.change_all_uuids()
for phase in parser.parse(filename):
phase.parent = self
self.phases.insert(insert_index, phase)
insert_index += 1
def load_phases(self, filename, parser, insert_index=0):
"""
Loads all :class:`~pyxrd.phase.models.Phase` objects from the file
'filename'. An optional index can be given where the phases need to be
inserted at.
"""
# make sure we have no duplicate UUID's
insert_index = not_none(insert_index, 0)
type(Project).object_pool.change_all_uuids()
for phase in parser.parse(filename):
phase.parent = self
self.phases.insert(insert_index, phase)
insert_index += 1
def __init__(self,
W1=0.75,
P112_or_P211=0.75,
P21=0.75,
P122_or_P221=0.75,
inherit_W1=False,
inherit_P112_or_P211=False,
inherit_P21=False,
inherit_P122_or_P221=False,
*args,
**kwargs):
super(R2G2Model, self).__init__(R=2, *args, **kwargs)
with self.data_changed.hold():
self.W1 = not_none(W1, 0.75)
self.inherit_W1 = inherit_W1
self.P112_or_P211 = not_none(P112_or_P211, 0.75)
self.inherit_P112_or_P211 = inherit_P112_or_P211
self.P21 = not_none(P21, 0.75)
self.inherit_P21 = inherit_P21
self.P122_or_P221 = not_none(P122_or_P221, 0.75)
self.inherit_P122_or_P221 = inherit_P122_or_P221
self.update()
def from_experimental_data(filename, parent, parser=None):
"""
Returns a list of new :class:`~.specimen.models.Specimen`'s loaded
from `filename`, setting thjeir parent to `parent` using the given
parser or :class:`~.generic.io.xrd_parsers.XRDParser` if none passed.
"""
specimens = list()
parser = not_none(parser, XRDParser)
xrdfiles = parser.parse(filename)
for xrdfile in xrdfiles:
name, sample, generator = xrdfile.filename, xrdfile.name, xrdfile.data
specimen = Specimen(parent=parent, name=name, sample_name=sample)
specimen.experimental_pattern.load_data_from_generator(generator, clear=True)
specimen.goniometer.reset_from_file(None, data=xrdfile.create_gon_file())
specimens.append(specimen)
return specimens
def get_kwarg(self, fun_kwargs, default, *keywords):
"""
Convenience function to get a certain keyword 'kw' value from the passed
keyword arguments 'fun_kwargs'. If the key 'kw' is not in 'fun_kwargs'
a list of deprecated keywords to be searched for can be passed as an
optional argument list 'depr_kws'. If one of these is found, its value
is returned and a deprecation warning is emitted.
If neither the 'kw' nor any of the 'depr_kws' are found the 'default'
value is returned.
"""
if len(keywords) < 1:
raise AttributeError, "get_kwarg() requires at least one keyword (%d given)" % (len(keywords))
value = default
for i, key in enumerate(keywords[::-1]):
if key in fun_kwargs:
value = not_none(fun_kwargs[key], default)
if i != 0:
warn("The use of the keyword '%s' is deprecated for %s!" %
(key, type(self)), DeprecationWarning)
return value
def setup(self, W1=0.6, P11_or_P22=0.3, G1=0.5, G2=0.4, G3=0.5, G4=0.2,
inherit_W1=False, inherit_P11_or_P22=False, inherit_G1=False,
inherit_G2=False, inherit_G3=False, inherit_G4=False, **kwargs):
_AbstractProbability.setup(self, R=1)
self.W1 = not_none(W1, 0.8)
self.inherit_W1 = bool(inherit_W1)
self.P11_or_P22 = not_none(P11_or_P22, 0.7)
self.inherit_P11_or_P22 = bool(inherit_P11_or_P22)
self.G1 = not_none(G1, 0.7)
self.inherit_G1 = bool(inherit_G1)
self.G2 = not_none(G2, 0.7)
self.inherit_G2 = bool(inherit_G2)
self.G3 = not_none(G3, 0.7)
self.inherit_G3 = bool(inherit_G3)
self.G4 = not_none(G4, 0.7)
self.inherit_G4 = bool(inherit_G4)
def setup(self, W1=0.8, P111_or_P212=0.9, G1=0.9, G2=0.9, G3=0.9, G4=0.9,
inherit_W1=False, inherit_P111_or_P212=False, inherit_G1=False,
inherit_G2=False, inherit_G3=False, inherit_G4=False, **kwargs):
_AbstractProbability.setup(self, R=2)
self.W1 = not_none(W1, 0.8)
self.inherit_W1 = inherit_W1
self.P111_or_P212 = not_none(P111_or_P212, 0.9)
self.inherit_P111_or_P212 = inherit_P111_or_P212
self.G1 = not_none(G1, 0.9)
self.inherit_G1 = inherit_G1
self.G2 = not_none(G2, 0.9)
self.inherit_G2 = inherit_G2
self.G3 = not_none(G3, 0.9)
self.inherit_G3 = inherit_G3
self.G4 = not_none(G4, 0.9)
self.inherit_G4 = inherit_G4
def get_kwarg(self, fun_kwargs, default, *keywords):
"""
Convenience function to get a certain keyword 'kw' value from the passed
keyword arguments 'fun_kwargs'. If the key 'kw' is not in 'fun_kwargs'
a list of deprecated keywords to be searched for can be passed as an
optional argument list 'depr_kws'. If one of these is found, its value
is returned and a deprecation warning is emitted.
If neither the 'kw' nor any of the 'depr_kws' are found the 'default'
value is returned.
"""
if len(keywords) < 1:
raise AttributeError, "get_kwarg() requires at least one keyword (%d given)" % (
len(keywords))
value = default
for i, key in enumerate(keywords[::-1]):
if key in fun_kwargs:
value = not_none(fun_kwargs[key], default)
if i != 0:
warn(
"The use of the keyword '%s' is deprecated for %s!" %
(key, type(self)), DeprecationWarning)
return value
def __init__(self, W1=0.8, P111_or_P212=0.9, G1=0.9, G2=0.9, G3=0.9, G4=0.9,
inherit_W1=False, inherit_P111_or_P212=False, inherit_G1=False,
inherit_G2=False, inherit_G3=False, inherit_G4=False, *args, **kwargs):
super(R2G3Model, self).__init__(R=2, *args, **kwargs)
with self.data_changed.hold():
self.W1 = not_none(W1, 0.8)
self.inherit_W1 = inherit_W1
self.P111_or_P212 = not_none(P111_or_P212, 0.9)
self.inherit_P111_or_P212 = inherit_P111_or_P212
self.G1 = not_none(G1, 0.9)
self.inherit_G1 = inherit_G1
self.G2 = not_none(G2, 0.9)
self.inherit_G2 = inherit_G2
self.G3 = not_none(G3, 0.9)
self.inherit_G3 = inherit_G3
self.G4 = not_none(G4, 0.9)
self.inherit_G4 = inherit_G4
self.update()
def parse(cls, fp, data_objects=None, close=False):
filename, fp, close = cls._get_file(fp, close=close)
try:
basename = os.path.basename(filename)
except AttributeError:
basename = None
num_samples = 0
zipinfos = fp.infolist()
processed_folders = []
data_objects = not_none(data_objects, [])
for zipinfo in zipinfos:
if zipinfo.filename.count('/') == 1 and "DataContainer.xml" in zipinfo.filename:
folder = os.path.dirname(zipinfo.filename)
if not folder in processed_folders:
processed_folders.append(folder)
header_d = cls._get_header_dict(fp, folder)
raw_data_files, sample_name = cls._get_raw_data_files(fp, folder)
for raw_data_filename in raw_data_files:
contf = fp.open(raw_data_filename)
_, root = cls.get_xml_for_file(contf)
for route in root.findall("./DataRoutes/DataRoute"):
# Adapt XRDFile list & get last addition:
data_objects = cls._adapt_data_object_list(
data_objects,
num_samples=(num_samples + 1),
only_extend=True
)
data_object = data_objects[num_samples]
# Get the Datum tags:
datums = route.findall("Datum")
data = []
# Parse the RawDataView tags to find out what index in
# the datum is used for what type of data:
enabled_datum_index = None
twotheta_datum_index = None
intensity_datum_index = None
steptime_datum_index = None
for dataview in route.findall("./DataViews/RawDataView"):
index = int(dataview.get("Start", 0))
name = dataview.get("LogicName", default="Undefined")
xsi_type = dataview.get("{http://www.w3.org/2001/XMLSchema-instance}type", default="Undefined")
if name == "MeasuredTime":
steptime_datum_index = index
elif name == "AbsorptionFactor":
enabled_datum_index = index
elif name == "Undefined" and xsi_type == "VaryingRawDataView":
for i, definition in enumerate(dataview.findall("./Varying/FieldDefinitions")):
if definition.get("TwoTheta"):
index += i
break
twotheta_datum_index = index
elif name == "Undefined" and xsi_type == "RecordedRawDataView":
intensity_datum_index = index
# Parse the SubScanInfo list (usually only one), and
# then parse the datums accordingly
twotheta_min = None
twotheta_max = None
twotheta_count = 0
for subscan in route.findall("./SubScans/SubScanInfo"):
# Get the steps, where to start and the planned
# time per step (measuredTimePerStep deviates
# if the recording was interrupted):
steps = int(subscan.get("MeasuredSteps"))
start = int(subscan.get("StartStepNo"))
steptime = float(subscan.get("PlannedTimePerStep"))
for datum in datums[start:start + steps]:
values = datum.text.split(",")
if values[enabled_datum_index] == "1":
# Fetch values from the list:
datum_steptime = float(values[steptime_datum_index])
intensity = float(values[intensity_datum_index])
intensity /= float(steptime * datum_steptime)
twotheta = float(values[twotheta_datum_index])
# Keep track of min 2theta:
if twotheta_min is None:
twotheta_min = twotheta
else:
twotheta_min = min(twotheta_min, twotheta)
# Keep track of max 2theta:
if twotheta_max is None:
twotheta_max = twotheta
else:
twotheta_max = min(twotheta_max, twotheta)
# Append point and increase count:
data.append([twotheta, intensity])
twotheta_count += 1
#Update header:
data_object.update(
filename=basename,
name=sample_name,
time_step=1, # we converted to CPS
twotheta_min=twotheta_min,
twotheta_max=twotheta_max,
twotheta_count=twotheta_count,
**header_d
)
data_object.data = data
num_samples += 1
#end for
contf.close()
#end for
#end if
#end if
#end for
if close: fp.close()
return data_objects
def setup(self,
W1=0.6,
P11_or_P22=0.25,
R1=0.5,
R2=0.5,
G1=0.5,
G2=0.4,
G11=0.5,
G12=0.2,
G21=0.8,
G22=0.75,
G31=0.7,
G32=0.5,
inherit_W1=False,
inherit_P11_or_P22=False,
inherit_R1=False,
inherit_R2=False,
inherit_G1=False,
inherit_G2=False,
inherit_G11=False,
inherit_G12=False,
inherit_G21=False,
inherit_G22=False,
inherit_G31=False,
inherit_G32=False,
**kwargs):
_AbstractProbability.setup(self, R=1)
self.W1 = not_none(W1, 0.6)
self.inherit_W1 = inherit_W1
self.P11_or_P22 = not_none(P11_or_P22, 0.25)
self.inherit_P11_or_P22 = inherit_P11_or_P22
self.R1 = not_none(R1, 0.5)
self.inherit_R1 = inherit_R1
self.R2 = not_none(R2, 0.5)
self.inherit_R2 = inherit_R2
self.G1 = not_none(G1, 0.5)
self.inherit_G1 = inherit_G1
self.G2 = not_none(G2, 0.4)
self.inherit_G2 = inherit_G2
self.G11 = not_none(G11, 0.5)
self.inherit_G11 = inherit_G11
self.G12 = not_none(G12, 0.2)
self.inherit_G12 = inherit_G12
self.G21 = not_none(G21, 0.8)
self.inherit_G21 = inherit_G21
self.G22 = not_none(G22, 0.75)
self.inherit_G22 = inherit_G22
self.G31 = not_none(G31, 0.7)
self.inherit_G31 = inherit_G31
self.G32 = not_none(G32, 0.5)
self.inherit_G32 = inherit_G32
def setup(self, W1=0.85, P1111_or_P2112=0.75, **kwargs):
_AbstractProbability.setup(self, R=3)
self.W1 = not_none(W1, 0.85)
self.P1111_or_P2112 = not_none(P1111_or_P2112, 0.75)
def get_best_threshold(self, max_threshold=None, steps=None):
"""
Estimates the best threshold for peak detection using an
iterative algorithm. Assumes there is a linear contribution from noise.
Returns a 4-tuple containing the selected threshold, the maximum
threshold, a list of threshold values and a list with the corresponding
number of peaks.
"""
length = self.data_x.size
steps = not_none(steps, 20)
threshold = 0.1
max_threshold = not_none(max_threshold, threshold * 3.2)
def get_new_threshold(threshold, deltas, num_peaks, ln):
# Left side line:
x = deltas[:ln]
y = num_peaks[:ln]
slope, intercept, R, _, _ = stats.linregress(x, y)
return R, -intercept / slope
if length > 2:
# Adjust the first distribution:
deltas, num_peaks = self.calculate_npeaks_for(max_threshold, steps)
# Fit several lines with increasing number of points from the
# generated threshold / marker count graph. Stop when the
# R-coefficiënt drops below 0.95 (past linear increase from noise)
# Then repeat this by increasing the resolution of data points
# and continue until the result does not change anymore
last_threshold = None
solution = False
max_iters = 10
min_iters = 3
itercount = 0
while not solution:
# Number of points to use for the lin regress:
ln = 4
# Maximum number of points to use:
max_ln = len(deltas)
# Flag indicating if we can stop searching for the linear part
stop = False
while not stop:
R, threshold = get_new_threshold(threshold, deltas,
num_peaks, ln)
max_threshold = threshold * 3.2
if abs(R) < 0.98 or ln >= max_ln:
stop = True
else:
ln += 1
itercount += 1 # Increase # of iterations
if last_threshold:
# Check if we have run at least `min_iters`, at most `max_iters`
# and have not reached an equilibrium.
solution = bool(
itercount > min_iters
and not (itercount <= max_iters
and last_threshold - threshold >= 0.001))
if not solution:
deltas, num_peaks = self.calculate_npeaks_for(
max_threshold, steps)
last_threshold = threshold
return (deltas, num_peaks), threshold, max_threshold
else:
return ([], []), threshold, max_threshold
def save_data(self, parser, filename, **kwargs):
if self.data_y.shape[1] > 1:
kwargs["header"] = ["2θ", ] + (not_none(self.y_names, []))
parser.write(filename, self.data_x, self._data_y.transpose(), **kwargs)
def setup(self, W1=0.6, P11_or_P22=0.25, R1=0.5, R2=0.5, G1=0.5, G2=0.4,
G11=0.5, G12=0.2, G21=0.8, G22=0.75, G31=0.7, G32=0.5,
inherit_W1=False, inherit_P11_or_P22=False, inherit_R1=False,
inherit_R2=False, inherit_G1=False, inherit_G2=False,
inherit_G11=False, inherit_G12=False, inherit_G21=False,
inherit_G22=False, inherit_G31=False, inherit_G32=False, **kwargs):
_AbstractProbability.setup(self, R=1)
self.W1 = not_none(W1, 0.6)
self.inherit_W1 = inherit_W1
self.P11_or_P22 = not_none(P11_or_P22, 0.25)
self.inherit_P11_or_P22 = inherit_P11_or_P22
self.R1 = not_none(R1, 0.5)
self.inherit_R1 = inherit_R1
self.R2 = not_none(R2, 0.5)
self.inherit_R2 = inherit_R2
self.G1 = not_none(G1, 0.5)
self.inherit_G1 = inherit_G1
self.G2 = not_none(G2, 0.4)
self.inherit_G2 = inherit_G2
self.G11 = not_none(G11, 0.5)
self.inherit_G11 = inherit_G11
self.G12 = not_none(G12, 0.2)
self.inherit_G12 = inherit_G12
self.G21 = not_none(G21, 0.8)
self.inherit_G21 = inherit_G21
self.G22 = not_none(G22, 0.75)
self.inherit_G22 = inherit_G22
self.G31 = not_none(G31, 0.7)
self.inherit_G31 = inherit_G31
self.G32 = not_none(G32, 0.5)
self.inherit_G32 = inherit_G32
def update_refinement_info(self, current_rp=None, message=None):
if not isnan(current_rp):
self["current_residual"].set_text("%.2f" % current_rp)
self["message"].set_text(not_none(message, ""))
def setup(self, W1=0.75, P11_or_P22=0.5, inherit_W1=False, inherit_P11_or_P22=False, **kwargs):
_AbstractProbability.setup(self, R=1)
self.W1 = not_none(W1, 0.75)
self.inherit_W1 = inherit_W1
self.P11_or_P22 = not_none(P11_or_P22, 0.5)
self.inherit_P11_or_P22 = inherit_P11_or_P22
def get_best_threshold(self, max_threshold=None, steps=None, status_dict=None):
"""
Estimates the best threshold for peak detection using an
iterative algorithm. Assumes there is a linear contribution from noise.
Returns a 4-tuple containing the selected threshold, the maximum
threshold, a list of threshold values and a list with the corresponding
number of peaks.
"""
length = self.data_x.size
steps = not_none(steps, 20)
threshold = 0.1
max_threshold = not_none(max_threshold, threshold * 3.2)
def get_new_threshold(threshold, deltas, num_peaks, ln):
# Left side line:
x = deltas[:ln]
y = num_peaks[:ln]
slope, intercept, R, _, _ = stats.linregress(x, y)
return R, -intercept / slope
if length > 2:
# Adjust the first distribution:
deltas, num_peaks = self.calculate_npeaks_for(max_threshold, steps)
# Fit several lines with increasing number of points from the
# generated threshold / marker count graph. Stop when the
# R-coefficiënt drops below 0.95 (past linear increase from noise)
# Then repeat this by increasing the resolution of data points
# and continue until the result does not change anymore
last_threshold = None
solution = False
max_iters = 10
min_iters = 3
itercount = 0
if status_dict is not None:
status_dict["progress"] = 0
while not solution:
# Number of points to use for the lin regress:
ln = 4
# Maximum number of points to use:
max_ln = len(deltas)
# Flag indicating if we can stop searching for the linear part
stop = False
while not stop:
R, threshold = get_new_threshold(threshold, deltas, num_peaks, ln)
max_threshold = threshold * 3.2
if abs(R) < 0.98 or ln >= max_ln:
stop = True
else:
ln += 1
itercount += 1 # Increase # of iterations
if last_threshold:
# Check if we have run at least `min_iters`, at most `max_iters`
# and have not reached an equilibrium.
solution = bool(
itercount > min_iters and not
(
itercount <= max_iters and
last_threshold - threshold >= 0.001
)
)
if not solution:
deltas, num_peaks = self.calculate_npeaks_for(max_threshold, steps)
last_threshold = threshold
if status_dict is not None:
status_dict["progress"] = float(itercount / max_iters)
return (deltas, num_peaks), threshold, max_threshold
else:
return ([], []), threshold, max_threshold
def setup(self, W1=0.85, P1111_or_P2112=0.75, **kwargs):
_AbstractProbability.setup(self, R=3)
self.W1 = not_none(W1, 0.85)
self.P1111_or_P2112 = not_none(P1111_or_P2112, 0.75)
def parse(cls, fp, data_objects=None, close=False):
filename, fp, close = cls._get_file(fp, close=close)
try:
basename = os.path.basename(filename)
except AttributeError:
basename = None
num_samples = 0
zipinfos = fp.infolist()
processed_folders = []
data_objects = not_none(data_objects, [])
for zipinfo in zipinfos:
if zipinfo.filename.count('/') == 1 and "DataContainer.xml" in zipinfo.filename:
folder = os.path.dirname(zipinfo.filename)
if not folder in processed_folders:
processed_folders.append(folder)
header_d = cls._get_header_dict(fp, folder)
raw_data_files, sample_name = cls._get_raw_data_files(fp, folder)
for raw_data_filename in raw_data_files:
contf = fp.open(raw_data_filename)
_, root = cls.get_xml_for_file(contf)
isScan = not ("NonAmbientModeData" in root.find("./DataRoutes/DataRoute/ScanInformation").get("ScanName"))
if isScan:
for route in root.findall("./DataRoutes/DataRoute"):
# Adapt XRDFile list & get last addition:
data_objects = cls._adapt_data_object_list(
data_objects,
num_samples=(num_samples + 1),
only_extend=True
)
data_object = data_objects[num_samples]
# Get the Datum tags:
datums = route.findall("Datum")
data = []
# Parse the RawDataView tags to find out what index in
# the datum is used for what type of data:
enabled_datum_index = None
twotheta_datum_index = None
intensity_datum_index = None
steptime_datum_index = None
relative_humidity_data, relative_humidity_index = None, None
temperature_data, temperature_index = None, None
temperature_index = None
for dataview in route.findall("./DataViews/RawDataView"):
index = int(dataview.get("Start", 0))
name = dataview.get("LogicName") or "Undefined"
xsi_type = dataview.get("{http://www.w3.org/2001/XMLSchema-instance}type") or "Undefined"
if name == "MeasuredTime":
steptime_datum_index = index
elif name == "AbsorptionFactor":
enabled_datum_index = index
elif name == "Undefined" and xsi_type == "VaryingRawDataView":
for i, definition in enumerate(dataview.findall("./Varying/FieldDefinitions")):
if definition.get("TwoTheta"):
index += i
break
twotheta_datum_index = index
elif name == "Undefined" and xsi_type == "RecordedRawDataView":
logic_name = dataview.find("Recording").get("LogicName")
if logic_name == "ScanCounter":
intensity_datum_index = index
elif logic_name == "modeActualHum":
relative_humidity_index = index
relative_humidity_data = []
elif logic_name == "modeActualTemp":
temperature_index = index
temperature_data = []
# Parse the SubScanInfo list (usually only one), and
# then parse the datums accordingly
twotheta_min = None
twotheta_max = None
twotheta_count = 0
for subscan in route.findall("./SubScans/SubScanInfo"):
# Get the steps, where to start and the planned
# time per step (measuredTimePerStep deviates
# if the recording was interrupted):
steps = int(subscan.get("MeasuredSteps"))
start = int(subscan.get("StartStepNo"))
steptime = float(subscan.get("PlannedTimePerStep"))
for datum in datums[start:start + steps]:
values = datum.text.split(",")
if values[enabled_datum_index] == "1":
# Fetch values from the list:
datum_steptime = float(values[steptime_datum_index])
intensity = float(values[intensity_datum_index])
intensity /= float(steptime * datum_steptime)
twotheta = float(values[twotheta_datum_index])
# If we have temperature or RH data, get them as well:
if temperature_index is not None:
temperature = float(values[temperature_index])
temperature_data.append(temperature)
if relative_humidity_index is not None:
relative_humidity = float(values[relative_humidity_index])
relative_humidity_data.append(relative_humidity)
# Keep track of min 2theta:
if twotheta_min is None:
twotheta_min = twotheta
else:
twotheta_min = min(twotheta_min, twotheta)
# Keep track of max 2theta:
if twotheta_max is None:
twotheta_max = twotheta
else:
twotheta_max = min(twotheta_max, twotheta)
# Append point and increase count:
data.append([twotheta, intensity])
twotheta_count += 1
#Update header:
data_object.update(
filename=basename,
name=sample_name,
time_step=1, # we converted to CPS
twotheta_min=twotheta_min,
twotheta_max=twotheta_max,
twotheta_count=twotheta_count,
**header_d
)
data_object.data = data
# These might be None:
data_object.temperature_data = temperature_data
data_object.relative_humidity_data = relative_humidity_data
num_samples += 1
#end for
contf.close()
#end for
#end if
#end if
#end for
if close: fp.close()
return data_objects
def __init__(self,
W1=0.6,
P11_or_P22=0.25,
R1=0.5,
R2=0.5,
G1=0.5,
G2=0.4,
G11=0.5,
G12=0.5,
G21=0.8,
G22=0.75,
G31=0.7,
G32=0.5,
inherit_W1=False,
inherit_P11_or_P22=False,
inherit_R1=False,
inherit_R2=False,
inherit_G1=False,
inherit_G2=False,
inherit_G11=False,
inherit_G12=False,
inherit_G21=False,
inherit_G22=False,
inherit_G31=False,
inherit_G32=False,
*args,
**kwargs):
super(R1G4Model, self).__init__(R=1, *args, **kwargs)
with self.data_changed.hold():
self.W1 = not_none(W1, 0.6)
self.inherit_W1 = inherit_W1
self.P11_or_P22 = not_none(P11_or_P22, 0.25)
self.inherit_P11_or_P22 = inherit_P11_or_P22
self.R1 = not_none(R1, 0.5)
self.inherit_R1 = inherit_R1
self.R2 = not_none(R2, 0.5)
self.inherit_R2 = inherit_R2
self.G1 = not_none(G1, 0.5)
self.inherit_G1 = inherit_G1
self.G2 = not_none(G2, 0.4)
self.inherit_G2 = inherit_G2
self.G11 = not_none(G11, 0.5)
self.inherit_G11 = inherit_G11
self.G12 = not_none(G12, 0.5)
self.inherit_G12 = inherit_G12
self.G21 = not_none(G21, 0.8)
self.inherit_G21 = inherit_G21
self.G22 = not_none(G22, 0.75)
self.inherit_G22 = inherit_G22
self.G31 = not_none(G31, 0.7)
self.inherit_G31 = inherit_G31
self.G32 = not_none(G32, 0.5)
self.inherit_G32 = inherit_G32
self.update()
def __init__(self, treemodel_property_name=None, treemodel_class_type=None, *args, **kwargs):
super(TreeModelMixin, self).__init__(*args, **kwargs)
self.treemodel_property_name = not_none(treemodel_property_name, self.treemodel_property_name)
self.treemodel_class_type = not_none(treemodel_class_type, self.treemodel_class_type)
def __init__(self, *args, **kwargs):
self.title = not_none(kwargs.pop("title", None), self.title)
super(ObjectListStoreController, self).__init__(*args, **kwargs)
def save_data(self, parser, filename, **kwargs):
if self.data_y.shape[1] > 1:
kwargs["header"] = [
"2θ",
] + (not_none(self.y_names, []))
parser.write(filename, self.data_x, self._data_y.transpose(), **kwargs)
def __init__(self, *args, **kwargs):
self.title = not_none(kwargs.pop("title", None), self.title)
super(ObjectListStoreController, self).__init__(*args, **kwargs)
