class RefinementInfo(PyXRDModel, Storable):
"""
A model that is used to store the refinement information for each
refinable value (in other models): minimum and maximum value and
a flag to indicate whether this value is selected for refinement.
"""
# MODEL INTEL:
class Meta(PyXRDModel.Meta, Storable.Meta):
store_id = "RefinementInfo"
minimum = FloatProperty(default=0.0, text="Minimum", persistent=True)
maximum = FloatProperty(default=1.0, text="Maximum", persistent=True)
refine = BoolProperty(default=False, text="Refine", persistent=True)
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 to_json(self):
return self.json_properties()
def json_properties(self):
return [self.minimum, self.maximum, self.refine]
pass # end of class
class _DummyObject(DataModel):
name = StringProperty(text="Name", tabular=True, default="")
number = FloatProperty(text="Number", tabular=True, default=0)
test = LabeledProperty(text="Test", tabular=True, default=[])
pass # end of class
class AtomContentObject(Model):
"""
Wrapper around an atom object used in the AtomContents model.
Stores the atom, the property to set and it's default amount.
"""
atom = LabeledProperty(
default=None,
text="Atom",
visible=False,
persistent=False,
tabular=True,
)
prop = LabeledProperty(
default=None,
text="Prop",
visible=False,
persistent=False,
tabular=True,
)
amount = FloatProperty(
default=0.0,
text="Amount",
minimum=0.0,
visible=False,
persistent=False,
tabular=True,
)
def __init__(self, atom, prop, amount, *args, **kwargs):
super(AtomContentObject, self).__init__(*args, **kwargs)
self.atom = atom
self.prop = prop
self.amount = amount
def update_atom(self, value):
if not (self.atom == "" or self.atom is None or self.prop is None):
with self.atom.data_changed.ignore():
setattr(self.atom, self.prop, self.amount * value)
if hasattr(self.atom, "_set_driven_flag_for_prop"):
self.atom._set_driven_flag_for_prop(self.prop)
pass
class R1G3Model(_AbstractProbability):
r"""
Probability model for Reichweite 1 with 3 components.
The 6 (=g*(g-1)) independent variables are:
.. math::
:nowrap:
\begin{align*}
& W_1
& \text{$P_{11} (W_1 < 0.5)$ or $P_{xx} (W_1 > 0.5)$}
with P_{xx} = \frac {W_{22} + W_{23} + W_{32} + W_{33} + W_{42} + W_{43}}{W_2 + W_3} \\
& G_1 = \frac{W_2}{W_2 + W_3}
& G_2 = \frac{W_{22} + W_{23}}{W_{22} + W_{23} + W_{32} + W_{33}} \\
& G_3 = \frac{W_{22}}{W_{22} + W_{23}}
& G_4 = \frac{W_{32}}{W_{32} + W_{33}} \\
\end{align*}
Calculation of the other variables happens as follows:
.. math::
:nowrap:
\begin{align*}
& \text{Calculate the 'inverted' ratios of $G_2$, $G_3$ and $G_4$ as follows:} \\
& \quad G_i^{\text{-1}} =
\begin{cases}
G_i^{-1} - 1.0, & \text{if } G_i > 0 \\
0, & \text{otherwise}
\end{cases} \quad \forall i \in \left\{ {2, 3, 4}\right\} \\
& \\
& \text{Calculate the base weight fractions of each component:} \\
& \quad W_2 = (1 - W_1) \cdot G_1\\
& \quad W_3 = 1.0 - W_1 - W_2 \\
& \\
& \text{if $W_1 \leq 0.5$:} \\
& \quad \text{$P_{11}$ is given and W_xx is derived as} \\
& \quad W_{xx} = W_{22} + W_{23} + W_{32} + W_{23} = W_1 \cdot (1 - P_{11}) + W_2 + W_3 \\
& \\
& \text{if $W_1 > 0.5$:} \\
& \quad \text{$P_{xx}$ is given and $P_{11}$ is derived further down} \\
& \quad W_{xx} = W_{22} + W_{23} + W_{32} + W_{23} = P_{xx} \cdot (W_2 + W_3) \\
& \\
& W_{22} = W_{xx} \cdot G_2 \cdot G_3 \\
& W_{23} = W_{22} \cdot G_3^{-1} \\
& W_{32} = W_{xx} \cdot (1 - G_2) \\
& W_{33} = G_4^{-1} \cdot W_{32} \\
& \\
& P_{23} =
\begin{dcases}
\dfrac{W_{23}}{W_2}, & \text{if $W_2 > 0$} \\
0, & \text{otherwise}
\end{dcases} \\
& P_{12} = 1 - P_{22} - P_{23} \\
& \\
& P_{32} =
\begin{dcases}
\frac{W_{32}}{W_3}, & \text{if $W_3 > 0$} \\
0, & \text{otherwise}
\end{dcases} \\
& P_{33} =
\begin{dcases}
\frac{W_{33}}{W_3}, & \text{if $W_3 > 0$} \\
0, & \text{otherwise}
\end{dcases} \\
& P_{31} = 1 - P_{32} - P_{33} \\
& \\
& P_{12} =
\begin{dcases}
\frac{W_2 - W_{22} - W_{32}}{W_1}, & \text{if $W_1 > 0$} \\
0, & \text{otherwise}
\end{dcases} \\
& P_{13} =
\begin{dcases}
\frac{W_3 - W_{23} - W_{33}}{W_1}, & \text{if $W_1 > 0$} \\
0, & \text{otherwise}
\end{dcases} \\
& \\
& \text{if $W_1 > 0.5$}: \\
& \quad P_{11} = 1 - P_{12} - P_{13} \\
& \\
& \text{Remainder of weight fraction can be calculated as follows:} \\
& \quad W_{ij} = {W_{ii}} \cdot {P_{ij}} \quad \forall {i,j} \in \left[ {1, 3} \right] \\
\end{align*}
"""
# MODEL METADATA:
class Meta(_AbstractProbability.Meta):
store_id = "R1G3Model"
# PROPERTIES
_G = 3
inherit_W1 = BoolProperty(default=False,
text="Inherit flag for W1",
persistent=True,
visible=True,
set_action_name="update",
mix_with=(SetActionMixin, ))
W1 = FloatProperty(default=0.8,
text="W1",
math_text=r"$W_1$",
persistent=True,
visible=True,
refinable=True,
store_private=True,
minimum=0.0,
maximum=1.0,
is_independent=True,
inheritable=True,
inherit_flag="inherit_W1",
inherit_from="parent.based_on.probabilities.W1",
set_action_name="update",
mix_with=(SetActionMixin, RefinableMixin,
InheritableMixin))
inherit_P11_or_P22 = BoolProperty(default=False,
text="Inherit flag for P11_or_P22",
persistent=True,
visible=True,
set_action_name="update",
mix_with=(SetActionMixin, ))
P11_or_P22 = FloatProperty(
default=0.7,
text="P11_or_P22",
math_text=r"$P_{11} %s$ or $\newline P_{22} %s$" %
(mt_range(0.0, "W_1", 0.5), mt_range(0.5, "W_1", 1.0)),
persistent=True,
visible=True,
refinable=True,
store_private=True,
minimum=0.0,
maximum=1.0,
is_independent=True,
inheritable=True,
inherit_flag="inherit_P11_or_P22",
inherit_from="parent.based_on.probabilities.P11_or_P22",
set_action_name="update",
mix_with=(SetActionMixin, RefinableMixin, InheritableMixin))
inherit_G1 = BoolProperty(default=False,
text="Inherit flag for G1",
persistent=True,
visible=True,
set_action_name="update",
mix_with=(SetActionMixin, ))
G1 = FloatProperty(default=0.7,
text="W2/(W2+W3)",
math_text=r"$\large\frac{W_2}{W_3 + W_2}$",
persistent=True,
visible=True,
refinable=True,
store_private=True,
minimum=0.0,
maximum=1.0,
is_independent=True,
inheritable=True,
inherit_flag="inherit_G1",
inherit_from="parent.based_on.probabilities.G1",
set_action_name="update",
mix_with=(SetActionMixin, RefinableMixin,
InheritableMixin))
inherit_G2 = BoolProperty(default=False,
text="Inherit flag for G2",
persistent=True,
visible=True,
set_action_name="update",
mix_with=(SetActionMixin, ))
G2 = FloatProperty(
default=0.7,
text="(W22+W23)/(W22+W23+W32+W33)",
math_text=
r"$\large\frac{W_{22} + W_{23}}{W_{22} + W_{23} + W_{32} + W_{33}}$",
persistent=True,
visible=True,
refinable=True,
store_private=True,
minimum=0.0,
maximum=1.0,
is_independent=True,
inheritable=True,
inherit_flag="inherit_G2",
inherit_from="parent.based_on.probabilities.G2",
set_action_name="update",
mix_with=(SetActionMixin, RefinableMixin, InheritableMixin))
inherit_G3 = BoolProperty(default=False,
text="Inherit flag for G3",
persistent=True,
visible=True,
set_action_name="update",
mix_with=(SetActionMixin, ))
G3 = FloatProperty(default=0.7,
text="W22/(W22+W23)",
math_text=r"$\large\frac{W_{22}}{W_{22} + W_{23}}$",
persistent=True,
visible=True,
refinable=True,
store_private=True,
minimum=0.0,
maximum=1.0,
is_independent=True,
inheritable=True,
inherit_flag="inherit_G3",
inherit_from="parent.based_on.probabilities.G3",
set_action_name="update",
mix_with=(SetActionMixin, RefinableMixin,
InheritableMixin))
inherit_G4 = BoolProperty(default=False,
text="Inherit flag for G4",
persistent=True,
visible=True,
set_action_name="update",
mix_with=(SetActionMixin, ))
G4 = FloatProperty(default=0.7,
text="W23/(W32+W33)",
math_text=r"$\large\frac{W_{22}}{W_{22} + W_{23}}$",
persistent=True,
visible=True,
refinable=True,
store_private=True,
minimum=0.0,
maximum=1.0,
is_independent=True,
inheritable=True,
inherit_flag="inherit_G4",
inherit_from="parent.based_on.probabilities.G4",
set_action_name="update",
mix_with=(SetActionMixin, RefinableMixin,
InheritableMixin))
# ------------------------------------------------------------
# Initialization and other internals
# ------------------------------------------------------------
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()
# ------------------------------------------------------------
# Methods & Functions
# ------------------------------------------------------------
def update(self):
with self.monitor_changes():
self.mW[0] = self.W1
self.mW[1] = (1 - self.mW[0]) * self.G1
self.mW[2] = 1.0 - self.mW[0] - self.mW[1]
W0inv = 1.0 / self.mW[0] if self.mW[0] > 0.0 else 0.0
Wxx = 0
if self.mW[0] <= 0.5: # P00 given
self.mP[0, 0] = self.P11_or_P22
# Wxx = W11 + W12 + W21 + W22
Wxx = self.mW[0] * (self.mP[0, 0] -
1) + self.mW[1] + self.mW[2]
else: # Pxx given
# Wxx = W11 + W12 + W21 + W22
Wxx = (1.0 - self.mW[0]) * self.P11_or_P22
# W11 + W12 = Wxx * G2:
self.mW[1, 1] = Wxx * self.G2 * self.G3
self.mW[1, 2] = Wxx * self.G2 * (1 - self.G3)
self.mP[1,
1] = self.mW[1,
1] / self.mW[1] if self.mW[1] > 0.0 else 0.0
self.mW[2, 1] = Wxx * (1 - self.G2) * self.G4
self.mW[2, 2] = Wxx * (1 - self.G2) * (1 - self.G4)
self.mP[1, 2] = (self.mW[1, 2] /
self.mW[1]) if self.mW[1] > 0.0 else 0.0
self.mP[1, 0] = 1 - self.mP[1, 1] - self.mP[1, 2]
self.mP[2, 1] = (self.mW[2, 1] /
self.mW[2]) if self.mW[2] > 0.0 else 0.0
self.mP[2, 2] = (self.mW[2, 2] /
self.mW[2]) if self.mW[2] > 0.0 else 0.0
self.mP[2, 0] = 1 - self.mP[2, 1] - self.mP[2, 2]
self.mP[0,
1] = (self.mW[1] - self.mW[1, 1] - self.mW[2, 1]) * W0inv
self.mP[0,
2] = (self.mW[2] - self.mW[1, 2] - self.mW[2, 2]) * W0inv
if self.mW[0] > 0.5:
self.mP[0, 0] = 1 - self.mP[0, 1] - self.mP[0, 2]
for i in range(3):
for j in range(3):
self.mW[i, j] = self.mW[i, i] * self.mP[i, j]
self.solve()
self.validate()
pass # end of class
class Specimen(DataModel, Storable):
# MODEL INTEL:
class Meta(DataModel.Meta):
store_id = "Specimen"
export_filters = xrd_parsers.get_export_file_filters()
excl_filters = exc_parsers.get_import_file_filters()
_data_object = None
@property
def data_object(self):
self._data_object.goniometer = self.goniometer.data_object
self._data_object.range_theta = self.__get_range_theta()
self._data_object.selected_range = self.get_exclusion_selector()
self._data_object.z_list = self.get_z_list()
try:
self._data_object.observed_intensity = np.copy(
self.experimental_pattern.data_y)
except IndexError:
self._data_object.observed_intensity = np.array([], dtype=float)
return self._data_object
def get_z_list(self):
return list(self.experimental_pattern.z_data)
project = property(DataModel.parent.fget, DataModel.parent.fset)
# PROPERTIES:
#: The sample name
sample_name = StringProperty(default="",
text="Sample",
visible=True,
persistent=True,
tabular=True,
signal_name="visuals_changed",
mix_with=(SignalMixin, ))
#: The sample name
name = StringProperty(default="",
text="Name",
visible=True,
persistent=True,
tabular=True,
signal_name="visuals_changed",
mix_with=(SignalMixin, ))
@StringProperty(default="",
text="Label",
visible=False,
persistent=False,
tabular=True,
mix_with=(ReadOnlyMixin, ))
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
display_calculated = BoolProperty(default=True,
text="Display calculated diffractogram",
visible=True,
persistent=True,
tabular=True,
signal_name="visuals_changed",
mix_with=(SignalMixin, ))
display_experimental = BoolProperty(
default=True,
text="Display experimental diffractogram",
visible=True,
persistent=True,
tabular=True,
signal_name="visuals_changed",
mix_with=(SignalMixin, ))
display_vshift = FloatProperty(default=0.0,
text="Vertical shift of the plot",
visible=True,
persistent=True,
tabular=True,
signal_name="visuals_changed",
widget_type="spin",
mix_with=(SignalMixin, ))
display_vscale = FloatProperty(default=0.0,
text="Vertical scale of the plot",
visible=True,
persistent=True,
tabular=True,
signal_name="visuals_changed",
widget_type="spin",
mix_with=(SignalMixin, ))
display_phases = BoolProperty(default=True,
text="Display phases seperately",
visible=True,
persistent=True,
tabular=True,
signal_name="visuals_changed",
mix_with=(SignalMixin, ))
display_stats_in_lbl = BoolProperty(default=True,
text="Display Rp in label",
visible=True,
persistent=True,
tabular=True,
signal_name="visuals_changed",
mix_with=(SignalMixin, ))
display_residuals = BoolProperty(default=True,
text="Display residual patterns",
visible=True,
persistent=True,
tabular=True,
signal_name="visuals_changed",
mix_with=(SignalMixin, ))
display_residual_scale = FloatProperty(default=1.0,
text="Residual pattern scale",
minimum=0.0,
visible=True,
persistent=True,
tabular=True,
signal_name="visuals_changed",
widget_type="spin",
mix_with=(SignalMixin, ))
display_derivatives = BoolProperty(default=False,
text="Display derivative patterns",
visible=True,
persistent=True,
tabular=True,
signal_name="visuals_changed",
mix_with=(SignalMixin, ))
#: A :class:`~pyxrd.generic.models.lines.CalculatedLine` instance
calculated_pattern = LabeledProperty(default=None,
text="Calculated diffractogram",
visible=True,
persistent=True,
tabular=True,
signal_name="data_changed",
widget_type="xy_list_view",
mix_with=(
SignalMixin,
ObserveMixin,
))
#: A :class:`~pyxrd.generic.models.lines.ExperimentalLine` instance
experimental_pattern = LabeledProperty(default=None,
text="Experimental diffractogram",
visible=True,
persistent=True,
tabular=True,
signal_name="data_changed",
widget_type="xy_list_view",
mix_with=(
SignalMixin,
ObserveMixin,
))
#: A list of 2-theta ranges to exclude for the calculation of the Rp factor
exclusion_ranges = LabeledProperty(default=None,
text="Excluded ranges",
visible=True,
persistent=True,
tabular=True,
signal_name="data_changed",
widget_type="xy_list_view",
mix_with=(SignalMixin, ObserveMixin))
#: A :class:`~pyxrd.goniometer.models.Goniometer` instance
goniometer = LabeledProperty(default=None,
text="Goniometer",
visible=True,
persistent=True,
tabular=True,
signal_name="data_changed",
mix_with=(
SignalMixin,
ObserveMixin,
))
#: A :class:`~pyxrd.specimen.models.Statistics` instance
statistics = LabeledProperty(
default=None,
text="Markers",
visible=False,
persistent=False,
tabular=True,
)
#: A list :class:`~pyxrd.specimen.models.Marker` instances
markers = ListProperty(default=None,
text="Markers",
data_type=Marker,
visible=False,
persistent=True,
tabular=True,
signal_name="visuals_changed",
widget_type="object_list_view",
mix_with=(SignalMixin, ))
@property
def max_display_y(self):
"""
The maximum intensity or z-value (display y axis)
of the current profile (both calculated and observed)
"""
_max = 0.0
if self.experimental_pattern is not None:
_max = max(_max, np.max(self.experimental_pattern.max_display_y))
if self.calculated_pattern is not None:
_max = max(_max, np.max(self.calculated_pattern.max_display_y))
return _max
# ------------------------------------------------------------
# Initialisation and other internals
# ------------------------------------------------------------
def __init__(self, *args, **kwargs):
"""
Valid keyword arguments for a Specimen are:
name: the name of the specimen
sample_name: the sample name of the specimen
calculated_pattern: the calculated pattern
experimental_pattern: the experimental pattern
exclusion_ranges: the exclusion ranges XYListStore
goniometer: the goniometer used for recording data
markers: the specimen's markers
display_vshift: the patterns vertical shift from its default position
display_vscale: the patterns vertical scale (default is 1.0)
display_calculated: whether or not to show the calculated pattern
display_experimental: whether or not to show the experimental pattern
display_residuals: whether or not to show the residuals
display_derivatives: whether or not to show the 1st derivative patterns
display_phases: whether or not to show the separate phase patterns
display_stats_in_lbl: whether or not to display the Rp values
in the pattern label
"""
my_kwargs = self.pop_kwargs(
kwargs, "data_name", "data_sample", "data_sample_length",
"data_calculated_pattern", "data_experimental_pattern",
"calc_color", "calc_lw", "inherit_calc_color", "inherit_calc_lw",
"exp_color", "exp_lw", "inherit_exp_color", "inherit_exp_lw",
"project_goniometer", "data_markers", "bg_shift", "abs_scale",
"exp_cap_value", "sample_length", "absorption", "sample_z_dev", *[
prop.label
for prop in Specimen.Meta.get_local_persistent_properties()
])
super(Specimen, self).__init__(*args, **kwargs)
kwargs = my_kwargs
self._data_object = SpecimenData()
with self.visuals_changed.hold_and_emit():
with self.data_changed.hold_and_emit():
self.name = self.get_kwarg(kwargs, "", "name", "data_name")
sample_name = self.get_kwarg(kwargs, "", "sample_name",
"data_sample")
if isinstance(sample_name, bytes):
sample_name = sample_name.decode("utf-8", "ignore")
self.sample_name = sample_name
calc_pattern_old_kwargs = {}
for kw in ("calc_color", "calc_lw", "inherit_calc_color",
"inherit_calc_lw"):
if kw in kwargs:
calc_pattern_old_kwargs[kw.replace(
"calc_", "")] = kwargs.pop(kw)
self.calculated_pattern = self.parse_init_arg(
self.get_kwarg(kwargs, None, "calculated_pattern",
"data_calculated_pattern"),
CalculatedLine,
child=True,
default_is_class=True,
label="Calculated Profile",
parent=self,
**calc_pattern_old_kwargs)
exp_pattern_old_kwargs = {}
for kw in ("exp_color", "exp_lw", "inherit_exp_color",
"inherit_exp_lw"):
if kw in kwargs:
exp_pattern_old_kwargs[kw.replace("exp_",
"")] = kwargs.pop(kw)
self.experimental_pattern = self.parse_init_arg(
self.get_kwarg(kwargs, None, "experimental_pattern",
"data_experimental_pattern"),
ExperimentalLine,
child=True,
default_is_class=True,
label="Experimental Profile",
parent=self,
**exp_pattern_old_kwargs)
self.exclusion_ranges = PyXRDLine(data=self.get_kwarg(
kwargs, None, "exclusion_ranges"),
parent=self)
# Extract old kwargs if they are there:
gonio_kwargs = {}
sample_length = self.get_kwarg(kwargs, None, "sample_length",
"data_sample_length")
if sample_length is not None:
gonio_kwargs["sample_length"] = float(sample_length)
absorption = self.get_kwarg(kwargs, None, "absorption")
if absorption is not None: # assuming a surface density of at least 20 mg/cm²:
gonio_kwargs["absorption"] = float(absorption) / 0.02
# Initialize goniometer (with optional old kwargs):
self.goniometer = self.parse_init_arg(self.get_kwarg(
kwargs, None, "goniometer", "project_goniometer"),
Goniometer,
child=True,
default_is_class=True,
parent=self,
**gonio_kwargs)
self.markers = self.get_list(kwargs,
None,
"markers",
"data_markers",
parent=self)
for marker in self.markers:
self.observe_model(marker)
self._specimens_observer = ListObserver(
self.on_marker_inserted,
self.on_marker_removed,
prop_name="markers",
model=self)
self.display_vshift = float(
self.get_kwarg(kwargs, 0.0, "display_vshift"))
self.display_vscale = float(
self.get_kwarg(kwargs, 1.0, "display_vscale"))
self.display_calculated = bool(
self.get_kwarg(kwargs, True, "display_calculated"))
self.display_experimental = bool(
self.get_kwarg(kwargs, True, "display_experimental"))
self.display_residuals = bool(
self.get_kwarg(kwargs, True, "display_residuals"))
self.display_residual_scale = float(
self.get_kwarg(kwargs, 1.0, "display_residual_scale"))
self.display_derivatives = bool(
self.get_kwarg(kwargs, False, "display_derivatives"))
self.display_phases = bool(
self.get_kwarg(kwargs, False, "display_phases"))
self.display_stats_in_lbl = bool(
self.get_kwarg(kwargs, True, "display_stats_in_lbl"))
self.statistics = Statistics(parent=self)
pass # end of with
pass # end of with
pass # end of __init__
def __str__(self):
return "<Specimen %s(%s)>" % (self.name, repr(self))
# ------------------------------------------------------------
# Notifications of observable properties
# ------------------------------------------------------------
@DataModel.observe("data_changed", signal=True)
def notify_data_changed(self, model, prop_name, info):
if model == self.calculated_pattern:
self.visuals_changed.emit() # don't propagate this as data_changed
else:
self.data_changed.emit() # propagate signal
@DataModel.observe("visuals_changed", signal=True)
def notify_visuals_changed(self, model, prop_name, info):
self.visuals_changed.emit() # propagate signal
def on_marker_removed(self, item):
with self.visuals_changed.hold_and_emit():
self.relieve_model(item)
item.parent = None
def on_marker_inserted(self, item):
with self.visuals_changed.hold_and_emit():
self.observe_model(item)
item.parent = self
# ------------------------------------------------------------
# Input/Output stuff
# ------------------------------------------------------------
@staticmethod
def from_experimental_data(filename,
parent,
parser=xrd_parsers._group_parser,
load_as_insitu=False):
"""
Returns a list of new :class:`~.specimen.models.Specimen`'s loaded
from `filename`, setting their parent to `parent` using the given
parser. If the load_as_insitu flag is set to true,
"""
specimens = list()
xrdfiles = parser.parse(filename)
if len(xrdfiles):
if getattr(xrdfiles[0], "relative_humidity_data",
None) is not None: # we have relative humidity data
specimen = None
# Setup list variables:
x_data = None
y_datas = []
rh_datas = []
for xrdfile in xrdfiles:
# Get data we need:
name, sample, xy_data, rh_data = (
xrdfile.filename, xrdfile.name, xrdfile.data,
xrdfile.relative_humidity_data)
# Transform into numpy array for column selection
xy_data = np.array(xy_data)
rh_data = np.array(rh_data)
if specimen is None:
specimen = Specimen(parent=parent,
name=name,
sample_name=sample)
specimen.goniometer.reset_from_file(
xrdfile.create_gon_file())
# Extract the 2-theta positions once:
x_data = np.copy(xy_data[:, 0])
# Add a new sub-pattern:
y_datas.append(np.copy(xy_data[:, 1]))
# Store the average RH for this pattern:
rh_datas.append(np.average(rh_data))
specimen.experimental_pattern.load_data_from_generator(
zip(x_data,
np.asanyarray(y_datas).transpose()),
clear=True)
specimen.experimental_pattern.y_names = [
"%.1f" % f for f in rh_datas
]
specimen.experimental_pattern.z_data = rh_datas
specimens.append(specimen)
else: # regular (might be multi-pattern) file
for xrdfile in xrdfiles:
name, sample, generator = xrdfile.filename, xrdfile.name, xrdfile.data
specimen = Specimen(parent=parent,
name=name,
sample_name=sample)
# TODO FIXME:
specimen.experimental_pattern.load_data_from_generator(
generator, clear=True)
specimen.goniometer.reset_from_file(
xrdfile.create_gon_file())
specimens.append(specimen)
return specimens
def json_properties(self):
props = Storable.json_properties(self)
props["exclusion_ranges"] = self.exclusion_ranges._serialize_data()
return props
def get_export_meta_data(self):
""" Returns a dictionary with common meta-data used in export functions
for experimental or calculated data """
return dict(
sample=self.label + " " + self.sample_name,
wavelength=self.goniometer.wavelength,
radius=self.goniometer.radius,
divergence=self.goniometer.divergence,
soller1=self.goniometer.soller1,
soller2=self.goniometer.soller2,
)
# ------------------------------------------------------------
# Methods & Functions
# ------------------------------------------------------------
def clear_markers(self):
with self.visuals_changed.hold():
for marker in list(self.markers)[::-1]:
self.markers.remove(marker)
def auto_add_peaks(self, tmodel):
"""
Automagically add peak markers
*tmodel* a :class:`~specimen.models.ThresholdSelector` model
"""
threshold = tmodel.sel_threshold
base = 1 if (tmodel.pattern == "exp") else 2
data_x, data_y = tmodel.get_xy()
maxtab, mintab = peakdetect(data_y, data_x, 5,
threshold) # @UnusedVariable
mpositions = [marker.position for marker in self.markers]
with self.visuals_changed.hold():
i = 1
for x, y in maxtab: # @UnusedVariable
if not x in mpositions:
nm = self.goniometer.get_nm_from_2t(x) if x != 0 else 0
new_marker = Marker(label="%%.%df" %
(3 + min(int(log(nm, 10)), 0)) % nm,
parent=self,
position=x,
base=base)
self.markers.append(new_marker)
i += 1
def get_exclusion_selector(self):
"""
Get the numpy selector array for non-excluded data
:rtype: a numpy ndarray
"""
x = self.__get_range_theta() * 360.0 / pi # convert to degrees
selector = np.ones(x.shape, dtype=bool)
data = np.sort(np.asarray(self.exclusion_ranges.get_xy_data()), axis=0)
for x0, x1 in zip(*data):
new_selector = ((x < x0) | (x > x1))
selector = selector & new_selector
return selector
def get_exclusion_xy(self):
"""
Get an numpy array containing only non-excluded data X and Y data
:rtype: a tuple containing 4 numpy ndarray's: the experimental X and Y
data and the calculated X and Y data
"""
ex, ey = self.experimental_pattern.get_xy_data()
cx, cy = self.calculated_pattern.get_xy_data()
selector = self.get_exclusion_selector(ex)
return ex[selector], ey[selector], cx[selector], cy[selector]
# ------------------------------------------------------------
# Draggable mix-in hook:
# ------------------------------------------------------------
def on_pattern_dragged(self, delta_y, button=1):
if button == 1:
self.display_vshift += delta_y
elif button == 3:
self.display_vscale += delta_y
elif button == 2:
self.project.display_plot_offset += delta_y
pass
def update_visuals(self, phases):
"""
Update visual representation of phase patterns (if any)
"""
if phases is not None:
self.calculated_pattern.y_names = [
phase.name if phase is not None else "" for phase in phases
]
self.calculated_pattern.phase_colors = [
phase.display_color if phase is not None else "#FF00FF"
for phase in phases
]
# ------------------------------------------------------------
# Intensity calculations:
# ------------------------------------------------------------
def update_pattern(self, total_intensity, phase_intensities, phases):
"""
Update calculated patterns using the provided total and phase intensities
"""
if len(phases) == 0:
self.calculated_pattern.clear()
else:
maxZ = len(self.get_z_list())
new_data = np.zeros(
(phase_intensities.shape[-1], maxZ + maxZ * len(phases)))
for z_index in range(maxZ):
# Set the total intensity for this z_index:
new_data[:, z_index] = total_intensity[z_index]
# Calculate phase intensity offsets:
phase_start_index = maxZ + z_index * len(phases)
phase_end_index = phase_start_index + len(phases)
# Set phase intensities for this z_index:
new_data[:, phase_start_index:
phase_end_index] = phase_intensities[:,
z_index, :].transpose(
)
# Store in pattern:
self.calculated_pattern.set_data(
self.__get_range_theta() * 360. / pi, new_data)
self.update_visuals(phases)
if settings.GUI_MODE:
self.statistics.update_statistics(derived=self.display_derivatives)
def convert_to_fixed(self):
"""
Converts the experimental data from ADS to fixed slits in-place
(disregards the `has_ads` flag in the goniometer, but uses the settings
otherwise)
"""
correction = self.goniometer.get_ADS_to_fixed_correction(
self.__get_range_theta())
self.experimental_pattern.apply_correction(correction)
def convert_to_ads(self):
"""
Converts the experimental data from fixed slits to ADS in-place
(disregards the `has_ads` flag in the goniometer, but uses the settings
otherwise)
"""
correction = 1.0 / self.goniometer.get_ADS_to_fixed_correction(
self.__get_range_theta())
self.experimental_pattern.apply_correction(correction)
def __get_range_theta(self):
if len(self.experimental_pattern) <= 1:
return self.goniometer.get_default_theta_range()
else:
return np.radians(self.experimental_pattern.data_x * 0.5)
def __repr__(self):
return "Specimen(name='%s')" % self.name
pass # end of class
class Project(DataModel, Storable):
"""
This is the top-level object that servers the purpose of combining the
different objects (most notably :class:`~.atoms.models.AtomType`'s,
:class:`~.phases.models.Phase`'s, :class:`~.specimen.models.Specimen`'s and
:class:`~.mixture.models.Mixture`'s).
It also provides a large number of display-related 'default' properties
(e.g. for patterns and their markers, axes etc.). For more details: see the
property descriptions.
Example usage:
.. code-block:: python
>>> from pyxrd.project.models import Project
>>> from pyxrd.generic.io.xrd_parsers import XRDParser
>>> from pyxrd.specimen.models import Specimen
>>> project = Project(name="New Project", author="Mr. X", layout_mode="FULL", axes_dspacing=True)
>>> for specimen in Specimen.from_experimental_data("/path/to/xrd_data_file.rd", parent=project):
... project.specimens.append(specimen)
...
"""
# MODEL INTEL:
class Meta(DataModel.Meta):
store_id = "Project"
file_filters = [
("PyXRD Project files",
get_case_insensitive_glob("*.pyxrd", "*.zpd")),
]
import_filters = [
("Sybilla XML files", get_case_insensitive_glob("*.xml")),
]
# PROPERTIES:
filename = None
#: The project name
name = StringProperty(default="",
text="Name",
visible=True,
persistent=True)
#: The project data (string)
date = StringProperty(default="",
text="Date",
visible=True,
persistent=True)
#: The project description
description = StringProperty(
default=None,
text="Description",
visible=True,
persistent=True,
widget_type="text_view",
)
#: The project author
author = StringProperty(default="",
text="Author",
visible=True,
persistent=True)
#: Flag indicating whether this project has been changed since it was last saved.
needs_saving = BoolProperty(default=True, visible=False, persistent=False)
#: The layout mode this project should be displayed in
layout_mode = StringChoiceProperty(default=settings.DEFAULT_LAYOUT,
text="Layout mode",
visible=True,
persistent=True,
choices=settings.DEFAULT_LAYOUTS,
mix_with=(SignalMixin, ),
signal_name="visuals_changed")
#: The manual lower limit for the X-axis
axes_xmin = FloatProperty(default=settings.AXES_MANUAL_XMIN,
text="min. [°2T]",
visible=True,
persistent=True,
minimum=0.0,
widget_type="spin",
mix_with=(SignalMixin, ),
signal_name="visuals_changed")
#: The manual upper limit for the X-axis
axes_xmax = FloatProperty(default=settings.AXES_MANUAL_XMAX,
text="max. [°2T]",
visible=True,
persistent=True,
minimum=0.0,
widget_type="spin",
mix_with=(SignalMixin, ),
signal_name="visuals_changed")
#: Whether or not to stretch the X-axis over the entire available display
axes_xstretch = BoolProperty(default=settings.AXES_XSTRETCH,
text="Stetch x-axis to fit window",
visible=True,
persistent=True,
mix_with=(SignalMixin, ),
signal_name="visuals_changed")
#: Flag toggling between d-spacing (when True) or 2-Theta axes (when False)
axes_dspacing = BoolProperty(default=settings.AXES_DSPACING,
text="Show d-spacing in x-axis",
visible=True,
persistent=True,
mix_with=(SignalMixin, ),
signal_name="visuals_changed")
#: Whether or not the y-axis should be shown
axes_yvisible = BoolProperty(default=settings.AXES_YVISIBLE,
text="Y-axis visible",
visible=True,
persistent=True,
mix_with=(SignalMixin, ),
signal_name="visuals_changed")
#: The manual lower limit for the Y-axis (in counts)
axes_ymin = FloatProperty(default=settings.AXES_MANUAL_YMIN,
text="min. [counts]",
visible=True,
persistent=True,
minimum=0.0,
widget_type="spin",
mix_with=(SignalMixin, ),
signal_name="visuals_changed")
#: The manual upper limit for the Y-axis (in counts)
axes_ymax = FloatProperty(default=settings.AXES_MANUAL_YMAX,
text="max. [counts]",
visible=True,
persistent=True,
minimum=0.0,
widget_type="spin",
mix_with=(SignalMixin, ),
signal_name="visuals_changed")
#: What type of y-axis to use: raw counts, single or multi-normalized units
axes_ynormalize = IntegerChoiceProperty(default=settings.AXES_YNORMALIZE,
text="Y scaling",
visible=True,
persistent=True,
choices=settings.AXES_YNORMALIZERS,
mix_with=(SignalMixin, ),
signal_name="visuals_changed")
#: Whether to use automatic or manual Y limits
axes_ylimit = IntegerChoiceProperty(default=settings.AXES_YLIMIT,
text="Y limit",
visible=True,
persistent=True,
choices=settings.AXES_YLIMITS,
mix_with=(SignalMixin, ),
signal_name="visuals_changed")
#: The offset between patterns as a fraction of the maximum intensity
display_plot_offset = FloatProperty(default=settings.PLOT_OFFSET,
text="Pattern offset",
visible=True,
persistent=True,
minimum=0.0,
widget_type="float_entry",
mix_with=(SignalMixin, ),
signal_name="visuals_changed")
#: The number of patterns to group ( = having no offset)
display_group_by = IntegerProperty(default=settings.PATTERN_GROUP_BY,
text="Group patterns by",
visible=True,
persistent=True,
minimum=1,
widget_type="spin",
mix_with=(SignalMixin, ),
signal_name="visuals_changed")
#: The relative position (from the pattern offset) for pattern labels
#: as a fraction of the patterns intensity
display_label_pos = FloatProperty(default=settings.LABEL_POSITION,
text="Default label position",
visible=True,
persistent=True,
widget_type="float_entry",
mix_with=(SignalMixin, ),
signal_name="visuals_changed")
#: What type of scale to use for X-axis, automatic or manual
axes_xlimit = IntegerChoiceProperty(default=settings.AXES_XLIMIT,
text="X limit",
visible=True,
persistent=True,
choices=settings.AXES_XLIMITS,
mix_with=(SignalMixin, ),
signal_name="visuals_changed")
#: The default angle at which marker labels are displayed
display_marker_angle = FloatProperty(default=settings.MARKER_ANGLE,
text="Angle",
visible=True,
persistent=True,
widget_type="float_entry",
mix_with=(SignalMixin, ),
signal_name="visuals_changed")
#: The default offset for marker labels
display_marker_top_offset = FloatProperty(
default=settings.MARKER_TOP_OFFSET,
text="Offset from base",
visible=True,
persistent=True,
widget_type="float_entry",
mix_with=(SignalMixin, ),
signal_name="visuals_changed")
#: The default marker label alignment (one of settings.MARKER_ALIGNS)
display_marker_align = StringChoiceProperty(default=settings.MARKER_ALIGN,
text="Label alignment",
visible=True,
persistent=True,
choices=settings.MARKER_ALIGNS,
mix_with=(SignalMixin, ),
signal_name="visuals_changed")
#: The default marker label base (one of settings.MARKER_BASES)
display_marker_base = IntegerChoiceProperty(default=settings.MARKER_BASE,
text="Base connection",
visible=True,
persistent=True,
choices=settings.MARKER_BASES,
mix_with=(SignalMixin, ),
signal_name="visuals_changed")
#: The default marker label top (one of settings.MARKER_TOPS)
display_marker_top = IntegerChoiceProperty(default=settings.MARKER_TOP,
text="Top connection",
visible=True,
persistent=True,
choices=settings.MARKER_TOPS,
mix_with=(SignalMixin, ),
signal_name="visuals_changed")
#: The default marker style (one of settings.MARKER_STYLES)
display_marker_style = StringChoiceProperty(default=settings.MARKER_STYLE,
text="Line style",
visible=True,
persistent=True,
choices=settings.MARKER_STYLES,
mix_with=(SignalMixin, ),
signal_name="visuals_changed")
#: The default marker color
display_marker_color = StringProperty(default=settings.MARKER_COLOR,
text="Color",
visible=True,
persistent=True,
widget_type="color",
mix_with=(SignalMixin, ),
signal_name="visuals_changed")
#: The default calculated profile color
display_calc_color = StringProperty(default=settings.CALCULATED_COLOR,
text="Calculated color",
visible=True,
persistent=True,
widget_type="color",
mix_with=(SignalMixin, ),
signal_name="visuals_changed")
#: The default experimental profile color
display_exp_color = StringProperty(default=settings.EXPERIMENTAL_COLOR,
text="Experimental color",
visible=True,
persistent=True,
widget_type="color",
mix_with=(SignalMixin, ),
signal_name="visuals_changed")
#: The default calculated profile line width
display_calc_lw = IntegerProperty(default=settings.CALCULATED_LINEWIDTH,
text="Calculated line width",
visible=True,
persistent=True,
widget_type="spin",
mix_with=(SignalMixin, ),
signal_name="visuals_changed")
#: The default experimental profile line width
display_exp_lw = IntegerProperty(default=settings.EXPERIMENTAL_LINEWIDTH,
text="Experimental line width",
visible=True,
persistent=True,
widget_type="spin",
mix_with=(SignalMixin, ),
signal_name="visuals_changed")
#: The default calculated profile line style
display_calc_ls = StringChoiceProperty(
default=settings.CALCULATED_LINESTYLE,
text="Calculated line style",
visible=True,
persistent=True,
choices=settings.PATTERN_LINE_STYLES,
mix_with=(SignalMixin, ),
signal_name="visuals_changed")
#: The default experimental profile line style
display_exp_ls = StringChoiceProperty(
default=settings.EXPERIMENTAL_LINESTYLE,
text="Experimental line style",
visible=True,
persistent=True,
choices=settings.PATTERN_LINE_STYLES,
mix_with=(SignalMixin, ),
signal_name="visuals_changed")
#: The default calculated profile line style
display_calc_marker = StringChoiceProperty(
default=settings.CALCULATED_MARKER,
text="Calculated line marker",
visible=True,
persistent=True,
choices=settings.PATTERN_MARKERS,
mix_with=(SignalMixin, ),
signal_name="visuals_changed")
#: The default calculated profile line style
display_exp_marker = StringChoiceProperty(
default=settings.EXPERIMENTAL_MARKER,
text="Experimental line marker",
visible=True,
persistent=True,
choices=settings.PATTERN_MARKERS,
mix_with=(SignalMixin, ),
signal_name="visuals_changed")
#: The list of specimens
specimens = ListProperty(
default=[],
text="Specimens",
data_type=Specimen,
visible=True,
persistent=True,
)
#: The list of phases
phases = ListProperty(default=[],
text="Phases",
data_type=Phase,
visible=False,
persistent=True)
#: The list of atom types
atom_types = ListProperty(default=[],
text="Atom types",
data_type=AtomType,
visible=False,
persistent=True)
#: The list of Behaviours
#behaviours = ListProperty(
# default=[], text="Behaviours", data_type=InSituBehaviour,
# visible=False, persistent=True
#)
#: The list of mixtures
mixtures = ListProperty(default=[],
text="Mixture",
data_type=Mixture,
visible=False,
persistent=True)
# ------------------------------------------------------------
# Initialization and other internals
# ------------------------------------------------------------
def __init__(self, *args, **kwargs):
"""
Constructor takes any of its properties as a keyword argument
except for:
- needs_saving
In addition to the above, the constructor still supports the
following deprecated keywords, mapping to a current keyword:
- goniometer: the project-level goniometer, is passed on to the
specimens
- axes_xscale: deprecated alias for axes_xlimit
- axes_yscale: deprecated alias for axes_ynormalize
Any other arguments or keywords are passed to the base class.
"""
my_kwargs = self.pop_kwargs(
kwargs, "goniometer", "data_goniometer", "data_atom_types",
"data_phases", "axes_yscale", "axes_xscale", "filename",
"behaviours", *[
prop.label
for prop in Project.Meta.get_local_persistent_properties()
])
super(Project, self).__init__(*args, **kwargs)
kwargs = my_kwargs
with self.data_changed.hold():
with self.visuals_changed.hold():
self.filename = self.get_kwarg(kwargs, self.filename,
"filename")
self.layout_mode = self.get_kwarg(kwargs, self.layout_mode,
"layout_mode")
self.display_marker_align = self.get_kwarg(
kwargs, self.display_marker_align, "display_marker_align")
self.display_marker_color = self.get_kwarg(
kwargs, self.display_marker_color, "display_marker_color")
self.display_marker_base = self.get_kwarg(
kwargs, self.display_marker_base, "display_marker_base")
self.display_marker_top = self.get_kwarg(
kwargs, self.display_marker_top, "display_marker_top")
self.display_marker_top_offset = self.get_kwarg(
kwargs, self.display_marker_top_offset,
"display_marker_top_offset")
self.display_marker_angle = self.get_kwarg(
kwargs, self.display_marker_angle, "display_marker_angle")
self.display_marker_style = self.get_kwarg(
kwargs, self.display_marker_style, "display_marker_style")
self.display_calc_color = self.get_kwarg(
kwargs, self.display_calc_color, "display_calc_color")
self.display_exp_color = self.get_kwarg(
kwargs, self.display_exp_color, "display_exp_color")
self.display_calc_lw = self.get_kwarg(kwargs,
self.display_calc_lw,
"display_calc_lw")
self.display_exp_lw = self.get_kwarg(kwargs,
self.display_exp_lw,
"display_exp_lw")
self.display_calc_ls = self.get_kwarg(kwargs,
self.display_calc_ls,
"display_calc_ls")
self.display_exp_ls = self.get_kwarg(kwargs,
self.display_exp_ls,
"display_exp_ls")
self.display_calc_marker = self.get_kwarg(
kwargs, self.display_calc_marker, "display_calc_marker")
self.display_exp_marker = self.get_kwarg(
kwargs, self.display_exp_marker, "display_exp_marker")
self.display_plot_offset = self.get_kwarg(
kwargs, self.display_plot_offset, "display_plot_offset")
self.display_group_by = self.get_kwarg(kwargs,
self.display_group_by,
"display_group_by")
self.display_label_pos = self.get_kwarg(
kwargs, self.display_label_pos, "display_label_pos")
self.axes_xlimit = self.get_kwarg(kwargs, self.axes_xlimit,
"axes_xlimit", "axes_xscale")
self.axes_xmin = self.get_kwarg(kwargs, self.axes_xmin,
"axes_xmin")
self.axes_xmax = self.get_kwarg(kwargs, self.axes_xmax,
"axes_xmax")
self.axes_xstretch = self.get_kwarg(kwargs, self.axes_xstretch,
"axes_xstretch")
self.axes_ylimit = self.get_kwarg(kwargs, self.axes_ylimit,
"axes_ylimit")
self.axes_ynormalize = self.get_kwarg(kwargs,
self.axes_ynormalize,
"axes_ynormalize",
"axes_yscale")
self.axes_yvisible = self.get_kwarg(kwargs, self.axes_yvisible,
"axes_yvisible")
self.axes_ymin = self.get_kwarg(kwargs, self.axes_ymin,
"axes_ymin")
self.axes_ymax = self.get_kwarg(kwargs, self.axes_ymax,
"axes_ymax")
goniometer = None
goniometer_kwargs = self.get_kwarg(kwargs, None, "goniometer",
"data_goniometer")
if goniometer_kwargs:
goniometer = self.parse_init_arg(goniometer_kwargs,
None,
child=True)
# Set up and observe atom types:
self.atom_types = self.get_list(kwargs, [],
"atom_types",
"data_atom_types",
parent=self)
self._atom_types_observer = ListObserver(
self.on_atom_type_inserted,
self.on_atom_type_removed,
prop_name="atom_types",
model=self)
# Resolve json references & observe phases
self.phases = self.get_list(kwargs, [],
"phases",
"data_phases",
parent=self)
for phase in self.phases:
phase.resolve_json_references()
self.observe_model(phase)
self._phases_observer = ListObserver(self.on_phase_inserted,
self.on_phase_removed,
prop_name="phases",
model=self)
# Set goniometer if required & observe specimens
self.specimens = self.get_list(kwargs, [],
"specimens",
"data_specimens",
parent=self)
for specimen in self.specimens:
if goniometer: specimen.goniometer = goniometer
self.observe_model(specimen)
self._specimens_observer = ListObserver(
self.on_specimen_inserted,
self.on_specimen_removed,
prop_name="specimens",
model=self)
# Observe behaviours:
#self.behaviours = self.get_list(kwargs, [], "behaviours", parent=self)
#for behaviour in self.behaviours:
# self.observe_model(behaviour)
#self._behaviours_observer = ListObserver(
# self.on_behaviour_inserted,
# self.on_behaviour_removed,
# prop_name="behaviours",
# model=self
#)
# Observe mixtures:
self.mixtures = self.get_list(kwargs, [],
"mixtures",
"data_mixtures",
parent=self)
for mixture in self.mixtures:
self.observe_model(mixture)
self._mixtures_observer = ListObserver(
self.on_mixture_inserted,
self.on_mixture_removed,
prop_name="mixtures",
model=self)
self.name = str(
self.get_kwarg(kwargs, "Project name", "name",
"data_name"))
self.date = str(
self.get_kwarg(kwargs, time.strftime("%d/%m/%Y"), "date",
"data_date"))
self.description = str(
self.get_kwarg(kwargs, "Project description",
"description", "data_description"))
self.author = str(
self.get_kwarg(kwargs, "Project author", "author",
"data_author"))
load_default_data = self.get_kwarg(kwargs, True,
"load_default_data")
if load_default_data and self.layout_mode != 1 and \
len(self.atom_types) == 0:
self.load_default_data()
self.needs_saving = True
pass # end with visuals_changed
pass # end with data_changed
def load_default_data(self):
for atom_type in AtomType.get_from_csv(
settings.DATA_REG.get_file_path("ATOM_SCAT_FACTORS")):
self.atom_types.append(atom_type)
# ------------------------------------------------------------
# Notifications of observable properties
# ------------------------------------------------------------
def on_phase_inserted(self, item):
# Set parent on the new phase:
if item.parent != self: item.parent = self
item.resolve_json_references()
def on_phase_removed(self, item):
with self.data_changed.hold_and_emit():
# Clear parent:
item.parent = None
# Clear links with other phases:
if getattr(item, "based_on", None) is not None:
item.based_on = None
for phase in self.phases:
if getattr(phase, "based_on", None) == item:
phase.based_on = None
# Remove phase from mixtures:
for mixture in self.mixtures:
mixture.unset_phase(item)
def on_atom_type_inserted(self, item, *data):
if item.parent != self: item.parent = self
# We do not observe AtomType's directly, if they change,
# Atoms containing them will be notified, and that event should bubble
# up to the project level.
def on_atom_type_removed(self, item, *data):
item.parent = None
# We do not emit a signal for AtomType's, if it was part of
# an Atom, the Atom will be notified, and the event should bubble
# up to the project level
def on_specimen_inserted(self, item):
# Set parent and observe the new specimen (visuals changed signals):
if item.parent != self: item.parent = self
self.observe_model(item)
def on_specimen_removed(self, item):
with self.data_changed.hold_and_emit():
# Clear parent & stop observing:
item.parent = None
self.relieve_model(item)
# Remove specimen from mixtures:
for mixture in self.mixtures:
mixture.unset_specimen(item)
def on_mixture_inserted(self, item):
# Set parent and observe the new mixture:
if item.parent != self: item.parent = self
self.observe_model(item)
def on_mixture_removed(self, item):
with self.data_changed.hold_and_emit():
# Clear parent & stop observing:
item.parent = None
self.relieve_model(item)
def on_behaviour_inserted(self, item):
# Set parent and observe the new mixture:
if item.parent != self: item.parent = self
self.observe_model(item)
def on_behaviour_removed(self, item):
with self.data_changed.hold_and_emit():
# Clear parent & stop observing:
item.parent = None
self.relieve_model(item)
@DataModel.observe("data_changed", signal=True)
def notify_data_changed(self, model, prop_name, info):
self.needs_saving = True
if isinstance(model, Mixture):
self.data_changed.emit()
@DataModel.observe("visuals_changed", signal=True)
def notify_visuals_changed(self, model, prop_name, info):
self.needs_saving = True
self.visuals_changed.emit() # propagate signal
# ------------------------------------------------------------
# Input/Output stuff
# ------------------------------------------------------------
@classmethod
def from_json(type, **kwargs): # @ReservedAssignment
project = type(**kwargs)
project.needs_saving = False # don't mark this when just loaded
return project
def to_json_multi_part(self):
to_json = self.to_json()
properties = to_json["properties"]
for name in ("phases", "specimens", "atom_types",
"mixtures"): #"behaviours"
yield (name, properties.pop(name))
properties[name] = "file://%s" % name
yield ("content", to_json)
yield ("version", __version__)
@staticmethod
def create_from_sybilla_xml(filename, **kwargs):
from pyxrd.project.importing import create_project_from_sybilla_xml
return create_project_from_sybilla_xml(filename, **kwargs)
# ------------------------------------------------------------
# Draggable mix-in hook:
# ------------------------------------------------------------
def on_label_dragged(self, delta_y, button=1):
if button == 1:
self.display_label_pos += delta_y
pass
# ------------------------------------------------------------
# Methods & Functions
# ------------------------------------------------------------
def get_scale_factor(self, specimen=None):
"""
Get the factor with which to scale raw data and the scaled offset
:rtype: tuple containing the scale factor and the (scaled) offset
"""
if self.axes_ynormalize == 0 or (self.axes_ynormalize == 1
and specimen is None):
return (1.0 / (self.get_max_display_y() or 1.0), 1.0)
elif self.axes_ynormalize == 1:
return (1.0 / (specimen.get_max_display_y or 1.0), 1.0)
elif self.axes_ynormalize == 2:
return (1.0, self.get_max_display_y())
else:
raise ValueError(
"Wrong value for 'axes_ysnormalize' in %s: is `%d`; should be 0, 1 or 2"
% (self, self.axes_ynormalize))
def get_max_display_y(self):
max_display_y = 0
if self.parent is not None:
for specimen in self.parent.current_specimens:
max_display_y = max(specimen.max_display_y, max_display_y)
return max_display_y
@contextmanager
def hold_child_signals(self):
logger.info("Holding back all project child object signals")
with self.hold_mixtures_needs_update():
with self.hold_mixtures_data_changed():
with self.hold_phases_data_changed():
with self.hold_specimens_data_changed():
with self.hold_atom_types_data_changed():
yield
@contextmanager
def hold_mixtures_needs_update(self):
logger.info("Holding back all 'needs_update' signals from Mixtures")
with EventContextManager(
*[mixture.needs_update.hold() for mixture in self.mixtures]):
yield
@contextmanager
def hold_mixtures_data_changed(self):
logger.info("Holding back all 'data_changed' signals from Mixtures")
with EventContextManager(
*[mixture.data_changed.hold() for mixture in self.mixtures]):
yield
@contextmanager
def hold_phases_data_changed(self):
logger.info("Holding back all 'data_changed' signals from Phases")
with EventContextManager(
*[phase.data_changed.hold() for phase in self.phases]):
yield
@contextmanager
def hold_atom_types_data_changed(self):
logger.info("Holding back all 'data_changed' signals from AtomTypes")
with EventContextManager(
*
[atom_type.data_changed.hold() for atom_type in self.atom_types]):
yield
@contextmanager
def hold_specimens_data_changed(self):
logger.info("Holding back all 'data_changed' signals from Specimens")
with EventContextManager(
*[specimen.data_changed.hold()
for specimen in self.specimens]):
yield
def update_all_mixtures(self):
"""
Forces all mixtures in this project to update. If they have auto
optimization enabled, this will also optimize them.
"""
for mixture in self.mixtures:
with self.data_changed.ignore():
mixture.update()
def get_mixtures_by_name(self, mixture_name):
"""
Convenience method that returns all the mixtures who's name match the
passed name as a list.
"""
return [
mixture for mixture in self.mixtures
if (mixture.name == mixture_name)
]
# ------------------------------------------------------------
# Specimen list related
# ------------------------------------------------------------
def move_specimen_up(self, specimen):
"""
Move the passed :class:`~pyxrd.specimen.models.Specimen` up one slot.
Will raise and IndexError if the passed specimen is not in this project.
"""
index = self.specimens.index(specimen)
self.specimens.insert(min(index + 1, len(self.specimens)),
self.specimens.pop(index))
def move_specimen_down(self, specimen):
"""
Move the passed :class:`~pyxrd.specimen.models.Specimen` down one slot
Will raise and IndexError if the passed specimen is not in this project.
"""
index = self.specimens.index(specimen)
self.specimens.insert(max(index - 1, 0), self.specimens.pop(index))
pass
# ------------------------------------------------------------
# Phases list related
# ------------------------------------------------------------
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
# ------------------------------------------------------------
# AtomType's list related
# ------------------------------------------------------------
def load_atom_types(self, filename, parser):
"""
Loads all :class:`~pyxrd.atoms.models.AtomType` objects from the
file specified by *filename*.
"""
# make sure we have no duplicate UUID's
type(Project).object_pool.change_all_uuids()
for atom_type in parser.parse(filename):
atom_type.parent = self
self.atom_types.append(atom_type)
pass # end of class
class DritsCSDSDistribution(_AbstractCSDSDistribution, RefinementValue):
# MODEL INTEL:
class Meta(_AbstractCSDSDistribution.Meta):
description = "Log-normal CSDS distr. (Drits et. al, 1997)"
store_id = "DritsCSDSDistribution"
# PROPERTIES:
alpha_scale = FloatProperty(default=0.9485,
text="α scale factor",
minimum=0.0,
maximum=10.0,
tabular=True,
persistent=False,
visible=False,
refinable=False,
mix_with=(ReadOnlyMixin, DataMixin,
RefinableMixin))
alpha_offset = FloatProperty(default=0.017,
text="α offset factor",
minimum=-5,
maximum=5,
tabular=True,
persistent=False,
visible=False,
refinable=False,
mix_with=(ReadOnlyMixin, DataMixin,
RefinableMixin))
beta_scale = FloatProperty(default=0.1032,
text="β² scale factor",
minimum=0.0,
maximum=10.0,
tabular=True,
persistent=False,
visible=False,
refinable=False,
mix_with=(ReadOnlyMixin, DataMixin,
RefinableMixin))
beta_offset = FloatProperty(default=0.0034,
text="β² offset factor",
minimum=-5,
maximum=5,
tabular=True,
persistent=False,
visible=False,
refinable=False,
mix_with=(ReadOnlyMixin, DataMixin,
RefinableMixin))
# REFINEMENT VALUE IMPLEMENTATION:
@property
def refine_title(self):
return "Average CSDS"
@property
def refine_descriptor_data(self):
return dict(phase_name=self.phase.name,
component_name="*",
property_name=self.refine_title)
@property
def refine_value(self):
return self.average
@refine_value.setter
def refine_value(self, value):
self.average = value
@property
def refine_info(self):
return self.average_ref_info
@property
def is_refinable(self):
return not self.inherited
# ------------------------------------------------------------
# Initialization and other internals
# ------------------------------------------------------------
def __init__(self, *args, **kwargs):
for key in [
"alpha_scale", "alpha_offset", "beta_scale", "beta_offset"
]:
kwargs.pop(key, None)
super(DritsCSDSDistribution, self).__init__(*args, **kwargs)
pass # end of class
class PyXRDLine(StorableXYData):
"""
A PyXRDLine is an abstract attribute holder for a real 'Line' object,
whatever the plotting library used may be. Attributes are line width and
color.
"""
# MODEL INTEL:
class Meta(StorableXYData.Meta):
store_id = "PyXRDLine"
# OBSERVABLE PROPERTIES:
#: The line label
label = StringProperty(default="", text="Label", persistent=True)
#: The line color
color = StringProperty(default="#000000",
text="Label",
visible=True,
persistent=True,
widget_type="color",
inherit_flag="inherit_color",
inherit_from="parent.parent.display_exp_color",
signal_name="visuals_changed",
mix_with=(InheritableMixin, SignalMixin))
#: Flag indicating whether to use the grandparents color yes/no
inherit_color = BoolProperty(default=True,
text="Inherit color",
visible=True,
persistent=True,
signal_name="visuals_changed",
mix_with=(SignalMixin, ))
#: The linewidth in points
lw = FloatProperty(
default=2.0,
text="Linewidth",
visible=True,
persistent=True,
widget_type="spin",
inherit_flag="inherit_lw",
inherit_from="parent.parent.display_exp_lw",
signal_name="visuals_changed",
mix_with=(InheritableMixin, SignalMixin),
)
#: Flag indicating whether to use the grandparents linewidth yes/no
inherit_lw = BoolProperty(
default=True,
text="Inherit linewidth",
visible=True,
persistent=True,
signal_name="visuals_changed",
mix_with=(SignalMixin, ),
)
#: A short string describing the (matplotlib) linestyle
ls = StringChoiceProperty(
default=settings.EXPERIMENTAL_LINESTYLE,
text="Linestyle",
visible=True,
persistent=True,
choices=settings.PATTERN_LINE_STYLES,
mix_with=(
InheritableMixin,
SignalMixin,
),
signal_name="visuals_changed",
inherit_flag="inherit_ls",
inherit_from="parent.parent.display_exp_ls",
)
#: Flag indicating whether to use the grandparents linestyle yes/no
inherit_ls = BoolProperty(default=True,
text="Inherit linestyle",
visible=True,
persistent=True,
mix_with=(SignalMixin, ),
signal_name="visuals_changed")
#: A short string describing the (matplotlib) marker
marker = StringChoiceProperty(
default=settings.EXPERIMENTAL_MARKER,
text="Marker",
visible=True,
persistent=True,
choices=settings.PATTERN_MARKERS,
mix_with=(
InheritableMixin,
SignalMixin,
),
signal_name="visuals_changed",
inherit_flag="inherit_marker",
inherit_from="parent.parent.display_exp_marker",
)
#: Flag indicating whether to use the grandparents linewidth yes/no
inherit_marker = BoolProperty(
default=True,
text="Inherit marker",
visible=True,
persistent=True,
mix_with=(SignalMixin, ),
signal_name="visuals_changed",
)
#: z-data (e.g. relative humidity, temperature, for multi-column 'lines')
z_data = ListProperty(default=None,
text="Z data",
data_type=float,
persistent=True,
visible=False)
# REGULAR PROPERTIES:
@property
def max_display_y(self):
if self.num_columns > 2:
# If there's several y-columns, check if we have z-data associated with them
# if so, it is a 2D pattern, otherwise this is a multi-line pattern
if len(self.z_data) > 2:
return np.max(self.z_data)
else:
return self.max_y
else:
# If there's a single comumn of y-data, just get the max value
return self.max_y
@property
def min_intensity(self):
if self.num_columns > 2:
return np.min(self.z_data)
else:
return self.min_y
@property
def abs_max_intensity(self):
return self.abs_max_y
# ------------------------------------------------------------
# Initialisation and other internals
# ------------------------------------------------------------
def __init__(self, *args, **kwargs):
"""
Valid keyword arguments for a PyXRDLine are:
data: the actual data containing x and y values
label: the label for this line
color: the color of this line
inherit_color: whether to use the parent-level color or its own
lw: the line width of this line
inherit_lw: whether to use the parent-level line width or its own
ls: the line style of this line
inherit_ls: whether to use the parent-level line style or its own
marker: the line marker of this line
inherit_marker: whether to use the parent-level line marker or its own
z_data: the z-data associated with the columns in a multi-column pattern
"""
my_kwargs = self.pop_kwargs(
kwargs, *[
prop.label
for prop in PyXRDLine.Meta.get_local_persistent_properties()
])
super(PyXRDLine, self).__init__(*args, **kwargs)
kwargs = my_kwargs
with self.visuals_changed.hold():
self.label = self.get_kwarg(kwargs, self.label, "label")
self.color = self.get_kwarg(kwargs, self.color, "color")
self.inherit_color = bool(
self.get_kwarg(kwargs, self.inherit_color, "inherit_color"))
self.lw = float(self.get_kwarg(kwargs, self.lw, "lw"))
self.inherit_lw = bool(
self.get_kwarg(kwargs, self.inherit_lw, "inherit_lw"))
self.ls = self.get_kwarg(kwargs, self.ls, "ls")
self.inherit_ls = bool(
self.get_kwarg(kwargs, self.inherit_ls, "inherit_ls"))
self.marker = self.get_kwarg(kwargs, self.marker, "marker")
self.inherit_marker = bool(
self.get_kwarg(kwargs, self.inherit_marker, "inherit_marker"))
self.z_data = list(self.get_kwarg(kwargs, [0], "z_data"))
# ------------------------------------------------------------
# Input/Output stuff
# ------------------------------------------------------------
@classmethod
def from_json(cls, **kwargs): # @ReservedAssignment
if "xy_store" in kwargs:
if "type" in kwargs["xy_store"]:
kwargs["data"] = kwargs["xy_store"]["properties"]["data"]
elif "xy_data" in kwargs:
if "type" in kwargs["xy_data"]:
kwargs["data"] = kwargs["xy_data"]["properties"]["data"]
kwargs["label"] = kwargs["data_label"]
del kwargs["data_name"]
del kwargs["data_label"]
del kwargs["xy_data"]
return cls(**kwargs)
# ------------------------------------------------------------
# Convenience Methods & Functions
# ------------------------------------------------------------
def interpolate(self, *x_vals, **kwargs):
"""
Returns a list of (x, y) tuples for the passed x values. An optional
column keyword argument can be passed to select a column, by default
the first y-column is used. Returned y-values are interpolated.
"""
column = kwargs.get("column", 0)
f = interp1d(self.data_x, self.data_y[:, column])
return list(zip(x_vals, f(x_vals)))
def get_plotted_y_at_x(self, x):
"""
Gets the (interpolated) plotted value at the given x position.
If this line has not been plotted (or does not have
access to a '__plot_line' attribute set by the plotting routines)
it will return 0.
"""
try:
xdata, ydata = getattr(self, "__plot_line").get_data()
except AttributeError:
logging.exception(
"Attribute error when trying to get plotter data at x position!"
)
else:
if len(xdata) > 0 and len(ydata) > 0:
return np.interp(x, xdata, ydata)
return 0
def calculate_npeaks_for(self, max_threshold, steps):
"""
Calculates the number of peaks for `steps` threshold values between
0 and `max_threshold`. Returns a tuple containing two lists with the
threshold values and the corresponding number of peaks.
"""
length = self.data_x.size
resolution = length / (self.data_x[-1] - self.data_x[0])
delta_angle = 0.05
window = int(delta_angle * resolution)
window += (window % 2) * 2
steps = max(steps, 2) - 1
factor = max_threshold / steps
deltas = [i * factor for i in range(0, steps)]
numpeaks = []
maxtabs, mintabs = multi_peakdetect(self.data_y[:, 0], self.data_x, 5,
deltas)
for maxtab, _ in zip(maxtabs, mintabs):
numpeak = len(maxtab)
numpeaks.append(numpeak)
numpeaks = list(map(float, numpeaks))
return deltas, numpeaks
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
pass # end of class
class R2G2Model(_AbstractProbability):
r"""
Probability model for Reichweite 2 with 2 components.
The 4 (=g^2) independent variables are:
.. math::
:nowrap:
\begin{align*}
& W_1
& P_{112} (W_1 leq \nicefrac{2}{3})
\text{ or }P_{211} (W_1 > \nicefrac{2}{3}) \\
& P_{21}
& P_{122} (P_{21} leq \nicefrac{1}{2})
\text{ or }P_{221} (P_{21} > \nicefrac{1}{2}) \\
\end{align*}
Calculation of the other variables happens as follows:
.. math::
:nowrap:
\begin{align*}
& W_2 = 1 - W_1 \\
& P_{22} = 1 - P_{21} \\
& \\
& W_{21} = W_2 \cdot P_{21} \\
& W_{21} = W_{12} \\
& W_{11} = W_1 - W_{21} \\
& W_{22} = W_{2} \cdot P_{22} \\
& \\
& \text{if $W_1 leq \nicefrac{2}{3}$:} \\
& \quad \text{$P_{112}$ is given}\\
& \quad P_{211} =
\begin{dcases}
\frac{W_{11}}{W_{21}} \cdot P_{112} , & \text{if $W_{21} > 0$} \\
0, & \text{otherwise}
\end{dcases} \\
& \\
& \text{if $W_1 > \nicefrac{2}{3}$:} \\
& \quad \text{$P_{211}$ is given}\\
& \quad P_{112} =
\begin{dcases}
\frac{W_{21}}{W_{11}} \cdot P_{211} , & \text{if $W_{11} > 0$} \\
0, & \text{otherwise}
\end{dcases} \\
& \\
& P_{212} = 1 - P_{211} \\
& P_{111} = 1 - P_{112} \\
& \\
& \text{if $P_{21} leq \nicefrac{1}{2}$:} \\
& \quad \text{$P_{122}$ is given}\\
& \quad P_{221} =
\begin{dcases}
\frac{W_{12}}{W_{22}} \cdot P_{122} , & \text{if $W_{22} > 0$} \\
0, & \text{otherwise}
\end{dcases} \\
& \\
& \text{if $P_{21} > \nicefrac{1}{2}$:} \\
& \quad \text{$P_{221}$ is given}\\
& \quad P_{122} =
\begin{dcases}
\frac{W_{22}}{W_{12}} \cdot P_{221} , & \text{if $W_{12} > 0$} \\
0, & \text{otherwise}
\end{dcases} \\
& P_{121} = 1 - P_{122} \\
& P_{222} = 1 - P_{221} \\
\end{align*}
"""
# MODEL METADATA:
class Meta(_AbstractProbability.Meta):
store_id = "R2G2Model"
# PROPERTIES:
_G = 2
twothirds = 2.0 / 3.0
inherit_W1 = BoolProperty(default=False,
text="Inherit flag for W1",
persistent=True,
visible=True,
set_action_name="update",
mix_with=(SetActionMixin, ))
W1 = FloatProperty(default=0.75,
text="W1 (> 0.5)",
math_text=r"$W_1 (> 0.5)$",
persistent=True,
visible=True,
refinable=True,
store_private=True,
minimum=0.5,
maximum=1.0,
is_independent=True,
inheritable=True,
inherit_flag="inherit_W1",
inherit_from="parent.based_on.probabilities.W1",
set_action_name="update",
mix_with=(SetActionMixin, RefinableMixin,
InheritableMixin))
inherit_P112_or_P211 = BoolProperty(default=False,
text="Inherit flag for P112_or_P211",
persistent=True,
visible=True,
set_action_name="update",
mix_with=(SetActionMixin, ))
P112_or_P211 = FloatProperty(
default=0.75,
text="P112 (W1 < 2/3) or\nP211 (W1 > 2/3)",
math_text=r"$P_{112} %s$ or $\newlineP_{211} %s$" % (mt_range(
1.0 / 2.0, "W_1", 2.0 / 3.0), mt_range(2.0 / 3.0, "W_1", 1.0)),
persistent=True,
visible=True,
refinable=True,
store_private=True,
minimum=0.0,
maximum=1.0,
is_independent=True,
inheritable=True,
inherit_flag="inherit_P112_or_P211",
inherit_from="parent.based_on.probabilities.P112_or_P211",
set_action_name="update",
mix_with=(SetActionMixin, RefinableMixin, InheritableMixin))
inherit_P21 = BoolProperty(default=False,
text="Inherit flag for P21",
persistent=True,
visible=True,
set_action_name="update",
mix_with=(SetActionMixin, ))
P21 = FloatProperty(default=0.75,
text="P21",
math_text=r"$P_{21}$",
persistent=True,
visible=True,
refinable=True,
store_private=True,
minimum=0.0,
maximum=1.0,
is_independent=True,
inheritable=True,
inherit_flag="inherit_P21",
inherit_from="parent.based_on.probabilities.P21",
set_action_name="update",
mix_with=(SetActionMixin, RefinableMixin,
InheritableMixin))
inherit_P122_or_P221 = BoolProperty(default=False,
text="Inherit flag for P122_or_P221",
persistent=True,
visible=True,
set_action_name="update",
mix_with=(SetActionMixin, ))
P122_or_P221 = FloatProperty(
default=0.75,
text="P112 (W1 < 1/2) or\nP221 (W1 > 1/2)",
math_text=r"$P_{122} %s$ or $\newlineP_{221} %s$" %
(mt_range(0.0, "W_1", 1.0 / 2.0), mt_range(1.0 / 2.0, "W_1", 1.0)),
persistent=True,
visible=True,
refinable=True,
store_private=True,
minimum=0.0,
maximum=1.0,
is_independent=True,
inheritable=True,
inherit_flag="inherit_P122_or_P221",
inherit_from="parent.based_on.probabilities.P122_or_P221",
set_action_name="update",
mix_with=(SetActionMixin, RefinableMixin, InheritableMixin))
# ------------------------------------------------------------
# Initialization and other internals
# ------------------------------------------------------------
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()
# ------------------------------------------------------------
# Methods & Functions
# ------------------------------------------------------------
def update(self):
with self.monitor_changes():
self.mW[0] = self.W1
self.mW[1] = 1.0 - self.mW[0]
self.mP[1, 0] = self.P21
self.mP[1, 1] = 1.0 - self.mP[1, 0]
self.mW[1, 0] = self.mW[1] * self.mP[1, 0]
self.mW[1, 1] = self.mW[1] * self.mP[1, 1]
self.mW[0, 1] = self.mW[1, 0]
self.mW[0, 0] = self.mW[0] - self.mW[1, 0]
if self.mW[0] <= self.twothirds:
self.mP[0, 0, 1] = self.P112_or_P211
if self.mW[1, 0] == 0.0:
self.mP[1, 0, 0] = 0.0
else:
self.mP[1, 0,
0] = self.mP[0, 0, 1] * self.mW[0, 0] / self.mW[1,
0]
else:
self.mP[1, 0, 0] = self.P112_or_P211
if self.mW[0, 0] == 0.0:
self.mP[0, 0, 1] = 0.0
else:
self.mP[0, 0,
1] = self.mP[1, 0, 0] * self.mW[1, 0] / self.mW[0,
0]
self.mP[1, 0, 1] = 1.0 - self.mP[1, 0, 0]
self.mP[0, 0, 0] = 1.0 - self.mP[0, 0, 1]
if self.mP[1, 0] <= 0.5:
self.mP[0, 1, 1] = self.P122_or_P221
self.mP[1, 1,
0] = self.mP[0, 1, 1] * self.mW[0, 1] / self.mW[1, 1]
else:
self.mP[1, 1, 0] = self.P122_or_P221
self.mP[0, 1,
1] = self.mP[1, 1, 0] * self.mW[1, 1] / self.mW[0, 1]
self.mP[0, 1, 0] = 1.0 - self.mP[0, 1, 1]
self.mP[1, 1, 1] = 1.0 - self.mP[1, 1, 0]
self.solve()
self.validate()
pass # end of class
class Statistics(ChildModel):
# PROPERTIES:
specimen = property(ChildModel.parent.fget, ChildModel.parent.fset)
@IntegerProperty(default=0,
label="Points",
visible=False,
mix_with=(ReadOnlyMixin, ))
def points(self):
try:
e_ex, e_ey, e_cx, e_cy = self.specimen.get_exclusion_xy(
) #@UnusedVariable
return e_ex.size
except:
pass
return 0
Rp = FloatProperty(default=None, label="Rp", visible=True)
Rwp = FloatProperty(default=None, label="Rwp", visible=True)
Rpder = FloatProperty(default=None, label="Rpder", visible=True)
residual_pattern = LabeledProperty(default=None, label="Residual pattern")
der_exp_pattern = LabeledProperty(default=None,
label="Derived experimental pattern")
der_calc_pattern = LabeledProperty(default=None,
label="Derived calculated pattern")
der_residual_pattern = LabeledProperty(default=None,
label="Derived residual pattern")
# ------------------------------------------------------------
# Initialization and other internals
# ------------------------------------------------------------
def __init__(self, *args, **kwargs):
super(Statistics, self).__init__(*args, **kwargs)
self.observe_model(self.parent)
# ------------------------------------------------------------
# Notifications of observable properties
# ------------------------------------------------------------
@ChildModel.observe("parent", assign=True, after=True)
def on_parent_changed(self, model, prop_name, info):
self.update_statistics()
# ------------------------------------------------------------
# Methods & Functions
# ------------------------------------------------------------
def _get_experimental(self):
if self.specimen is not None:
x, y = self.specimen.experimental_pattern.get_xy_data()
return x.copy(), y.copy()
else:
return None, None
def _get_calculated(self):
if self.specimen is not None:
x, y = self.specimen.calculated_pattern.get_xy_data()
return x.copy(), y.copy()
else:
return None, None
def scale_factor_y(self, offset):
return self.specimen.scale_factor_y(offset) if self.specimen else (
1.0, offset)
def update_statistics(self, derived=False):
# Clear factors:
self.Rp = 0
self.Rwp = 0
self.Rpder = 0
# Setup lines if not yet done:
if self.residual_pattern == None:
self.residual_pattern = PyXRDLine(label="Residual",
color="#000000",
lw=0.5,
parent=self)
if self.der_exp_pattern == None:
self.der_exp_pattern = PyXRDLine(label="Exp. 1st der.",
color="#000000",
lw=2,
parent=self)
if self.der_calc_pattern == None:
self.der_calc_pattern = PyXRDLine(label="Calc. 1st der.",
color="#AA0000",
lw=2,
parent=self)
if self.der_residual_pattern == None:
self.der_residual_pattern = PyXRDLine(label="1st der. residual",
color="#AA00AA",
lw=1,
parent=self)
# Get data:
exp_x, exp_y = self._get_experimental()
cal_x, cal_y = self._get_calculated()
der_exp_y, der_cal_y = None, None
del cal_x # don't need this, is the same as exp_x
# Try to get statistics, if it fails, just clear and inform the user
try:
if cal_y is not None and exp_y is not None and cal_y.size > 0 and exp_y.size > 0:
# Get the selector for areas to consider in the statistics:
selector = self.specimen.get_exclusion_selector()
if derived:
# Calculate and set first derivate patterns:
der_exp_y, der_cal_y = derive(exp_y), derive(cal_y)
self.der_exp_pattern.set_data(exp_x, der_exp_y)
self.der_calc_pattern.set_data(exp_x, der_cal_y)
# Calculate and set residual pattern:
self.residual_pattern.set_data(exp_x, exp_y - cal_y)
if derived:
self.der_residual_pattern.set_data(exp_x,
der_exp_y - der_cal_y)
# Calculate 'included' R values:
self.Rp = Rp(exp_y[selector], cal_y[selector])
self.Rwp = Rpw(exp_y[selector], cal_y[selector])
if derived:
self.Rpder = Rp(der_exp_y[selector], der_cal_y[selector])
else:
self.residual_pattern.clear()
self.der_exp_pattern.clear()
self.der_calc_pattern.clear()
except:
self.residual_pattern.clear()
self.der_exp_pattern.clear()
self.der_calc_pattern.clear()
logger.error(
"Error occurred when trying to calculate statistics, aborting calculation!"
)
raise
pass # end of class
class UnitCellProperty(ComponentPropMixin,
RefinementValue,
DataModel,
Storable,
metaclass=PyXRDRefinableMeta):
"""
UnitCellProperty's are an integral part of a component and allow to
calculate the dimensions of the unit cell based on compositional
information such as the iron content.
This class is not responsible for keeping its value up-to-date.
With other words, it is the responsibility of the higher-level class
to call the 'update_value' method on this object whenever it emits a
'data_changed' signal. The reason for this is to prevent infinite
recursion errors.
"""
class Meta(DataModel.Meta):
store_id = "UnitCellProperty"
component = property(DataModel.parent.fget, DataModel.parent.fset)
# PROPERTIES:
#: The UnitCellProperty name
name = StringProperty(default="",
text="Name",
visible=False,
persistent=False,
signal_name="visuals_changed",
mix_with=(SignalMixin, ))
#: Flag indicating if this UnitCellProperty is enabled
enabled = BoolProperty(default=False,
text="Enabled",
visible=True,
persistent=True,
set_action_name="update_value",
mix_with=(SetActionMixin, ))
#: Flag indicating if this UnitCellProperty is inherited
inherited = BoolProperty(default=False,
text="Inherited",
visible=False,
persistent=False,
set_action_name="update_value",
mix_with=(SetActionMixin, ))
#: The value of the UnitCellProperty
value = FloatProperty(default=0.0,
text="Value",
visible=True,
persistent=True,
refinable=True,
widget_type='float_entry',
set_action_name="update_value",
mix_with=(SetActionMixin, RefinableMixin))
#: The factor of the UnitCellProperty (if enabled and not constant)
factor = FloatProperty(default=1.0,
text="Factor",
visible=True,
persistent=True,
widget_type='float_entry',
set_action_name="update_value",
mix_with=(SetActionMixin, ))
#: The constant of the UnitCellProperty (if enabled and not constant)
constant = FloatProperty(default=0.0,
text="Constant",
visible=True,
persistent=True,
widget_type='float_entry',
set_action_name="update_value",
mix_with=(SetActionMixin, ))
_temp_prop = None # temporary, JSON-style prop
prop = LabeledProperty(default=None,
text="Property",
visible=True,
persistent=True,
widget_type='combo',
set_action_name="update_value",
mix_with=(SetActionMixin, ))
# REFINEMENT VALUE IMPLEMENTATION:
@property
def refine_title(self):
return self.name
@property
def refine_descriptor_data(self):
return dict(phase_name=self.component.phase.refine_title,
component_name=self.component.refine_title)
@property
def refine_value(self):
return self.value
@refine_value.setter
def refine_value(self, value):
if not self.enabled:
self.value = value
@property
def refine_info(self):
return self.value_ref_info
@property
def is_refinable(self):
return not (self.enabled or self.inherited)
# ------------------------------------------------------------
# Initialisation and other internals
# ------------------------------------------------------------
def __init__(self, *args, **kwargs):
keys = [
prop.label for prop in
UnitCellProperty.Meta.get_local_persistent_properties()
]
keys.extend([
"data_%s" % prop.label for prop in
UnitCellProperty.Meta.get_local_persistent_properties()
])
my_kwargs = self.pop_kwargs(kwargs, "name", *keys)
super(UnitCellProperty, self).__init__(*args, **kwargs)
kwargs = my_kwargs
with self.data_changed.hold_and_emit():
self.name = self.get_kwarg(kwargs, self.name, "name", "data_name")
self.value = self.get_kwarg(kwargs, self.value, "value",
"data_value")
self.factor = self.get_kwarg(kwargs, self.factor, "factor",
"data_factor")
self.constant = self.get_kwarg(kwargs, self.constant, "constant",
"data_constant")
self.enabled = self.get_kwarg(kwargs, self.enabled, "enabled",
"data_enabled")
self._temp_prop = self.get_kwarg(kwargs, self.prop, "prop",
"data_prop")
# ------------------------------------------------------------
# Input/Output stuff
# ------------------------------------------------------------
def json_properties(self):
retval = Storable.json_properties(self)
if retval["prop"]:
# Try to replace objects with their uuid's:
try:
retval["prop"] = [
getattr(retval["prop"][0], 'uuid', retval["prop"][0]),
retval["prop"][1]
]
except:
logger.exception(
"Error when trying to interpret UCP JSON properties")
pass # ignore
return retval
def resolve_json_references(self):
if getattr(self, "_temp_prop", None):
self._temp_prop = list(self._temp_prop)
if isinstance(self._temp_prop[0], str):
obj = type(type(self)).object_pool.get_object(
self._temp_prop[0])
if obj:
self._temp_prop[0] = obj
self.prop = self._temp_prop
else:
self._temp_prop = None
self.prop = self._temp_prop
del self._temp_prop
# ------------------------------------------------------------
# Methods & Functions
# ------------------------------------------------------------
def create_prop_store(self, extra_props=[]):
assert (self.component is not None)
store = Gtk.ListStore(object, str, str)
# use private properties so we connect to the actual object stores and not the inherited ones
for atom in self.component._layer_atoms:
store.append([atom, "pn", atom.name])
for atom in self.component._interlayer_atoms:
store.append([atom, "pn", atom.name])
for prop in extra_props:
store.append(prop)
return store
def get_value_of_prop(self):
try:
return getattr(*self.prop)
except:
return 0.0
def update_value(self):
if self.enabled:
self._value = float(self.factor * self.get_value_of_prop() +
self.constant)
self.data_changed.emit()
pass # end of class
class AtomRelation(ComponentPropMixin,
RefinementValue,
DataModel,
Storable,
metaclass=PyXRDRefinableMeta):
# MODEL INTEL:
class Meta(DataModel.Meta):
store_id = "AtomRelation"
file_filters = [
("Atom relation", get_case_insensitive_glob("*.atr")),
]
allowed_relations = {}
component = property(DataModel.parent.fget, DataModel.parent.fset)
# PROPERTIES:
#: The name of this AtomRelation
name = StringProperty(default="",
text="Name",
visible=True,
persistent=True,
tabular=True,
signal_name="visuals_changed",
mix_with=(SignalMixin, ))
#: The value of this AtomRelation
value = FloatProperty(default=0.0,
text="Value",
visible=True,
persistent=True,
tabular=True,
widget_type='float_entry',
signal_name="data_changed",
refinable=True,
mix_with=(SignalMixin, RefinableMixin))
#: Flag indicating whether this AtomRelation is enabled or not
enabled = BoolProperty(default=True,
text="Enabled",
visible=True,
persistent=True,
tabular=True,
signal_name="data_changed",
mix_with=(SignalMixin, ))
#: Is True when this AtomRelation's value is driven by another AtomRelation.
#: Should never be set directly or things might break!
driven_by_other = BoolProperty(default=False,
text="Driven by other",
visible=False,
persistent=False,
tabular=True)
@property
def applicable(self):
"""
Is True when this AtomRelation was passed a component of which the atom
ratios are not set to be inherited from another component.
"""
return (self.parent is not None
and not self.parent.inherit_atom_relations)
# REFINEMENT VALUE IMPLEMENTATION:
@property
def refine_title(self):
return self.name
@property
def refine_descriptor_data(self):
return dict(phase_name=self.component.phase.refine_title,
component_name=self.component.refine_title)
@property
def refine_value(self):
return self.value
@refine_value.setter
def refine_value(self, value):
self.value = value
@property
def inside_linked_component(self):
return (self.component.linked_with
is not None) and self.component.inherit_atom_relations
@property
def is_refinable(self):
return self.enabled and not self.driven_by_other and not self.inside_linked_component
@property
def refine_info(self):
return self.value_ref_info
# ------------------------------------------------------------
# Initialisation and other internals
# ------------------------------------------------------------
def __init__(self, *args, **kwargs):
"""
Valid keyword arguments for an AtomRelation are:
name: the name of this AtomRelation
value: the value for this AtomRelation
enabled: boolean indicating whether or not this AtomRelation is
enabled
"""
my_kwargs = self.pop_kwargs(
kwargs, "data_name", "data_ratio", "ratio", *[
prop.label for prop in
AtomRelation.Meta.get_local_persistent_properties()
])
super(AtomRelation, self).__init__(*args, **kwargs)
kwargs = my_kwargs
self.name = self.get_kwarg(kwargs, "", "name", "data_name")
self.value = self.get_kwarg(kwargs, 0.0, "value", "ratio",
"data_ratio")
self.enabled = bool(self.get_kwarg(kwargs, True, "enabled"))
# ------------------------------------------------------------
# Input/Output stuff
# ------------------------------------------------------------
def resolve_relations(self):
raise NotImplementedError(
"Subclasses should implement the resolve_relations method!")
# ------------------------------------------------------------
# Methods & Functions
# ------------------------------------------------------------
def create_prop_store(self, prop=None):
if self.component is not None:
store = Gtk.ListStore(object, str, str)
for atom in self.component._layer_atoms:
store.append([atom, "pn", atom.name])
for atom in self.component._interlayer_atoms:
store.append([atom, "pn", atom.name])
for relation in self.component._atom_relations:
tp = relation.Meta.store_id
if tp in self.Meta.allowed_relations:
for prop, name in self.Meta.allowed_relations[tp]:
if callable(name):
name = name(relation)
store.append([relation, prop, name])
return store
def iter_references(self):
raise NotImplementedError(
"'iter_references' should be implemented by subclasses!")
def _safe_is_referring(self, value):
if value is not None and hasattr(value, "is_referring"):
return value.is_referring([
self,
])
else:
return False
def is_referring(self, references=None):
"""
Checks whether this AtomRelation is causing circular references.
Can be used to check this before actually setting references by
setting the 'references' keyword argument to a list containing the
new reference value(s).
"""
if references == None:
references = []
# 1. Bluntly check if we're not already somewhere referred to,
# if not, add ourselves to the list of references
if self in references:
return True
references.append(self)
# 2. Loop over our own references, check if they cause a circular
# reference, if not add them to the list of references.
for reference in self.iter_references():
if reference is not None and hasattr(reference, "is_referring"):
if reference.is_referring(references):
return True
else:
references.append(reference)
return False
def _set_driven_flag_for_prop(self, prop=None):
"""Internal method used to safely set the driven_by_other flag on an object.
Subclasses can override to provide a check on the property set by the driver."""
self.driven_by_other = True
def apply_relation(self):
raise NotImplementedError(
"Subclasses should implement the apply_relation method!")
pass # end of class
class AtomRatio(AtomRelation):
# MODEL INTEL:
class Meta(AtomRelation.Meta):
store_id = "AtomRatio"
allowed_relations = {
"AtomRatio": [
("__internal_sum__", lambda o: "%s: SUM" % o.name),
("value", lambda o: "%s: RATIO" % o.name),
],
"AtomContents": [("value", lambda o: o.name)],
}
# SIGNALS:
# PROPERTIES:
#: The sum of the two atoms
sum = FloatProperty(default=1.0,
text="Sum",
minimum=0.0,
visible=True,
persistent=True,
tabular=True,
widget_type='float_entry',
signal_name="data_changed",
mix_with=(SignalMixin, ))
def __internal_sum__(self, value):
"""
Special setter for other AtomRelation objects depending on the value of
the sum of the AtomRatio. This can be used to have multi-substitution by
linking two (or more) AtomRatio's. Eg Al-by-Mg-&-Fe:
AtomRatioMgAndFeForAl -> links together Al content and Fe+Mg content
=> sum = e.g. 4 set by user
AtomRatioMgForFe -> links together the Fe and Mg content
=> sum = set by previous ratio.
"""
self._sum = float(value)
self.apply_relation()
__internal_sum__ = property(fset=__internal_sum__)
def _set_driven_flag_for_prop(self, prop, *args):
"""Internal method used to safely set the driven_by_other flag on an object.
Subclasses can override to provide a check on the property set by the driver."""
if prop != "__internal_sum__":
super(AtomRatio, self)._set_driven_flag_for_prop(prop)
def _set_atom(self, value, label=None):
if not self._safe_is_referring(value[0]):
getattr(type(self), label)._set(self, value)
#: The substituting atom
atom1 = LabeledProperty(default=[None, None],
text="Substituting Atom",
visible=True,
persistent=True,
tabular=True,
signal_name="data_changed",
fset=partial(_set_atom, label="atom1"),
mix_with=(SignalMixin, ))
#: The Original atom
atom2 = LabeledProperty(default=[None, None],
text="Original Atom",
visible=True,
persistent=True,
tabular=True,
signal_name="data_changed",
fset=partial(_set_atom, label="atom2"),
mix_with=(SignalMixin, ))
# ------------------------------------------------------------
# Initialization and other internals
# ------------------------------------------------------------
def __init__(self, *args, **kwargs): # @ReservedAssignment
"""
Valid keyword arguments for an AtomRatio are:
sum: the sum of the atoms contents
atom1: a tuple containing the first atom and its property name to read/set
atom2: a tuple containing the first atom and its property name to read/set
The value property is the 'ratio' of the first atom over the sum of both
"""
my_kwargs = self.pop_kwargs(
kwargs, "data_sum", "prop1", "data_prop1", "data_prop2", "prop2",
*[
prop.label
for prop in AtomRatio.Meta.get_local_persistent_properties()
])
super(AtomRatio, self).__init__(*args, **kwargs)
kwargs = my_kwargs
self.sum = self.get_kwarg(kwargs, self.sum, "sum", "data_sum")
self._unresolved_atom1 = self._parseattr(
self.get_kwarg(kwargs, [None, None], "atom1", "prop1",
"data_prop1"))
self._unresolved_atom2 = self._parseattr(
self.get_kwarg(kwargs, [None, None], "atom2", "prop2",
"data_prop2"))
# ------------------------------------------------------------
# Input/Output stuff
# ------------------------------------------------------------
def json_properties(self):
retval = Storable.json_properties(self)
retval["atom1"] = [
retval["atom1"][0].uuid if retval["atom1"][0] else None,
retval["atom1"][1]
]
retval["atom2"] = [
retval["atom2"][0].uuid if retval["atom2"][0] else None,
retval["atom2"][1]
]
return retval
def resolve_relations(self):
if isinstance(self._unresolved_atom1[0], str):
self._unresolved_atom1[0] = type(
type(self)).object_pool.get_object(self._unresolved_atom1[0])
self.atom1 = list(self._unresolved_atom1)
del self._unresolved_atom1
if isinstance(self._unresolved_atom2[0], str):
self._unresolved_atom2[0] = type(
type(self)).object_pool.get_object(self._unresolved_atom2[0])
self.atom2 = list(self._unresolved_atom2)
del self._unresolved_atom2
# ------------------------------------------------------------
# Methods & Functions
# ------------------------------------------------------------
def apply_relation(self):
if self.enabled and self.applicable:
for value, (atom, prop) in [(self.value, self.atom1),
(1.0 - self.value, self.atom2)]:
if atom and prop:
# do not fire events, just set attributes:
with atom.data_changed.ignore():
setattr(atom, prop, value * self.sum)
if hasattr(atom, "_set_driven_flag_for_prop"):
atom._set_driven_flag_for_prop(prop)
def iter_references(self):
for atom in [self.atom1[0], self.atom2[0]]:
yield atom
pass # end of class
class Component(RefinementGroup,
DataModel,
Storable,
metaclass=PyXRDRefinableMeta):
# MODEL INTEL:
class Meta(DataModel.Meta):
store_id = "Component"
_data_object = None
@property
def data_object(self):
weight = 0.0
self._data_object.layer_atoms = [None] * len(self.layer_atoms)
for i, atom in enumerate(self.layer_atoms):
self._data_object.layer_atoms[i] = atom.data_object
weight += atom.weight
self._data_object.interlayer_atoms = [None] * len(
self.interlayer_atoms)
for i, atom in enumerate(self.interlayer_atoms):
self._data_object.interlayer_atoms[i] = atom.data_object
weight += atom.weight
self._data_object.volume = self.get_volume()
self._data_object.weight = weight
self._data_object.d001 = self.d001
self._data_object.default_c = self.default_c
self._data_object.delta_c = self.delta_c
self._data_object.lattice_d = self.lattice_d
return self._data_object
phase = property(DataModel.parent.fget, DataModel.parent.fset)
# SIGNALS:
atoms_changed = SignalProperty()
# UNIT CELL DIMENSION SHORTCUTS:
@property
def cell_a(self):
return self._ucp_a.value
@property
def cell_b(self):
return self._ucp_b.value
@property
def cell_c(self):
return self.d001
# PROPERTIES:
#: The name of the Component
name = StringProperty(default="",
text="Name",
visible=True,
persistent=True,
tabular=True,
signal_name="visuals_changed",
mix_with=(SignalMixin, ))
#: Flag indicating whether to inherit the UCP a from :attr:`~linked_with`
@BoolProperty(
default=False,
text="Inh. cell length a",
visible=True,
persistent=True,
tabular=True,
)
def inherit_ucp_a(self):
return self._ucp_a.inherited
@inherit_ucp_a.setter
def inherit_ucp_a(self, value):
self._ucp_a.inherited = value
#: Flag indicating whether to inherit the UCP b from :attr:`~linked_with`
@BoolProperty(
default=False,
text="Inh. cell length b",
visible=True,
persistent=True,
tabular=True,
)
def inherit_ucp_b(self):
return self._ucp_b.inherited
@inherit_ucp_b.setter
def inherit_ucp_b(self, value):
self._ucp_b.inherited = value
#: Flag indicating whether to inherit d001 from :attr:`~linked_with`
inherit_d001 = BoolProperty(default=False,
text="Inh. cell length c",
visible=True,
persistent=True,
tabular=True,
signal_name="data_changed",
mix_with=(SignalMixin, ))
#: Flag indicating whether to inherit default_c from :attr:`~linked_with`
inherit_default_c = BoolProperty(default=False,
text="Inh. default length c",
visible=True,
persistent=True,
tabular=True,
signal_name="data_changed",
mix_with=(SignalMixin, ))
#: Flag indicating whether to inherit delta_c from :attr:`~linked_with`
inherit_delta_c = BoolProperty(default=False,
text="Inh. c length dev.",
visible=True,
persistent=True,
tabular=True,
signal_name="data_changed",
mix_with=(SignalMixin, ))
#: Flag indicating whether to inherit layer_atoms from :attr:`~linked_with`
inherit_layer_atoms = BoolProperty(default=False,
text="Inh. layer atoms",
visible=True,
persistent=True,
tabular=True,
signal_name="data_changed",
mix_with=(SignalMixin, ))
#: Flag indicating whether to inherit interlayer_atoms from :attr:`~linked_with`
inherit_interlayer_atoms = BoolProperty(default=False,
text="Inh. interlayer atoms",
visible=True,
persistent=True,
tabular=True,
signal_name="data_changed",
mix_with=(SignalMixin, ))
#: Flag indicating whether to inherit atom_relations from :attr:`~linked_with`
inherit_atom_relations = BoolProperty(default=False,
text="Inh. atom relations",
visible=True,
persistent=True,
tabular=True,
signal_name="data_changed",
mix_with=(SignalMixin, ))
_linked_with_index = None
_linked_with_uuid = None
#: The :class:`~Component` this component is linked with
linked_with = LabeledProperty(default=None,
text="Linked with",
visible=True,
persistent=True,
signal_name="data_changed",
mix_with=(SignalMixin, ))
@linked_with.setter
def linked_with(self, value):
old = type(self).linked_with._get(self)
if old != value:
if old is not None:
self.relieve_model(old)
type(self).linked_with._set(self, value)
if value is not None:
self.observe_model(value)
else:
for prop in self.Meta.get_inheritable_properties():
setattr(self, prop.inherit_flag, False)
#: The silicate lattice's c length
lattice_d = FloatProperty(default=0.0,
text="Lattice c length [nm]",
visible=False,
persistent=True,
signal_name="data_changed")
ucp_a = LabeledProperty(default=None,
text="Cell length a [nm]",
visible=True,
persistent=True,
tabular=True,
refinable=True,
inheritable=True,
inherit_flag="inherit_ucp_a",
inherit_from="linked_with.ucp_a",
signal_name="data_changed",
mix_with=(SignalMixin, InheritableMixin,
ObserveMixin, RefinableMixin))
ucp_b = LabeledProperty(default=None,
text="Cell length b [nm]",
visible=True,
persistent=True,
tabular=True,
refinable=True,
inheritable=True,
inherit_flag="inherit_ucp_b",
inherit_from="linked_with.ucp_b",
signal_name="data_changed",
mix_with=(SignalMixin, InheritableMixin,
ObserveMixin, RefinableMixin))
d001 = FloatProperty(default=1.0,
text="Cell length c [nm]",
minimum=0.0,
maximum=5.0,
visible=True,
persistent=True,
tabular=True,
refinable=True,
inheritable=True,
inherit_flag="inherit_default_c",
inherit_from="linked_with.d001",
signal_name="data_changed",
mix_with=(SignalMixin, InheritableMixin,
RefinableMixin))
default_c = FloatProperty(default=1.0,
text="Default c length [nm]",
minimum=0.0,
maximum=5.0,
visible=True,
persistent=True,
tabular=True,
inheritable=True,
inherit_flag="inherit_default_c",
inherit_from="linked_with.default_c",
signal_name="data_changed",
mix_with=(SignalMixin, InheritableMixin))
delta_c = FloatProperty(default=0.0,
text="C length dev. [nm]",
minimum=0.0,
maximum=0.05,
visible=True,
persistent=True,
tabular=True,
inheritable=True,
inherit_flag="inherit_delta_c",
inherit_from="linked_with.delta_c",
signal_name="data_changed",
mix_with=(SignalMixin, InheritableMixin,
RefinableMixin))
layer_atoms = ListProperty(default=None,
text="Layer atoms",
visible=True,
persistent=True,
tabular=True,
widget_type="custom",
inheritable=True,
inherit_flag="inherit_layer_atoms",
inherit_from="linked_with.layer_atoms",
signal_name="data_changed",
data_type=Atom,
mix_with=(SignalMixin, InheritableMixin))
interlayer_atoms = ListProperty(
default=None,
text="Interlayer atoms",
visible=True,
persistent=True,
tabular=True,
widget_type="custom",
inheritable=True,
inherit_flag="inherit_interlayer_atoms",
inherit_from="linked_with.interlayer_atoms",
signal_name="data_changed",
data_type=Atom,
mix_with=(SignalMixin, InheritableMixin))
atom_relations = ListProperty(default=None,
text="Atom relations",
widget_type="custom",
visible=True,
persistent=True,
tabular=True,
refinable=True,
inheritable=True,
inherit_flag="inherit_atom_relations",
inherit_from="linked_with.atom_relations",
signal_name="data_changed",
data_type=AtomRelation,
mix_with=(SignalMixin, InheritableMixin,
RefinableMixin))
# Instance flag indicating whether or not linked_with & inherit flags should be saved
save_links = True
# Class flag indicating whether or not atom types in the component should be
# exported using their name rather then their project-uuid.
export_atom_types = False
# REFINEMENT GROUP IMPLEMENTATION:
@property
def refine_title(self):
return self.name
@property
def refine_descriptor_data(self):
return dict(phase_name=self.phase.refine_title,
component_name=self.refine_title)
# ------------------------------------------------------------
# Initialization and other internals
# ------------------------------------------------------------
def __init__(self, **kwargs):
"""
Valid keyword arguments for a Component are:
*ucp_a*: unit cell property along a axis
*ucp_b*: unit cell property along b axis
*d001*: unit cell length c (aka d001)
*default_c*: default c-value
*delta_c*: the variation in basal spacing due to defects
*layer_atoms*: ObjectListStore of layer Atoms
*interlayer_atoms*: ObjectListStore of interlayer Atoms
*atom_relations*: ObjectListStore of AtomRelations
*inherit_ucp_a*: whether or not to inherit the ucp_a property from
the linked component (if linked)
*inherit_ucp_b*: whether or not to inherit the ucp_b property from
the linked component (if linked)
*inherit_d001*: whether or not to inherit the d001 property from
the linked component (if linked)
*inherit_default_c*: whether or not to inherit the default_c
property from the linked component (if linked)
*inherit_delta_c*: whether or not to inherit the delta_c
property from the linked component (if linked)
*inherit_layer_atoms*: whether or not to inherit the layer_atoms
property from the linked component (if linked)
*inherit_interlayer_atoms*: whether or not to inherit the
interlayer_atoms property from the linked component (if linked)
*inherit_atom_relations*: whether or not to inherit the
atom_relations property from the linked component (if linked)
*linked_with_uuid*: the UUID for the component this one is linked
with
Deprecated, but still supported:
*linked_with_index*: the index of the component this one is
linked with in the ObjectListStore of the parent based on phase.
"""
my_kwargs = self.pop_kwargs(
kwargs, "data_name", "data_layer_atoms", "data_interlayer_atoms",
"data_atom_relations", "data_atom_ratios", "data_d001",
"data_default_c", "data_delta_c", "lattice_d", "data_cell_a",
"data_ucp_a", "data_cell_b", "data_ucp_b", "linked_with_uuid",
"linked_with_index", "inherit_cell_a", "inherit_cell_b", *[
prop.label
for prop in Component.Meta.get_local_persistent_properties()
])
super(Component, self).__init__(**kwargs)
kwargs = my_kwargs
# Set up data object
self._data_object = ComponentData(d001=0.0, delta_c=0.0)
# Set attributes:
self.name = self.get_kwarg(kwargs, "", "name", "data_name")
# Load lists:
self.layer_atoms = self.get_list(kwargs, [],
"layer_atoms",
"data_layer_atoms",
parent=self)
self.interlayer_atoms = self.get_list(kwargs, [],
"interlayer_atoms",
"data_interlayer_atoms",
parent=self)
self.atom_relations = self.get_list(kwargs, [],
"atom_relations",
"data_atom_relations",
parent=self)
# Add all atom ratios to the AtomRelation list
for atom_ratio in self.get_list(kwargs, [],
"atom_ratios",
"data_atom_ratios",
parent=self):
self.atom_relations.append(atom_ratio)
# Observe the inter-layer atoms, and make sure they get stretched
for atom in self.interlayer_atoms:
atom.stretch_values = True
self.observe_model(atom)
# Observe the layer atoms
for atom in self.layer_atoms:
self.observe_model(atom)
# Resolve their relations and observe the atom relations
for relation in self.atom_relations:
relation.resolve_relations()
self.observe_model(relation)
# Connect signals to lists and dicts:
self._layer_atoms_observer = ListObserver(self._on_layer_atom_inserted,
self._on_layer_atom_removed,
prop_name="layer_atoms",
model=self)
self._interlayer_atoms_observer = ListObserver(
self._on_interlayer_atom_inserted,
self._on_interlayer_atom_removed,
prop_name="interlayer_atoms",
model=self)
self._atom_relations_observer = ListObserver(
self._on_atom_relation_inserted,
self._on_atom_relation_removed,
prop_name="atom_relations",
model=self)
# Update lattice values:
self.d001 = self.get_kwarg(kwargs, self.d001, "d001", "data_d001")
self._default_c = float(
self.get_kwarg(kwargs, self.d001, "default_c", "data_default_c"))
self.delta_c = float(
self.get_kwarg(kwargs, self.delta_c, "delta_c", "data_delta_c"))
self.update_lattice_d()
# Set/Create & observe unit cell properties:
ucp_a = self.get_kwarg(kwargs, None, "ucp_a", "data_ucp_a",
"data_cell_a")
if isinstance(ucp_a, float):
ucp_a = UnitCellProperty(name="cell length a",
value=ucp_a,
parent=self)
ucp_a = self.parse_init_arg(ucp_a,
UnitCellProperty,
child=True,
default_is_class=True,
name="Cell length a [nm]",
parent=self)
type(self).ucp_a._set(self, ucp_a)
self.observe_model(ucp_a)
ucp_b = self.get_kwarg(kwargs, None, "ucp_b", "data_ucp_b",
"data_cell_b")
if isinstance(ucp_b, float):
ucp_b = UnitCellProperty(name="cell length b",
value=ucp_b,
parent=self)
ucp_b = self.parse_init_arg(ucp_b,
UnitCellProperty,
child=True,
default_is_class=True,
name="Cell length b [nm]",
parent=self)
type(self).ucp_b._set(self, ucp_b)
self.observe_model(ucp_b)
# Set links:
self._linked_with_uuid = self.get_kwarg(kwargs, "", "linked_with_uuid")
self._linked_with_index = self.get_kwarg(kwargs, -1,
"linked_with_index")
# Set inherit flags:
self.inherit_d001 = self.get_kwarg(kwargs, False, "inherit_d001")
self.inherit_ucp_a = self.get_kwarg(kwargs, False, "inherit_ucp_a",
"inherit_cell_a")
self.inherit_ucp_b = self.get_kwarg(kwargs, False, "inherit_ucp_b",
"inherit_cell_b")
self.inherit_default_c = self.get_kwarg(kwargs, False,
"inherit_default_c")
self.inherit_delta_c = self.get_kwarg(kwargs, False, "inherit_delta_c")
self.inherit_layer_atoms = self.get_kwarg(kwargs, False,
"inherit_layer_atoms")
self.inherit_interlayer_atoms = self.get_kwarg(
kwargs, False, "inherit_interlayer_atoms")
self.inherit_atom_relations = self.get_kwarg(kwargs, False,
"inherit_atom_relations")
def __repr__(self):
return "Component(name='%s', linked_with=%r)" % (self.name,
self.linked_with)
# ------------------------------------------------------------
# Notifications of observable properties
# ------------------------------------------------------------
@DataModel.observe("data_changed", signal=True)
def _on_data_model_changed(self, model, prop_name, info):
# Check whether the changed model is an AtomRelation or Atom, if so
# re-apply the atom_relations.
with self.data_changed.hold():
if isinstance(model, AtomRelation) or isinstance(model, Atom):
self._apply_atom_relations()
self._update_ucp_values()
if isinstance(model, UnitCellProperty):
self.data_changed.emit() # propagate signal
@DataModel.observe("removed", signal=True)
def _on_data_model_removed(self, model, prop_name, info):
# Check whether the removed component is linked with this one, if so
# clears the link and emits the data_changed signal.
if model != self and self.linked_with is not None and self.linked_with == model:
with self.data_changed.hold_and_emit():
self.linked_with = None
def _on_layer_atom_inserted(self, atom):
"""Sets the atoms parent and stretch_values property,
updates the components lattice d-value, and emits a data_changed signal"""
with self.data_changed.hold_and_emit():
with self.atoms_changed.hold_and_emit():
atom.parent = self
atom.stretch_values = False
self.observe_model(atom)
self.update_lattice_d()
def _on_layer_atom_removed(self, atom):
"""Clears the atoms parent, updates the components lattice d-value, and
emits a data_changed signal"""
with self.data_changed.hold_and_emit():
with self.atoms_changed.hold_and_emit():
self.relieve_model(atom)
atom.parent = None
self.update_lattice_d()
def _on_interlayer_atom_inserted(self, atom):
"""Sets the atoms parent and stretch_values property,
and emits a data_changed signal"""
with self.data_changed.hold_and_emit():
with self.atoms_changed.hold_and_emit():
atom.stretch_values = True
atom.parent = self
def _on_interlayer_atom_removed(self, atom):
"""Clears the atoms parent property,
and emits a data_changed signal"""
with self.data_changed.hold_and_emit():
with self.atoms_changed.hold_and_emit():
atom.parent = None
def _on_atom_relation_inserted(self, item):
item.parent = self
self.observe_model(item)
self._apply_atom_relations()
def _on_atom_relation_removed(self, item):
self.relieve_model(item)
item.parent = None
self._apply_atom_relations()
# ------------------------------------------------------------
# Input/Output stuff
# ------------------------------------------------------------
def resolve_json_references(self):
for atom in type(self).layer_atoms._get(self):
atom.resolve_json_references()
for atom in type(self).interlayer_atoms._get(self):
atom.resolve_json_references()
type(self).ucp_a._get(self).resolve_json_references()
type(self).ucp_a._get(self).update_value()
type(self).ucp_b._get(self).resolve_json_references()
type(self).ucp_b._get(self).update_value()
if getattr(self, "_linked_with_uuid", None):
self.linked_with = type(type(self)).object_pool.get_object(
self._linked_with_uuid)
del self._linked_with_uuid
elif getattr(self, "_linked_with_index",
None) and self._linked_with_index != -1:
warn(
"The use of object indeces is deprected since version 0.4. Please switch to using object UUIDs.",
DeprecationWarning)
self.linked_with = self.parent.based_on.components.get_user_from_index(
self._linked_with_index)
del self._linked_with_index
@classmethod
def save_components(cls, components, filename):
"""
Saves multiple components to a single file.
"""
Component.export_atom_types = True
for comp in components:
comp.save_links = False
with zipfile.ZipFile(filename, 'w', compression=COMPRESSION) as zfile:
for component in components:
zfile.writestr(component.uuid, component.dump_object())
for comp in components:
comp.save_links = True
Component.export_atom_types = False
# After export we change all the UUID's
# This way, we're sure that we're not going to import objects with
# duplicate UUID's!
type(cls).object_pool.change_all_uuids()
@classmethod
def load_components(cls, filename, parent=None):
"""
Returns multiple components loaded from a single file.
"""
# Before import, we change all the UUID's
# This way we're sure that we're not going to import objects
# with duplicate UUID's!
type(cls).object_pool.change_all_uuids()
if zipfile.is_zipfile(filename):
with zipfile.ZipFile(filename, 'r') as zfile:
for uuid in zfile.namelist():
obj = JSONParser.parse(zfile.open(uuid))
obj.parent = parent
yield obj
else:
obj = JSONParser.parse(filename)
obj.parent = parent
yield obj
def json_properties(self):
if self.phase == None or not self.save_links:
retval = Storable.json_properties(self)
for prop in self.Meta.all_properties:
if getattr(prop, "inherit_flag", False):
retval[prop.inherit_flag] = False
else:
retval = Storable.json_properties(self)
retval[
"linked_with_uuid"] = self.linked_with.uuid if self.linked_with is not None else ""
return retval
# ------------------------------------------------------------
# Methods & Functions
# ------------------------------------------------------------
def get_factors(self, range_stl):
"""
Get the structure factor for the given range of sin(theta)/lambda values.
:param range_stl: A 1D numpy ndarray
"""
return get_factors(range_stl, self.data_object)
def get_interlayer_stretch_factors(self):
z_factor = (self.cell_c - self.lattice_d) / (self.default_c -
self.lattice_d)
return self.lattice_d, z_factor
def update_lattice_d(self):
"""
Updates the lattice_d attribute for this :class:`~.Component`.
Should normally not be called from outside the component.
"""
for atom in self.layer_atoms:
self.lattice_d = float(max(self.lattice_d, atom.default_z))
def _apply_atom_relations(self):
"""
Applies the :class:`~..atom_relations.AtomRelation` objects
in this component. Should normally not be called from outside the component.
"""
with self.data_changed.hold_and_emit():
for relation in self.atom_relations:
# Clear the 'driven by' flags:
relation.driven_by_other = False
for relation in self.atom_relations:
# Apply the relations, will also take care of flag setting:
relation.apply_relation()
def _update_ucp_values(self):
"""
Updates the :class:`~..unit_cell_prop.UnitCellProperty` objects in this
component. Should normally not be called from outside the component.
"""
with self.data_changed.hold():
for ucp in [self._ucp_a, self._ucp_b]:
ucp.update_value()
def get_volume(self):
"""
Get the volume for this :class:`~.Component`.
Will always return a value >= 1e-25, to prevent division-by-zero
errors in calculation code.
"""
return max(self.cell_a * self.cell_b * self.cell_c, 1e-25)
def get_weight(self):
"""
Get the total atomic weight for this
:class:`~.Component`.
"""
weight = 0
for atom in (self.layer_atoms + self.interlayer_atoms):
weight += atom.weight
return weight
# ------------------------------------------------------------
# AtomRelation list related
# ------------------------------------------------------------
def move_atom_relation_up(self, relation):
"""
Move the passed :class:`~..atom_relations.AtomRelation`
up one slot
"""
index = self.atom_relations.index(relation)
del self.atom_relations[index]
self.atom_relations.insert(max(index - 1, 0), relation)
def move_atom_relation_down(self, relation):
"""
Move the passed :class:`~..atom_relations.AtomRelation`
down one slot
"""
index = self.atom_relations.index(relation)
del self.atom_relations[index]
self.atom_relations.insert(min(index + 1, len(self.atom_relations)),
relation)
pass # end of class
class R1G2Model(_AbstractProbability):
r"""
Probability model for Reichweite 1 with 2 components.
The 2(=g*(g-1)) independent variables are:
.. math::
:nowrap:
\begin{flalign*}
& W_1 \\
& \text{$P_{11} (W_1 < 0.5)$ or $P_{22} (W_1 > 0.5)$}
\end{flalign*}
Calculation of the other variables happens as follows:
.. math::
:nowrap:
\begin{align*}
& W_2 = 1 – W_1 \\
& \begin{aligned}
& \text{$P_{11}$ is given:} \\
& \quad P_{12} = 1 - P_{11} \\
& \quad P_{21} = \frac{W_1 \cdot P_{12}}{W2} \\
& \quad P_{22} = 1 - P_{21} \\
\end{aligned}
\quad \quad
\begin{aligned}
& \text{$P_{22}$ is given:} \\
& \quad P_{21} = 1 - P_{22} \\
& \quad P_{12} = \frac{W_2 \cdot P_{21}}{W1} \\
& \quad P_{11} = 1 - P_{12} \\
\end{aligned} \\
\end{align*}
"""
# MODEL METADATA:
class Meta(_AbstractProbability.Meta):
store_id = "R1G2Model"
# PROPERTIES:
_G = 2
inherit_W1 = BoolProperty(default=False,
text="Inherit flag for W1",
persistent=True,
visible=True,
set_action_name="update",
mix_with=(SetActionMixin, ))
W1 = FloatProperty(default=0.0,
text="W1",
math_text=r"$W_1$",
persistent=True,
visible=True,
refinable=True,
store_private=True,
minimum=0.0,
maximum=1.0,
is_independent=True,
inheritable=True,
inherit_flag="inherit_W1",
inherit_from="parent.based_on.probabilities.W1",
set_action_name="update",
mix_with=(SetActionMixin, RefinableMixin,
InheritableMixin))
inherit_P11_or_P22 = BoolProperty(default=False,
text="Inherit flag for P11_or_P22",
persistent=True,
visible=True,
set_action_name="update",
mix_with=(SetActionMixin, ))
P11_or_P22 = FloatProperty(
default=0.0,
text="P11_or_P22",
math_text=r"$P_{11} %s$ or $\newline P_{22} %s$" %
(mt_range(0.0, "W_1", 0.5), mt_range(0.5, "W_1", 1.0)),
persistent=True,
visible=True,
refinable=True,
store_private=True,
minimum=0.0,
maximum=1.0,
is_independent=True,
inheritable=True,
inherit_flag="inherit_P11_or_P22",
inherit_from="parent.based_on.probabilities.P11_or_P22",
set_action_name="update",
mix_with=(SetActionMixin, RefinableMixin, InheritableMixin))
# ------------------------------------------------------------
# Initialization and other internals
# ------------------------------------------------------------
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()
# ------------------------------------------------------------
# Methods & Functions
# ------------------------------------------------------------
def update(self):
with self.monitor_changes():
self.mW[0] = self.W1
self.mW[1] = 1.0 - self.mW[0]
if self.mW[0] <= 0.5:
self.mP[0, 0] = self.P11_or_P22
self.mP[0, 1] = 1.0 - self.mP[0, 0]
self.mP[1, 0] = self.mW[0] * self.mP[0, 1] / self.mW[1]
self.mP[1, 1] = 1.0 - self.mP[1, 0]
else:
self.mP[1, 1] = self.P11_or_P22
self.mP[1, 0] = 1.0 - self.mP[1, 1]
self.mP[0, 1] = self.mW[1] * self.mP[1, 0] / self.mW[0]
self.mP[0, 0] = 1.0 - self.mP[0, 1]
self.solve()
self.validate()
pass # end of class
class Goniometer(DataModel, Storable):
"""
The Goniometer class contains all the information related to the
X-ray diffraction goniometer, e.g. wavelength, radius, slit sizes, ...
"""
# MODEL INTEL:
class Meta(DataModel.Meta):
store_id = "Goniometer"
_data_object = None
@property
def data_object(self):
self._data_object.wavelength = self.wavelength
x, y = self.wavelength_distribution.get_xy_data()
self._data_object.wavelength_distribution = list(
zip(x.tolist(), y.tolist()))
return self._data_object
specimen = property(DataModel.parent.fget, DataModel.parent.fset)
# PROPERTIES:
#: Start angle (in °2-theta, only used when calculating without
#: experimental data)
min_2theta = FloatProperty(default=3.0,
text="Start angle",
minimum=0.0,
maximum=180.0,
tabular=True,
persistent=True,
visible=True,
signal_name="data_changed",
widget_type="spin",
mix_with=(DataMixin, SignalMixin))
#: End angle (in °2-theta, only used when calculating without
#: experimental data)
max_2theta = FloatProperty(default=3.0,
text="End angle",
minimum=0.0,
maximum=180.0,
tabular=True,
persistent=True,
visible=True,
signal_name="data_changed",
widget_type="spin",
mix_with=(DataMixin, SignalMixin))
#: The number of steps between start and end angle
steps = IntegerProperty(default=2500,
text="Steps",
minimum=0,
maximum=10000,
tabular=True,
persistent=True,
visible=True,
signal_name="data_changed",
widget_type="spin",
mix_with=(DataMixin, SignalMixin))
#: The wavelength distribution
wavelength_distribution = LabeledProperty(default=None,
text="Wavelength distribution",
tabular=False,
persistent=True,
visible=True,
signal_name="data_changed",
widget_type="xy_list_view",
mix_with=(SignalMixin,
ObserveMixin))
@FloatProperty(default=0.154056,
text="Wavelength",
tabular=True,
persistent=False,
visible=False,
signal_name="data_changed",
mix_with=(ReadOnlyMixin, ))
def wavelength(self):
"""The wavelength of the generated X-rays (in nm)"""
# Get the dominant wavelength in the distribution:
x, y = self.wavelength_distribution.get_xy_data()
wl = float(x[np.argmax(y)])
return wl
#: Flag indicating if the first soller slit is present or not
has_soller1 = BoolProperty(default=True,
text="Soller 1",
tabular=True,
persistent=True,
visible=True,
signal_name="data_changed",
mix_with=(DataMixin, SignalMixin))
#: The first Soller slit size (in °)
soller1 = FloatProperty(default=2.3,
text="Soller 1",
minimum=0.0,
maximum=10.0,
tabular=True,
persistent=True,
visible=True,
signal_name="data_changed",
widget_type="spin",
mix_with=(DataMixin, SignalMixin))
#: Flag indicating if the second soller slit is present or not
has_soller2 = BoolProperty(default=True,
text="Soller 2",
tabular=True,
persistent=True,
visible=True,
signal_name="data_changed",
mix_with=(DataMixin, SignalMixin))
#: The second Soller slit size (in °)
soller2 = FloatProperty(default=2.3,
text="Soller 2",
minimum=0.0,
maximum=10.0,
tabular=True,
persistent=True,
visible=True,
signal_name="data_changed",
widget_type="spin",
mix_with=(DataMixin, SignalMixin))
#: The radius of the goniometer (in cm)
radius = FloatProperty(default=24.0,
text="Radius",
minimum=0.0,
maximum=200.0,
tabular=True,
persistent=True,
visible=True,
signal_name="data_changed",
widget_type="spin",
mix_with=(DataMixin, SignalMixin))
#: The divergence slit mode of the goniometer
divergence_mode = StringChoiceProperty(
default=settings.DEFAULT_DIVERGENCE_MODE,
text="Divergence mode",
visible=True,
persistent=True,
choices=settings.DIVERGENCE_MODES,
signal_name="data_changed",
mix_with=(
DataMixin,
SignalMixin,
))
#: The divergence slit size (if fixed) or irradiated sample length (if automatic)
divergence = FloatProperty(default=0.5,
text="Divergence",
minimum=0.0,
maximum=90.0,
tabular=True,
persistent=True,
visible=True,
signal_name="data_changed",
widget_type="spin",
mix_with=(DataMixin, SignalMixin))
#: Flag indicating if the second soller slit is present or not
has_absorption_correction = BoolProperty(default=False,
text="Absorption correction",
tabular=True,
persistent=True,
visible=True,
signal_name="data_changed",
mix_with=(DataMixin, SignalMixin))
#: The actual sample length
sample_length = FloatProperty(default=1.25,
text="Sample length [cm]",
minimum=0.0,
visible=True,
persistent=True,
tabular=True,
signal_name="data_changed",
widget_type="spin",
mix_with=(DataMixin, SignalMixin))
#: The sample surface density
sample_surf_density = FloatProperty(default=20.0,
text="Sample surface density [mg/cm²]",
minimum=0.0,
visible=True,
persistent=True,
tabular=True,
signal_name="data_changed",
widget_type="spin",
mix_with=(DataMixin, SignalMixin))
#: The sample mass absorption coefficient
absorption = FloatProperty(default=45.0,
text="Mass attenuation coeff. [cm²/g]",
minimum=0.0,
visible=True,
persistent=True,
tabular=True,
signal_name="data_changed",
widget_type="spin",
mix_with=(DataMixin, SignalMixin))
#: Angular value (in degrees) for a monochromator correction - use 28.44 for silicon and 26.53 for carbon.
mcr_2theta = FloatProperty(default=0.0,
text="Monochromator 2θ",
minimum=0.0,
maximum=90.0,
tabular=True,
persistent=True,
visible=True,
signal_name="data_changed",
widget_type="spin",
mix_with=(
DataMixin,
SignalMixin,
))
# ------------------------------------------------------------
# Initialization and other internals
# ------------------------------------------------------------
def __init__(self, *args, **kwargs):
"""
Constructor takes any of its properties as a keyword argument.
In addition to the above, the constructor still supports the
following deprecated keywords, mapping to a current keyword:
- lambda: maps to wavelength
Any other arguments or keywords are passed to the base class.
"""
my_kwargs = self.pop_kwargs(
kwargs, "data_radius", "data_divergence", "data_soller1",
"data_soller2", "data_min_2theta", "data_max_2theta",
"data_lambda", "lambda", "wavelength", "has_ads", "ads_fact",
"ads_phase_fact", "ads_phase_shift", "ads_const", *[
prop.label
for prop in Goniometer.Meta.get_local_persistent_properties()
])
super(Goniometer, self).__init__(*args, **kwargs)
kwargs = my_kwargs
self._data_object = GonioData()
with self.data_changed.hold():
self.radius = self.get_kwarg(kwargs, 24.0, "radius", "data_radius")
#: Parse divergence mode (including old-style keywords):
new_div_mode = self.get_kwarg(kwargs, None, "divergence_mode")
if new_div_mode is None: # old style project
old_ads = self.get_kwarg(kwargs, None, "has_ads")
if old_ads is not None and old_ads: # if we have ads, set as such:
new_div_mode = "AUTOMATIC"
else: # otherwise it was angular fixed slits
new_div_mode = settings.DEFAULT_DIVERGENCE_MODE
self.divergence_mode = new_div_mode
# Divergence value:
self.divergence = self.get_kwarg(kwargs, 0.5, "divergence",
"data_divergence")
# Monochromator correction:
self.mcr_2theta = float(self.get_kwarg(kwargs, 0, "mcr_2theta"))
# Soller slits:
self.has_soller1 = self.get_kwarg(kwargs, True, "has_soller1")
self.soller1 = float(
self.get_kwarg(kwargs, 2.3, "soller1", "data_soller1"))
self.has_soller2 = self.get_kwarg(kwargs, True, "has_soller2")
self.soller2 = float(
self.get_kwarg(kwargs, 2.3, "soller2", "data_soller2"))
# Angular range settings for calculated patterns:
self.min_2theta = float(
self.get_kwarg(kwargs, 3.0, "min_2theta", "data_min_2theta"))
self.max_2theta = float(
self.get_kwarg(kwargs, 45.0, "max_2theta", "data_max_2theta"))
self.steps = int(self.get_kwarg(kwargs, 2500, "steps"))
# Sample characteristics
self.sample_length = float(
self.get_kwarg(kwargs, settings.DEFAULT_SAMPLE_LENGTH,
"sample_length"))
self.absorption = float(self.get_kwarg(kwargs, 45.0, "absorption"))
self.sample_surf_density = float(
self.get_kwarg(kwargs, 20.0, "sample_surf_density"))
self.has_absorption_correction = bool(
self.get_kwarg(kwargs, False, "has_absorption_correction"))
wavelength = self.get_kwarg(kwargs, None, "wavelength",
"data_lambda", "lambda")
if not "wavelength_distribution" in kwargs and wavelength is not None:
default_wld = [
[wavelength, 1.0],
]
else:
# A Cu wld:
default_wld = [
[0.1544426, 0.955148885],
[0.153475, 0.044851115],
]
self.wavelength_distribution = StorableXYData(
data=self.get_kwarg(kwargs, list(zip(
*default_wld)), "wavelength_distribution"))
# ------------------------------------------------------------
# Input/Output stuff
# ------------------------------------------------------------
def __reduce__(self):
return (type(self), ((), self.json_properties()))
def json_properties(self):
props = Storable.json_properties(self)
props[
"wavelength_distribution"] = self.wavelength_distribution._serialize_data(
)
return props
def reset_from_file(self, gonfile):
"""
Loads & sets the parameters from the goniometer JSON file
specified by `gonfile`, can be a filename or a file-like object.
"""
new_gonio = JSONParser.parse(gonfile)
with self.data_changed.hold():
for prop in self.Meta.all_properties:
if prop.persistent:
if prop.label == "wavelength_distribution":
self.wavelength_distribution.clear()
self.wavelength_distribution.set_data(
*new_gonio.wavelength_distribution.get_xy_data())
elif prop.label != "uuid":
setattr(self, prop.label,
getattr(new_gonio, prop.label))
# ------------------------------------------------------------
# Methods & Functions
# ------------------------------------------------------------
def get_nm_from_t(self, theta):
"""Converts a theta position to a nanometer value"""
return get_nm_from_t(theta,
wavelength=self.wavelength,
zero_for_inf=True)
def get_nm_from_2t(self, twotheta):
"""Converts a 2-theta position to a nanometer value"""
return get_nm_from_2t(twotheta,
wavelength=self.wavelength,
zero_for_inf=True)
def get_t_from_nm(self, nm):
""" Converts a nanometer value to a theta position"""
return get_t_from_nm(nm, wavelength=self.wavelength)
def get_2t_from_nm(self, nm):
""" Converts a nanometer value to a 2-theta position"""
return get_2t_from_nm(nm, wavelength=self.wavelength)
def get_default_theta_range(self, as_radians=True):
"""
Returns a numpy array containing the theta values as radians from
`min_2theta` to `max_2theta` with `steps` controlling the interval.
When `as_radians` is set to False the values are returned as degrees.
"""
def torad(val):
if as_radians:
return radians(val)
else:
return val
min_theta = torad(self.min_2theta * 0.5)
max_theta = torad(self.max_2theta * 0.5)
delta_theta = float(max_theta - min_theta) / float(self.steps)
theta_range = (min_theta + delta_theta * np.arange(
0, self.steps, dtype=float)) + delta_theta * 0.5
return theta_range
def get_lorentz_polarisation_factor(self, range_theta, sigma_star):
"""
Calculates Lorentz polarization factor for the given theta range
and sigma-star value using the information about the goniometer's
geometry.
"""
return get_lorentz_polarisation_factor(range_theta, sigma_star,
self.soller1, self.soller2,
self.mcr_2theta)
def get_ADS_to_fixed_correction(self, range_theta):
"""
Returns a correction range that will convert ADS data to fixed slit
data. Use with caution.
"""
return 1.0 / get_fixed_to_ads_correction_range(range_theta,
self.data_object)
pass # end of class
class R1G4Model(_AbstractProbability):
r"""
Probability model for Reichweite 1 with 4 components.
The independent variables (# = g*(g-1) = 12) are:
.. math::
:nowrap:
\begin{align*}
& W_1
& P_{11} (W_1 < 0,5)\text{ or }P_{xx} (W_1 > 0,5)
with P_{xx} = \frac {W_{22} + W_{23} + W_{24} + W_{32} + W_{33} + W_{34} + W_{42} + W_{43} + W_{44}}{W_2 + W_3 + W_4} \\
& R_2 = \frac{ W_2 }{W_2 + W_3 + W_4}
& R_3 = \frac{ W_3 }{W_3 + W_4} \\
& G_2 = \frac{W_{22} + W_{23} + W_{24}}{\sum_{i=2}^{4}\sum_{j=2}^4{W_{ij}}}
& G_3 = \frac{W_{32} + W_{33} + W_{34}}{\sum_{i=3}^{4}\sum_{j=2}^4{W_{ij}}} \\
& G_{22} = \frac{W_{22}}{W_{22} + W_{23} + W_{24}}
& G_{23} = \frac{W_{23}}{W_{23} + W_{24}} \\
& G_{32} = \frac{W_{32}}{W_{32} + W_{33} + W_{34}}
& G_{33} = \frac{W_{33}}{W_{33} + W_{34}} \\
& G_{42} = \frac{W_{42}}{W_{42} + W_{43} + W_{44}}
& G_{44} = \frac{W_{43}}{W_{43} + W_{44}}
\end{align*}
Calculation of the other variables happens as follows:
.. math::
:nowrap:
\begin{align*}
& \text{Calculate the base weight fractions of each component:} \\
& W_2 = (1 - W_1) \cdot R_1 \\
& W_3 = (1 - W_1 - W_2) \cdot R_2 \\
& W_4 = (1 - W_1 - W_2 - W_3) \\
& \\
& \text{if $W_1 \leq 0.5$:} \\
& \quad \text{$P_{11}$ is given}\\
& \quad W_{xx} = W_{22} + W_{23} + W_{24} + W_{32} + W_{33} + W_{34} + W_{42} + W_{43} + W_{44} = W_1 \cdot (1 - P_{11}) + W_2 + W_3 + W_4 \\
& \text{if $W_1 > 0.5$:} \\
& \quad \text{$P_{xx}$ is given and $P_{11}$ is derived further down} \\
& \quad W_{xx} = W_{22} + W_{23} + W_{24} + W_{32} + W_{33} + W_{34} + W_{42} + W_{43} + W_{44} = P_{xx} \cdot (W_2 + W_3 + W_4) \\
& \\
& \text{Caclulate a partial sum of the $2^{nd}$ component's contributions: } \\
& W_{2x} = W_{xx} \cdot G_2 \\
& \text{Calculate a partial sum of the $3^{d}$ and $4^{th}$ component's contributions:} \\
& W_{yx} = W_{xx} - W_{2x} \\
& \text{Calculate a partial sum of the $3^{d}$ component's contributions:} \\
& W_{3x} = W_{yx} \cdot G_3 \\
& \text{Calculate a partial sum of the $4^{th}$ component's contributions:} \\
& W_{4x} = W_{yx} - W_{3x} \\
& \\
& W_{22} = G_{22} \cdot W_{2x} \\
& W_{23} = G_{23} \cdot (W_{2x} - W_{22}) \\
& W_{24} = W{2x} - W_{22} - W_{23} \\
& \\
& W_{32} = G_{32} \cdot W_{3x} \\
& W_{33} = G_{33} \cdot (W_{3x} - W_{32}) \\
& W_{34} = W{3x} - W_{32} - W_{33} \\
& \\
& W_{42} = G_{42} \cdot W_{4x} \\
& W_{43} = G_{43} \cdot (W_{4x} - W_{42}) \\
& W_{44} = W{4x} - W_{42} - W_{43} \\
& \\
& \text{ From the above weight fractions the junction probabilities
for any combination of $2^{nd}$, $3^{d}$ and $4^{th}$ type
components can be calculated. } \\
& \text{ The remaining probabilities are: } \\
& P_{12} = \begin{dcases}
\frac{W_2 - W_{22} - W_{32} - W_{42}}{W_1}, & \text{if $W_1 > 0$} \\
0, & \text{otherwise}
\end{dcases} \\
& P_{13} = \begin{dcases}
\frac{W_3 - W_{23} - W_{33} - W_{43}}{W_1}, & \text{if $W_1 > 0$} \\
0, & \text{otherwise}
\end{dcases} \\
& P_{14} = \begin{dcases}
\frac{W_4 - W_{24} - W_{34} - W_{44}}{W_1}, & \text{if $W_1 > 0$} \\
0, & \text{otherwise}
\end{dcases} \\
& \\
& \text{if $W_1 \leq 0.5$}: \\
& \quad P_{11} = 1 - P_{12} - P_{13} - P_{14} \\
& \text{Remainder of weight fraction can now be calculated as follows:} \\
& \quad W_{ij} = {W_{ii}} \cdot {P_{ij}} \quad \forall {i,j} \in \left[ {1, 4} \right] \\
\end{align*}
"""
# MODEL METADATA:
class Meta(_AbstractProbability.Meta):
store_id = "R1G4Model"
# PROPERTIES
_G = 4
inherit_W1 = BoolProperty(default=False,
text="Inherit flag for W1",
persistent=True,
visible=True,
set_action_name="update",
mix_with=(SetActionMixin, ))
W1 = FloatProperty(default=0.6,
text="W1",
math_text=r"$W_1$",
persistent=True,
visible=True,
refinable=True,
store_private=True,
minimum=0.0,
maximum=1.0,
is_independent=True,
inheritable=True,
inherit_flag="inherit_W1",
inherit_from="parent.based_on.probabilities.W1",
set_action_name="update",
mix_with=(SetActionMixin, RefinableMixin,
InheritableMixin))
inherit_P11_or_P22 = BoolProperty(default=False,
text="Inherit flag for P11_or_P22",
persistent=True,
visible=True,
set_action_name="update",
mix_with=(SetActionMixin, ))
P11_or_P22 = FloatProperty(
default=0.25,
text="P11_or_P22",
math_text=r"$P_{11} %s$ or $\newline P_{22} %s$" %
(mt_range(0.0, "W_1", 0.5), mt_range(0.5, "W_1", 1.0)),
persistent=True,
visible=True,
refinable=True,
store_private=True,
minimum=0.0,
maximum=1.0,
is_independent=True,
inheritable=True,
inherit_flag="inherit_P11_or_P22",
inherit_from="parent.based_on.probabilities.P11_or_P22",
set_action_name="update",
mix_with=(SetActionMixin, RefinableMixin, InheritableMixin))
inherit_R1 = BoolProperty(default=False,
text="Inherit flag for R1",
persistent=True,
visible=True,
set_action_name="update",
mix_with=(SetActionMixin, ))
R1 = FloatProperty(default=0.5,
text="W2/(W2+W3+W4)",
math_text=r"$\large\frac{W_2}{W_2 + W_3 + W_4}$",
persistent=True,
visible=True,
refinable=True,
store_private=True,
minimum=0.0,
maximum=1.0,
is_independent=True,
inheritable=True,
inherit_flag="inherit_R1",
inherit_from="parent.based_on.probabilities.R1",
set_action_name="update",
mix_with=(SetActionMixin, RefinableMixin,
InheritableMixin))
inherit_R2 = BoolProperty(default=False,
text="Inherit flag for R2",
persistent=True,
visible=True,
set_action_name="update",
mix_with=(SetActionMixin, ))
R2 = FloatProperty(default=0.5,
text="W3/(W3+W4)",
math_text=r"$\large\frac{W_3}{W_3 + W_4}$",
persistent=True,
visible=True,
refinable=True,
store_private=True,
minimum=0.0,
maximum=1.0,
is_independent=True,
inheritable=True,
inherit_flag="inherit_R2",
inherit_from="parent.based_on.probabilities.R2",
set_action_name="update",
mix_with=(SetActionMixin, RefinableMixin,
InheritableMixin))
inherit_G1 = BoolProperty(default=False,
text="Inherit flag for G1",
persistent=True,
visible=True,
set_action_name="update",
mix_with=(SetActionMixin, ))
G1 = FloatProperty(
default=0.5,
text="(W22+W23+W24)/(W22+W23+W24+W32+W33+W34+W42+W43+W44)",
math_text=
r"$\large\frac{\sum_{j=2}^{4} W_{2j}}{\sum_{i=2}^{4} \sum_{j=2}^{4} W_{ij}}$",
persistent=True,
visible=True,
refinable=True,
store_private=True,
minimum=0.0,
maximum=1.0,
is_independent=True,
inheritable=True,
inherit_flag="inherit_G1",
inherit_from="parent.based_on.probabilities.G1",
set_action_name="update",
mix_with=(SetActionMixin, RefinableMixin, InheritableMixin))
inherit_G2 = BoolProperty(default=False,
text="Inherit flag for G2",
persistent=True,
visible=True,
set_action_name="update",
mix_with=(SetActionMixin, ))
G2 = FloatProperty(
default=0.4,
text="(W32+W33+W34)/(W32+W33+W34+W42+W43+W44)",
math_text=
r"$\large\frac{\sum_{j=2}^{4} W_{3j}}{\sum_{i=3}^{4} \sum_{j=2}^{4} W_{ij}}$",
persistent=True,
visible=True,
refinable=True,
store_private=True,
minimum=0.0,
maximum=1.0,
is_independent=True,
inheritable=True,
inherit_flag="inherit_G2",
inherit_from="parent.based_on.probabilities.G2",
set_action_name="update",
mix_with=(SetActionMixin, RefinableMixin, InheritableMixin))
inherit_G11 = BoolProperty(default=False,
text="Inherit flag for G11",
persistent=True,
visible=True,
set_action_name="update",
mix_with=(SetActionMixin, ))
G11 = FloatProperty(
default=0.5,
text="W22/(W22+W23+W24)",
math_text=r"$\large\frac{W_{22}}{\sum_{j=2}^{4} W_{2j}}$",
persistent=True,
visible=True,
refinable=True,
store_private=True,
minimum=0.0,
maximum=1.0,
is_independent=True,
inheritable=True,
inherit_flag="inherit_G11",
inherit_from="parent.based_on.probabilities.G11",
set_action_name="update",
mix_with=(SetActionMixin, RefinableMixin, InheritableMixin))
inherit_G12 = BoolProperty(default=False,
text="Inherit flag for G12",
persistent=True,
visible=True,
set_action_name="update",
mix_with=(SetActionMixin, ))
G12 = FloatProperty(
default=0.5,
text="W23/(W23+W24)",
math_text=r"$\large\frac{W_{23}}{\sum_{j=3}^{4} W_{2j}}$",
persistent=True,
visible=True,
refinable=True,
store_private=True,
minimum=0.0,
maximum=1.0,
is_independent=True,
inheritable=True,
inherit_flag="inherit_G12",
inherit_from="parent.based_on.probabilities.G12",
set_action_name="update",
mix_with=(SetActionMixin, RefinableMixin, InheritableMixin))
inherit_G21 = BoolProperty(default=False,
text="Inherit flag for G21",
persistent=True,
visible=True,
set_action_name="update",
mix_with=(SetActionMixin, ))
G21 = FloatProperty(
default=0.8,
text="W32/(W32+W33+W34)",
math_text=r"$\large\frac{W_{32}}{\sum_{j=2}^{4} W_{3j}}$",
persistent=True,
visible=True,
refinable=True,
store_private=True,
minimum=0.0,
maximum=1.0,
is_independent=True,
inheritable=True,
inherit_flag="inherit_G21",
inherit_from="parent.based_on.probabilities.G21",
set_action_name="update",
mix_with=(SetActionMixin, RefinableMixin, InheritableMixin))
inherit_G22 = BoolProperty(default=False,
text="Inherit flag for G22",
persistent=True,
visible=True,
set_action_name="update",
mix_with=(SetActionMixin, ))
G22 = FloatProperty(
default=0.8,
text="W33/(W32+W34)",
math_text=r"$\large\frac{W_{33}}{\sum_{j=3}^{4} W_{3j}}$",
persistent=True,
visible=True,
refinable=True,
store_private=True,
minimum=0.0,
maximum=1.0,
is_independent=True,
inheritable=True,
inherit_flag="inherit_G22",
inherit_from="parent.based_on.probabilities.G22",
set_action_name="update",
mix_with=(SetActionMixin, RefinableMixin, InheritableMixin))
inherit_G31 = BoolProperty(default=False,
text="Inherit flag for G31",
persistent=True,
visible=True,
set_action_name="update",
mix_with=(SetActionMixin, ))
G31 = FloatProperty(
default=0.7,
text="W42/(W42+W43+W44)",
math_text=r"$\large\frac{W_{42}}{\sum_{j=2}^{4} W_{4j}}$",
persistent=True,
visible=True,
refinable=True,
store_private=True,
minimum=0.0,
maximum=1.0,
is_independent=True,
inheritable=True,
inherit_flag="inherit_G31",
inherit_from="parent.based_on.probabilities.G31",
set_action_name="update",
mix_with=(SetActionMixin, RefinableMixin, InheritableMixin))
inherit_G32 = BoolProperty(default=False,
text="Inherit flag for G32",
persistent=True,
visible=True,
set_action_name="update",
mix_with=(SetActionMixin, ))
G32 = FloatProperty(
default=0.5,
text="W43/(W43+W44)",
math_text=r"$\large\frac{W_{43}}{\sum_{j=3}^{4} W_{4j}}$",
persistent=True,
visible=True,
refinable=True,
store_private=True,
minimum=0.0,
maximum=1.0,
is_independent=True,
inheritable=True,
inherit_flag="inherit_G32",
inherit_from="parent.based_on.probabilities.G32",
set_action_name="update",
mix_with=(SetActionMixin, RefinableMixin, InheritableMixin))
# ------------------------------------------------------------
# Initialization and other internals
# ------------------------------------------------------------
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()
# ------------------------------------------------------------
# Methods & Functions
# ------------------------------------------------------------
def update(self):
with self.monitor_changes():
self.mW[0] = self.W1
self.mW[1] = (1.0 - self.mW[0]) * self.R1
self.mW[2] = (1.0 - self.mW[0] - self.mW[1]) * self.R2
self.mW[3] = 1.0 - self.mW[0] - self.mW[1] - self.mW[2]
W0inv = 1.0 / self.mW[0] if self.mW[0] > 0.0 else 0.0
if self.mW[0] < 0.5: # P11 is given
self.mP[0, 0] = self.P11_or_P22
Wxx = self.mW[0] * (self.mP[0, 0] -
1) + self.mW[1] + self.mW[2] + self.mW[3]
else: # P22 is given
Wxx = self.P11_or_P22 * (self.mW[1] + self.mW[2] + self.mW[3])
W1x = Wxx * self.G1 # = W11 + W12 + W13
Wyx = (Wxx - W1x) # = W21 + W22 + W23 + W31 + W32 + W33
W2x = Wyx * self.G2 # = W21 + W22 + W23
W3x = Wyx - W2x # = W31 + W32 + W33
self.mW[1, 1] = self.G11 * W1x
self.mW[1, 2] = self.G12 * (W1x - self.mW[1, 1])
self.mW[1, 3] = W1x - self.mW[1, 1] - self.mW[1, 2]
self.mW[2, 1] = self.G21 * W2x
self.mW[2, 2] = self.G22 * (W2x - self.mW[2, 1])
self.mW[2, 3] = W2x - self.mW[2, 1] - self.mW[2, 2]
self.mW[3, 1] = self.G31 * W3x
self.mW[3, 2] = self.G32 * (W3x - self.mW[3, 1])
self.mW[3, 3] = W3x - self.mW[3, 1] - self.mW[3, 2]
for i in range(1, 4):
self.mP[i, 0] = 1
for j in range(1, 4):
self.mP[i, j] = self.mW[
i, j] / self.mW[i] if self.mW[i] > 0 else 0
self.mP[i, 0] -= self.mP[i, j]
self.mW[i, 0] = self.mW[i] * self.mP[i, 0]
self.mP[0, 1] = (self.mW[1] - self.mW[1, 1] - self.mW[2, 1] -
self.mW[3, 1]) * W0inv
self.mP[0, 2] = (self.mW[2] - self.mW[1, 2] - self.mW[2, 2] -
self.mW[3, 2]) * W0inv
self.mP[0, 3] = (self.mW[3] - self.mW[1, 3] - self.mW[2, 3] -
self.mW[3, 3]) * W0inv
if self.mW[0] >= 0.5:
self.mP[0,
0] = 1 - self.mP[0, 1] - self.mP[0, 2] - self.mP[0, 3]
for i in range(4):
for j in range(4):
self.mW[i, j] = self.mW[i] * self.mP[i, j]
self.solve()
self.validate()
pass # end of class
class Phase(RefinementGroup, AbstractPhase, metaclass=PyXRDRefinableMeta):
# MODEL INTEL:
class Meta(AbstractPhase.Meta):
store_id = "Phase"
file_filters = [
("Phase file", get_case_insensitive_glob("*.PHS")),
]
_data_object = None
@property
def data_object(self):
self._data_object.type = "Phase"
self._data_object.valid_probs = (all(self.probabilities.P_valid)
and all(self.probabilities.W_valid))
if self._data_object.valid_probs:
self._data_object.sigma_star = self.sigma_star
self._data_object.CSDS = self.CSDS_distribution.data_object
self._data_object.G = self.G
self._data_object.W = self.probabilities.get_distribution_matrix()
self._data_object.P = self.probabilities.get_probability_matrix()
self._data_object.components = [None] * len(self.components)
for i, comp in enumerate(self.components):
self._data_object.components[i] = comp.data_object
else:
self._data_object.sigma_star = None
self._data_object.CSDS = None
self._data_object.G = None
self._data_object.W = None
self._data_object.P = None
self._data_object.components = None
return self._data_object
project = property(AbstractPhase.parent.fget, AbstractPhase.parent.fset)
# PROPERTIES:
#: Flag indicating whether the CSDS distribution is inherited from the
#: :attr:`based_on` phase or not.
@BoolProperty(default=False,
text="Inh. mean CSDS",
visible=True,
persistent=True,
tabular=True)
def inherit_CSDS_distribution(self):
return self._CSDS_distribution.inherited
@inherit_CSDS_distribution.setter
def inherit_CSDS_distribution(self, value):
self._CSDS_distribution.inherited = value
#: Flag indicating whether to inherit the display color from the
#: :attr:`based_on` phase or not.
inherit_display_color = BoolProperty(default=False,
text="Inh. display color",
visible=True,
persistent=True,
tabular=True,
signal_name="visuals_changed",
mix_with=(SignalMixin, ))
#: Flag indicating whether to inherit the sigma start value from the
#: :attr:`based_on` phase or not.
inherit_sigma_star = BoolProperty(default=False,
text="Inh. sigma star",
visible=True,
persistent=True,
tabular=True,
signal_name="data_changed",
mix_with=(SignalMixin, ))
_based_on_index = None # temporary property
_based_on_uuid = None # temporary property
#: The :class:`~Phase` instance this phase is based on
based_on = LabeledProperty(default=None,
text="Based on phase",
visible=True,
persistent=False,
tabular=True,
signal_name="data_changed",
mix_with=(SignalMixin, ObserveChildMixin))
@based_on.setter
def based_on(self, value):
old = type(self).based_on._get(self)
if value == None or value.get_based_on_root(
) == self or value.parent != self.parent:
value = None
if value != old:
type(self).based_on._set(self, value)
for component in self.components:
component.linked_with = None
# INHERITABLE PROPERTIES:
#: The sigma star orientation factor
sigma_star = FloatProperty(default=3.0,
text="σ* [°]",
math_text="$\sigma^*$ [°]",
minimum=0.0,
maximum=90.0,
visible=True,
persistent=True,
tabular=True,
refinable=True,
inheritable=True,
inherit_flag="inherit_sigma_star",
inherit_from="based_on.sigma_star",
signal_name="data_changed",
mix_with=(SignalMixin, RefinableMixin,
InheritableMixin))
# A :class:`~pyxrd.phases.models.CSDS` instance
CSDS_distribution = LabeledProperty(
default=None,
text="CSDS Distribution",
visible=True,
persistent=True,
tabular=True,
refinable=True,
inheritable=True,
inherit_flag="inherit_CSDS_distribution",
inherit_from="based_on.CSDS_distribution",
signal_name="data_changed",
mix_with=(SignalMixin, RefinableMixin, InheritableMixin,
ObserveChildMixin))
# A :class:`~pyxrd._probabilities.models._AbstractProbability` subclass instance
probabilities = LabeledProperty(default=None,
text="Probablities",
visible=True,
persistent=True,
tabular=True,
refinable=True,
signal_name="data_changed",
mix_with=(SignalMixin, RefinableMixin,
ObserveChildMixin))
@probabilities.setter
def probabilities(self, value):
type(self).probabilities._set(self, value)
if value is not None:
value.update()
#: The color this phase's X-ray diffraction pattern should have.
display_color = StringProperty(fset=AbstractPhase.display_color.fset,
fget=AbstractPhase.display_color.fget,
fdel=AbstractPhase.display_color.fdel,
doc=AbstractPhase.display_color.__doc__,
default="#008600",
text="Display color",
visible=True,
persistent=True,
tabular=True,
widget_type='color',
inheritable=True,
inherit_flag="inherit_display_color",
inherit_from="based_on.display_color",
signal_name="visuals_changed",
mix_with=(SignalMixin, InheritableMixin))
#: The list of components this phase consists of
components = ListProperty(default=None,
text="Components",
visible=True,
persistent=True,
tabular=True,
refinable=True,
widget_type="custom",
data_type=Component,
mix_with=(RefinableMixin, ))
#: The # of components
@AbstractPhase.G.getter
def G(self):
if self.components is not None:
return len(self.components)
else:
return 0
#: The # of components
@AbstractPhase.R.getter
def R(self):
if self.probabilities:
return self.probabilities.R
# Flag indicating whether or not the links (based_on and linked_with) should
# be saved as well.
save_links = True
# REFINEMENT GROUP IMPLEMENTATION:
@property
def refine_title(self):
return self.name
@property
def refine_descriptor_data(self):
return dict(phase_name=self.refine_title, component_name="*")
# ------------------------------------------------------------
# Initialization and other internals
# ------------------------------------------------------------
def __init__(self, *args, **kwargs):
my_kwargs = self.pop_kwargs(
kwargs, "data_CSDS_distribution", "data_sigma_star",
"data_components", "data_G", "G", "data_R", "R",
"data_probabilities", "based_on_uuid", "based_on_index",
"inherit_probabilities", *[
prop.label
for prop in Phase.Meta.get_local_persistent_properties()
])
super(Phase, self).__init__(*args, **kwargs)
kwargs = my_kwargs
with self.data_changed.hold():
CSDS_distribution = self.get_kwarg(kwargs, None,
"CSDS_distribution",
"data_CSDS_distribution")
self.CSDS_distribution = self.parse_init_arg(CSDS_distribution,
DritsCSDSDistribution,
child=True,
default_is_class=True,
parent=self)
self.inherit_CSDS_distribution = self.get_kwarg(
kwargs, False, "inherit_CSDS_distribution")
self.display_color = self.get_kwarg(kwargs,
choice(self.line_colors),
"display_color")
self.inherit_display_color = self.get_kwarg(
kwargs, False, "inherit_display_color")
self.sigma_star = self.get_kwarg(kwargs, self.sigma_star,
"sigma_star", "data_sigma_star")
self.inherit_sigma_star = self.get_kwarg(kwargs, False,
"inherit_sigma_star")
self.components = self.get_list(kwargs, [],
"components",
"data_components",
parent=self)
G = int(self.get_kwarg(kwargs, 1, "G", "data_G"))
R = int(self.get_kwarg(kwargs, 0, "R", "data_R"))
if G is not None and G > 0:
for i in range(len(self.components), G):
new_comp = Component(name="Component %d" % (i + 1),
parent=self)
self.components.append(new_comp)
self.observe_model(new_comp)
# Observe components
for component in self.components:
self.observe_model(component)
# Connect signals to lists and dicts:
self._components_observer = ListObserver(
self.on_component_inserted,
self.on_component_removed,
prop_name="components",
model=self)
self.probabilities = self.parse_init_arg(
self.get_kwarg(kwargs, None, "probabilities",
"data_probabilities"),
get_correct_probability_model(R, G),
default_is_class=True,
child=True)
self.probabilities.update() # force an update
inherit_probabilities = kwargs.pop("inherit_probabilities", None)
if inherit_probabilities is not None:
for prop in self.probabilities.Meta.get_inheritable_properties(
):
setattr(self.probabilities, prop.inherit_flag,
bool(inherit_probabilities))
self._based_on_uuid = self.get_kwarg(kwargs, None, "based_on_uuid")
self._based_on_index = self.get_kwarg(kwargs, None,
"based_on_index")
def __repr__(self):
return "Phase(name='%s', based_on=%r)" % (self.name, self.based_on)
# ------------------------------------------------------------
# Notifications of observable properties
# ------------------------------------------------------------
def on_component_inserted(self, item):
# Set parent and observe the new component (visuals changed signals):
if item.parent != self: item.parent = self
self.observe_model(item)
def on_component_removed(self, item):
with self.data_changed.hold_and_emit():
# Clear parent & stop observing:
item.parent = None
self.relieve_model(item)
@Observer.observe("data_changed", signal=True)
def notify_data_changed(self, model, prop_name, info):
if isinstance(model, Phase) and model == self.based_on:
with self.data_changed.hold():
# make sure inherited probabilities are up-to-date
self.probabilities.update()
self.data_changed.emit(arg="based_on")
else:
self.data_changed.emit()
@Observer.observe("visuals_changed", signal=True)
def notify_visuals_changed(self, model, prop_name, info):
self.visuals_changed.emit()
# ------------------------------------------------------------
# Input/Output stuff
# ------------------------------------------------------------
def resolve_json_references(self):
# Set the based on and linked with variables:
if hasattr(self, "_based_on_uuid") and self._based_on_uuid is not None:
self.based_on = type(type(self)).object_pool.get_object(
self._based_on_uuid)
del self._based_on_uuid
elif hasattr(
self, "_based_on_index"
) and self._based_on_index is not None and self._based_on_index != -1:
warn(
"The use of object indices is deprecated since version 0.4. Please switch to using object UUIDs.",
DeprecationWarning)
self.based_on = self.parent.phases.get_user_from_index(
self._based_on_index)
del self._based_on_index
for component in self.components:
component.resolve_json_references()
with self.data_changed.hold():
# make sure inherited probabilities are up-to-date
self.probabilities.update()
def _pre_multi_save(self, phases, ordered_phases):
## Override from base class
if self.based_on != "" and not self.based_on in phases:
self.save_links = False
Component.export_atom_types = True
for component in self.components:
component.save_links = self.save_links
# Make sure parent is first in ordered list:
if self.based_on in phases:
index = ordered_phases.index(self)
index2 = ordered_phases.index(self.based_on)
if index < index2:
ordered_phases.remove(self.based_on)
ordered_phases.insert(index, self.based_on)
def _post_multi_save(self):
## Override from base class
self.save_links = True
for component in self.components:
component.save_links = True
Component.export_atom_types = False
def json_properties(self):
retval = super(Phase, self).json_properties()
if not self.save_links:
for prop in self.Meta.all_properties:
if getattr(prop, "inherit_flag", False):
retval[prop.inherit_flag] = False
retval["based_on_uuid"] = ""
else:
retval[
"based_on_uuid"] = self.based_on.uuid if self.based_on else ""
return retval
# ------------------------------------------------------------
# Methods & Functions
# ------------------------------------------------------------
def _update_interference_distributions(self):
return self.CSDS_distribution.distrib
def get_based_on_root(self):
"""
Gets the root object in the based_on chain
"""
if self.based_on is not None:
return self.based_on.get_based_on_root()
else:
return self
pass # end of class
class R2G3Model(_AbstractProbability):
r"""
(Restricted) probability model for Reichweite 2 with 3 components.
The (due to restrictions only) 6 independent variables are:
.. math::
:nowrap:
\begin{align*}
& W_{1}
& P_{111} \text{(if $\nicefrac{1}{2} \leq W_1 < \nicefrac{2}{3}$) or} P_{x1x} \text{(if $\nicefrac{2}{3} \leq W_1 \leq 1)$ with $x \in \left\{ {2,3} \right\}$} \\
& G_1 = \frac{W_2}{W_2 + W_3}
& G_2 = \frac{W_{212} + W_{213}}{W_{212} + W_{213} + W_{312} + W_{313}} \\
& G_3 = \frac{W_{212}}{W_{212} + W_{213}}
& G_4 = \frac{W_{312}}{W_{312} + W_{313}} \\
\end{align*}
This model can not describe mixed layers in which the last two components
occur right after each other in a stack. In other words there is always
an alternation between (one or more) layers of the first component and a
single layer of the second or third component. Therefore, the weight
fraction of the first component (:math:`W_1`) needs to be > than 1/2.
The restriction also translates in the following:
.. math::
:nowrap:
\begin{align*}
& P_{22} = P_{23} = P_{32} = P_{33} = 0 \\
& P_{21} = P_{31} = 1 \\
& \\
& P_{122} = P_{123} = P_{132} = P_{133} = 0 \\
& P_{121} = P_{131} = 1 \\
& \\
& P_{222} = P_{223} = P_{232} = P_{233} = 0 \\
& P_{221} = P_{231} = 1 \\
& \\
& P_{322} = P_{323} = P_{332} = P_{333} = 0 \\
& P_{321} = P_{331} = 1 \\
\end{align*}
Using the above, we can calculate a lot of the weight fractions of stacks:
.. math::
:nowrap:
\begin{align*}
& W_{22} = W_{23} = W_{32} = W_{33} 0 \\
& W_{21} = W_{2} \\
& W_{31} = W_{3} \\
& \\
& W_{122} = W_{123} = W_{132} = W_{133} = 0 \\
& W_{121} = W_{12} = W_{21} = W_2 \\
& W_{131} = W_{13} = W_{31} = W_3 \\
& W_{11} = W_1 - W_{12} - W_{13} \\
& \\
& W_{221} = W_{231} = W_{222} = W_{223} = W_{232} = W_{233} = 0 \\
& W_{331} = W_{331} = W_{322} = W_{323} = W_{332} = W_{333} = 0 \\
\end{align*}
Then the remaining fractions and probablities can be calculated as follows:
.. math::
:nowrap:
\begin{align*}
& W_2 = G_1 * (1 - W_1) \\
& W_3 = 1 - W_1 - W_2 \\
& \\
& W_x = W_2 + W_3 &
& \text{if $W_1 < \nicefrac{2}{3}$:} \\
& \quad \text{$P_{111}$ is given}\\
& \quad P_{x1x} =
\begin{dcases}
1 - \frac{W_1 - W_x}{W_x} \cdot (1 - P_{111}, & \text{if $W_x > 0$} \\
0, & \text{otherwise}
\end{dcases} \\
& \\
& \text{if $W_1 \geq \nicefrac{2}{3}$:} \\
& \quad \text{$P_{x1x}$ is given}\\
& \quad P_{111} =
\begin{dcases}
1 - \frac{W_x}{W_1 - W_x} \cdot (1 - P_{x1x}, & \text{if $(W_1 - W_x) > 0$} \\
0, & \text{otherwise}
\end{dcases} \\
& \\
& W_{x1x} = W_x \cdot P_{x1x} \\
& W_{21x} = G_2 \cdot W_{x1x} \\
& W_{31x} = W_{x1x} - W_{21x} \\
& \\
& W_{212} = G_3 \cdot W_{21x} \\
& W_{213} = (1 - G_3) \cdot W_{21x} \\
& W_{211} = W_{21} - W_{212} - W_{213} \\
& \\
& W_{312} = G_4 \cdot W_{31x} \\
& W_{313} = (1 - G_4) \cdot W_{31x} \\
& W_{311} = W_{31} - W_{312} - W_{313} \\
& \\
& W_{111} = W_{11} \cdot P_{111} \\
& W_{112} = W_{12} - W_{212} - W_{312} \\
& W_{112} = W_{13} - W_{213} - W_{313} \\
& \\
& \text{Calculate the remaining P using:} \\
& P_{ijk} =
\begin{dcases}
\frac{W_{ijk}}{W_{ij}}, & \text{if $W_{ij} > 0$} \\
0, & \text{otherwise}
\end{dcases} \\
\end{align*}
"""
# MODEL METADATA:
class Meta(_AbstractProbability.Meta):
store_id = "R2G3Model"
# PROPERTIES:
_G = 3
twothirds = 2.0 / 3.0
inherit_W1 = BoolProperty(default=False,
text="Inherit flag for W1",
persistent=True,
visible=True,
set_action_name="update",
mix_with=(SetActionMixin, ))
W1 = FloatProperty(default=0.8,
text="W1 (> 0.5)",
math_text=r"$W_1 (> 0.5)$",
persistent=True,
visible=True,
refinable=True,
store_private=True,
minimum=0.5,
maximum=1.0,
is_independent=True,
inheritable=True,
inherit_flag="inherit_W1",
inherit_from="parent.based_on.probabilities.W1",
set_action_name="update",
mix_with=(SetActionMixin, RefinableMixin,
InheritableMixin))
inherit_P111_or_P212 = BoolProperty(default=False,
text="Inherit flag for P112_or_P211",
persistent=True,
visible=True,
set_action_name="update",
mix_with=(SetActionMixin, ))
P111_or_P212 = FloatProperty(
default=0.9,
text="P111 (W1 < 2/3) or\nPx1x (W1 > 2/3)",
math_text=r"$P_{111} %s$ or $\newline P_{x1x} %s$" %
(mt_range(0.5, "W_1", 2.0 / 3.0), mt_range(2.0 / 3.0, "W_1", 1.0)),
persistent=True,
visible=True,
refinable=True,
store_private=True,
minimum=0.0,
maximum=1.0,
is_independent=True,
inheritable=True,
inherit_flag="inherit_P111_or_P212",
inherit_from="parent.based_on.probabilities.P111_or_P212",
set_action_name="update",
mix_with=(SetActionMixin, RefinableMixin, InheritableMixin))
inherit_G1 = BoolProperty(default=False,
text="Inherit flag for G1",
persistent=True,
visible=True,
set_action_name="update",
mix_with=(SetActionMixin, ))
G1 = FloatProperty(default=0.9,
text="W2/(W2+W3)",
math_text=r"$\large\frac{W_2}{W_3 + W_2}$",
persistent=True,
visible=True,
refinable=True,
store_private=True,
minimum=0.0,
maximum=1.0,
is_independent=True,
inheritable=True,
inherit_flag="inherit_G1",
inherit_from="parent.based_on.probabilities.G1",
set_action_name="update",
mix_with=(SetActionMixin, RefinableMixin,
InheritableMixin))
inherit_G2 = BoolProperty(default=False,
text="Inherit flag for G2",
persistent=True,
visible=True,
set_action_name="update",
mix_with=(SetActionMixin, ))
G2 = FloatProperty(
default=0.9,
text="(W212+W213)/(W212+W213+W312+W313)",
math_text=
r"$\large\frac{W_{212} + W_{213}}{W_{212} + W_{213} + W_{312} + W_{313}}$",
persistent=True,
visible=True,
refinable=True,
store_private=True,
minimum=0.0,
maximum=1.0,
is_independent=True,
inheritable=True,
inherit_flag="inherit_G2",
inherit_from="parent.based_on.probabilities.G2",
set_action_name="update",
mix_with=(SetActionMixin, RefinableMixin, InheritableMixin))
inherit_G3 = BoolProperty(default=False,
text="Inherit flag for G3",
persistent=True,
visible=True,
set_action_name="update",
mix_with=(SetActionMixin, ))
G3 = FloatProperty(default=0.9,
text="W212/(W212+W213)",
math_text=r"$\large\frac{W_{212}}{W_{212} + W_{213}}$",
persistent=True,
visible=True,
refinable=True,
store_private=True,
minimum=0.0,
maximum=1.0,
is_independent=True,
inheritable=True,
inherit_flag="inherit_G3",
inherit_from="parent.based_on.probabilities.G3",
set_action_name="update",
mix_with=(SetActionMixin, RefinableMixin,
InheritableMixin))
inherit_G4 = BoolProperty(default=False,
text="Inherit flag for G4",
persistent=True,
visible=True,
set_action_name="update",
mix_with=(SetActionMixin, ))
G4 = FloatProperty(default=0.9,
text="W312/(W312+W313)",
math_text=r"$\large\frac{W_{312}}{W_{312} + W_{313}}$",
persistent=True,
visible=True,
refinable=True,
store_private=True,
minimum=0.0,
maximum=1.0,
is_independent=True,
inheritable=True,
inherit_flag="inherit_G4",
inherit_from="parent.based_on.probabilities.G4",
set_action_name="update",
mix_with=(SetActionMixin, RefinableMixin,
InheritableMixin))
# ------------------------------------------------------------
# Initialization and other internals
# ------------------------------------------------------------
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()
# ------------------------------------------------------------
# Methods & Functions
# ------------------------------------------------------------
def update(self):
with self.monitor_changes():
# calculate Wx's:
self.mW[0] = self.W1
self.mW[1] = (1.0 - self.mW[0]) * self.G1
self.mW[2] = 1.0 - self.mW[0] - self.mW[1]
# consequences of restrictions:
self.mW[1, 1] = 0
self.mW[1, 2] = 0
self.mW[2, 1] = 0
self.mW[2, 2] = 0
self.mW[0, 1, 0] = self.mW[0, 1] = self.mW[1, 0] = self.mW[1]
self.mW[0, 2, 0] = self.mW[0, 2] = self.mW[2, 0] = self.mW[2]
self.mW[0, 0] = self.mW[0] - self.mW[0, 1] - self.mW[0, 2]
# continue calculations:
Wx = self.mW[1] + self.mW[2]
if self.mW[0] < self.twothirds:
self.mP[0, 0, 0] = self.P111_or_P212
Px0x = 1 - (self.mW[0] - Wx) / Wx * (
1 - self.mP[0, 0, 0]) if Wx != 0 else 0.0
else:
Px0x = self.P111_or_P212
self.mP[0, 0, 0] = 1 - Wx / (self.mW[0] - Wx) * (1 - Px0x) if (
self.mW[0] - Wx) != 0 else 0.0
Wx0x = Wx * Px0x
W10x = self.G2 * Wx0x
W20x = Wx0x - W10x
self.mW[1, 0, 1] = self.G3 * W10x
self.mW[1, 0, 2] = (1 - self.G3) * W10x
self.mW[1, 0,
0] = self.mW[1, 0] - self.mW[1, 0, 1] - self.mW[1, 0, 2]
self.mW[2, 0, 1] = self.G4 * W20x
self.mW[2, 0, 2] = (1 - self.G4) * W20x
self.mW[2, 0,
0] = self.mW[2, 0] - self.mW[2, 0, 1] - self.mW[2, 0, 2]
self.mW[0, 0, 0] = self.mW[0, 0] * self.mP[0, 0, 0]
self.mW[0, 0,
1] = self.mW[0, 1] - self.mW[1, 0, 1] - self.mW[2, 0, 1]
self.mW[0, 0,
2] = self.mW[0, 2] - self.mW[1, 0, 2] - self.mW[2, 0, 2]
# Calculate remaining P:
for i in range(3):
for j in range(3):
for k in range(3):
self.mP[i, j, k] = self.mW[i, j, k] / self.mW[
i, j] if self.mW[i, j] > 0 else 0.0
self.solve()
self.validate()
pass # end of class
class ThresholdSelector(ChildModel):
# MODEL INTEL:
specimen = property(DataModel.parent.fget, DataModel.parent.fset)
# PROPERTIES:
pattern = StringChoiceProperty(
default="exp",
text="Pattern",
visible=True,
persistent=False,
choices={
"exp": "Experimental Pattern",
"calc": "Calculated Pattern"
},
set_action_name="update_threshold_plot_data",
mix_with=(SetActionMixin, ))
max_threshold = FloatProperty(default=0.32,
text="Maximum threshold",
visible=True,
persistent=False,
minimum=0.0,
maximum=1.0,
widget_type="float_entry",
set_action_name="update_threshold_plot_data",
mix_with=(SetActionMixin, ))
steps = IntegerProperty(default=20,
text="Steps",
visible=True,
persistent=False,
minimum=3,
maximum=50,
set_action_name="update_threshold_plot_data",
mix_with=(SetActionMixin, ))
sel_num_peaks = IntegerProperty(default=0,
text="Selected number of peaks",
visible=True,
persistent=False,
widget_type="label")
def set_sel_threshold(self, value):
_sel_threshold = type(self).sel_threshold._get(self)
if value != _sel_threshold and len(self.threshold_plot_data[0]) > 0:
_sel_threshold = value
if _sel_threshold >= self.threshold_plot_data[0][-1]:
self.sel_num_peaks = self.threshold_plot_data[1][-1]
elif _sel_threshold <= self.threshold_plot_data[0][0]:
self.sel_num_peaks = self.threshold_plot_data[1][0]
else:
self.sel_num_peaks = int(
interp1d(*self.threshold_plot_data)(_sel_threshold))
type(self).sel_threshold._set(self, _sel_threshold)
sel_threshold = FloatProperty(default=0.1,
text="Selected threshold",
visible=True,
persistent=False,
widget_type="float_entry",
fset=set_sel_threshold)
threshold_plot_data = LabeledProperty(default=None,
text="Threshold plot data",
visible=False,
persistent=False)
# ------------------------------------------------------------
# Initialisation and other internals
# ------------------------------------------------------------
def __init__(self, *args, **kwargs):
super(ThresholdSelector, self).__init__(*args, **kwargs)
self.max_threshold = self.get_kwarg(kwargs, self.max_threshold,
"max_threshold")
self.steps = self.get_kwarg(kwargs, self.steps, "steps")
self.sel_threshold = self.get_kwarg(kwargs, self.sel_threshold,
"sel_threshold")
if self.parent.experimental_pattern.size > 0:
self.pattern = "exp"
else:
self.pattern = "calc"
# ------------------------------------------------------------
# Methods & Functions
# ------------------------------------------------------------
def get_xy(self):
if self.pattern == "exp":
data_x, data_y = self.parent.experimental_pattern.get_xy_data()
elif self.pattern == "calc":
data_x, data_y = self.parent.calculated_pattern.get_xy_data()
if data_y.size > 0:
data_y = data_y / np.max(data_y)
return data_x, data_y
_updating_plot_data = False
def update_threshold_plot_data(self, status_dict=None):
if self.parent is not None and not self._updating_plot_data:
self._updating_plot_data = True
if self.pattern == "exp":
p, t, m = self.parent.experimental_pattern.get_best_threshold(
self.max_threshold, self.steps, status_dict)
elif self.pattern == "calc":
p, t, m = self.parent.calculated_pattern.get_best_threshold(
self.max_threshold, self.steps, status_dict)
self.threshold_plot_data = p
self.sel_threshold = t
self.max_threshold = m
self._updating_plot_data = False
pass # end of class
class ExperimentalLine(PyXRDLine):
# MODEL INTEL:
class Meta(PyXRDLine.Meta):
store_id = "ExperimentalLine"
specimen = property(DataModel.parent.fget, DataModel.parent.fset)
# PROPERTIES:
#: The line color
color = modify(PyXRDLine.color,
default=settings.EXPERIMENTAL_COLOR,
inherit_from="parent.parent.display_exp_color")
#: The linewidth in points
lw = modify(PyXRDLine.lw,
default=settings.EXPERIMENTAL_LINEWIDTH,
inherit_from="parent.parent.display_exp_lw")
#: A short string describing the (matplotlib) linestyle
ls = modify(PyXRDLine.ls,
default=settings.EXPERIMENTAL_LINESTYLE,
inherit_from="parent.parent.display_exp_ls")
#: A short string describing the (matplotlib) marker
marker = modify(PyXRDLine.marker,
default=settings.EXPERIMENTAL_MARKER,
inherit_from="parent.parent.display_exp_marker")
#: The value to cap the pattern at (in raw values)
cap_value = FloatProperty(default=0.0,
text="Cap value",
persistent=True,
visible=True,
widget_type="float_entry",
signal_name="visuals_changed",
mix_with=(SignalMixin, ))
@property
def max_display_y(self):
max_value = super(ExperimentalLine, self).max_display_y
# Only cap single and multi-line patterns, not 2D images:
if self.cap_value > 0 and not (self.num_columns > 2
and len(self.z_data)):
max_value = min(max_value, self.cap_value)
return max_value
###########################################################################
#: The background offset value
bg_position = FloatProperty(default=0.0,
text="Background offset",
persistent=False,
visible=True,
widget_type="float_entry",
signal_name="visuals_changed",
mix_with=(SignalMixin, ))
#: The background scale
bg_scale = FloatProperty(default=1.0,
text="Background scale",
persistent=False,
visible=True,
widget_type="float_entry",
signal_name="visuals_changed",
mix_with=(SignalMixin, ))
#: The background pattern or None for linear patterns
bg_pattern = LabeledProperty(default=None,
text="Background pattern",
persistent=False,
visible=False,
signal_name="visuals_changed",
mix_with=(SignalMixin, ))
#: The background type: pattern or linear
bg_type = IntegerChoiceProperty(default=0,
text="Background type",
choices=settings.PATTERN_BG_TYPES,
persistent=False,
visible=True,
signal_name="visuals_changed",
set_action_name="find_bg_position",
mix_with=(
SignalMixin,
SetActionMixin,
))
def get_bg_type_lbl(self):
return settings.PATTERN_BG_TYPES[self.bg_type]
###########################################################################
#: Pattern smoothing type
smooth_type = IntegerChoiceProperty(
default=0,
text="Smooth type",
choices=settings.PATTERN_SMOOTH_TYPES,
persistent=False,
visible=True,
signal_name="visuals_changed",
set_action_name="setup_smooth_variables",
mix_with=(
SignalMixin,
SetActionMixin,
))
smooth_pattern = None
#: The smooth degree
smooth_degree = IntegerProperty(default=0,
text="Smooth degree",
persistent=False,
visible=True,
signal_name="visuals_changed",
mix_with=(SignalMixin, ))
###########################################################################
#: The noise fraction to add
noise_fraction = FloatProperty(default=0.0,
text="Noise fraction",
persistent=False,
visible=True,
widget_type="spin",
signal_name="visuals_changed",
mix_with=(SignalMixin, ))
###########################################################################
#: The pattern shift correction value
shift_value = FloatProperty(default=0.0,
text="Shift value",
persistent=False,
visible=True,
widget_type="float_entry",
signal_name="visuals_changed",
mix_with=(SignalMixin, ))
#: Shift reference position
shift_position = FloatChoiceProperty(
default=0.42574,
text="Shift position",
choices=settings.PATTERN_SHIFT_POSITIONS,
persistent=False,
visible=True,
signal_name="visuals_changed",
set_action_name="setup_shift_variables",
mix_with=(
SignalMixin,
SetActionMixin,
))
###########################################################################
#: The peak properties calculation start position
peak_startx = FloatProperty(default=0.0,
text="Peak properties start position",
persistent=False,
visible=True,
widget_type="float_entry",
set_action_name="update_peak_properties",
mix_with=(SetActionMixin, ))
#: The peak properties calculation end position
peak_endx = FloatProperty(default=0.0,
text="Peak properties end position",
persistent=False,
visible=True,
widget_type="float_entry",
set_action_name="update_peak_properties",
mix_with=(SetActionMixin, ))
#: The peak fwhm value
peak_fwhm_result = FloatProperty(
default=0.0,
text="Peak FWHM value",
persistent=False,
visible=True,
widget_type="label",
)
#: The peak area value
peak_area_result = FloatProperty(
default=0.0,
text="Peak area value",
persistent=False,
visible=True,
widget_type="label",
)
#: The patterns peak properties are calculated from
peak_properties_pattern = LabeledProperty(default=None,
text="Peak properties pattern",
persistent=False,
visible=False,
signal_name="visuals_changed",
mix_with=(SignalMixin, ))
###########################################################################
#: The strip peak start position
strip_startx = FloatProperty(default=0.0,
text="Strip peak start position",
persistent=False,
visible=True,
widget_type="float_entry",
set_action_name="update_strip_pattern",
mix_with=(SetActionMixin, ))
#: The strip peak end position
strip_endx = FloatProperty(default=0.0,
text="Strip peak end position",
persistent=False,
visible=True,
widget_type="float_entry",
set_action_name="update_strip_pattern",
mix_with=(SetActionMixin, ))
#: The stripped peak pattern
stripped_pattern = LabeledProperty(default=None,
text="Strip peak pattern",
persistent=False,
visible=False,
signal_name="visuals_changed",
mix_with=(SignalMixin, ))
#: The stripped peak pattern noise
noise_level = FloatProperty(default=0.0,
text="Strip peak noise level",
persistent=False,
visible=True,
widget_type="float_entry",
set_action_name="update_strip_pattern_noise",
mix_with=(SetActionMixin, ))
# ------------------------------------------------------------
# Initialization and other internals
# ------------------------------------------------------------
def __init__(self, cap_value=0.0, *args, **kwargs):
"""
Valid keyword arguments for a ExperimentalLine are:
cap_value: the value (in raw counts) at which to cap
the experimental pattern
"""
super(ExperimentalLine, self).__init__(*args, **kwargs)
self.cap_value = cap_value
# ------------------------------------------------------------
# Background Removal
# ------------------------------------------------------------
def remove_background(self):
with self.data_changed.hold_and_emit():
bg = None
if self.bg_type == 0:
bg = self.bg_position
elif self.bg_type == 1 and self.bg_pattern is not None and not (
self.bg_position == 0 and self.bg_scale == 0):
bg = self.bg_pattern * self.bg_scale + self.bg_position
if bg is not None and self.data_y.size > 0:
self.data_y[:, 0] -= bg
self.clear_bg_variables()
def find_bg_position(self):
try:
self.bg_position = np.min(self.data_y)
except ValueError:
return 0.0
def clear_bg_variables(self):
with self.visuals_changed.hold_and_emit():
self.bg_pattern = None
self.bg_scale = 0.0
self.bg_position = 0.0
# ------------------------------------------------------------
# Data Smoothing
# ------------------------------------------------------------
def smooth_data(self):
with self.data_changed.hold_and_emit():
if self.smooth_degree > 0:
degree = int(self.smooth_degree)
self.data_y[:, 0] = smooth(self.data_y[:, 0], degree)
self.smooth_degree = 0.0
def setup_smooth_variables(self):
with self.visuals_changed.hold_and_emit():
self.smooth_degree = 5.0
def clear_smooth_variables(self):
with self.visuals_changed.hold_and_emit():
self.smooth_degree = 0.0
# ------------------------------------------------------------
# Noise adding
# ------------------------------------------------------------
def add_noise(self):
with self.data_changed.hold_and_emit():
if self.noise_fraction > 0:
noisified = add_noise(self.data_y[:, 0], self.noise_fraction)
self.set_data(self.data_x, noisified)
self.noise_fraction = 0.0
def clear_noise_variables(self):
with self.visuals_changed.hold_and_emit():
self.noise_fraction = 0.0
# ------------------------------------------------------------
# Data Shifting
# ------------------------------------------------------------
def shift_data(self):
with self.data_changed.hold_and_emit():
if self.shift_value != 0.0:
if settings.PATTERN_SHIFT_TYPE == "Linear":
self.data_x = self.data_x - self.shift_value
if self.specimen is not None:
with self.specimen.visuals_changed.hold():
for marker in self.specimen.markers:
marker.position = marker.position - self.shift_value
elif settings.PATTERN_SHIFT_TYPE == "Displacement":
position = self.specimen.goniometer.get_t_from_nm(
self.shift_position)
displacement = 0.5 * self.specimen.goniometer.radius * self.shift_value / np.cos(
position / 180 * np.pi)
correction = 2 * displacement * np.cos(
self.data_x / 2 / 180 *
np.pi) / self.specimen.goniometer.radius
self.data_x = self.data_x - correction
self.shift_value = 0.0
def setup_shift_variables(self):
with self.visuals_changed.hold_and_emit():
position = self.specimen.goniometer.get_2t_from_nm(
self.shift_position)
if position > 0.1:
max_x = position + 0.5
min_x = position - 0.5
condition = (self.data_x >= min_x) & (self.data_x <= max_x)
section_x, section_y = np.extract(condition,
self.data_x), np.extract(
condition,
self.data_y[:, 0])
try:
#TODO to exclude noise it'd be better to first interpolate
# or smooth the data and then find the max.
actual_position = section_x[np.argmax(section_y)]
except ValueError:
actual_position = position
self.shift_value = actual_position - position
def clear_shift_variables(self):
with self.visuals_changed.hold_and_emit():
self.shift_value = 0
# ------------------------------------------------------------
# Peak area calculation
# ------------------------------------------------------------
peak_bg_slope = 0.0
avg_starty = 0.0
avg_endy = 0.0
def update_peak_properties(self):
with self.visuals_changed.hold_and_emit():
if self.peak_endx < self.peak_startx:
self.peak_endx = self.peak_startx + 1.0
return # previous line will have re-invoked this method
# calculate average starting point y value
condition = (self.data_x >= self.peak_startx - 0.1) & (
self.data_x <= self.peak_startx + 0.1)
section = np.extract(condition, self.data_y[:, 0])
self.avg_starty = np.min(section)
# calculate average ending point y value
condition = (self.data_x >= self.peak_endx - 0.1) & (
self.data_x <= self.peak_endx + 0.1)
section = np.extract(condition, self.data_y[:, 0])
self.avg_endy = np.min(section)
# Calculate new bg slope
self.peak_bg_slope = (self.avg_starty - self.avg_endy) / (
self.peak_startx - self.peak_endx)
# Get the x-values in between start and end point:
condition = (self.data_x >= self.peak_startx) & (self.data_x <=
self.peak_endx)
section_x = np.extract(condition, self.data_x)
section_y = np.extract(condition, self.data_y)
bg_curve = (self.peak_bg_slope * (section_x - self.peak_startx) +
self.avg_starty)
#Calculate the peak area:
self.peak_area_result = abs(
trapz(section_y, x=section_x) - trapz(bg_curve, x=section_x))
# create a spline of of the peak (shifted down by half of its maximum)
fwhm_curve = section_y - bg_curve
peak_half_max = np.max(fwhm_curve) * 0.5
spline = UnivariateSpline(section_x,
fwhm_curve - peak_half_max,
s=0)
roots = spline.roots() # find the roots = where the splin = 0
self.peak_fwhm_result = np.abs(roots[0] - roots[-1]) if (
len(roots) >= 2) else 0
# Calculate the new y-values: x values, bg_curve y values, original pattern y values, x values for the FWHM, y values for the FWHM
self.peak_properties_pattern = (section_x,
bg_curve, section_y, roots,
spline(roots) + peak_half_max)
def clear_peak_properties_variables(self):
with self.visuals_changed.hold_and_emit():
self._peak_startx = 0.0
self._peak_properties_pattern = None
self._peak_endx = 0.0
self.peak_properties = 0.0
# ------------------------------------------------------------
# Peak stripping
# ------------------------------------------------------------
def strip_peak(self):
with self.data_changed.hold_and_emit():
if self.stripped_pattern is not None:
stripx, stripy = self.stripped_pattern
indeces = ((self.data_x >= self.strip_startx) &
(self.data_x <= self.strip_endx)).nonzero()[0]
np.put(self.data_y[:, 0], indeces, stripy)
self._strip_startx = 0.0
self._stripped_pattern = None
self.strip_endx = 0.0
strip_slope = 0.0
avg_starty = 0.0
avg_endy = 0.0
block_strip = False
def update_strip_pattern_noise(self):
with self.visuals_changed.hold_and_emit():
# Get the x-values in between start and end point:
condition = (self.data_x >= self.strip_startx) & (self.data_x <=
self.strip_endx)
section_x = np.extract(condition, self.data_x)
# Calculate the new y-values, add noise according to noise_level
noise = self.avg_endy * 2 * (np.random.rand(*section_x.shape) -
0.5) * self.noise_level
section_y = (self.strip_slope * (section_x - self.strip_startx) +
self.avg_starty) + noise
self.stripped_pattern = (section_x, section_y)
def update_strip_pattern(self):
with self.visuals_changed.hold_and_emit():
if self.strip_endx < self.strip_startx:
self.strip_endx = self.strip_startx + 1.0
return # previous line will have re-invoked this method
if not self.block_strip:
self.block_strip = True
# calculate average starting point y value
condition = (self.data_x >= self.strip_startx - 0.1) & (
self.data_x <= self.strip_startx + 0.1)
section = np.extract(condition, self.data_y[:, 0])
self.avg_starty = np.average(section)
noise_starty = 2 * np.std(section) / self.avg_starty
# calculate average ending point y value
condition = (self.data_x >= self.strip_endx - 0.1) & (
self.data_x <= self.strip_endx + 0.1)
section = np.extract(condition, self.data_y[:, 0])
self.avg_endy = np.average(section)
noise_endy = 2 * np.std(section) / self.avg_starty
# Calculate new slope and noise level
self.strip_slope = (self.avg_starty - self.avg_endy) / (
self.strip_startx - self.strip_endx)
self.noise_level = (noise_starty + noise_endy) * 0.5
self.update_strip_pattern_noise()
def clear_strip_variables(self):
with self.visuals_changed.hold_and_emit():
self._strip_startx = 0.0
self._strip_pattern = None
self.strip_start_x = 0.0
pass # end of class
class Marker(DataModel, Storable, CSVMixin):
# MODEL INTEL:
class Meta(DataModel.Meta):
store_id = "Marker"
specimen = property(DataModel.parent.fget, DataModel.parent.fset)
# PROPERTIES:
#: This marker's label
label = StringProperty(default="New Marker",
text="Label",
persistent=True,
visible=True,
tabular=True,
signal_name="visuals_changed",
mix_with=(SignalMixin, ))
#: Flag indicating whether the color of this marker is inherited
inherit_color = BoolProperty(default=settings.MARKER_INHERIT_COLOR,
text="Inherit color",
persistent=True,
visible=True,
tabular=True,
signal_name="visuals_changed",
mix_with=(SignalMixin, ))
#: This maker's color:
color = ColorProperty(default=settings.MARKER_COLOR,
text="Color",
persistent=True,
visible=True,
tabular=True,
inheritable=True,
signal_name="visuals_changed",
inherit_flag="inherit_color",
inherit_from="specimen.project.display_marker_color",
mix_with=(
InheritableMixin,
SignalMixin,
))
#: Flag indicating whether the angle of this marker is inherited
inherit_angle = BoolProperty(default=settings.MARKER_INHERIT_ANGLE,
text="Inherit angle",
persistent=True,
visible=True,
tabular=True,
signal_name="visuals_changed",
mix_with=(SignalMixin, ))
#: This maker's angle:
angle = FloatProperty(default=settings.MARKER_ANGLE,
text="Angle",
widget_type="spin",
persistent=True,
visible=True,
tabular=True,
inheritable=True,
signal_name="visuals_changed",
inherit_flag="inherit_angle",
inherit_from="specimen.project.display_marker_angle",
mix_with=(
InheritableMixin,
SignalMixin,
))
#: Flag indicating whether the top offset of this marker is inherited
inherit_top_offset = BoolProperty(
default=settings.MARKER_INHERIT_TOP_OFFSET,
text="Inherit top offset",
persistent=True,
visible=True,
tabular=True,
signal_name="visuals_changed",
mix_with=(SignalMixin, ))
#: This maker's top offset:
top_offset = FloatProperty(
default=settings.MARKER_TOP_OFFSET,
text="Top offset",
widget_type="spin",
persistent=True,
visible=True,
tabular=True,
inheritable=True,
signal_name="visuals_changed",
inherit_flag="inherit_top_offset",
inherit_from="specimen.project.display_marker_top_offset",
mix_with=(
InheritableMixin,
SignalMixin,
))
#: Whether this marker is visible
visible = BoolProperty(default=settings.MARKER_VISIBLE,
text="Visible",
persistent=True,
visible=True,
tabular=True,
signal_name="visuals_changed",
mix_with=(SignalMixin, ))
#: The marker's position
position = FloatProperty(default=settings.MARKER_POSITION,
text="Position",
widget_type="spin",
persistent=True,
visible=True,
tabular=True,
signal_name="visuals_changed",
mix_with=(SignalMixin, ))
#: The marker's x offset
x_offset = FloatProperty(default=settings.MARKER_X_OFFSET,
text="X offset",
widget_type="spin",
persistent=True,
visible=True,
tabular=True,
signal_name="visuals_changed",
mix_with=(SignalMixin, ))
#: The marker's y offset
y_offset = FloatProperty(default=settings.MARKER_Y_OFFSET,
text="Y offset",
widget_type="spin",
persistent=True,
visible=True,
tabular=True,
signal_name="visuals_changed",
mix_with=(SignalMixin, ))
#: Flag indicating whether the alignment of this marker is inherited
inherit_align = BoolProperty(default=settings.MARKER_INHERIT_ALIGN,
text="Inherit align",
persistent=True,
visible=True,
tabular=True,
signal_name="visuals_changed",
mix_with=(SignalMixin, ))
#: This marker's alignment
align = StringChoiceProperty(
default=settings.MARKER_ALIGN,
text="Align",
choices=settings.MARKER_ALIGNS,
persistent=True,
visible=True,
tabular=True,
inheritable=True,
signal_name="visuals_changed",
inherit_flag="inherit_align",
inherit_from="specimen.project.display_marker_align",
mix_with=(
InheritableMixin,
SignalMixin,
))
#: Flag indicating whether the base of this marker is inherited
inherit_base = BoolProperty(default=settings.MARKER_INHERIT_BASE,
text="Inherit base",
persistent=True,
visible=True,
tabular=True,
signal_name="visuals_changed",
mix_with=(SignalMixin, ))
#: This marker's base
base = IntegerChoiceProperty(
default=settings.MARKER_BASE,
text="Base",
choices=settings.MARKER_BASES,
persistent=True,
visible=True,
tabular=True,
inheritable=True,
signal_name="visuals_changed",
inherit_flag="inherit_base",
inherit_from="specimen.project.display_marker_base",
mix_with=(
InheritableMixin,
SignalMixin,
))
#: Flag indicating whether the top of this marker is inherited
inherit_top = BoolProperty(default=settings.MARKER_INHERIT_TOP,
text="Inherit top",
persistent=True,
visible=True,
tabular=True,
signal_name="visuals_changed",
mix_with=(SignalMixin, ))
#: This marker's top
top = IntegerChoiceProperty(
default=settings.MARKER_TOP,
text="Top",
choices=settings.MARKER_TOPS,
persistent=True,
visible=True,
tabular=True,
inheritable=True,
signal_name="visuals_changed",
inherit_flag="inherit_top",
inherit_from="specimen.project.display_marker_top",
mix_with=(
InheritableMixin,
SignalMixin,
))
#: Flag indicating whether the line style of this marker is inherited
inherit_style = BoolProperty(default=settings.MARKER_INHERIT_STYLE,
text="Inherit line style",
persistent=True,
visible=True,
tabular=True,
signal_name="visuals_changed",
mix_with=(SignalMixin, ))
#: This marker's line style
style = StringChoiceProperty(
default=settings.MARKER_STYLE,
text="Line style",
choices=settings.MARKER_STYLES,
persistent=True,
visible=True,
tabular=True,
inheritable=True,
signal_name="visuals_changed",
inherit_flag="inherit_style",
inherit_from="specimen.project.display_marker_style",
mix_with=(
InheritableMixin,
SignalMixin,
))
# ------------------------------------------------------------
# Initialisation and other internals
# ------------------------------------------------------------
def __init__(self, *args, **kwargs):
my_kwargs = self.pop_kwargs(
kwargs, "data_label", "data_visible", "data_position",
"data_x_offset", "data_y_offset"
"data_color", "data_base", "data_angle", "data_align", *[
prop.label
for prop in type(self).Meta.get_local_persistent_properties()
])
super(Marker, self).__init__(*args, **kwargs)
kwargs = my_kwargs
self.label = self.get_kwarg(kwargs, "", "label", "data_label")
self.visible = self.get_kwarg(kwargs, True, "visible", "data_visible")
self.position = float(
self.get_kwarg(kwargs, 0.0, "position", "data_position"))
self.x_offset = float(
self.get_kwarg(kwargs, 0.0, "x_offset", "data_x_offset"))
self.y_offset = float(
self.get_kwarg(kwargs, 0.05, "y_offset", "data_y_offset"))
self.top_offset = float(self.get_kwarg(kwargs, 0.0, "top_offset"))
self.color = self.get_kwarg(kwargs, settings.MARKER_COLOR, "color",
"data_color")
self.base = int(
self.get_kwarg(kwargs, settings.MARKER_BASE, "base", "data_base"))
self.angle = float(self.get_kwarg(kwargs, 0.0, "angle", "data_angle"))
self.align = self.get_kwarg(kwargs, settings.MARKER_ALIGN, "align")
self.style = self.get_kwarg(kwargs, settings.MARKER_STYLE, "style",
"data_align")
# if top is not set and style is not "none",
# assume top to be "Top of plot", otherwise (style is not "none")
# assume top to be relative to the base point (using top_offset)
self.top = int(
self.get_kwarg(kwargs, 0 if self.style == "none" else 1, "top"))
self.inherit_align = self.get_kwarg(kwargs, True, "inherit_align")
self.inherit_color = self.get_kwarg(kwargs, True, "inherit_color")
self.inherit_base = self.get_kwarg(kwargs, True, "inherit_base")
self.inherit_top = self.get_kwarg(kwargs, True, "inherit_top")
self.inherit_top_offset = self.get_kwarg(kwargs, True,
"inherit_top_offset")
self.inherit_angle = self.get_kwarg(kwargs, True, "inherit_angle")
self.inherit_style = self.get_kwarg(kwargs, True, "inherit_style")
# ------------------------------------------------------------
# Methods & Functions
# ------------------------------------------------------------
def get_nm_position(self):
if self.parent is not None:
return self.parent.goniometer.get_nm_from_2t(self.position)
else:
return 0.0
def set_nm_position(self, position):
if self.parent is not None:
self.position = self.parent.goniometer.get_2t_from_nm(position)
else:
self.position = 0.0
pass # end of class
class _AbstractCSDSDistribution(DataModel,
Storable,
metaclass=PyXRDRefinableMeta):
# MODEL INTEL:
class Meta(DataModel.Meta):
description = "Abstract CSDS distr."
explanation = ""
phase = property(DataModel.parent.fget, DataModel.parent.fset)
# PROPERTIES:
_data_object = None
@property
def data_object(self):
return self._data_object
inherited = BoolProperty(default=False,
text="Inherited",
visible=False,
persistent=False,
signal_name="data_changed",
mix_with=(SignalMixin, ))
distrib = LabeledProperty(default=None,
text="CSDS Distribution",
tabular=True,
visible=False,
persistent=False,
get_action_name="_update_distribution",
signal_name="data_changed",
mix_with=(
SignalMixin,
GetActionMixin,
))
# PROPERTIES:
#: The maximum value of this distribution
maximum = FloatProperty(default=0.0,
text="Maximum CSDS",
minimum=1,
maximum=1000,
tabular=True,
persistent=False,
visible=False,
mix_with=(ReadOnlyMixin, DataMixin))
#: The minimum value of this distribution
minimum = FloatProperty(default=0.0,
text="Maximum CSDS",
minimum=1,
maximum=1000,
tabular=True,
persistent=False,
visible=False,
mix_with=(ReadOnlyMixin, DataMixin))
average = FloatProperty(default=0.0,
text="Average CSDS",
minimum=1,
maximum=200,
tabular=True,
persistent=True,
visible=True,
refinable=True,
signal_name="data_changed",
set_action_name="_update_distribution",
mix_with=(SignalMixin, DataMixin, RefinableMixin,
SetActionMixin))
alpha_scale = FloatProperty(default=0.0,
text="α scale factor",
minimum=0.0,
maximum=10.0,
tabular=True,
persistent=True,
visible=True,
refinable=True,
signal_name="data_changed",
set_action_name="_update_distribution",
mix_with=(SignalMixin, DataMixin,
RefinableMixin, SetActionMixin))
alpha_offset = FloatProperty(default=0.0,
text="α offset factor",
minimum=-5,
maximum=5,
tabular=True,
persistent=True,
visible=True,
refinable=True,
signal_name="data_changed",
set_action_name="_update_distribution",
mix_with=(SignalMixin, DataMixin,
RefinableMixin, SetActionMixin))
beta_scale = FloatProperty(default=0.0,
text="β² scale factor",
minimum=0.0,
maximum=10.0,
tabular=True,
persistent=True,
visible=True,
refinable=True,
signal_name="data_changed",
set_action_name="_update_distribution",
mix_with=(SignalMixin, DataMixin,
RefinableMixin, SetActionMixin))
beta_offset = FloatProperty(default=0.0,
text="β² offset factor",
minimum=-5,
maximum=5,
tabular=True,
persistent=True,
visible=True,
refinable=True,
signal_name="data_changed",
set_action_name="_update_distribution",
mix_with=(SignalMixin, DataMixin,
RefinableMixin, SetActionMixin))
# ------------------------------------------------------------
# Initialization and other internals
# ------------------------------------------------------------
def __init__(self,
average=10,
alpha_scale=0.9485,
alpha_offset=-0.0017,
beta_scale=0.1032,
beta_offset=0.0034,
*args,
**kwargs):
super(_AbstractCSDSDistribution, self).__init__(*args, **kwargs)
self._data_object = CSDSData()
type(self).average._set(self, average)
type(self).maximum._set(
self, int(settings.LOG_NORMAL_MAX_CSDS_FACTOR * average))
type(self).minimum._set(self, 1)
type(self).alpha_scale._set(self, alpha_scale)
type(self).alpha_offset._set(self, alpha_offset)
type(self).beta_scale._set(self, beta_scale)
type(self).beta_offset._set(self, beta_offset)
self._update_distribution()
# ------------------------------------------------------------
# Methods & Functions
# ------------------------------------------------------------
def _update_distribution(self):
type(self).maximum._set(
self, int(settings.LOG_NORMAL_MAX_CSDS_FACTOR * self.average))
self._distrib = calculate_distribution(self.data_object)
pass # end of class
def R0_model_generator(pasG):
class _R0Meta(_AbstractProbability.Meta):
store_id = "R0G%dModel" % pasG
class _BaseR0Model(object):
"""
Probability model for Reichweite = 0
(g-1) independent variables:
W0 = W0/sum(W0>Wg)
W1/sum(W1>Wg)
W2/sum(W2>Wg)
etc.
Pij = Wj
∑W = 1
∑P = 1
indexes are NOT zero-based in external property names!
"""
# ------------------------------------------------------------
# Initialization and other internals
# ------------------------------------------------------------
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()
# ------------------------------------------------------------
# Methods & Functions
# ------------------------------------------------------------
def update(self):
with self.monitor_changes():
if self.G > 1:
for i in range(self.G - 1):
name = "F%d" % (i + 1)
if i > 0:
self.mW[i] = getattr(self, name) * (
1.0 - np.sum(np.diag(self._W)[0:i]))
else:
self.mW[i] = getattr(self, name)
self.mW[self.G - 1] = 1.0 - np.sum(np.diag(self._W)[:-1])
else:
self.mW[0] = 1.0
self._P[:] = np.repeat(
np.diag(self._W)[np.newaxis, :], self.G, 0)
self.solve()
self.validate()
pass # end of class
_dict = dict()
_dict["Meta"] = _R0Meta
def set_attribute(name, value): # @NoSelf
"""Sets an attribute on the class and the dict"""
_dict[name] = value
setattr(_BaseR0Model, name, value)
set_attribute("G", pasG)
# PROPERTIES:
for g in range(pasG - 1):
label = "F%d" % (g + 1)
text = "W%(g)d/Sum(W%(g)d+...+W%(G)d)" % {'g': g + 1, 'G': pasG}
math_text = r"$\large\frac{W_{%(g)d}}{\sum_{i=%(g)d}^{%(G)d} W_i}$" % {
'g': g + 1,
'G': pasG
}
inh_flag = "inherit_F%d" % (g + 1)
inh_from = "parent.based_on.probabilities.F%d" % (g + 1)
set_attribute(
label,
FloatProperty(default=0.8,
text=text,
math_text=math_text,
refinable=True,
persistent=True,
visible=True,
minimum=0.0,
maximum=1.0,
inheritable=True,
inherit_flag=inh_flag,
inherit_from=inh_from,
is_independent=True,
store_private=True,
set_action_name="update",
mix_with=(SetActionMixin, RefinableMixin,
InheritableMixin)))
label = "inherit_F%d" % (g + 1)
text = "Inherit flag for F%d" % (g + 1)
set_attribute(
label,
BoolProperty(default=False,
text=text,
refinable=False,
persistent=True,
visible=True,
set_action_name="update",
mix_with=(SetActionMixin, )))
# CREATE TYPE AND REGISTER AS STORABLE:
cls = type("R0G%dModel" % pasG, (_BaseR0Model, _AbstractProbability),
_dict)
storables.register_decorator(cls)
return cls