class CommitInfo(tr.HasStrictTraits):
hash = tr.Str
parents = tr.List(tr.This)
children = tr.Set(tr.This)
diff = tr.Either(None, tr.CLong)
gitdiff = tr.Either(None, tr.CLong)
def test_trait_utils4(self):
trait = traits.Either(traits.Int(47), traits.Str("vovo")).as_ctrait()
trait.ouptut = False
trait.optional = False
manhelp = get_trait_desc("choice", trait, None)
desc = ' '.join([x.strip() for x in manhelp[:-1]])
self.assertTrue(
desc in ("choice: an integer (int or long) or a string "
"(['Int', 'Str'] - mandatory)",
"choice: an integer or a string "
"(['Int', 'Str'] - mandatory)"))
self.assertEqual(manhelp[-1], " No description.")
def __init__(self,
pipeline,
name,
input_names=['input_%d'],
output_names=['outputs'],
input_types=None):
in_traits = [{
'name': 'lengths',
'optional': True
}, {
'name': 'skip_empty',
'optional': True
}]
out_traits = []
if input_types:
ptypes = input_types
else:
ptypes = [traits.File(traits.Undefined, output=False)] \
* len(input_names)
self.input_types = ptypes
for tr in output_names:
out_traits.append({'name': tr, 'optional': False})
super(ReduceNode, self).__init__(pipeline, name, in_traits, out_traits)
for tr, ptype in zip(output_names, ptypes):
self.add_trait(
tr,
traits.List(traits.Either(ptype, traits.Undefined),
output=True))
self.add_trait(
'lengths',
traits.List(traits.Int(), output=False, desc='lists lengths'))
self.add_trait(
'skip_empty',
traits.Bool(False,
output=False,
desc='remove empty (Undefined, None, empty strings) '
'from the output lists'))
self.input_names = input_names
self.output_names = output_names
self.lengths = [0] * len(input_names)
self.set_callbacks()
self.lengths = [1] * len(input_names)
class Parameter(t.HasTraits):
"""Model parameter
Attributes
----------
value : float or array
The value of the parameter for the current location. The value
for other locations is stored in map.
bmin, bmax: float
Lower and upper bounds of the parameter value.
twin : {None, Parameter}
If it is not None, the value of the current parameter is
a function of the given Parameter. The function is by default
the identity function, but it can be defined by twin_function
twin_function_expr: str
Expression of the ``twin_function`` that enables setting a functional
relationship between the parameter and its twin. If ``twin`` is not
``None``, the parameter value is calculated as the output of calling the
twin function with the value of the twin parameter. The string is
parsed using sympy, so permitted values are any valid sympy expressions
of one variable. If the function is invertible the twin inverse function
is set automatically.
twin_inverse_function : str
Expression of the ``twin_inverse_function`` that enables setting the
value of the twin parameter. If ``twin`` is not
``None``, its value is set to the output of calling the
twin inverse function with the value provided. The string is
parsed using sympy, so permitted values are any valid sympy expressions
of one variable.
twin_function : function
**Setting this attribute manually
is deprecated in HyperSpy newer than 1.1.2. It will become private in
HyperSpy 2.0. Please use ``twin_function_expr`` instead.**
twin_inverse_function : function
**Setting this attribute manually
is deprecated in HyperSpy newer than 1.1.2. It will become private in
HyperSpy 2.0. Please use ``twin_inverse_function_expr`` instead.**
ext_force_positive : bool
If True, the parameter value is set to be the absolute value
of the input value i.e. if we set Parameter.value = -3, the
value stored is 3 instead. This is useful to bound a value
to be positive in an optimization without actually using an
optimizer that supports bounding.
ext_bounded : bool
Similar to ext_force_positive, but in this case the bounds are
defined by bmin and bmax. It is a better idea to use
an optimizer that supports bounding though.
Methods
-------
as_signal(field = 'values')
Get a parameter map as a signal object
plot()
Plots the value of the Parameter at all locations.
export(folder=None, name=None, format=None, save_std=False)
Saves the value of the parameter map to the specified format
connect, disconnect(function)
Call the functions connected when the value attribute changes.
"""
__number_of_elements = 1
__value = 0
__free = True
_bounds = (None, None)
__twin = None
_axes_manager = None
__ext_bounded = False
__ext_force_positive = False
# traitsui bugs out trying to make an editor for this, so always specify!
# (it bugs out, because both editor shares the object, and Array editors
# don't like non-sequence objects). TextEditor() works well, so does
# RangeEditor() as it works with bmin/bmax.
value = t.Property(t.Either([t.CFloat(0), Array()]))
units = t.Str('')
free = t.Property(t.CBool(True))
bmin = t.Property(NoneFloat(), label="Lower bounds")
bmax = t.Property(NoneFloat(), label="Upper bounds")
_twin_function_expr = ""
_twin_inverse_function_expr = ""
twin_function = None
_twin_inverse_function = None
_twin_inverse_sympy = None
def __init__(self):
self._twins = set()
self.events = Events()
self.events.value_changed = Event("""
Event that triggers when the `Parameter.value` changes.
The event triggers after the internal state of the `Parameter` has
been updated.
Arguments
---------
obj : Parameter
The `Parameter` that the event belongs to
value : {float | array}
The new value of the parameter
""",
arguments=["obj", 'value'])
self.std = None
self.component = None
self.grad = None
self.name = ''
self.units = ''
self.map = None
self.model = None
self._whitelist = {
'_id_name': None,
'value': None,
'std': None,
'free': None,
'units': None,
'map': None,
'_bounds': None,
'ext_bounded': None,
'name': None,
'ext_force_positive': None,
'twin_function_expr': None,
'twin_inverse_function_expr': None,
'self': ('id', None),
}
self._slicing_whitelist = {'map': 'inav'}
def _load_dictionary(self, dictionary):
"""Load data from dictionary
Parameters
----------
dict : dictionary
A dictionary containing at least the following items:
_id_name : string
_id_name of the original parameter, used to create the
dictionary. Has to match with the self._id_name
_whitelist : dictionary
a dictionary, which keys are used as keywords to match with the
parameter attributes. For more information see
:meth:`hyperspy.misc.export_dictionary.load_from_dictionary`
* any field from _whitelist.keys() *
Returns
-------
id_value : int
the ID value of the original parameter, to be later used for setting
up the correct twins
"""
if dictionary['_id_name'] == self._id_name:
load_from_dictionary(self, dictionary)
return dictionary['self']
else:
raise ValueError(
"_id_name of parameter and dictionary do not match, \nparameter._id_name = %s\
\ndictionary['_id_name'] = %s" %
(self._id_name, dictionary['_id_name']))
def __repr__(self):
text = ''
text += 'Parameter %s' % self.name
if self.component is not None:
text += ' of %s' % self.component._get_short_description()
text = '<' + text + '>'
return text
def __len__(self):
return self._number_of_elements
@property
def twin_function_expr(self):
return self._twin_function_expr
@twin_function_expr.setter
def twin_function_expr(self, value):
if not value:
self.twin_function = None
self.twin_inverse_function = None
self._twin_function_expr = ""
self._twin_inverse_sympy = None
return
expr = sympy.sympify(value)
if len(expr.free_symbols) > 1:
raise ValueError("The expression must contain only one variable.")
elif len(expr.free_symbols) == 0:
raise ValueError("The expression must contain one variable, "
"it contains none.")
x = tuple(expr.free_symbols)[0]
self.twin_function = lambdify(x, expr.evalf())
self._twin_function_expr = value
if not self.twin_inverse_function:
y = sympy.Symbol(x.name + "2")
try:
inv = sympy.solveset(sympy.Eq(y, expr), x)
self._twin_inverse_sympy = lambdify(y, inv)
self._twin_inverse_function = None
except BaseException:
# Not all may have a suitable solution.
self._twin_inverse_function = None
self._twin_inverse_sympy = None
_logger.warning(
"The function {} is not invertible. Setting the value of "
"{} will raise an AttributeError unless you set manually "
"``twin_inverse_function_expr``. Otherwise, set the "
"value of its twin parameter instead.".format(value, self))
@property
def twin_inverse_function_expr(self):
if self.twin:
return self._twin_inverse_function_expr
else:
return ""
@twin_inverse_function_expr.setter
def twin_inverse_function_expr(self, value):
if not value:
self.twin_inverse_function = None
self._twin_inverse_function_expr = ""
return
expr = sympy.sympify(value)
if len(expr.free_symbols) > 1:
raise ValueError("The expression must contain only one variable.")
elif len(expr.free_symbols) == 0:
raise ValueError("The expression must contain one variable, "
"it contains none.")
x = tuple(expr.free_symbols)[0]
self._twin_inverse_function = lambdify(x, expr.evalf())
self._twin_inverse_function_expr = value
@property
def twin_inverse_function(self):
if (not self.twin_inverse_function_expr and self.twin_function_expr
and self._twin_inverse_sympy):
return lambda x: self._twin_inverse_sympy(x).pop()
else:
return self._twin_inverse_function
@twin_inverse_function.setter
def twin_inverse_function(self, value):
self._twin_inverse_function = value
def _get_value(self):
if self.twin is None:
return self.__value
else:
if self.twin_function:
return self.twin_function(self.twin.value)
else:
return self.twin.value
def _set_value(self, value):
try:
# Use try/except instead of hasattr("__len__") because a numpy
# memmap has a __len__ wrapper even for numbers that raises a
# TypeError when calling. See issue #349.
if len(value) != self._number_of_elements:
raise ValueError("The length of the parameter must be ",
self._number_of_elements)
else:
if not isinstance(value, tuple):
value = tuple(value)
except TypeError:
if self._number_of_elements != 1:
raise ValueError("The length of the parameter must be ",
self._number_of_elements)
old_value = self.__value
if self.twin is not None:
if self.twin_function is not None:
if self.twin_inverse_function is not None:
self.twin.value = self.twin_inverse_function(value)
return
else:
raise AttributeError(
"This parameter has a ``twin_function`` but"
"its ``twin_inverse_function`` is not defined.")
else:
self.twin.value = value
return
if self.ext_bounded is False:
self.__value = value
else:
if self.ext_force_positive is True:
value = np.abs(value)
if self._number_of_elements == 1:
if self.bmin is not None and value <= self.bmin:
self.__value = self.bmin
elif self.bmax is not None and value >= self.bmax:
self.__value = self.bmax
else:
self.__value = value
else:
bmin = (self.bmin if self.bmin is not None else -np.inf)
bmax = (self.bmax if self.bmin is not None else np.inf)
self.__value = np.clip(value, bmin, bmax)
if (self._number_of_elements != 1
and not isinstance(self.__value, tuple)):
self.__value = tuple(self.__value)
if old_value != self.__value:
self.events.value_changed.trigger(value=self.__value, obj=self)
self.trait_property_changed('value', old_value, self.__value)
# Fix the parameter when coupled
def _get_free(self):
if self.twin is None:
return self.__free
else:
return False
def _set_free(self, arg):
old_value = self.__free
self.__free = arg
if self.component is not None:
self.component._update_free_parameters()
self.trait_property_changed('free', old_value, self.__free)
def _on_twin_update(self, value, twin=None):
if (twin is not None and hasattr(twin, 'events')
and hasattr(twin.events, 'value_changed')):
with twin.events.value_changed.suppress_callback(
self._on_twin_update):
self.events.value_changed.trigger(value=value, obj=self)
else:
self.events.value_changed.trigger(value=value, obj=self)
def _set_twin(self, arg):
if arg is None:
if self.twin is not None:
# Store the value of the twin in order to set the
# value of the parameter when it is uncoupled
twin_value = self.value
if self in self.twin._twins:
self.twin._twins.remove(self)
self.twin.events.value_changed.disconnect(
self._on_twin_update)
self.__twin = arg
self.value = twin_value
else:
if self not in arg._twins:
arg._twins.add(self)
arg.events.value_changed.connect(self._on_twin_update,
["value"])
self.__twin = arg
if self.component is not None:
self.component._update_free_parameters()
def _get_twin(self):
return self.__twin
twin = property(_get_twin, _set_twin)
def _get_bmin(self):
if self._number_of_elements == 1:
return self._bounds[0]
else:
return self._bounds[0][0]
def _set_bmin(self, arg):
old_value = self.bmin
if self._number_of_elements == 1:
self._bounds = (arg, self.bmax)
else:
self._bounds = ((arg, self.bmax), ) * self._number_of_elements
# Update the value to take into account the new bounds
self.value = self.value
self.trait_property_changed('bmin', old_value, arg)
def _get_bmax(self):
if self._number_of_elements == 1:
return self._bounds[1]
else:
return self._bounds[0][1]
def _set_bmax(self, arg):
old_value = self.bmax
if self._number_of_elements == 1:
self._bounds = (self.bmin, arg)
else:
self._bounds = ((self.bmin, arg), ) * self._number_of_elements
# Update the value to take into account the new bounds
self.value = self.value
self.trait_property_changed('bmax', old_value, arg)
@property
def _number_of_elements(self):
return self.__number_of_elements
@_number_of_elements.setter
def _number_of_elements(self, arg):
# Do nothing if the number of arguments stays the same
if self.__number_of_elements == arg:
return
if arg <= 1:
raise ValueError("Please provide an integer number equal "
"or greater to 1")
self._bounds = ((self.bmin, self.bmax), ) * arg
self.__number_of_elements = arg
if arg == 1:
self._Parameter__value = 0
else:
self._Parameter__value = (0, ) * arg
if self.component is not None:
self.component.update_number_parameters()
@property
def ext_bounded(self):
return self.__ext_bounded
@ext_bounded.setter
def ext_bounded(self, arg):
if arg is not self.__ext_bounded:
self.__ext_bounded = arg
# Update the value to take into account the new bounds
self.value = self.value
@property
def ext_force_positive(self):
return self.__ext_force_positive
@ext_force_positive.setter
def ext_force_positive(self, arg):
if arg is not self.__ext_force_positive:
self.__ext_force_positive = arg
# Update the value to take into account the new bounds
self.value = self.value
def store_current_value_in_array(self):
"""Store the value and std attributes.
See also
--------
fetch, assign_current_value_to_all
"""
indices = self._axes_manager.indices[::-1]
# If it is a single spectrum indices is ()
if not indices:
indices = (0, )
self.map['values'][indices] = self.value
self.map['is_set'][indices] = True
if self.std is not None:
self.map['std'][indices] = self.std
def fetch(self):
"""Fetch the stored value and std attributes.
See Also
--------
store_current_value_in_array, assign_current_value_to_all
"""
indices = self._axes_manager.indices[::-1]
# If it is a single spectrum indices is ()
if not indices:
indices = (0, )
if self.map['is_set'][indices]:
value = self.map['values'][indices]
std = self.map['std'][indices]
if isinstance(value, dArray):
value = value.compute()
if isinstance(std, dArray):
std = std.compute()
self.value = value
self.std = std
def assign_current_value_to_all(self, mask=None):
"""Assign the current value attribute to all the indices
Parameters
----------
mask: {None, boolean numpy array}
Set only the indices that are not masked i.e. where
mask is False.
See Also
--------
store_current_value_in_array, fetch
"""
if mask is None:
mask = np.zeros(self.map.shape, dtype='bool')
self.map['values'][mask == False] = self.value
self.map['is_set'][mask == False] = True
def _create_array(self):
"""Create the map array to store the information in
multidimensional datasets.
"""
shape = self._axes_manager._navigation_shape_in_array
if not shape:
shape = [
1,
]
# Shape-1 fields in dtypes won’t be collapsed to scalars in a future
# numpy version (see release notes numpy 1.17.0)
if self._number_of_elements > 1:
dtype_ = np.dtype([('values', 'float', self._number_of_elements),
('std', 'float', self._number_of_elements),
('is_set', 'bool')])
else:
dtype_ = np.dtype([('values', 'float'), ('std', 'float'),
('is_set', 'bool')])
if (self.map is None or self.map.shape != shape
or self.map.dtype != dtype_):
self.map = np.zeros(shape, dtype_)
self.map['std'].fill(np.nan)
# TODO: in the future this class should have access to
# axes manager and should be able to fetch its own
# values. Until then, the next line is necessary to avoid
# erros when self.std is defined and the shape is different
# from the newly defined arrays
self.std = None
def as_signal(self, field='values'):
"""Get a parameter map as a signal object.
Please note that this method only works when the navigation
dimension is greater than 0.
Parameters
----------
field : {'values', 'std', 'is_set'}
Raises
------
NavigationDimensionError : if the navigation dimension is 0
"""
from hyperspy.signal import BaseSignal
s = BaseSignal(data=self.map[field],
axes=self._axes_manager._get_navigation_axes_dicts())
if self.component is not None and \
self.component.active_is_multidimensional:
s.data[np.logical_not(self.component._active_array)] = np.nan
s.metadata.General.title = ("%s parameter" %
self.name if self.component is None else
"%s parameter of %s component" %
(self.name, self.component.name))
for axis in s.axes_manager._axes:
axis.navigate = False
if self._number_of_elements > 1:
s.axes_manager._append_axis(size=self._number_of_elements,
name=self.name,
navigate=True)
s._assign_subclass()
if field == "values":
# Add the variance if available
std = self.as_signal(field="std")
if not np.isnan(std.data).all():
std.data = std.data**2
std.metadata.General.title = "Variance"
s.metadata.set_item("Signal.Noise_properties.variance", std)
return s
def plot(self, **kwargs):
"""Plot parameter signal.
Parameters
----------
**kwargs
Any extra keyword arguments are passed to the signal plot.
Example
-------
>>> parameter.plot() #doctest: +SKIP
Set the minimum and maximum displayed values
>>> parameter.plot(vmin=0, vmax=1) #doctest: +SKIP
"""
self.as_signal().plot(**kwargs)
def export(self, folder=None, name=None, format="hspy", save_std=False):
"""Save the data to a file.
All the arguments are optional.
Parameters
----------
folder : str or None
The path to the folder where the file will be saved.
If `None` the current folder is used by default.
name : str or None
The name of the file. If `None` the Components name followed
by the Parameter `name` attributes will be used by default.
If a file with the same name exists the name will be
modified by appending a number to the file path.
save_std : bool
If True, also the standard deviation will be saved
format: str
The extension of any file format supported by HyperSpy, default hspy
"""
if format is None:
format = "hspy"
if name is None:
name = self.component.name + '_' + self.name
filename = incremental_filename(slugify(name) + '.' + format)
if folder is not None:
filename = os.path.join(folder, filename)
self.as_signal().save(filename)
if save_std is True:
self.as_signal(field='std').save(append2pathname(filename, '_std'))
def as_dictionary(self, fullcopy=True):
"""Returns parameter as a dictionary, saving all attributes from
self._whitelist.keys() For more information see
:meth:`hyperspy.misc.export_dictionary.export_to_dictionary`
Parameters
----------
fullcopy : Bool (optional, False)
Copies of objects are stored, not references. If any found,
functions will be pickled and signals converted to dictionaries
Returns
-------
dic : dictionary with the following keys:
_id_name : string
_id_name of the original parameter, used to create the
dictionary. Has to match with the self._id_name
_twins : list
a list of ids of the twins of the parameter
_whitelist : dictionary
a dictionary, which keys are used as keywords to match with the
parameter attributes. For more information see
:meth:`hyperspy.misc.export_dictionary.export_to_dictionary`
* any field from _whitelist.keys() *
"""
dic = {'_twins': [id(t) for t in self._twins]}
export_to_dictionary(self, self._whitelist, dic, fullcopy)
return dic
def default_traits_view(self):
# As mentioned above, the default editor for
# value = t.Property(t.Either([t.CFloat(0), Array()]))
# gives a ValueError. We therefore implement default_traits_view so
# that configure/edit_traits will still work straight out of the box.
# A whitelist controls which traits to include in this view.
from traitsui.api import RangeEditor, View, Item
whitelist = ['bmax', 'bmin', 'free', 'name', 'std', 'units', 'value']
editable_traits = [
trait for trait in self.editable_traits() if trait in whitelist
]
if 'value' in editable_traits:
i = editable_traits.index('value')
v = editable_traits.pop(i)
editable_traits.insert(
i,
Item(v, editor=RangeEditor(low_name='bmin', high_name='bmax')))
view = View(editable_traits, buttons=['OK', 'Cancel'])
return view
class Parameter(t.HasTraits):
"""Model parameter
Attributes
----------
value : float or array
The value of the parameter for the current location. The value
for other locations is stored in map.
bmin, bmax: float
Lower and upper bounds of the parameter value.
twin : {None, Parameter}
If it is not None, the value of the current parameter is
a function of the given Parameter. The function is by default
the identity function, but it can be defined by twin_function
twin_function : function
Function that, if selt.twin is not None, takes self.twin.value
as its only argument and returns a float or array that is
returned when getting Parameter.value
twin_inverse_function : function
The inverse of twin_function. If it is None then it is not
possible to set the value of the parameter twin by setting
the value of the current parameter.
ext_force_positive : bool
If True, the parameter value is set to be the absolute value
of the input value i.e. if we set Parameter.value = -3, the
value stored is 3 instead. This is useful to bound a value
to be positive in an optimization without actually using an
optimizer that supports bounding.
ext_bounded : bool
Similar to ext_force_positive, but in this case the bounds are
defined by bmin and bmax. It is a better idea to use
an optimizer that supports bounding though.
Methods
-------
as_signal(field = 'values')
Get a parameter map as a signal object
plot()
Plots the value of the Parameter at all locations.
export(folder=None, name=None, format=None, save_std=False)
Saves the value of the parameter map to the specified format
connect, disconnect(function)
Call the functions connected when the value attribute changes.
"""
__number_of_elements = 1
__value = 0
__free = True
_bounds = (None, None)
__twin = None
_axes_manager = None
__ext_bounded = False
__ext_force_positive = False
# traitsui bugs out trying to make an editor for this, so always specify!
# (it bugs out, because both editor shares the object, and Array editors
# don't like non-sequence objects). TextEditor() works well, so does
# RangeEditor() as it works with bmin/bmax.
value = t.Property(t.Either([t.CFloat(0), Array()]))
units = t.Str('')
free = t.Property(t.CBool(True))
bmin = t.Property(NoneFloat(), label="Lower bounds")
bmax = t.Property(NoneFloat(), label="Upper bounds")
def __init__(self):
self._twins = set()
self.connected_functions = list()
self.twin_function = lambda x: x
self.twin_inverse_function = lambda x: x
self.std = None
self.component = None
self.grad = None
self.name = ''
self.map = None
self.model = None
def __repr__(self):
text = ''
text += 'Parameter %s' % self.name
if self.component is not None:
text += ' of %s' % self.component._get_short_description()
text = '<' + text + '>'
return text.encode('utf8')
def __len__(self):
return self._number_of_elements
def connect(self, f):
if f not in self.connected_functions:
self.connected_functions.append(f)
if self.twin:
self.twin.connect(f)
def disconnect(self, f):
if f in self.connected_functions:
self.connected_functions.remove(f)
if self.twin:
self.twin.disconnect(f)
def _get_value(self):
if self.twin is None:
return self.__value
else:
return self.twin_function(self.twin.value)
def _set_value(self, arg):
try:
# Use try/except instead of hasattr("__len__") because a numpy
# memmap has a __len__ wrapper even for numbers that raises a
# TypeError when calling. See issue #349.
if len(arg) != self._number_of_elements:
raise ValueError("The length of the parameter must be ",
self._number_of_elements)
else:
if not isinstance(arg, tuple):
arg = tuple(arg)
except TypeError:
if self._number_of_elements != 1:
raise ValueError("The length of the parameter must be ",
self._number_of_elements)
old_value = self.__value
if self.twin is not None:
if self.twin_inverse_function is not None:
self.twin.value = self.twin_inverse_function(arg)
return
if self.ext_bounded is False:
self.__value = arg
else:
if self.ext_force_positive is True:
arg = np.abs(arg)
if self._number_of_elements == 1:
if self.bmin is not None and arg <= self.bmin:
self.__value = self.bmin
elif self.bmax is not None and arg >= self.bmax:
self.__value = self.bmax
else:
self.__value = arg
else:
bmin = (self.bmin if self.bmin is not None else -np.inf)
bmax = (self.bmax if self.bmin is not None else np.inf)
self.__value = np.clip(arg, bmin, bmax)
if (self._number_of_elements != 1
and not isinstance(self.__value, tuple)):
self.__value = tuple(self.__value)
if old_value != self.__value:
for f in self.connected_functions:
try:
f()
except:
self.disconnect(f)
self.trait_property_changed('value', old_value, self.__value)
# Fix the parameter when coupled
def _get_free(self):
if self.twin is None:
return self.__free
else:
return False
def _set_free(self, arg):
old_value = self.__free
self.__free = arg
if self.component is not None:
self.component._update_free_parameters()
self.trait_property_changed('free', old_value, self.__free)
def _set_twin(self, arg):
if arg is None:
if self.twin is not None:
# Store the value of the twin in order to set the
# value of the parameter when it is uncoupled
twin_value = self.value
if self in self.twin._twins:
self.twin._twins.remove(self)
for f in self.connected_functions:
self.twin.disconnect(f)
self.__twin = arg
self.value = twin_value
else:
if self not in arg._twins:
arg._twins.add(self)
for f in self.connected_functions:
arg.connect(f)
self.__twin = arg
if self.component is not None:
self.component._update_free_parameters()
def _get_twin(self):
return self.__twin
twin = property(_get_twin, _set_twin)
def _get_bmin(self):
if self._number_of_elements == 1:
return self._bounds[0]
else:
return self._bounds[0][0]
def _set_bmin(self, arg):
old_value = self.bmin
if self._number_of_elements == 1:
self._bounds = (arg, self.bmax)
else:
self._bounds = ((arg, self.bmax), ) * self._number_of_elements
# Update the value to take into account the new bounds
self.value = self.value
self.trait_property_changed('bmin', old_value, arg)
def _get_bmax(self):
if self._number_of_elements == 1:
return self._bounds[1]
else:
return self._bounds[0][1]
def _set_bmax(self, arg):
old_value = self.bmax
if self._number_of_elements == 1:
self._bounds = (self.bmin, arg)
else:
self._bounds = ((self.bmin, arg), ) * self._number_of_elements
# Update the value to take into account the new bounds
self.value = self.value
self.trait_property_changed('bmax', old_value, arg)
@property
def _number_of_elements(self):
return self.__number_of_elements
@_number_of_elements.setter
def _number_of_elements(self, arg):
# Do nothing if the number of arguments stays the same
if self.__number_of_elements == arg:
return
if arg <= 1:
raise ValueError("Please provide an integer number equal "
"or greater to 1")
self._bounds = ((self.bmin, self.bmax), ) * arg
self.__number_of_elements = arg
if arg == 1:
self._Parameter__value = 0
else:
self._Parameter__value = (0, ) * arg
if self.component is not None:
self.component.update_number_parameters()
@property
def ext_bounded(self):
return self.__ext_bounded
@ext_bounded.setter
def ext_bounded(self, arg):
if arg is not self.__ext_bounded:
self.__ext_bounded = arg
# Update the value to take into account the new bounds
self.value = self.value
@property
def ext_force_positive(self):
return self.__ext_force_positive
@ext_force_positive.setter
def ext_force_positive(self, arg):
if arg is not self.__ext_force_positive:
self.__ext_force_positive = arg
# Update the value to take into account the new bounds
self.value = self.value
def store_current_value_in_array(self):
"""Store the value and std attributes.
See also
--------
fetch, assign_current_value_to_all
"""
indices = self._axes_manager.indices[::-1]
# If it is a single spectrum indices is ()
if not indices:
indices = (0, )
self.map['values'][indices] = self.value
self.map['is_set'][indices] = True
if self.std is not None:
self.map['std'][indices] = self.std
def fetch(self):
"""Fetch the stored value and std attributes.
See Also
--------
store_current_value_in_array, assign_current_value_to_all
"""
indices = self._axes_manager.indices[::-1]
# If it is a single spectrum indices is ()
if not indices:
indices = (0, )
if self.map['is_set'][indices]:
self.value = self.map['values'][indices]
self.std = self.map['std'][indices]
def assign_current_value_to_all(self, mask=None):
'''Assign the current value attribute to all the indices
Parameters
----------
mask: {None, boolean numpy array}
Set only the indices that are not masked i.e. where
mask is False.
See Also
--------
store_current_value_in_array, fetch
'''
if mask is None:
mask = np.zeros(self.map.shape, dtype='bool')
self.map['values'][mask == False] = self.value
self.map['is_set'][mask == False] = True
def _create_array(self):
"""Create the map array to store the information in
multidimensional datasets.
"""
shape = self._axes_manager._navigation_shape_in_array
if not shape:
shape = [
1,
]
dtype_ = np.dtype([('values', 'float', self._number_of_elements),
('std', 'float', self._number_of_elements),
('is_set', 'bool', 1)])
if (self.map is None or self.map.shape != shape
or self.map.dtype != dtype_):
self.map = np.zeros(shape, dtype_)
self.map['std'].fill(np.nan)
# TODO: in the future this class should have access to
# axes manager and should be able to fetch its own
# values. Until then, the next line is necessary to avoid
# erros when self.std is defined and the shape is different
# from the newly defined arrays
self.std = None
def as_signal(self, field='values'):
"""Get a parameter map as a signal object.
Please note that this method only works when the navigation
dimension is greater than 0.
Parameters
----------
field : {'values', 'std', 'is_set'}
Raises
------
NavigationDimensionError : if the navigation dimension is 0
"""
from hyperspy.signal import Signal
if self._axes_manager.navigation_dimension == 0:
raise NavigationDimensionError(0, '>0')
s = Signal(data=self.map[field],
axes=self._axes_manager._get_navigation_axes_dicts())
if self.component.active_is_multidimensional:
s.data[np.logical_not(self.component._active_array)] = np.nan
s.metadata.General.title = ("%s parameter" %
self.name if self.component is None else
"%s parameter of %s component" %
(self.name, self.component.name))
for axis in s.axes_manager._axes:
axis.navigate = False
if self._number_of_elements > 1:
s.axes_manager.append_axis(size=self._number_of_elements,
name=self.name,
navigate=True)
return s
def plot(self):
self.as_signal().plot()
def export(self, folder=None, name=None, format=None, save_std=False):
'''Save the data to a file.
All the arguments are optional.
Parameters
----------
folder : str or None
The path to the folder where the file will be saved.
If `None` the current folder is used by default.
name : str or None
The name of the file. If `None` the Components name followed
by the Parameter `name` attributes will be used by default.
If a file with the same name exists the name will be
modified by appending a number to the file path.
save_std : bool
If True, also the standard deviation will be saved
'''
if format is None:
format = preferences.General.default_export_format
if name is None:
name = self.component.name + '_' + self.name
filename = incremental_filename(slugify(name) + '.' + format)
if folder is not None:
filename = os.path.join(folder, filename)
self.as_signal().save(filename)
if save_std is True:
self.as_signal(field='std').save(append2pathname(filename, '_std'))
def default_traits_view(self):
# As mentioned above, the default editor for
# value = t.Property(t.Either([t.CFloat(0), Array()]))
# gives a ValueError. We therefore implement default_traits_view so
# that configure/edit_traits will still work straight out of the box.
# A whitelist controls which traits to include in this view.
from traitsui.api import RangeEditor, View, Item
whitelist = ['bmax', 'bmin', 'free', 'name', 'std', 'units', 'value']
editable_traits = [
trait for trait in self.editable_traits() if trait in whitelist
]
if 'value' in editable_traits:
i = editable_traits.index('value')
v = editable_traits.pop(i)
editable_traits.insert(
i,
Item(v, editor=RangeEditor(low_name='bmin', high_name='bmax')))
view = View(editable_traits, buttons=['OK', 'Cancel'])
return view
class Agent(t.HasTraits):
"""
An Agent is the basic class of the simulation.
Agents communicate asynchronously.
"""
#DEBUG_RECEIVE = True
#DEBUG_SEND = True
_address_book = t.Instance(addressing.AddressBook,
transient=True,
allow_none=False)
_default_queue = t.Instance(queue.Queue, transient=True, allow_none=False)
_greenlet = t.Instance(gevent.Greenlet, transient=True, allow_none=False)
_node_db = t.Instance(agent_db.IAgentStorage,
transient=True,
allow_none=False)
id = t.Either(t.CInt, t.Str)
__ = t.PythonValue(transient=True)
def join(self):
try:
return self._greenlet.join()
except (KeyboardInterrupt, SystemExit) as e:
print "[%s] I was caught with the hands in the jam!" % self.id
print self._create_error_text(self._greenlet)
raise AgentError(e)
def sleep(self, seconds):
gevent.sleep(seconds)
def setup(self):
"""
Additional initialization before the run loop goes here.
The default implementation is empty.
"""
def _compute_identifier(self, identifier):
if identifier is None:
try:
self.id = self.name
except Exception:
raise AgentError("%r is not a valid Agent Id." % identifier)
else:
self.id = identifier
def start(self, address_book, agent_db, identifier=None):
"""
Start executing the agent.
:param address_book: the address book where the other agents references can be found.
:type address_book: :class:`addressing.AddressBook`
:param agent_db: the agent database that is used if this agent needs to be serialized
:type agent_db: :class:`agent_db.IAgentStorage`
:param identifier: this agent identifier. It is also added to the address book.
:type identifier: str | int
:return: the agent itself
:rtype: :class:`Agent`
"""
self._compute_identifier(identifier)
self._address_book = address_book
self._address_book.register(self, self.id)
self._default_queue = queue.Queue()
self._node_db = agent_db
self._greenlet = gevent.Greenlet(self._start)
self._greenlet.link_exception(self.on_error)
if hasattr(self, 'on_completion'):
self._greenlet.link_value(self.on_completion)
self._greenlet.start()
return self
@property
def started(self):
"""
True if the agent has been started.
"""
return ((self.id is not None) and (self._address_book is not None)
and (self._greenlet is not None))
def _revert_start(self, _source):
self._address_book.unregister(self.id)
del self._address_book
del self._default_queue
del self._node_db
del self._greenlet
def _create_error_text(self, source):
ss = StringIO()
print >> ss, self
print >> ss, source
print >> ss, 'succesful:', source.successful()
print >> ss, 'value:', source.value
print >> ss, 'exception:', source.exception
if self._default_queue is not None:
print >> ss, 'messages:', self._default_queue.qsize()
if self._default_queue.qsize():
import pprint
pprint.pprint([
message for message, _result in self._default_queue.queue
],
stream=ss)
return ss.getvalue()
def on_error(self, source):
"""
This handler is called when an error arises in the greenlet executing the agent.
The default implementation logs the error. Other implementations may
take different actions.
:param source: the greenlet where the error originated.
:type source: gevent.Greenlet
"""
text = self._create_error_text(source)
self._get_logger().put_error(self, text)
def kill(self):
return self._greenlet.kill()
def read(self, timeout=None):
"""
Reads the next message that was sent to this agent.
:param timeout: can specify a timeout
:return: (Message, event.AsyncResult)
:raise: NoMessage if no message arrives after timeout
.. warning::
It is not really meant to be called directly unless you are building a runloop.
Strictly speaking it is not non public, though.
"""
try:
entry = self._default_queue.get(timeout=timeout)
if getattr(self, 'DEBUG_RECEIVE', False):
self.log_received(entry[0])
return entry
except queue.Empty, e:
raise NoMessage(e)
class Parameter(t.HasTraits):
"""Model parameter
Attributes
----------
value : float or array
The value of the parameter for the current location. The value
for other locations is stored in map.
bmin, bmax: float
Lower and upper bounds of the parameter value.
twin : {None, Parameter}
If it is not None, the value of the current parameter is
a function of the given Parameter. The function is by default
the identity function, but it can be defined by twin_function
twin_function : function
Function that, if selt.twin is not None, takes self.twin.value
as its only argument and returns a float or array that is
returned when getting Parameter.value
twin_inverse_function : function
The inverse of twin_function. If it is None then it is not
possible to set the value of the parameter twin by setting
the value of the current parameter.
ext_force_positive : bool
If True, the parameter value is set to be the absolute value
of the input value i.e. if we set Parameter.value = -3, the
value stored is 3 instead. This is useful to bound a value
to be positive in an optimization without actually using an
optimizer that supports bounding.
ext_bounded : bool
Similar to ext_force_positive, but in this case the bounds are
defined by bmin and bmax. It is a better idea to use
an optimizer that supports bounding though.
Methods
-------
as_signal(field = 'values')
Get a parameter map as a signal object
plot()
Plots the value of the Parameter at all locations.
export(folder=None, name=None, format=None, save_std=False)
Saves the value of the parameter map to the specified format
connect, disconnect(function)
Call the functions connected when the value attribute changes.
"""
__number_of_elements = 1
__value = 0
__free = True
_bounds = (None, None)
__twin = None
_axes_manager = None
__ext_bounded = False
__ext_force_positive = False
# traitsui bugs out trying to make an editor for this, so always specify!
# (it bugs out, because both editor shares the object, and Array editors
# don't like non-sequence objects). TextEditor() works well, so does
# RangeEditor() as it works with bmin/bmax.
value = t.Property(t.Either([t.CFloat(0), Array()]))
units = t.Str('')
free = t.Property(t.CBool(True))
bmin = t.Property(NoneFloat(), label="Lower bounds")
bmax = t.Property(NoneFloat(), label="Upper bounds")
def __init__(self):
self._twins = set()
self.events = Events()
self.events.value_changed = Event("""
Event that triggers when the `Parameter.value` changes.
The event triggers after the internal state of the `Parameter` has
been updated.
Arguments
---------
obj : Parameter
The `Parameter` that the event belongs to
value : {float | array}
The new value of the parameter
""",
arguments=["obj", 'value'])
self.twin_function = lambda x: x
self.twin_inverse_function = lambda x: x
self.std = None
self.component = None
self.grad = None
self.name = ''
self.units = ''
self.map = None
self.model = None
self._whitelist = {
'_id_name': None,
'value': None,
'std': None,
'free': None,
'units': None,
'map': None,
'_bounds': None,
'ext_bounded': None,
'name': None,
'ext_force_positive': None,
'self': ('id', None),
'twin_function': ('fn', None),
'twin_inverse_function': ('fn', None),
}
self._slicing_whitelist = {'map': 'inav'}
def _load_dictionary(self, dictionary):
"""Load data from dictionary
Parameters
----------
dict : dictionary
A dictionary containing at least the following items:
_id_name : string
_id_name of the original parameter, used to create the
dictionary. Has to match with the self._id_name
_whitelist : dictionary
a dictionary, which keys are used as keywords to match with the
parameter attributes. For more information see
:meth:`hyperspy.misc.export_dictionary.load_from_dictionary`
* any field from _whitelist.keys() *
Returns
-------
id_value : int
the ID value of the original parameter, to be later used for setting
up the correct twins
"""
if dictionary['_id_name'] == self._id_name:
load_from_dictionary(self, dictionary)
return dictionary['self']
else:
raise ValueError(
"_id_name of parameter and dictionary do not match, \nparameter._id_name = %s\
\ndictionary['_id_name'] = %s" %
(self._id_name, dictionary['_id_name']))
def __repr__(self):
text = ''
text += 'Parameter %s' % self.name
if self.component is not None:
text += ' of %s' % self.component._get_short_description()
text = '<' + text + '>'
return text
def __len__(self):
return self._number_of_elements
def _get_value(self):
if self.twin is None:
return self.__value
else:
return self.twin_function(self.twin.value)
def _set_value(self, value):
try:
# Use try/except instead of hasattr("__len__") because a numpy
# memmap has a __len__ wrapper even for numbers that raises a
# TypeError when calling. See issue #349.
if len(value) != self._number_of_elements:
raise ValueError("The length of the parameter must be ",
self._number_of_elements)
else:
if not isinstance(value, tuple):
value = tuple(value)
except TypeError:
if self._number_of_elements != 1:
raise ValueError("The length of the parameter must be ",
self._number_of_elements)
old_value = self.__value
if self.twin is not None:
if self.twin_inverse_function is not None:
self.twin.value = self.twin_inverse_function(value)
return
if self.ext_bounded is False:
self.__value = value
else:
if self.ext_force_positive is True:
value = np.abs(value)
if self._number_of_elements == 1:
if self.bmin is not None and value <= self.bmin:
self.__value = self.bmin
elif self.bmax is not None and value >= self.bmax:
self.__value = self.bmax
else:
self.__value = value
else:
bmin = (self.bmin if self.bmin is not None else -np.inf)
bmax = (self.bmax if self.bmin is not None else np.inf)
self.__value = np.clip(value, bmin, bmax)
if (self._number_of_elements != 1
and not isinstance(self.__value, tuple)):
self.__value = tuple(self.__value)
if old_value != self.__value:
self.events.value_changed.trigger(value=self.__value, obj=self)
self.trait_property_changed('value', old_value, self.__value)
# Fix the parameter when coupled
def _get_free(self):
if self.twin is None:
return self.__free
else:
return False
def _set_free(self, arg):
old_value = self.__free
self.__free = arg
if self.component is not None:
self.component._update_free_parameters()
self.trait_property_changed('free', old_value, self.__free)
def _on_twin_update(self, value, twin=None):
if (twin is not None and hasattr(twin, 'events')
and hasattr(twin.events, 'value_changed')):
with twin.events.value_changed.suppress_callback(
self._on_twin_update):
self.events.value_changed.trigger(value=value, obj=self)
else:
self.events.value_changed.trigger(value=value, obj=self)
def _set_twin(self, arg):
if arg is None:
if self.twin is not None:
# Store the value of the twin in order to set the
# value of the parameter when it is uncoupled
twin_value = self.value
if self in self.twin._twins:
self.twin._twins.remove(self)
self.twin.events.value_changed.disconnect(
self._on_twin_update)
self.__twin = arg
self.value = twin_value
else:
if self not in arg._twins:
arg._twins.add(self)
arg.events.value_changed.connect(self._on_twin_update,
["value"])
self.__twin = arg
if self.component is not None:
self.component._update_free_parameters()
def _get_twin(self):
return self.__twin
twin = property(_get_twin, _set_twin)
def _get_bmin(self):
if self._number_of_elements == 1:
return self._bounds[0]
else:
return self._bounds[0][0]
def _set_bmin(self, arg):
old_value = self.bmin
if self._number_of_elements == 1:
self._bounds = (arg, self.bmax)
else:
self._bounds = ((arg, self.bmax), ) * self._number_of_elements
# Update the value to take into account the new bounds
self.value = self.value
self.trait_property_changed('bmin', old_value, arg)
def _get_bmax(self):
if self._number_of_elements == 1:
return self._bounds[1]
else:
return self._bounds[0][1]
def _set_bmax(self, arg):
old_value = self.bmax
if self._number_of_elements == 1:
self._bounds = (self.bmin, arg)
else:
self._bounds = ((self.bmin, arg), ) * self._number_of_elements
# Update the value to take into account the new bounds
self.value = self.value
self.trait_property_changed('bmax', old_value, arg)
@property
def _number_of_elements(self):
return self.__number_of_elements
@_number_of_elements.setter
def _number_of_elements(self, arg):
# Do nothing if the number of arguments stays the same
if self.__number_of_elements == arg:
return
if arg <= 1:
raise ValueError("Please provide an integer number equal "
"or greater to 1")
self._bounds = ((self.bmin, self.bmax), ) * arg
self.__number_of_elements = arg
if arg == 1:
self._Parameter__value = 0
else:
self._Parameter__value = (0, ) * arg
if self.component is not None:
self.component.update_number_parameters()
@property
def ext_bounded(self):
return self.__ext_bounded
@ext_bounded.setter
def ext_bounded(self, arg):
if arg is not self.__ext_bounded:
self.__ext_bounded = arg
# Update the value to take into account the new bounds
self.value = self.value
@property
def ext_force_positive(self):
return self.__ext_force_positive
@ext_force_positive.setter
def ext_force_positive(self, arg):
if arg is not self.__ext_force_positive:
self.__ext_force_positive = arg
# Update the value to take into account the new bounds
self.value = self.value
def store_current_value_in_array(self):
"""Store the value and std attributes.
See also
--------
fetch, assign_current_value_to_all
"""
indices = self._axes_manager.indices[::-1]
# If it is a single spectrum indices is ()
if not indices:
indices = (0, )
self.map['values'][indices] = self.value
self.map['is_set'][indices] = True
if self.std is not None:
self.map['std'][indices] = self.std
def fetch(self):
"""Fetch the stored value and std attributes.
See Also
--------
store_current_value_in_array, assign_current_value_to_all
"""
indices = self._axes_manager.indices[::-1]
# If it is a single spectrum indices is ()
if not indices:
indices = (0, )
if self.map['is_set'][indices]:
self.value = self.map['values'][indices]
self.std = self.map['std'][indices]
def assign_current_value_to_all(self, mask=None):
"""Assign the current value attribute to all the indices
Parameters
----------
mask: {None, boolean numpy array}
Set only the indices that are not masked i.e. where
mask is False.
See Also
--------
store_current_value_in_array, fetch
"""
if mask is None:
mask = np.zeros(self.map.shape, dtype='bool')
self.map['values'][mask == False] = self.value
self.map['is_set'][mask == False] = True
def _create_array(self):
"""Create the map array to store the information in
multidimensional datasets.
"""
shape = self._axes_manager._navigation_shape_in_array
if not shape:
shape = [
1,
]
dtype_ = np.dtype([('values', 'float', self._number_of_elements),
('std', 'float', self._number_of_elements),
('is_set', 'bool', 1)])
if (self.map is None or self.map.shape != shape
or self.map.dtype != dtype_):
self.map = np.zeros(shape, dtype_)
self.map['std'].fill(np.nan)
# TODO: in the future this class should have access to
# axes manager and should be able to fetch its own
# values. Until then, the next line is necessary to avoid
# erros when self.std is defined and the shape is different
# from the newly defined arrays
self.std = None
def as_signal(self, field='values'):
"""Get a parameter map as a signal object.
Please note that this method only works when the navigation
dimension is greater than 0.
Parameters
----------
field : {'values', 'std', 'is_set'}
Raises
------
NavigationDimensionError : if the navigation dimension is 0
"""
from hyperspy.signal import BaseSignal
s = BaseSignal(data=self.map[field],
axes=self._axes_manager._get_navigation_axes_dicts())
if self.component is not None and \
self.component.active_is_multidimensional:
s.data[np.logical_not(self.component._active_array)] = np.nan
s.metadata.General.title = ("%s parameter" %
self.name if self.component is None else
"%s parameter of %s component" %
(self.name, self.component.name))
for axis in s.axes_manager._axes:
axis.navigate = False
if self._number_of_elements > 1:
s.axes_manager._append_axis(size=self._number_of_elements,
name=self.name,
navigate=True)
s._assign_subclass()
if field == "values":
# Add the variance if available
std = self.as_signal(field="std")
if not np.isnan(std.data).all():
std.data = std.data**2
std.metadata.General.title = "Variance"
s.metadata.set_item("Signal.Noise_properties.variance", std)
return s
def plot(self, **kwargs):
"""Plot parameter signal.
Parameters
----------
**kwargs
Any extra keyword arguments are passed to the signal plot.
Example
-------
>>> parameter.plot()
Set the minimum and maximum displayed values
>>> parameter.plot(vmin=0, vmax=1)
"""
self.as_signal().plot(**kwargs)
def export(self, folder=None, name=None, format=None, save_std=False):
"""Save the data to a file.
All the arguments are optional.
Parameters
----------
folder : str or None
The path to the folder where the file will be saved.
If `None` the current folder is used by default.
name : str or None
The name of the file. If `None` the Components name followed
by the Parameter `name` attributes will be used by default.
If a file with the same name exists the name will be
modified by appending a number to the file path.
save_std : bool
If True, also the standard deviation will be saved
"""
if format is None:
format = preferences.General.default_export_format
if name is None:
name = self.component.name + '_' + self.name
filename = incremental_filename(slugify(name) + '.' + format)
if folder is not None:
filename = os.path.join(folder, filename)
self.as_signal().save(filename)
if save_std is True:
self.as_signal(field='std').save(append2pathname(filename, '_std'))
def as_dictionary(self, fullcopy=True):
"""Returns parameter as a dictionary, saving all attributes from
self._whitelist.keys() For more information see
:meth:`hyperspy.misc.export_dictionary.export_to_dictionary`
Parameters
----------
fullcopy : Bool (optional, False)
Copies of objects are stored, not references. If any found,
functions will be pickled and signals converted to dictionaries
Returns
-------
dic : dictionary with the following keys:
_id_name : string
_id_name of the original parameter, used to create the
dictionary. Has to match with the self._id_name
_twins : list
a list of ids of the twins of the parameter
_whitelist : dictionary
a dictionary, which keys are used as keywords to match with the
parameter attributes. For more information see
:meth:`hyperspy.misc.export_dictionary.export_to_dictionary`
* any field from _whitelist.keys() *
"""
dic = {'_twins': [id(t) for t in self._twins]}
export_to_dictionary(self, self._whitelist, dic, fullcopy)
return dic
def default_traits_view(self):
# As mentioned above, the default editor for
# value = t.Property(t.Either([t.CFloat(0), Array()]))
# gives a ValueError. We therefore implement default_traits_view so
# that configure/edit_traits will still work straight out of the box.
# A whitelist controls which traits to include in this view.
from traitsui.api import RangeEditor, View, Item
whitelist = ['bmax', 'bmin', 'free', 'name', 'std', 'units', 'value']
editable_traits = [
trait for trait in self.editable_traits() if trait in whitelist
]
if 'value' in editable_traits:
i = editable_traits.index('value')
v = editable_traits.pop(i)
editable_traits.insert(
i,
Item(v, editor=RangeEditor(low_name='bmin', high_name='bmax')))
view = View(editable_traits, buttons=['OK', 'Cancel'])
return view
def _interactive_slider_bounds(self, index=None):
"""Guesstimates the bounds for the slider. They will probably have to
be changed later by the user.
"""
fraction = 10.
_min, _max, step = None, None, None
value = self.value if index is None else self.value[index]
if self.bmin is not None:
_min = self.bmin
if self.bmax is not None:
_max = self.bmax
if _max is None and _min is not None:
_max = value + fraction * (value - _min)
if _min is None and _max is not None:
_min = value - fraction * (_max - value)
if _min is None and _max is None:
if self is self.component._position:
axis = self._axes_manager.signal_axes[-1]
_min = axis.axis.min()
_max = axis.axis.max()
step = np.abs(axis.scale)
else:
_max = value + np.abs(value * fraction)
_min = value - np.abs(value * fraction)
if step is None:
step = (_max - _min) * 0.001
return {'min': _min, 'max': _max, 'step': step}
def _interactive_update(self, value=None, index=None):
"""Callback function for the widgets, to update the value
"""
if value is not None:
if index is None:
self.value = value['new']
else:
self.value = self.value[:index] + (value['new'],) +\
self.value[index + 1:]
def notebook_interaction(self, display=True):
"""Creates interactive notebook widgets for the parameter, if
available.
Requires `ipywidgets` to be installed.
Parameters
----------
display : bool
if True (default), attempts to display the parameter widget.
Otherwise returns the formatted widget object.
"""
from ipywidgets import VBox
from traitlets import TraitError as TraitletError
from IPython.display import display as ip_display
try:
if self._number_of_elements == 1:
container = self._create_notebook_widget()
else:
children = [
self._create_notebook_widget(index=i)
for i in range(self._number_of_elements)
]
container = VBox(children)
if not display:
return container
ip_display(container)
except TraitletError:
if display:
_logger.info('This function is only avialable when running in'
' a notebook')
else:
raise
def _create_notebook_widget(self, index=None):
from ipywidgets import (FloatSlider, FloatText, Layout, HBox)
widget_bounds = self._interactive_slider_bounds(index=index)
thismin = FloatText(
value=widget_bounds['min'],
description='min',
layout=Layout(flex='0 1 auto', width='auto'),
)
thismax = FloatText(
value=widget_bounds['max'],
description='max',
layout=Layout(flex='0 1 auto', width='auto'),
)
current_value = self.value if index is None else self.value[index]
current_name = self.name
if index is not None:
current_name += '_{}'.format(index)
widget = FloatSlider(value=current_value,
min=thismin.value,
max=thismax.value,
step=widget_bounds['step'],
description=current_name,
layout=Layout(flex='1 1 auto', width='auto'))
def on_min_change(change):
if widget.max > change['new']:
widget.min = change['new']
widget.step = np.abs(widget.max - widget.min) * 0.001
def on_max_change(change):
if widget.min < change['new']:
widget.max = change['new']
widget.step = np.abs(widget.max - widget.min) * 0.001
thismin.observe(on_min_change, names='value')
thismax.observe(on_max_change, names='value')
this_observed = functools.partial(self._interactive_update,
index=index)
widget.observe(this_observed, names='value')
container = HBox((thismin, widget, thismax))
return container
