Constraints#

Computation of the real-space axis positions given a set of reciprocal-space coordinates can have many solutions. One or more constraints (Constraint) (a.k.a, cut points), together with a choice of operating mode, can:

Limit the range of forward() solutions accepted for that positioner.
Declare the value to use when the positioner should be kept constant. (not implemented yet)

Source Code Documentation#

Limitations on acceptable positions for computed ‘forward()’ solutions.

`RealAxisConstraints`(reals)	Constraints for every (real) axis of the diffractometer.
`ConstraintBase`()	Base class for all constraints for selecting 'forward()' solutions.
`LimitsConstraint`([low_limit, high_limit, label])	Value must fall between low & high limits.

From hklpy, these TODO items:

_constraints_dict
_constraints_for_databroker
_push_current_constraints
_set_constraints

class hklpy2.operations.constraints.ConstraintBase[source]#

Bases: ABC

Base class for all constraints for selecting ‘forward()’ solutions.

`_asdict`()	Return a new dict which maps field names to their values.
`valid`(**values)	Is this constraint satisifed by current value(s)?

_asdict()[source]#: Return a new dict which maps field names to their values.

_fromdict(config)[source]#: Redefine this constraint from a (configuration) dictionary.

abstract valid(**values: Dict[str, int | float]) → bool[source]#

Is this constraint satisifed by current value(s)?

PARAMETERS

values dict:: Dictionary of current ‘axis: value’ pairs for comparison.

class hklpy2.operations.constraints.LimitsConstraint(low_limit=-180, high_limit=180, label=None)[source]#

Bases: ConstraintBase

Value must fall between low & high limits.

Parameters:

low_limit (float) – Lowest acceptable value for this axis when computing real-space solutions from given reciprocal-space positions.
high_limit (float) – Highest acceptable value for this axis when computing real-space solutions from given reciprocal-space positions.
label (str) – Name of the axis for these limits.
autosummary:: (..) – ~limits ~valid

_asdict()#: Return a new dict which maps field names to their values.

_fromdict(config)#: Redefine this constraint from a (configuration) dictionary.

property limits#: Return the low and high limits of this constraint.

valid(**values: Dict[str, int | float]) → bool[source]#

True if low <= value <= high.

PARAMETERS

reals dict:: Dictionary of current ‘axis: value’ pairs for comparison.

class hklpy2.operations.constraints.RealAxisConstraints(reals: List[str])[source]#

Bases: dict

Constraints for every (real) axis of the diffractometer.

`_asdict`()	Return all constraints as a dictionary.
`_fromdict`(config)	Redefine existing constraints from a (configuration) dictionary.
`valid`(**reals)	Are all constraints satisfied?

_asdict()[source]#: Return all constraints as a dictionary.

_fromdict(config)[source]#: Redefine existing constraints from a (configuration) dictionary.

clear() → None. Remove all items from D.#

copy() → a shallow copy of D#

fromkeys(value=None, /)#: Create a new dictionary with keys from iterable and values set to value.

get(key, default=None, /)#: Return the value for key if key is in the dictionary, else default.

items() → a set-like object providing a view on D's items#

keys() → a set-like object providing a view on D's keys#

pop(k[, d]) → v, remove specified key and return the corresponding value.#: If the key is not found, return the default if given; otherwise, raise a KeyError.

popitem()#

Remove and return a (key, value) pair as a 2-tuple.

Pairs are returned in LIFO (last-in, first-out) order. Raises KeyError if the dict is empty.

setdefault(key, default=None, /)#

Insert key with a value of default if key is not in the dictionary.

Return the value for key if key is in the dictionary, else default.

update([E, ]**F) → None. Update D from dict/iterable E and F.#: If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

valid(**reals: Dict[str, int | float]) → bool[source]#: Are all constraints satisfied?

values() → an object providing a view on D's values#