Package: wield.declarative
Declarative PythonCollection of decorators and base classes to allow a declarative style of programming. The
underlying philosophy can be described as "init considered harmful". Instead, object attributes
are constructed from decorator functions and then stored. This is essentially like the @property
decorator, but @declarative.mproperty additionallystores(memoizes) the result. Unlike the @property
builtin, @declarative.mproperty can take an argument, providing convenient parameterization and transformation
of inputs.For classes inheriting declarative.OverridableObject, the @declarative.dproperty attribute can be used
and all properties will be called/accessed within theinitconstructor to ensure construction. This allows
objects to register with other objects and is convenient for event-loop reactor programming.The Argparse sub-library allows the user to create quick command-line interfaces to create and run methods in declarative fashion.The technique of access->construction means that the dependencies between class attributes are resolved
automatically. During the construction of each attribute, any required attributes are accessed and therefore
constructed if they haven't already been.The price for the convenience is that construction becomes implicit and recursive. The wrappers in this library
do some error checking to aid with this and to properly report AttributeError. Code also ends up somewhat more
verbose with the decorator boilerplate.Quick ExampleimportdeclarativeclassChild(object):id=NoneclassParent(object):@declarative.mpropertydefchild_registry(self):returnset()@declarative.mpropertydefc1(self):print("made Parent.c1")child=Child()child.id=1self.child_registry.add(child)returnchild@declarative.mpropertydefc2(self):print("made Parent.c2")child=Child()child.id=2self.child_registry.add(child)returnchildparent=Parent()parent.c1#>> made Parent.c1parent.c2#>> made Parent.c2print(parent.child_registry)Ok, so now as the child object attributes are accessed, they are also registered.More automatic ExampleimportdeclarativeclassChild(declarative.OverridableObject):id=NoneclassParent(declarative.OverridableObject):@declarative.mpropertydefchild_registry(self):returnset()@declarative.dpropertydefc1(self,val=None):ifvalisNone:child=Child(id=1,)print("made Parent.c1")else:print("Using outside c1")child=valself.child_registry.add(child)returnchild@declarative.dpropertydefc2(self):child=Child(id=2,)print("made Parent.c2")self.child_registry.add(child)returnchild@declarative.dpropertydefc2b(self):child=Child(id=self.c2.id+0.5)print("made Parent.c2b")self.child_registry.add(child)returnchildparent=Parent()#>> made Parent.c2#>> made Parent.c2b#>> made Parent.c1print(parent.child_registry)Now the registry is filled instantly.Alternatively, c1 for this object can be replaced.parent = Parent(
    c1 = Child(id = 8)
)
#>> made Parent.c2
#>> made Parent.c2b
#>> using outside c1
print(parent.child_registry)Noinitfunction!Numerical UsageThis technique can be applied for memoized numerical results, particularly when you might
want to canonicalize the inputs to use a numpy representation.importdeclarativeclassMultiFunction(declarative.OverridableObject):@declarative.dpropertydefinput_A(self,val):#not providing a default makes them required keyword arguments#during constructionreturnnumpy.asarray(val)@declarative.dpropertydefinput_B(self,val):returnnumpy.asarray(val)@declarative.mpropertydefoutput_A(self):#note usage of mproperty. This will only be computed if accessed, not at constructionreturnself.input_A+self.input_B@declarative.mpropertydefoutput_B(self):#note the use of incremental computing into output_Areturnself.input_A*self.input_B-self.output_Adata=MultiFunction(input_A=[1,2,3],input_B=[4,5,6],)print(data.output_A)Additional FeaturesArgparse interfaceMentioned above. Some additional annotations and run methods can allow objects to be called and accessed from the command line, without a special interface while providing improved composition of declarative programming.BunchesThese are dictionary objects that also allow indexing through the '.' attribute access operator. Other libraries provide these, but the ones included here areBunch - just a dictionary wrapper. It also wraps any dictionary's that are stored to provide a consistent interface.DeepBunch - Allows nested access without construction of intermediate dictionary's. Extremely convenient for configuration management of hierarchical systems.HDFDeepBunch - DeepBunch adapted so that the underlying storage are HDF5 data groups using the h5py library. Automatically converts to/from numpy arrays and unwraps values. DeepBunch's containing compatible numbers/arrays can be directly embedded into hdf. This allows configuration storage with datasets.See the Bunch page for more.Relays and CallbacksA number of objects are provided for reactor programming. These are RelayValue and RelayBool which store values and run callbacks upon their change. This is similar Qt's signal/socket programming but lightweight for python.Substrate SystemThis is the culmination of the declarative techniques to hierarchical simulation and modeling. Child objects automatically embed into parent objects and gain access to nonlocal data and registration interfaces. It invokes considerably more "magic" than this library typically need. Currently used by the python physics/controls simulation software openLoop.DevelopmentThis library was developed in initial form to generate the Control System and interfaces of the Holometer experiment at Fermilab. The underlying technology for that is the EPICS distributed experimental control library (developed at Argonne).RelayBool and RelayValue objects were bound to EPICS variables using the declarative construction methods of this library. Further logic cross-linked variables and interfaced to hardware. Bunches were used for configuration management. HDFDeepBunch was used for data analysis.Related DocumentationUsing multiple inheritance and mixins becomes very simple with this style of programming, but super is often needed, but forces the use of keyword arguments. Since this library forces them anyway, this site details other considerations for using super:https://fuhm.net/super-harmful/