Source code for param.parameterized

"""
Generic support for objects with full-featured Parameters and
messaging.
"""

import copy
import re
import sys
import inspect
import random
import numbers
import operator

from collections import namedtuple, OrderedDict
from operator import itemgetter,attrgetter
from types import FunctionType
from functools import partial, wraps, reduce

import logging
from contextlib import contextmanager
from logging import DEBUG, INFO, WARNING, ERROR, CRITICAL

try:
    # In case the optional ipython module is unavailable
    from .ipython import ParamPager
    param_pager = ParamPager(metaclass=True)  # Generates param description
except:
    param_pager = None


VERBOSE = INFO - 1
logging.addLevelName(VERBOSE, "VERBOSE")

# Logger instance to use for param; if "logger" is set to None, the root logger
# will be used.
logger = None
def get_logger():
    if logger is None:
        # If it was not configured before, do default initialization
        if not logging.getLogger().handlers:
            logging.basicConfig(level=INFO)
        return logging.getLogger()
    else:
        return logger

# Indicates whether warnings should be raised as errors, stopping
# processing.
warnings_as_exceptions = False

docstring_signature = True        # Add signature to class docstrings
docstring_describe_params = True  # Add parameter description to class
                                  # docstrings (requires ipython module)
object_count = 0
warning_count = 0


[docs]@contextmanager def logging_level(level): """ Temporarily modify param's logging level. """ level = level.upper() levels = [DEBUG, INFO, WARNING, ERROR, CRITICAL, VERBOSE] level_names = ['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL', 'VERBOSE'] if level not in level_names: raise Exception("Level %r not in %r" % (level, levels)) param_logger = get_logger() logging_level = param_logger.getEffectiveLevel() param_logger.setLevel(levels[level_names.index(level)]) try: yield None finally: param_logger.setLevel(logging_level)
[docs]def classlist(class_): """ Return a list of the class hierarchy above (and including) the given class. Same as inspect.getmro(class_)[::-1] """ return inspect.getmro(class_)[::-1]
[docs]def descendents(class_): """ Return a list of the class hierarchy below (and including) the given class. The list is ordered from least- to most-specific. Can be useful for printing the contents of an entire class hierarchy. """ assert isinstance(class_,type) q = [class_] out = [] while len(q): x = q.pop(0) out.insert(0,x) for b in x.__subclasses__(): if b not in q and b not in out: q.append(b) return out[::-1]
[docs]def get_all_slots(class_): """ Return a list of slot names for slots defined in class_ and its superclasses. """ # A subclass's __slots__ attribute does not contain slots defined # in its superclass (the superclass' __slots__ end up as # attributes of the subclass). all_slots = [] parent_param_classes = [c for c in classlist(class_)[1::]] for c in parent_param_classes: if hasattr(c,'__slots__'): all_slots+=c.__slots__ return all_slots
[docs]def get_occupied_slots(instance): """ Return a list of slots for which values have been set. (While a slot might be defined, if a value for that slot hasn't been set, then it's an AttributeError to request the slot's value.) """ return [slot for slot in get_all_slots(type(instance)) if hasattr(instance,slot)]
[docs]def all_equal(arg1,arg2): """ Return a single boolean for arg1==arg2, even for numpy arrays using element-wise comparison. Uses all(arg1==arg2) for sequences, and arg1==arg2 otherwise. If both objects have an '_infinitely_iterable' attribute, they are not be zipped together and are compared directly instead. """ if all(hasattr(el, '_infinitely_iterable') for el in [arg1,arg2]): return arg1==arg2 try: return all(a1 == a2 for a1, a2 in zip(arg1, arg2)) except TypeError: return arg1==arg2
# For Python 2 compatibility. # # The syntax to use a metaclass changed incompatibly between 2 and # 3. The add_metaclass() class decorator below creates a class using a # specified metaclass in a way that works on both 2 and 3. For 3, can # remove this decorator and specify metaclasses in a simpler way # (https://docs.python.org/3/reference/datamodel.html#customizing-class-creation) # # Code from six (https://bitbucket.org/gutworth/six; version 1.4.1).
[docs]def add_metaclass(metaclass): """Class decorator for creating a class with a metaclass.""" def wrapper(cls): orig_vars = cls.__dict__.copy() orig_vars.pop('__dict__', None) orig_vars.pop('__weakref__', None) for slots_var in orig_vars.get('__slots__', ()): orig_vars.pop(slots_var) return metaclass(cls.__name__, cls.__bases__, orig_vars) return wrapper
[docs]class bothmethod(object): # pylint: disable-msg=R0903 """ 'optional @classmethod' A decorator that allows a method to receive either the class object (if called on the class) or the instance object (if called on the instance) as its first argument. Code (but not documentation) copied from: http://aspn.activestate.com/ASPN/Cookbook/Python/Recipe/523033. """ # pylint: disable-msg=R0903 def __init__(self, func): self.func = func # i.e. this is also a non-data descriptor def __get__(self, obj, type_=None): if obj is None: return wraps(self.func)(partial(self.func, type_)) else: return wraps(self.func)(partial(self.func, obj))
def _getattrr(obj, attr, *args): def _getattr(obj, attr): return getattr(obj, attr, *args) return reduce(_getattr, [obj] + attr.split('.')) # (thought I was going to have a few decorators following this pattern) def accept_arguments(f): @wraps(f) def _f(*args, **kwargs): return lambda actual_f: f(actual_f, *args, **kwargs) return _f
[docs]@accept_arguments def depends(func, *dependencies, **kw): """ Annotates a Parameterized method to express its dependencies. The specified dependencies can be either be Parameters of this class, or Parameters of subobjects (Parameterized objects that are values of this object's parameters). Dependencies can either be on Parameter values, or on other metadata about the Parameter. """ # python3 would allow kw-only args # (i.e. "func,*dependencies,watch=False" rather than **kw and the check below) watch = kw.pop("watch",False) assert len(kw)==0, "@depends accepts only 'watch' kw" # TODO: rename dinfo _dinfo = getattr(func, '_dinfo', {}) _dinfo.update({'dependencies': dependencies, 'watch': watch}) @wraps(func) def _depends(*args,**kw): return func(*args,**kw) # storing here risks it being tricky to find if other libraries # mess around with methods _depends._dinfo = _dinfo return _depends
[docs]@accept_arguments def output(func, *output, **kw): """ output allows annotating a method on a Parameterized class to declare that it returns an output of a specific type. The outputs of a Parameterized class can be queried using the Parameterized.param.outputs method. By default the output will inherit the method name but a custom name can be declared by expressing the Parameter type using a keyword argument. Declaring multiple return types using keywords is only supported in Python >= 3.6. The simplest declaration simply declares the method returns an object without any type guarantees, e.g.: @output() If a specific parameter type is specified this is a declaration that the method will return a value of that type, e.g.: @output(param.Number()) To override the default name of the output the type may be declared as a keyword argument, e.g.: @output(custom_name=param.Number()) Multiple outputs may be declared using keywords mapping from output name to the type for Python >= 3.6 or using tuples of the same format, which is supported for earlier versions, i.e. these two declarations are equivalent: @output(number=param.Number(), string=param.String()) @output(('number', param.Number()), ('string', param.String())) output also accepts Python object types which will be upgraded to a ClassSelector, e.g.: @output(int) """ if output: outputs = [] for i, out in enumerate(output): i = i if len(output) > 1 else None if isinstance(out, tuple) and len(out) == 2 and isinstance(out[0], str): outputs.append(out+(i,)) elif isinstance(out, str): outputs.append((out, Parameter(), i)) else: outputs.append((None, out, i)) elif kw: py_major = sys.version_info.major py_minor = sys.version_info.minor if (py_major < 3 or (py_major == 3 and py_minor < 6)) and len(kw) > 1: raise ValueError('Multiple output declaration using keywords ' 'only supported in Python >= 3.6.') # (requires keywords to be kept ordered, which was not true in previous versions) outputs = [(name, otype, i if len(kw) > 1 else None) for i, (name, otype) in enumerate(kw.items())] else: outputs = [(None, Parameter(), None)] names, processed = [], [] for name, otype, i in outputs: if isinstance(otype, type): if issubclass(otype, Parameter): otype = otype() else: from .import ClassSelector otype = ClassSelector(class_=otype) elif isinstance(otype, tuple) and all(isinstance(t, type) for t in otype): from .import ClassSelector otype = ClassSelector(class_=otype) if not isinstance(otype, Parameter): raise ValueError('output type must be declared with a Parameter class, ' 'instance or a Python object type.') processed.append((name, otype, i)) names.append(name) if len(set(names)) != len(names): raise ValueError('When declaring multiple outputs each value ' 'must be unique.') _dinfo = getattr(func, '_dinfo', {}) _dinfo.update({'outputs': processed}) @wraps(func) def _output(*args,**kw): return func(*args,**kw) _output._dinfo = _dinfo return _output
def _params_depended_on(minfo): params = [] dinfo = getattr(minfo.method,"_dinfo", {}) for d in dinfo.get('dependencies',list(minfo.cls.param.params())): things = (minfo.inst or minfo.cls).param._spec_to_obj(d) for thing in things: if isinstance(thing,PInfo): params.append(thing) else: params += _params_depended_on(thing) return params def _m_caller(self,n): return lambda event: getattr(self,n)() PInfo = namedtuple("PInfo","inst cls name pobj what") MInfo = namedtuple("MInfo","inst cls name method") Event = namedtuple("Event","what name obj cls old new type") Watcher = namedtuple("Watcher","inst cls fn mode onlychanged parameter_names")
[docs]class ParameterMetaclass(type): """ Metaclass allowing control over creation of Parameter classes. """ def __new__(mcs,classname,bases,classdict): # store the class's docstring in __classdoc if '__doc__' in classdict: classdict['__classdoc']=classdict['__doc__'] # when asking for help on Parameter *object*, return the doc # slot classdict['__doc__']=property(attrgetter('doc')) # To get the benefit of slots, subclasses must themselves define # __slots__, whether or not they define attributes not present in # the base Parameter class. That's because a subclass will have # a __dict__ unless it also defines __slots__. if '__slots__' not in classdict: classdict['__slots__']=[] return type.__new__(mcs,classname,bases,classdict) def __getattribute__(mcs,name): if name=='__doc__': # when asking for help on Parameter *class*, return the # stored class docstring return type.__getattribute__(mcs,'__classdoc') else: return type.__getattribute__(mcs,name)
# CEBALERT: we break some aspects of slot handling for Parameter and # Parameterized. The __new__ methods in the metaclasses for those two # classes omit to handle the case where __dict__ is passed in # __slots__ (and they possibly omit other things too). Additionally, # various bits of code in the Parameterized class assumes that all # Parameterized instances have a __dict__, but I'm not sure that's # guaranteed to be true (although it's true at the moment). # CB: we could maybe reduce the complexity by doing something to allow # a parameter to discover things about itself when created (would also # allow things like checking a Parameter is owned by a # Parameterized). I have some vague ideas about what to do.
[docs]@add_metaclass(ParameterMetaclass) class Parameter(object): """ An attribute descriptor for declaring parameters. Parameters are a special kind of class attribute. Setting a Parameterized class attribute to be a Parameter instance causes that attribute of the class (and the class's instances) to be treated as a Parameter. This allows special behavior, including dynamically generated parameter values, documentation strings, constant and read-only parameters, and type or range checking at assignment time. For example, suppose someone wants to define two new kinds of objects Foo and Bar, such that Bar has a parameter delta, Foo is a subclass of Bar, and Foo has parameters alpha, sigma, and gamma (and delta inherited from Bar). She would begin her class definitions with something like this: class Bar(Parameterized): delta = Parameter(default=0.6, doc='The difference between steps.') ... class Foo(Bar): alpha = Parameter(default=0.1, doc='The starting value.') sigma = Parameter(default=0.5, doc='The standard deviation.', constant=True) gamma = Parameter(default=1.0, doc='The ending value.') ... Class Foo would then have four parameters, with delta defaulting to 0.6. Parameters have several advantages over plain attributes: 1. Parameters can be set automatically when an instance is constructed: The default constructor for Foo (and Bar) will accept arbitrary keyword arguments, each of which can be used to specify the value of a Parameter of Foo (or any of Foo's superclasses). E.g., if a script does this: myfoo = Foo(alpha=0.5) myfoo.alpha will return 0.5, without the Foo constructor needing special code to set alpha. If Foo implements its own constructor, keyword arguments will still be accepted if the constructor accepts a dictionary of keyword arguments (as in ``def __init__(self,**params):``), and then each class calls its superclass (as in ``super(Foo,self).__init__(**params)``) so that the Parameterized constructor will process the keywords. 2. A Parameterized class need specify only the attributes of a Parameter whose values differ from those declared in superclasses; the other values will be inherited. E.g. if Foo declares delta = Parameter(default=0.2) the default value of 0.2 will override the 0.6 inherited from Bar, but the doc will be inherited from Bar. 3. The Parameter descriptor class can be subclassed to provide more complex behavior, allowing special types of parameters that, for example, require their values to be numbers in certain ranges, generate their values dynamically from a random distribution, or read their values from a file or other external source. 4. The attributes associated with Parameters provide enough information for automatically generating property sheets in graphical user interfaces, allowing Parameterized instances to be edited by users. Note that Parameters can only be used when set as class attributes of Parameterized classes. Parameters used as standalone objects, or as class attributes of non-Parameterized classes, will not have the behavior described here. """ # Because they implement __get__ and __set__, Parameters are known # as 'descriptors' in Python; see "Implementing Descriptors" and # "Invoking Descriptors" in the 'Customizing attribute access' # section of the Python reference manual: # http://docs.python.org/ref/attribute-access.html # # Overview of Parameters for programmers # ====================================== # # Consider the following code: # # # class A(Parameterized): # p = Parameter(default=1) # # a1 = A() # a2 = A() # # # * a1 and a2 share one Parameter object (A.__dict__['p']). # # * The default (class) value of p is stored in this Parameter # object (A.__dict__['p'].default). # # * If the value of p is set on a1 (e.g. a1.p=2), a1's value of p # is stored in a1 itself (a1.__dict__['_p_param_value']) # # * When a1.p is requested, a1.__dict__['_p_param_value'] is # returned. When a2.p is requested, '_p_param_value' is not # found in a2.__dict__, so A.__dict__['p'].default (i.e. A.p) is # returned instead. # # # Be careful when referring to the 'name' of a Parameter: # # * A Parameterized class has a name for the attribute which is # being represented by the Parameter ('p' in the example above); # in the code, this is called the 'attrib_name'. # # * When a Parameterized instance has its own local value for a # parameter, it is stored as '_X_param_value' (where X is the # attrib_name for the Parameter); in the code, this is called # the internal_name. # So that the extra features of Parameters do not require a lot of # overhead, Parameters are implemented using __slots__ (see # http://www.python.org/doc/2.4/ref/slots.html). Instead of having # a full Python dictionary associated with each Parameter instance, # Parameter instances have an enumerated list (named __slots__) of # attributes, and reserve just enough space to store these # attributes. Using __slots__ requires special support for # operations to copy and restore Parameters (e.g. for Python # persistent storage pickling); see __getstate__ and __setstate__. __slots__ = ['_attrib_name','_internal_name','default','doc', 'precedence','instantiate','constant','readonly', 'pickle_default_value','allow_None', 'watchers','_owner'] # Note: When initially created, a Parameter does not know which # Parameterized class owns it, nor does it know its names # (attribute name, internal name). Once the owning Parameterized # class is created, _owner, _attrib_name, and _internal name are # set. # TODO regarding _attrib_name, _owner: what if someone re-uses # a parameter object across different classes? we should raise # an error if attrib name,owner already set def __init__(self,default=None,doc=None,precedence=None, # pylint: disable-msg=R0913 instantiate=False,constant=False,readonly=False, pickle_default_value=True, allow_None=False): """ Initialize a new Parameter object: store the supplied attributes. default: the owning class's value for the attribute represented by this Parameter. precedence is a value, usually in the range 0.0 to 1.0, that allows the order of Parameters in a class to be defined (for e.g. in GUI menus). A negative precedence indicates a parameter that should be hidden in e.g. GUI menus. default, doc, and precedence default to None. This is to allow inheritance of Parameter slots (attributes) from the owning-class' class hierarchy (see ParameterizedMetaclass). In rare cases where the default value should not be pickled, set pickle_default_value=False (e.g. for file search paths). """ self._attrib_name = None self._internal_name = None self._owner = None self.precedence = precedence self.default = default self.doc = doc self.constant = constant or readonly # readonly => constant self.readonly = readonly self._set_instantiate(instantiate) self.pickle_default_value = pickle_default_value self.allow_None = (default is None or allow_None) self.watchers = {} def _set_instantiate(self,instantiate): """Constant parameters must be instantiated.""" # CB: instantiate doesn't actually matter for read-only # parameters, since they can't be set even on a class. But # this avoids needless instantiation. if self.readonly: self.instantiate = False else: self.instantiate = instantiate or self.constant # pylint: disable-msg=W0201 # TODO: quick trick to allow subscription to the setting of # parameter metadata. ParameterParameter? # Note that unlike with parameter value setting, there's no access # to the Parameterized instance, so no per-instance subscription. def __setattr__(self,attribute,value): implemented = (attribute!="default" and hasattr(self,'watchers') and attribute in self.watchers) try: old = getattr(self,attribute) if implemented else NotImplemented except AttributeError as e: if attribute in self.__slots__: # If Parameter slot is defined but an AttributeError was raised # we are in __setstate__ and watchers should not be triggered old = NotImplemented else: raise e super(Parameter, self).__setattr__(attribute, value) if old is not NotImplemented: event = Event(what=attribute,name=self._attrib_name,obj=None,cls=self._owner,old=old,new=value, type=None) for watcher in self.watchers[attribute]: self._owner.param._call_watcher(watcher, event) def __get__(self,obj,objtype): # pylint: disable-msg=W0613 """ Return the value for this Parameter. If called for a Parameterized class, produce that class's value (i.e. this Parameter object's 'default' attribute). If called for a Parameterized instance, produce that instance's value, if one has been set - otherwise produce the class's value (default). """ # NB: obj can be None (when __get__ called for a # Parameterized class); objtype is never None if obj is None: result = self.default else: result = obj.__dict__.get(self._internal_name,self.default) return result def __set__(self,obj,val): """ Set the value for this Parameter. If called for a Parameterized class, set that class's value (i.e. set this Parameter object's 'default' attribute). If called for a Parameterized instance, set the value of this Parameter on that instance (i.e. in the instance's __dict__, under the parameter's internal_name). If the Parameter's constant attribute is True, only allows the value to be set for a Parameterized class or on uninitialized Parameterized instances. If the Parameter's readonly attribute is True, only allows the value to be specified in the Parameter declaration inside the Parameterized source code. A read-only parameter also cannot be set on a Parameterized class. Note that until we support some form of read-only object, it is still possible to change the attributes of the object stored in a constant or read-only Parameter (e.g. the left bound of a BoundingBox). """ # TODO: simplify this method! _old = NotImplemented # NB: obj can be None (when __set__ called for a # Parameterized class) if self.constant or self.readonly: if self.readonly: raise TypeError("Read-only parameter '%s' cannot be modified"%self._attrib_name) elif obj is None: #not obj _old = self.default self.default = val elif not obj.initialized: _old = obj.__dict__.get(self._internal_name,self.default) obj.__dict__[self._internal_name] = val else: raise TypeError("Constant parameter '%s' cannot be modified"%self._attrib_name) else: if obj is None: _old = self.default self.default = val else: _old = obj.__dict__.get(self._internal_name,self.default) obj.__dict__[self._internal_name] = val if obj is None: watchers = self.watchers.get("value",[]) else: watchers = getattr(obj,"_param_watchers",{}).get(self._attrib_name,{}).get('value',self.watchers.get("value",[])) event = Event(what='value',name=self._attrib_name,obj=obj,cls=self._owner,old=_old,new=val, type=None) obj = self._owner if obj is None else obj for s in watchers: obj.param._call_watcher(s, event) def __delete__(self,obj): raise TypeError("Cannot delete '%s': Parameters deletion not allowed."%self._attrib_name) def _set_names(self,attrib_name): self._attrib_name = attrib_name self._internal_name = "_%s_param_value"%attrib_name def __getstate__(self): """ All Parameters have slots, not a dict, so we have to support pickle and deepcopy ourselves. """ state = {} for slot in get_occupied_slots(self): state[slot] = getattr(self,slot) return state def __setstate__(self,state): # set values of __slots__ (instead of in non-existent __dict__) for (k,v) in state.items(): setattr(self,k,v)
# Define one particular type of Parameter that is used in this file
[docs]class String(Parameter): """ A String Parameter, with a default value and optional regular expression (regex) matching. Example of using a regex to implement IPv4 address matching:: class IPAddress(String): '''IPv4 address as a string (dotted decimal notation)''' def __init__(self, default="0.0.0.0", allow_None=False, **kwargs): ip_regex = '^((25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\.){3}(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)$' super(IPAddress, self).__init__(default=default, regex=ip_regex, **kwargs) """ __slots__ = ['regex'] basestring = basestring if sys.version_info[0]==2 else str # noqa: it is defined def __init__(self, default="", regex=None, allow_None=False, **kwargs): super(String, self).__init__(default=default, allow_None=allow_None, **kwargs) self.regex = regex self.allow_None = (default is None or allow_None) self._check_value(default) def _check_value(self,val): if self.allow_None and val is None: return if not isinstance(val, self.basestring): raise ValueError("String '%s' only takes a string value."%self._attrib_name) if self.regex is not None and re.match(self.regex, val) is None: raise ValueError("String '%s': '%s' does not match regex '%s'."%(self._attrib_name,val,self.regex)) def __set__(self,obj,val): self._check_value(val) super(String,self).__set__(obj,val)
[docs]class shared_parameters(object): """ Context manager to share parameter instances when creating multiple Parameterized objects of the same type. Parameter default values are instantiated once and cached to be reused when another Parameterized object of the same type is instantiated. Can be useful to easily modify large collections of Parameterized objects at once and can provide a significant speedup. """ _share = False _shared_cache = {} def __enter__(self): shared_parameters._share = True def __exit__(self, exc_type, exc_val, exc_tb): shared_parameters._share = False shared_parameters._shared_cache = {}
[docs]def as_uninitialized(fn): """ Decorator: call fn with the parameterized_instance's initialization flag set to False, then revert the flag. (Used to decorate Parameterized methods that must alter a constant Parameter.) """ @wraps(fn) def override_initialization(self_,*args,**kw): parameterized_instance = self_.self original_initialized=parameterized_instance.initialized parameterized_instance.initialized=False fn(parameterized_instance,*args,**kw) parameterized_instance.initialized=original_initialized return override_initialization
[docs]class Comparator(object): """ Comparator defines methods for determining whether two objects should be considered equal. It works by registering custom comparison functions, which may either be registed by type or with a predicate function. If no matching comparison can be found for the two objects the comparison will return False. If registered by type the Comparator will check whether both objects are of that type and apply the comparison. If the equality function is instead registered with a function it will call the function with each object individually to check if the comparison applies. This is useful for defining comparisons for objects without explicitly importing them. To use the Comparator simply call the is_equal function. """ equalities = { numbers.Number: operator.eq, String.basestring: operator.eq, bytes: operator.eq, type(None): operator.eq } @classmethod def is_equal(cls, obj1, obj2): for eq_type, eq in cls.equalities.items(): if ((isinstance(eq_type, FunctionType) and eq_type(obj1) and eq_type(obj2)) or (isinstance(obj1, eq_type) and isinstance(obj2, eq_type))): return eq(obj1, obj2) if isinstance(obj2, (list, set, tuple)): return cls.compare_iterator(obj1, obj2) elif isinstance(obj2, dict): return cls.compare_mapping(obj1, obj2) return False @classmethod def compare_iterator(cls, obj1, obj2): if type(obj1) != type(obj2) or len(obj1) != len(obj2): return False for o1, o2 in zip(obj1, obj2): if not cls.is_equal(o1, o2): return False return True @classmethod def compare_mapping(cls, obj1, obj2): if type(obj1) != type(obj2) or len(obj1) != len(obj2): return False for k in obj1: if k in obj2: if not cls.is_equal(obj1[k], obj2[k]): return False else: return False return True
[docs]class Parameters(object): """Object that holds the namespace and implementation of Parameterized methods as well as any state that is not in __slots__ or the Parameters themselves. Exists at both the metaclass level (instantiated by the metaclass) and at the instance level. Can contain state specific to either the class or the instance as necessary. """ _disable_stubs = None # Flag used to disable stubs in the API1 tests # None for no action, True to raise and False to warn. def __init__(self_, cls, self=None): """ cls is the Parameterized class which is always set. self is the instance if set. """ self_.cls = cls self_.self = self self_._BATCH_WATCH = False # If true, Event and watcher objects are queued. self_._TRIGGER = False self_._events = [] # Queue of batched eventd self_._watchers = [] # Queue of batched watchers @property def self_or_cls(self_): return self_.cls if self_.self is None else self_.self @as_uninitialized def _set_name(self_, name): self = self_.param.self self.name=name @as_uninitialized def _generate_name(self_): self = self_.param.self self.param._set_name('%s%05d' % (self.__class__.__name__ ,object_count)) @as_uninitialized def _setup_params(self_,**params): """ Initialize default and keyword parameter values. First, ensures that all Parameters with 'instantiate=True' (typically used for mutable Parameters) are copied directly into each object, to ensure that there is an independent copy (to avoid suprising aliasing errors). Then sets each of the keyword arguments, warning when any of them are not defined as parameters. Constant Parameters can be set during calls to this method. """ self = self_.param.self ## Deepcopy all 'instantiate=True' parameters # (build a set of names first to avoid redundantly instantiating # a later-overridden parent class's parameter) params_to_instantiate = {} for class_ in classlist(type(self)): if not issubclass(class_, Parameterized): continue for (k,v) in class_.__dict__.items(): # (avoid replacing name with the default of None) if isinstance(v,Parameter) and v.instantiate and k!="name": params_to_instantiate[k]=v for p in params_to_instantiate.values(): self.param._instantiate_param(p) ## keyword arg setting for name,val in params.items(): desc = self.__class__.get_param_descriptor(name)[0] # pylint: disable-msg=E1101 if not desc: self.param.warning("Setting non-parameter attribute %s=%s using a mechanism intended only for parameters",name,val) # i.e. if not desc it's setting an attribute in __dict__, not a Parameter setattr(self,name,val)
[docs] @classmethod def deprecate(cls, fn): """ Decorator to issue warnings for API moving onto the param namespace and to add a docstring directing people to the appropriate method. """ def inner(*args, **kwargs): info = (args[0].__class__.__name__, fn.__name__) if cls._disable_stubs: raise AssertionError('Stubs supporting old API disabled') elif cls._disable_stubs is None: pass elif cls._disable_stubs is False: get_logger().log(WARNING, '%s: Use method %r via param namespace ' % info) return fn(*args, **kwargs) inner.__doc__= "Inspect .param.%s method for the full docstring" % fn.__name__ return inner
@classmethod def _changed(cls, event): """ Predicate that determines whether a Event object has actually changed such that old != new. """ return not Comparator.is_equal(event.old, event.new) # CEBALERT: this is a bit ugly def _instantiate_param(self_,param_obj,dict_=None,key=None): # deepcopy param_obj.default into self.__dict__ (or dict_ if supplied) # under the parameter's _internal_name (or key if supplied) self = self_.self dict_ = dict_ or self.__dict__ key = key or param_obj._internal_name param_key = (str(type(self)), param_obj._attrib_name) if shared_parameters._share: if param_key in shared_parameters._shared_cache: new_object = shared_parameters._shared_cache[param_key] else: new_object = copy.deepcopy(param_obj.default) shared_parameters._shared_cache[param_key] = new_object else: new_object = copy.deepcopy(param_obj.default) dict_[key]=new_object if isinstance(new_object,Parameterized): global object_count object_count+=1 # CB: writes over name given to the original object; # should it instead keep the same name? new_object.param._generate_name() # Classmethods
[docs] def print_param_defaults(self_): """Print the default values of all cls's Parameters.""" cls = self_.cls for key,val in cls.__dict__.items(): if isinstance(val,Parameter): print(cls.__name__+'.'+key+ '='+ repr(val.default))
[docs] def set_default(self_,param_name,value): """ Set the default value of param_name. Equivalent to setting param_name on the class. """ cls = self_.cls setattr(cls,param_name,value)
def _add_parameter(self_, param_name,param_obj): """ Add a new Parameter object into this object's class. Supposed to result in a Parameter equivalent to one declared in the class's source code. """ # CEBALERT: can't we just do # setattr(cls,param_name,param_obj)? The metaclass's # __setattr__ is actually written to handle that. (Would also # need to do something about the params() cache. That cache # is a pain, but it definitely improved the startup time; it # would be worthwhile making sure no method except for one # "add_param()" method has to deal with it (plus any future # remove_param() method.) cls = self_.cls type.__setattr__(cls,param_name,param_obj) ParameterizedMetaclass._initialize_parameter(cls,param_name,param_obj) # delete cached params() try: delattr(cls,'_%s__params'%cls.__name__) except AttributeError: pass
[docs] def params(self_, parameter_name=None): """ Return the Parameters of this class as the dictionary {name: parameter_object} Includes Parameters from this class and its superclasses. """ cls = self_.cls # CB: we cache the parameters because this method is called often, # and parameters are rarely added (and cannot be deleted) try: pdict=getattr(cls,'_%s__params'%cls.__name__) except AttributeError: paramdict = {} for class_ in classlist(cls): for name,val in class_.__dict__.items(): if isinstance(val,Parameter): paramdict[name] = val # We only want the cache to be visible to the cls on which # params() is called, so we mangle the name ourselves at # runtime (if we were to mangle it now, it would be # _Parameterized.__params for all classes). setattr(cls,'_%s__params'%cls.__name__,paramdict) pdict= paramdict if parameter_name is None: return pdict else: return pdict[parameter_name]
# Bothmethods
[docs] def set_param(self_, *args,**kwargs): """ For each param=value keyword argument, sets the corresponding parameter of this object or class to the given value. For backwards compatibility, also accepts set_param("param",value) for a single parameter value using positional arguments, but the keyword interface is preferred because it is more compact and can set multiple values. """ self_.self_or_cls.param._BATCH_WATCH = True self_or_cls = self_.self_or_cls if args: if len(args)==2 and not args[0] in kwargs and not kwargs: kwargs[args[0]]=args[1] else: raise ValueError("Invalid positional arguments for %s.set_param" % (self_or_cls.name)) for (k,v) in kwargs.items(): if k not in self_or_cls.param.params(): raise ValueError("'%s' is not a parameter of %s"%(k,self_or_cls.name)) setattr(self_or_cls,k,v) self_.self_or_cls.param._BATCH_WATCH = False self_._batch_call_watchers()
[docs] def trigger(self_, *param_names): """ Trigger watchers for the given set of parameter names. Watchers will be triggered whether or not the parameter values have actually changed. """ events = self_.self_or_cls.param._events watchers = self_.self_or_cls.param._watchers self_.self_or_cls.param._events = [] self_.self_or_cls.param._watchers = [] param_values = dict(self_.get_param_values()) params = {name: param_values[name] for name in param_names} self_.self_or_cls.param._TRIGGER = True self_.set_param(**params) self_.self_or_cls.param._TRIGGER = False self_.self_or_cls.param._events = events self_.self_or_cls.param._watchers = watchers
def _update_event_type(self_, watcher, event, triggered): """ Returns an updated Event object with the type field set appropriately. """ if triggered: event_type = 'triggered' else: event_type = 'changed' if watcher.onlychanged else 'set' return Event(what=event.what,name=event.name,obj=event.obj,cls=event.cls, old=event.old, new=event.new, type=event_type) def _call_watcher(self_, watcher, event): """ Invoke the given the watcher appropriately given a Event object. """ if self_.self_or_cls.param._TRIGGER: pass elif watcher.onlychanged and (not self_._changed(event)): return if self_.self_or_cls.param._BATCH_WATCH: self_._events.append(event) if watcher not in self_._watchers: self_._watchers.append(watcher) elif watcher.mode == 'args': watcher.fn(self_._update_event_type(watcher, event, self_.self_or_cls.param._TRIGGER)) else: event = self_._update_event_type(watcher, event, self_.self_or_cls.param._TRIGGER) watcher.fn(**{event.name: event.new}) def _batch_call_watchers(self_): """ Batch call a set of watchers based on the parameter value settings in kwargs using the queued Event and watcher objects. """ event_dict = OrderedDict([(c.name,c) for c in self_.self_or_cls.param._events]) watchers = self_.self_or_cls.param._watchers[:] self_.self_or_cls.param._events = [] self_.self_or_cls.param._watchers = [] for watcher in watchers: events = [self_._update_event_type(watcher, event_dict[name], self_.self_or_cls.param._TRIGGER) for name in watcher.parameter_names if name in event_dict] if watcher.mode == 'args': watcher.fn(*events) else: watcher.fn(**{c.name:c.new for c in events})
[docs] def set_dynamic_time_fn(self_,time_fn,sublistattr=None): """ Set time_fn for all Dynamic Parameters of this class or instance object that are currently being dynamically generated. Additionally, sets _Dynamic_time_fn=time_fn on this class or instance object, so that any future changes to Dynamic Parmeters can inherit time_fn (e.g. if a Number is changed from a float to a number generator, the number generator will inherit time_fn). If specified, sublistattr is the name of an attribute of this class or instance that contains an iterable collection of subobjects on which set_dynamic_time_fn should be called. If the attribute sublistattr is present on any of the subobjects, set_dynamic_time_fn() will be called for those, too. """ self_or_cls = self_.self_or_cls self_or_cls._Dynamic_time_fn = time_fn if isinstance(self_or_cls,type): a = (None,self_or_cls) else: a = (self_or_cls,) for n,p in self_or_cls.param.params().items(): if hasattr(p,'_value_is_dynamic'): if p._value_is_dynamic(*a): g = self_or_cls.param.get_value_generator(n) g._Dynamic_time_fn = time_fn if sublistattr: try: sublist = getattr(self_or_cls,sublistattr) except AttributeError: sublist = [] for obj in sublist: obj.param.set_dynamic_time_fn(time_fn,sublistattr)
[docs] def get_param_values(self_,onlychanged=False): """ Return a list of name,value pairs for all Parameters of this object. When called on an instance with onlychanged set to True, will only return values that are not equal to the default value (onlychanged has no effect when called on a class). """ self_or_cls = self_.self_or_cls # CEB: we'd actually like to know whether a value has been # explicitly set on the instance, but I'm not sure that's easy # (would need to distinguish instantiation of default from # user setting of value). vals = [] for name,val in self_or_cls.param.params().items(): value = self_or_cls.param.get_value_generator(name) # (this is pointless for cls) if not onlychanged or not all_equal(value,val.default): vals.append((name,value)) vals.sort(key=itemgetter(0)) return vals
# CB: is there a more obvious solution than making these # 'bothmethod's? # An alternative would be to lose these methods completely and # make users do things via the Parameter object directly. # CB: is there a performance hit for doing this decoration? It # would show up in lissom_oo_or because separated composite uses # this method.
[docs] def force_new_dynamic_value(self_,name): # pylint: disable-msg=E0213 """ Force a new value to be generated for the dynamic attribute name, and return it. If name is not dynamic, its current value is returned (i.e. equivalent to getattr(name). """ cls_or_slf = self_.self_or_cls param_obj = cls_or_slf.param.params().get(name) if not param_obj: return getattr(cls_or_slf,name) cls,slf=None,None if isinstance(cls_or_slf,type): cls = cls_or_slf else: slf = cls_or_slf if not hasattr(param_obj,'_force'): return param_obj.__get__(slf,cls) else: return param_obj._force(slf,cls)
[docs] def get_value_generator(self_,name): # pylint: disable-msg=E0213 """ Return the value or value-generating object of the named attribute. For most parameters, this is simply the parameter's value (i.e. the same as getattr()), but Dynamic parameters have their value-generating object returned. """ cls_or_slf = self_.self_or_cls param_obj = cls_or_slf.param.params().get(name) if not param_obj: value = getattr(cls_or_slf,name) # CompositeParameter detected by being a Parameter and having 'attribs' elif hasattr(param_obj,'attribs'): value = [cls_or_slf.param.get_value_generator(a) for a in param_obj.attribs] # not a Dynamic Parameter elif not hasattr(param_obj,'_value_is_dynamic'): value = getattr(cls_or_slf,name) # Dynamic Parameter... else: internal_name = "_%s_param_value"%name if hasattr(cls_or_slf,internal_name): # dealing with object and it's been set on this object value = getattr(cls_or_slf,internal_name) else: # dealing with class or isn't set on the object value = param_obj.default return value
[docs] def inspect_value(self_,name): # pylint: disable-msg=E0213 """ Return the current value of the named attribute without modifying it. Same as getattr() except for Dynamic parameters, which have their last generated value returned. """ cls_or_slf = self_.self_or_cls param_obj = cls_or_slf.param.params().get(name) if not param_obj: value = getattr(cls_or_slf,name) elif hasattr(param_obj,'attribs'): value = [cls_or_slf.param.inspect_value(a) for a in param_obj.attribs] elif not hasattr(param_obj,'_inspect'): value = getattr(cls_or_slf,name) else: if isinstance(cls_or_slf,type): value = param_obj._inspect(None,cls_or_slf) else: value = param_obj._inspect(cls_or_slf,None) return value
def params_depended_on(self_,name): return _params_depended_on(MInfo(cls=self_.cls,inst=self_.self,name=name,method=getattr(self_.self_or_cls,name)))
[docs] def outputs(self_): """ Returns a mapping between any declared outputs and a tuple of the declared Parameter type, the output method, and the index into the output if multiple outputs are returned. """ outputs = {} for name in dir(self_.self_or_cls): method = getattr(self_.self_or_cls, name) dinfo = getattr(method, '_dinfo', {}) if 'outputs' not in dinfo: continue for override, otype, idx in dinfo['outputs']: if override is not None: name = override outputs[name] = (otype, method, idx) return outputs
def _spec_to_obj(self_,spec): # TODO: when we decide on spec, this method should be # rewritten assert spec.count(":")<=1 spec = spec.strip() m = re.match("(?P<path>[^:]*):?(?P<what>.*)", spec) what = m.group('what') path = "."+m.group('path') m = re.match(r"(?P<obj>.*)(\.)(?P<attr>.*)",path) obj = m.group('obj') attr = m.group("attr") src = self_.self_or_cls if obj=='' else _getattrr(self_.self_or_cls,obj[1::]) cls,inst = (src,None) if isinstance(src,type) else (type(src),src) if attr == 'param': dependencies = self_._spec_to_obj(obj[1:]) for p in src.param.params(): dependencies += src.param._spec_to_obj(p) return dependencies elif attr in src.param.params(): info = PInfo(inst=inst,cls=cls,name=attr,pobj=src.param.params(attr), what=what if what!='' else 'value') else: info = MInfo(inst=inst,cls=cls,name=attr,method=getattr(src,attr)) return [info] def _watch(self_,action,watcher,what='value', operation='add'): #'add' | 'remove' #cls,obj = (slf_or_cls,None) if isinstance(slf_or_cls,ParameterizedMetaclass) else (slf_or_cls.__class__,slf_or_cls) parameter_names = watcher.parameter_names for parameter_name in parameter_names: assert parameter_name in self_.cls.param.params() if self_.self is not None and what=="value": watchers = self_.self._param_watchers if parameter_name not in watchers: watchers[parameter_name] = {} if what not in watchers[parameter_name]: watchers[parameter_name][what] = [] getattr(watchers[parameter_name][what],action)(watcher) else: watchers = self_.cls.param.params(parameter_name).watchers if what not in watchers: watchers[what] = [] getattr(watchers[what],action)(watcher) def watch(self_,fn,parameter_names, what='value', onlychanged=True): parameter_names = tuple(parameter_names) if isinstance(parameter_names, list) else (parameter_names,) watcher = Watcher(inst=self_.self, cls=self_.cls, fn=fn, mode='args', onlychanged=onlychanged, parameter_names=parameter_names) self_._watch('append', watcher, what) return watcher
[docs] def unwatch(self_,watcher): """ Unwatch watchers set either with watch or watch_values. """ try: self_._watch('remove',watcher) except: self_.warning('No such watcher {watcher} to remove.'.format(watcher=watcher))
def watch_values(self_,fn,parameter_names,what='value', onlychanged=True): parameter_names = tuple(parameter_names) if isinstance(parameter_names, list) else (parameter_names,) watcher = Watcher(inst=self_.self, cls=self_.cls, fn=fn, mode='kwargs', onlychanged=onlychanged, parameter_names=parameter_names) self_._watch('append', watcher, what) return watcher # Instance methods
[docs] def defaults(self_): """ Return {parameter_name:parameter.default} for all non-constant Parameters. Note that a Parameter for which instantiate==True has its default instantiated. """ self = self_.self d = {} for param_name,param in self.param.params().items(): if param.constant: pass elif param.instantiate: self.param._instantiate_param(param,dict_=d,key=param_name) else: d[param_name]=param.default return d
# CEBALERT: designed to avoid any processing unless the print # level is high enough, but not all callers of message(), # verbose(), debug(), etc are taking advantage of this. Need to # document, and also check other ioam projects. def __db_print(self_,level,msg,*args,**kw): """ Calls the logger returned by the get_logger() function, prepending the result of calling dbprint_prefix() (if any). See python's logging module for details. """ self_or_cls = self_.self_or_cls if get_logger().isEnabledFor(level): if dbprint_prefix and callable(dbprint_prefix): prefix=dbprint_prefix() # pylint: disable-msg=E1102 else: prefix="" get_logger().log(level, '%s%s: '+msg, prefix, self_or_cls.name, *args, **kw)
[docs] def print_param_values(self_): """Print the values of all this object's Parameters.""" self = self_.self for name,val in self.param.get_param_values(): print('%s.%s = %s' % (self.name,name,val))
[docs] def warning(self_, msg,*args,**kw): """ Print msg merged with args as a warning, unless module variable warnings_as_exceptions is True, then raise an Exception containing the arguments. See Python's logging module for details of message formatting. """ if not warnings_as_exceptions: global warning_count warning_count+=1 self_.__db_print(WARNING,msg,*args,**kw) else: raise Exception("Warning: " + msg % args)
[docs] def message(self_,msg,*args,**kw): """ Print msg merged with args as a message. See Python's logging module for details of message formatting. """ self_.__db_print(INFO,msg,*args,**kw)
[docs] def verbose(self_,msg,*args,**kw): """ Print msg merged with args as a verbose message. See Python's logging module for details of message formatting. """ self_.__db_print(VERBOSE,msg,*args,**kw)
[docs] def debug(self_,msg,*args,**kw): """ Print msg merged with args as a debugging statement. See Python's logging module for details of message formatting. """ self_.__db_print(DEBUG,msg,*args,**kw)
# CEBALERT: I think I've noted elsewhere the fact that we # sometimes have a method on Parameter that requires passing the # owning Parameterized instance or class, and other times we have # the method on Parameterized itself. In case I haven't written # that down elsewhere, here it is again. We should clean that up # (at least we should be consistent). # cebalert: it's really time to stop and clean up this bothmethod # stuff and repeated code in methods using it.
[docs]class ParameterizedMetaclass(type): """ The metaclass of Parameterized (and all its descendents). The metaclass overrides type.__setattr__ to allow us to set Parameter values on classes without overwriting the attribute descriptor. That is, for a Parameterized class of type X with a Parameter y, the user can type X.y=3, which sets the default value of Parameter y to be 3, rather than overwriting y with the constant value 3 (and thereby losing all other info about that Parameter, such as the doc string, bounds, etc.). The __init__ method is used when defining a Parameterized class, usually when the module where that class is located is imported for the first time. That is, the __init__ in this metaclass initializes the *class* object, while the __init__ method defined in each Parameterized class is called for each new instance of that class. Additionally, a class can declare itself abstract by having an attribute __abstract set to True. The 'abstract' attribute can be used to find out if a class is abstract or not. """ def __init__(mcs,name,bases,dict_): """ Initialize the class object (not an instance of the class, but the class itself). Initializes all the Parameters by looking up appropriate default values (see __param_inheritance()) and setting attrib_names (see _set_names()). """ type.__init__(mcs,name,bases,dict_) # Give Parameterized classes a useful 'name' attribute. # (Could instead consider changing the instance Parameter # 'name' to '__name__'?) mcs.name = name mcs.param = Parameters(mcs) # All objects (with their names) of type Parameter that are # defined in this class parameters = [(n,o) for (n,o) in dict_.items() if isinstance(o,Parameter)] for param_name,param in parameters: mcs._initialize_parameter(param_name,param) # retrieve depends info from methods and store more conveniently dependers = [(n,m._dinfo) for (n,m) in dict_.items() if hasattr(m,'_dinfo')] _watch = [] # TODO: probably copy dependencies here too and have # everything else access from here rather than from method # object for n,dinfo in dependers: if dinfo.get('watch', False): _watch.append(n) mcs.param._depends = {'watch':_watch} if docstring_signature: mcs.__class_docstring_signature() def __class_docstring_signature(mcs, max_repr_len=15): """ Autogenerate a keyword signature in the class docstring for all available parameters. This is particularly useful in the IPython Notebook as IPython will parse this signature to allow tab-completion of keywords. max_repr_len: Maximum length (in characters) of value reprs. """ processed_kws, keyword_groups = set(), [] for cls in reversed(mcs.mro()): keyword_group = [] for (k,v) in sorted(cls.__dict__.items()): if isinstance(v, Parameter) and k not in processed_kws: param_type = v.__class__.__name__ keyword_group.append("%s=%s" % (k, param_type)) processed_kws.add(k) keyword_groups.append(keyword_group) keywords = [el for grp in reversed(keyword_groups) for el in grp] class_docstr = "\n"+mcs.__doc__ if mcs.__doc__ else '' signature = "params(%s)" % (", ".join(keywords)) description = param_pager(mcs) if (docstring_describe_params and param_pager) else '' mcs.__doc__ = signature + class_docstr + '\n' + description def _initialize_parameter(mcs,param_name,param): # parameter has no way to find out the name a # Parameterized class has for it param._set_names(param_name) mcs.__param_inheritance(param_name,param) # Python 2.6 added abstract base classes; see # https://github.com/ioam/param/issues/84 def __is_abstract(mcs): """ Return True if the class has an attribute __abstract set to True. Subclasses will return False unless they themselves have __abstract set to true. This mechanism allows a class to declare itself to be abstract (e.g. to avoid it being offered as an option in a GUI), without the "abstract" property being inherited by its subclasses (at least one of which is presumably not abstract). """ # Can't just do ".__abstract", because that is mangled to # _ParameterizedMetaclass__abstract before running, but # the actual class object will have an attribute # _ClassName__abstract. So, we have to mangle it ourselves at # runtime. try: return getattr(mcs,'_%s__abstract'%mcs.__name__) except AttributeError: return False abstract = property(__is_abstract) def __setattr__(mcs,attribute_name,value): """ Implements 'self.attribute_name=value' in a way that also supports Parameters. If there is already a descriptor named attribute_name, and that descriptor is a Parameter, and the new value is *not* a Parameter, then call that Parameter's __set__ method with the specified value. In all other cases set the attribute normally (i.e. overwrite the descriptor). If the new value is a Parameter, once it has been set we make sure that the value is inherited from Parameterized superclasses as described in __param_inheritance(). """ # Find out if there's a Parameter called attribute_name as a # class attribute of this class - if not, parameter is None. parameter,owning_class = mcs.get_param_descriptor(attribute_name) if parameter and not isinstance(value,Parameter): if owning_class != mcs: parameter = copy.copy(parameter) parameter._owner = mcs type.__setattr__(mcs,attribute_name,parameter) mcs.__dict__[attribute_name].__set__(None,value) else: type.__setattr__(mcs,attribute_name,value) if isinstance(value,Parameter): mcs.__param_inheritance(attribute_name,value) elif isinstance(value,Parameters): pass else: # the purpose of the warning below is to catch # mistakes ("thinking you are setting a parameter, but # you're not"). There are legitimate times when # something needs be set on the class, and we don't # want to see a warning then. Such attributes should # presumably be prefixed by at least one underscore. # (For instance, python's own pickling mechanism # caches __slotnames__ on the class: # http://mail.python.org/pipermail/python-checkins/2003-February/033517.html.) # CEBALERT: this warning bypasses the usual # mechanisms, which has have consequences for warning # counts, warnings as exceptions, etc. if not attribute_name.startswith('_'): get_logger().log(WARNING, "Setting non-Parameter class attribute %s.%s = %s ", mcs.__name__,attribute_name,repr(value)) def __param_inheritance(mcs,param_name,param): """ Look for Parameter values in superclasses of this Parameterized class. Ordinarily, when a Python object is instantiated, attributes not given values in the constructor will inherit the value given in the object's class, or in its superclasses. For Parameters owned by Parameterized classes, we have implemented an additional level of default lookup, should this ordinary lookup return only None. In such a case, i.e. when no non-None value was found for a Parameter by the usual inheritance mechanisms, we explicitly look for Parameters with the same name in superclasses of this Parameterized class, and use the first such value that we find. The goal is to be able to set the default value (or other slots) of a Parameter within a Parameterized class, just as we can set values for non-Parameter objects in Parameterized classes, and have the values inherited through the Parameterized hierarchy as usual. Note that instantiate is handled differently: if there is a parameter with the same name in one of the superclasses with instantiate set to True, this parameter will inherit instatiate=True. """ # get all relevant slots (i.e. slots defined in all # superclasses of this parameter) slots = {} for p_class in classlist(type(param))[1::]: slots.update(dict.fromkeys(p_class.__slots__)) # note for some eventual future: python 3.6+ descriptors grew # __set_name__, which could replace this and _set_names setattr(param,'_owner',mcs) del slots['_owner'] # backwards compatibility (see Composite parameter) if 'objtype' in slots: setattr(param,'objtype',mcs) del slots['objtype'] # instantiate is handled specially for superclass in classlist(mcs)[::-1]: super_param = superclass.__dict__.get(param_name) if isinstance(super_param, Parameter) and super_param.instantiate is True: param.instantiate=True del slots['instantiate'] for slot in slots.keys(): superclasses = iter(classlist(mcs)[::-1]) # Search up the hierarchy until param.slot (which has to # be obtained using getattr(param,slot)) is not None, or # we run out of classes to search. while getattr(param,slot) is None: try: param_super_class = next(superclasses) except StopIteration: break new_param = param_super_class.__dict__.get(param_name) if new_param is not None and hasattr(new_param,slot): # (slot might not be there because could be a more # general type of Parameter) new_value = getattr(new_param,slot) setattr(param,slot,new_value)
[docs] def get_param_descriptor(mcs,param_name): """ Goes up the class hierarchy (starting from the current class) looking for a Parameter class attribute param_name. As soon as one is found as a class attribute, that Parameter is returned along with the class in which it is declared. """ classes = classlist(mcs) for c in classes[::-1]: attribute = c.__dict__.get(param_name) if isinstance(attribute,Parameter): return attribute,c return None,None
# JABALERT: Only partially achieved so far -- objects of the same # type and parameter values are treated as different, so anything # for which instantiate == True is reported as being non-default. # Whether script_repr should avoid reporting the values of parameters # that are just inheriting their values from the class defaults. script_repr_suppress_defaults=True # CEBALERT: How about some defaults? # Also, do we need an option to return repr without path, if desired? # E.g. to get 'pre_plot_hooks()' instead of # 'topo.command.analysis.pre_plot_hooks()' in the gui?
[docs]def script_repr(val,imports,prefix,settings): """ Variant of repr() designed for generating a runnable script. Instances of types that require special handling can use the script_repr_reg dictionary. Using the type as a key, add a function that returns a suitable representation of instances of that type, and adds the required import statement. The repr of a parameter can be suppressed by returning None from the appropriate hook in script_repr_reg. """ return pprint(val,imports,prefix,settings,unknown_value=None, qualify=True,separator="\n")
# CB: when removing script_repr, merge its docstring here and improve. # And the ALERT by script_repr about defaults can go. # CEBALERT: remove settings, add default argument for imports
[docs]def pprint(val,imports, prefix="\n ", settings=[], unknown_value='<?>', qualify=False, separator=''): """ (Experimental) Pretty printed representation of a parameterized object that may be evaluated with eval. Similar to repr except introspection of the constructor (__init__) ensures a valid and succinct representation is generated. Only parameters are represented (whether specified as standard, positional, or keyword arguments). Parameters specified as positional arguments are always shown, followed by modified parameters specified as keyword arguments, sorted by precedence. unknown_value determines what to do where a representation cannot be generated for something required to recreate the object. Such things include non-parameter positional and keyword arguments, and certain values of parameters (e.g. some random state objects). Supplying an unknown_value of None causes unrepresentable things to be silently ignored. If unknown_value is a string, that string will appear in place of any unrepresentable things. If unknown_value is False, an Exception will be raised if an unrepresentable value is encountered. If supplied, imports should be a list, and it will be populated with the set of imports required for the object and all of its parameter values. If qualify is True, the class's path will be included (e.g. "a.b.C()"), otherwise only the class will appear ("C()"). Parameters will be separated by a comma only by default, but the separator parameter allows an additional separator to be supplied (e.g. a newline could be supplied to have each Parameter appear on a separate line). NOTE: pprint will replace script_repr in a future version of param, but is not yet a complete replacement for script_repr. """ # CB: doc prefix & settings or realize they don't need to be # passed around, etc. # JLS: The settings argument is not used anywhere. To be removed # in a separate PR. if isinstance(val,type): rep = type_script_repr(val,imports,prefix,settings) elif type(val) in script_repr_reg: rep = script_repr_reg[type(val)](val,imports,prefix,settings) # CEBALERT: remove with script_repr elif hasattr(val,'script_repr'): rep=val.script_repr(imports, prefix+" ") elif hasattr(val,'pprint'): rep=val.pprint(imports=imports, prefix=prefix+" ", qualify=qualify, unknown_value=unknown_value, separator=separator) else: rep=repr(val) return rep
#: see script_repr() script_repr_reg = {} # currently only handles list and tuple def container_script_repr(container,imports,prefix,settings): result=[] for i in container: result.append(pprint(i,imports,prefix,settings)) ## (hack to get container brackets) if isinstance(container,list): d1,d2='[',']' elif isinstance(container,tuple): d1,d2='(',')' else: raise NotImplementedError rep=d1+','.join(result)+d2 # no imports to add for built-in types return rep def empty_script_repr(*args): # pyflakes:ignore (unused arguments): return None try: # Suppress scriptrepr for objects not yet having a useful string representation import numpy script_repr_reg[random.Random] = empty_script_repr script_repr_reg[numpy.random.RandomState] = empty_script_repr except ImportError: pass # Support added only if those libraries are available # why I have to type prefix and settings? def function_script_repr(fn,imports,prefix,settings): name = fn.__name__ module = fn.__module__ imports.append('import %s'%module) return module+'.'+name def type_script_repr(type_,imports,prefix,settings): module = type_.__module__ if module!='__builtin__': imports.append('import %s'%module) return module+'.'+type_.__name__ script_repr_reg[list]=container_script_repr script_repr_reg[tuple]=container_script_repr script_repr_reg[FunctionType]=function_script_repr #: If not None, the value of this Parameter will be called (using '()') #: before every call to __db_print, and is expected to evaluate to a #: string that is suitable for prefixing messages and warnings (such #: as some indicator of the global state). dbprint_prefix=None
[docs]@add_metaclass(ParameterizedMetaclass) class Parameterized(object): """ Base class for named objects that support Parameters and message formatting. Automatic object naming: Every Parameterized instance has a name parameter. If the user doesn't designate a name=<str> argument when constructing the object, the object will be given a name consisting of its class name followed by a unique 5-digit number. Automatic parameter setting: The Parameterized __init__ method will automatically read the list of keyword parameters. If any keyword matches the name of a Parameter (see Parameter class) defined in the object's class or any of its superclasses, that parameter in the instance will get the value given as a keyword argument. For example: class Foo(Parameterized): xx = Parameter(default=1) foo = Foo(xx=20) in this case foo.xx gets the value 20. When initializing a Parameterized instance ('foo' in the example above), the values of parameters can be supplied as keyword arguments to the constructor (using parametername=parametervalue); these values will override the class default values for this one instance. If no 'name' parameter is supplied, self.name defaults to the object's class name with a unique number appended to it. Message formatting: Each Parameterized instance has several methods for optionally printing output. This functionality is based on the standard Python 'logging' module; using the methods provided here, wraps calls to the 'logging' module's root logger and prepends each message with information about the instance from which the call was made. For more information on how to set the global logging level and change the default message prefix, see documentation for the 'logging' module. """ name = String(default=None,constant=True,doc=""" String identifier for this object.""") def __init__(self,**params): global object_count # Flag that can be tested to see if e.g. constant Parameters # can still be set self.initialized=False # Override class level param namespace with instance namespace self.param = Parameters(self.__class__, self=self) self.param._generate_name() self.param._setup_params(**params) object_count += 1 # TODO: should move to param namespace? (like _param_value # etc should also move) self._param_watchers = {} # add watched dependencies # for n in self.__class__.param._depends['watch']: # TODO: should improve this - will happen for every # instantiation of Parameterized with watched deps. Will # probably store expanded deps on class - see metaclass # 'dependers'. for p in self.param.params_depended_on(n): # TODO: can't remember why not just pass m (rather than _m_caller) here (p.inst or p.cls).param.watch(_m_caller(self,n),p.name,p.what) self.initialized=True # 'Special' methods def __getstate__(self): """ Save the object's state: return a dictionary that is a shallow copy of the object's __dict__ and that also includes the object's __slots__ (if it has any). """ # remind me, why is it a copy? why not just state.update(self.__dict__)? state = self.__dict__.copy() for slot in get_occupied_slots(self): state[slot] = getattr(self,slot) # Note that Parameterized object pickling assumes that # attributes to be saved are only in __dict__ or __slots__ # (the standard Python places to store attributes, so that's a # reasonable assumption). (Additionally, class attributes that # are Parameters are also handled, even when they haven't been # instantiated - see PickleableClassAttributes.) return state def __setstate__(self,state): """ Restore objects from the state dictionary to this object. During this process the object is considered uninitialized. """ self.initialized=False for name,value in state.items(): setattr(self,name,value) self.initialized=True def __repr__(self): """ Provide a nearly valid Python representation that could be used to recreate the item with its parameters, if executed in the appropriate environment. Returns 'classname(parameter1=x,parameter2=y,...)', listing all the parameters of this object. """ settings = ['%s=%s' % (name,repr(val)) for name,val in self.param.get_param_values()] return self.__class__.__name__ + "(" + ", ".join(settings) + ")" def __str__(self): """Return a short representation of the name and class of this object.""" return "<%s %s>" % (self.__class__.__name__,self.name)
[docs] def script_repr(self,imports=[],prefix=" "): """ Variant of __repr__ designed for generating a runnable script. """ return self.pprint(imports,prefix, unknown_value=None, qualify=True, separator="\n")
# CEBALERT: not yet properly documented
[docs] def pprint(self, imports=None, prefix=" ", unknown_value='<?>', qualify=False, separator=""): """ (Experimental) Pretty printed representation that may be evaluated with eval. See pprint() function for more details. """ if imports is None: imports = [] # CEBALERT: imports should just be a set rather than a list; # change in next release? imports[:] = list(set(imports)) # Generate import statement mod = self.__module__ bits = mod.split('.') imports.append("import %s"%mod) imports.append("import %s"%bits[0]) changed_params = dict(self.param.get_param_values(onlychanged=script_repr_suppress_defaults)) values = dict(self.param.get_param_values()) spec = inspect.getargspec(self.__init__) args = spec.args[1:] if spec.args[0] == 'self' else spec.args if spec.defaults is not None: posargs = spec.args[:-len(spec.defaults)] kwargs = dict(zip(spec.args[-len(spec.defaults):], spec.defaults)) else: posargs, kwargs = args, [] ordering = sorted( sorted(changed_params.keys()), # alphanumeric tie-breaker key=lambda k: (- float('inf') # No precedence is lowest possible precendence if self.param.params(k).precedence is None else self.param.params(k).precedence)) arglist, keywords, processed = [], [], [] for k in args + ordering: if k in processed: continue # Suppresses automatically generated names. if k == 'name' and (values[k] is not None and re.match('^'+self.__class__.__name__+'[0-9]+$', values[k])): continue value = pprint(values[k], imports, prefix=prefix,settings=[], unknown_value=unknown_value, qualify=qualify) if k in values else None if value is None: if unknown_value is False: raise Exception("%s: unknown value of %r" % (self.name,k)) elif unknown_value is None: # i.e. suppress repr continue else: value = unknown_value # Explicit kwarg (unchanged, known value) if (k in kwargs) and (k in values) and kwargs[k] == values[k]: continue if k in posargs: # value will be unknown_value unless k is a parameter arglist.append(value) elif k in kwargs or (spec.keywords is not None): # Explicit modified keywords or parameters in # precendence order (if **kwargs present) keywords.append('%s=%s' % (k, value)) processed.append(k) qualifier = mod + '.' if qualify else '' arguments = arglist + keywords + (['**%s' % spec.varargs] if spec.varargs else []) return qualifier + '%s(%s)' % (self.__class__.__name__, (','+separator+prefix).join(arguments))
# CEBALERT: note there's no state_push method on the class, so # dynamic parameters set on a class can't have state saved. This # is because, to do this, state_push() would need to be a # @bothmethod, but that complicates inheritance in cases where we # already have a state_push() method. I need to decide what to do # about that. (isinstance(g,Parameterized) below is used to exclude classes.)
[docs] def state_push(self): """ Save this instance's state. For Parameterized instances, this includes the state of dynamically generated values. Subclasses that maintain short-term state should additionally save and restore that state using state_push() and state_pop(). Generally, this method is used by operations that need to test something without permanently altering the objects' state. """ for pname,p in self.param.params().items(): g = self.param.get_value_generator(pname) if hasattr(g,'_Dynamic_last'): g._saved_Dynamic_last.append(g._Dynamic_last) g._saved_Dynamic_time.append(g._Dynamic_time) # CB: not storing the time_fn: assuming that doesn't # change. elif hasattr(g,'state_push') and isinstance(g,Parameterized): g.state_push()
[docs] def state_pop(self): """ Restore the most recently saved state. See state_push() for more details. """ for pname,p in self.param.params().items(): g = self.param.get_value_generator(pname) if hasattr(g,'_Dynamic_last'): g._Dynamic_last = g._saved_Dynamic_last.pop() g._Dynamic_time = g._saved_Dynamic_time.pop() elif hasattr(g,'state_pop') and isinstance(g,Parameterized): g.state_pop()
# API to be accessed via param namespace @classmethod @Parameters.deprecate def _add_parameter(cls, param_name,param_obj): return cls.param._add_parameter(param_name,param_obj)
[docs] @classmethod @Parameters.deprecate def params(cls,parameter_name=None): return cls.param.params(parameter_name=parameter_name)
[docs] @classmethod @Parameters.deprecate def set_default(cls,param_name,value): return cls.param.set_default(param_name,value)
[docs] @classmethod @Parameters.deprecate def print_param_defaults(cls): return cls.param.print_param_defaults()
[docs] @bothmethod @Parameters.deprecate def set_param(self_or_cls,*args,**kwargs): return self_or_cls.param.set_param(*args,**kwargs)
[docs] @bothmethod @Parameters.deprecate def set_dynamic_time_fn(self_or_cls,time_fn,sublistattr=None): return self_or_cls.param.set_dynamic_time_fn(time_fn,sublistattr=sublistattr)
[docs] @bothmethod @Parameters.deprecate def get_param_values(self_or_cls,onlychanged=False): return self_or_cls.param.get_param_values(onlychanged=onlychanged)
[docs] @bothmethod @Parameters.deprecate def force_new_dynamic_value(cls_or_slf,name): # pylint: disable-msg=E0213 return cls_or_slf.param.force_new_dynamic_value(name)
[docs] @bothmethod @Parameters.deprecate def get_value_generator(cls_or_slf,name): # pylint: disable-msg=E0213 return cls_or_slf.param.get_value_generator(name)
[docs] @bothmethod @Parameters.deprecate def inspect_value(cls_or_slf,name): # pylint: disable-msg=E0213 return cls_or_slf.param.inspect_value(name)
@Parameters.deprecate def _set_name(self,name): return self.param._set_name(name) @Parameters.deprecate def __db_print(self,level,msg,*args,**kw): return self.param.__db_print(level,msg,*args,**kw)
[docs] @Parameters.deprecate def warning(self,msg,*args,**kw): return self.param.warning(msg,*args,**kw)
[docs] @Parameters.deprecate def message(self,msg,*args,**kw): return self.param.message(msg,*args,**kw)
[docs] @Parameters.deprecate def verbose(self,msg,*args,**kw): return self.param.verbose(msg,*args,**kw)
[docs] @Parameters.deprecate def debug(self,msg,*args,**kw): return self.param.debug(msg,*args,**kw)
[docs] @Parameters.deprecate def print_param_values(self): return self.param.print_param_values()
[docs] @Parameters.deprecate def defaults(self): return self.param.defaults()
# CB: seems to work, but conflicts with (hides) # Simulation(OptionalSingleton)'s __deepcopy__ method. Guess it's # finally time to clean up that inheritance mess... ## def __deepcopy__(self,memo=None): ## # Deepcopy all attributes in __slots__ and __dict__, except ## # for attributes which are ObjectSelector parameters (which ## # are not copied at all). ## # ## # Should be equivalent to copy.deepcopy(self), but without copying ## # ObjectSelector parameters. ## if memo is None: ## memo = {} ## class_ = self.__class__ ## new_instance = class_.__new__(class_) ## memo[id(self)]=new_instance ## ## attributes are in __dict__ and __slots__ ## all_attributes = [] ## if hasattr(self,'__dict__'): ## all_attributes+=self.__dict__.keys() ## if hasattr(self,'__slots__'): ## all_attributes+=self.__slots__ ## attributes_to_copy = all_attributes[:] ## ## remove ObjectSelector parameters from list to be copied ## for param_name,param_obj in self.params().items(): ## internal_param_name = "_%s_param_value"%param_name ## # (if param_obj has 'objects' slot, it's assumed to be an ObjectSelector) ## if hasattr(param_obj,'objects') and internal_param_name in attributes_to_copy: ## attributes_to_copy.remove(internal_param_name) ## for attr in all_attributes: ## if attr in attributes_to_copy: ## obj = copy.deepcopy(getattr(self,attr),memo) ## else: ## obj = getattr(self,attr) ## setattr(new_instance,attr,obj) ## return new_instance # Note that with Python 2.6, a fn's **args no longer has to be a # dictionary. This might allow us to use a decorator to simplify using # ParamOverrides (if that does indeed make them simpler to use). # http://docs.python.org/whatsnew/2.6.html
[docs]class ParamOverrides(dict): """ A dictionary that returns the attribute of a specified object if that attribute is not present in itself. Used to override the parameters of an object. """ # NOTE: Attribute names of this object block parameters of the # same name, so all attributes of this object should have names # starting with an underscore (_). def __init__(self,overridden,dict_,allow_extra_keywords=False): """ If allow_extra_keywords is False, then all keys in the supplied dict_ must match parameter names on the overridden object (otherwise a warning will be printed). If allow_extra_keywords is True, then any items in the supplied dict_ that are not also parameters of the overridden object will be available via the extra_keywords() method. """ # we'd like __init__ to be fast because it's going to be # called a lot. What's the fastest way to move the existing # params dictionary into this one? Would # def __init__(self,overridden,**kw): # ... # dict.__init__(self,**kw) # be faster/easier to use? self._overridden = overridden dict.__init__(self,dict_) if allow_extra_keywords: self._extra_keywords=self._extract_extra_keywords(dict_) else: self._check_params(dict_)
[docs] def extra_keywords(self): """ Return a dictionary containing items from the originally supplied dict_ whose names are not parameters of the overridden object. """ return self._extra_keywords
[docs] def param_keywords(self): """ Return a dictionary containing items from the originally supplied dict_ whose names are parameters of the overridden object (i.e. not extra keywords/parameters). """ return dict((key, self[key]) for key in self if key not in self.extra_keywords())
def __missing__(self,name): # Return 'name' from the overridden object return getattr(self._overridden,name) def __repr__(self): # As dict.__repr__, but indicate the overridden object return dict.__repr__(self)+" overriding params from %s"%repr(self._overridden) def __getattr__(self,name): # Provide 'dot' access to entries in the dictionary. # (This __getattr__ method is called only if 'name' isn't an # attribute of self.) return self.__getitem__(name) def __setattr__(self,name,val): # Attributes whose name starts with _ are set on self (as # normal), but all other attributes are inserted into the # dictionary. if not name.startswith('_'): self.__setitem__(name,val) else: dict.__setattr__(self,name,val)
[docs] def get(self, key, default=None): try: return self[key] except KeyError: return default
def __contains__(self, key): return key in self.__dict__ or key in self._overridden.param.params() def _check_params(self,params): """ Print a warning if params contains something that is not a Parameter of the overridden object. """ overridden_object_params = list(self._overridden.param.params().keys()) for item in params: if item not in overridden_object_params: self.param.warning("'%s' will be ignored (not a Parameter).",item) def _extract_extra_keywords(self,params): """ Return any items in params that are not also parameters of the overridden object. """ extra_keywords = {} overridden_object_params = self._overridden.param.params() for name,val in params.items(): if name not in overridden_object_params: extra_keywords[name]=val # CEBALERT: should we remove name from params # (i.e. del params[name]) so that it's only available # via extra_keywords()? return extra_keywords
# Helper function required by ParameterizedFunction.__reduce__ def _new_parameterized(cls): return Parameterized.__new__(cls)
[docs]class ParameterizedFunction(Parameterized): """ Acts like a Python function, but with arguments that are Parameters. Implemented as a subclass of Parameterized that, when instantiated, automatically invokes __call__ and returns the result, instead of returning an instance of the class. To obtain an instance of this class, call instance(). """ __abstract = True # CEBALERT: shouldn't this have come from a parent class # somewhere? def __str__(self): return self.__class__.__name__+"()"
[docs] @bothmethod def instance(self_or_cls,**params): """ Return an instance of this class, copying parameters from any existing instance provided. """ if isinstance (self_or_cls,ParameterizedMetaclass): cls = self_or_cls else: p = params params = dict(self_or_cls.get_param_values()) params.update(p) params.pop('name') cls = self_or_cls.__class__ inst=Parameterized.__new__(cls) Parameterized.__init__(inst,**params) if 'name' in params: inst.__name__ = params['name'] else: inst.__name__ = self_or_cls.name return inst
def __new__(class_,*args,**params): # Create and __call__() an instance of this class. inst = class_.instance() inst.param._set_name(class_.__name__) return inst.__call__(*args,**params) def __call__(self,*args,**kw): raise NotImplementedError("Subclasses must implement __call__.") def __reduce__(self): # Control reconstruction (during unpickling and copying): # ensure that ParameterizedFunction.__new__ is skipped state = ParameterizedFunction.__getstate__(self) # CB: here it's necessary to use a function defined at the # module level rather than Parameterized.__new__ directly # because otherwise pickle will find .__new__'s module to be # __main__. Pretty obscure aspect of pickle.py, or a bug? return (_new_parameterized,(self.__class__,),state)
[docs] def script_repr(self,imports=[],prefix=" "): """ Same as Parameterized.script_repr, except that X.classname(Y is replaced with X.classname.instance(Y """ return self.pprint(imports,prefix,unknown_value='',qualify=True, separator="\n")
[docs] def pprint(self, imports=None, prefix="\n ",unknown_value='<?>', qualify=False, separator=""): """ Same as Parameterized.pprint, except that X.classname(Y is replaced with X.classname.instance(Y """ r = Parameterized.pprint(self,imports,prefix, unknown_value=unknown_value, qualify=qualify,separator=separator) classname=self.__class__.__name__ return r.replace(".%s("%classname,".%s.instance("%classname)
# CBENHANCEMENT: should be able to remove overridable_property when we # switch to Python 2.6: # "Properties now have three attributes, getter, setter and deleter, # that are decorators providing useful shortcuts for adding a getter, # setter or deleter function to an existing property." # http://docs.python.org/whatsnew/2.6.html # Renamed & documented version of OProperty from # infinitesque.net/articles/2005/enhancing%20Python's%20property.xhtml
[docs]class overridable_property(object): """ The same as Python's "property" attribute, but allows the accessor methods to be overridden in subclasses. """ # Delays looking up the accessors until they're needed, rather # than finding them when the class is first created. # Based on the emulation of PyProperty_Type() in Objects/descrobject.c def __init__(self, fget=None, fset=None, fdel=None, doc=None): self.fget = fget self.fset = fset self.fdel = fdel self.__doc__ = doc def __get__(self, obj, objtype=None): if obj is None: return self if self.fget is None: raise AttributeError("unreadable attribute") if self.fget.__name__ == '<lambda>' or not self.fget.__name__: return self.fget(obj) else: return getattr(obj, self.fget.__name__)() def __set__(self, obj, value): if self.fset is None: raise AttributeError("can't set attribute") if self.fset.__name__ == '<lambda>' or not self.fset.__name__: self.fset(obj, value) else: getattr(obj, self.fset.__name__)(value) def __delete__(self, obj): if self.fdel is None: raise AttributeError("can't delete attribute") if self.fdel.__name__ == '<lambda>' or not self.fdel.__name__: self.fdel(obj) else: getattr(obj, self.fdel.__name__)()