"""
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
basestring = basestring if sys.version_info[0]==2 else str # noqa: it is defined
VERBOSE = INFO - 1
logging.addLevelName(VERBOSE, "VERBOSE")
# Get the appropriate logging.Logger instance. If `logger` is None, a
# logger named `"param"` will be instantiated. If `name` is set, a descendant
# logger with the name ``"param.<name>"`` is returned (or
# ``logger.name + ".<name>"``)
logger = None
def get_logger(name=None):
if logger is None:
root_logger = logging.getLogger('param')
if not root_logger.handlers:
root_logger.setLevel(logging.INFO)
formatter = logging.Formatter(
fmt='%(levelname)s:%(name)s: %(message)s')
handler = logging.StreamHandler()
handler.setFormatter(formatter)
root_logger.addHandler(handler)
else:
root_logger = logger
if name is None:
return root_logger
else:
return logging.getLogger(root_logger.name + '.' + name)
# 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]@contextmanager
def batch_watch(parameterized, enable=True, run=True):
"""
Context manager to batch watcher events on a parameterized object.
The context manager will queue any events triggered by setting a
parameter on the supplied parameterized object and dispatch them
all at once when the context manager exits. If run=False the
queued events are not dispatched and should be processed manually.
"""
BATCH_WATCH = parameterized.param._BATCH_WATCH
parameterized.param._BATCH_WATCH = enable or parameterized.param._BATCH_WATCH
try:
yield
finally:
parameterized.param._BATCH_WATCH = BATCH_WATCH
if run and not BATCH_WATCH:
parameterized.param._batch_call_watchers()
[docs]@contextmanager
def edit_constant(parameterized):
"""
Temporarily set parameters on Parameterized object to constant=False
to allow editing them.
"""
params = parameterized.param.objects('existing').values()
constants = [p.constant for p in params]
for p in params:
p.constant = False
try:
yield
except:
raise
finally:
for (p, const) in zip(params, constants):
p.constant = const
[docs]@contextmanager
def discard_events(parameterized):
"""
Context manager that discards any events within its scope
triggered on the supplied parameterized object.
"""
batch_watch = parameterized.param._BATCH_WATCH
parameterized.param._BATCH_WATCH = True
watchers, events = parameterized.param._watchers, parameterized.param._events
try:
yield
except:
raise
finally:
parameterized.param._BATCH_WATCH = batch_watch
parameterized.param._watchers = watchers
parameterized.param._events = events
[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]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]def no_instance_params(cls):
"""
Disables instance parameters on the class
"""
cls._disable_instance__params = True
return cls
def instance_descriptor(f):
# If parameter has an instance Parameter delegate setting
def _f(self, obj, val):
instance_param = getattr(obj, '_instance__params', {}).get(self.name)
if instance_param is not None and self is not instance_param:
instance_param.__set__(obj, val)
return
return f(self, obj, val)
return _f
[docs]@accept_arguments
def depends(func, *dependencies, **kw):
"""
Annotates a function or Parameterized method to express its
dependencies. The specified dependencies can be either be
Parameter instances or if a method is supplied they can be
defined as strings referring to Parameters of the 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)
@wraps(func)
def _depends(*args,**kw):
return func(*args,**kw)
deps = list(dependencies)+list(kw.values())
string_specs = False
for dep in deps:
if isinstance(dep, basestring):
string_specs = True
elif not isinstance(dep, Parameter):
raise ValueError('The depends decorator only accepts string '
'types referencing a parameter or parameter '
'instances, found %s type instead.' %
type(dep).__name__)
elif not (isinstance(dep.owner, Parameterized) or
(isinstance(dep.owner, ParameterizedMetaclass))):
owner = 'None' if dep.owner is None else '%s class' % type(dep.owner).__name__
raise ValueError('Parameters supplied to the depends decorator, '
'must be bound to a Parameterized class or '
'instance not %s.' % owner)
if (any(isinstance(dep, Parameter) for dep in deps) and
any(isinstance(dep, basestring) for dep in deps)):
raise ValueError('Dependencies must either be defined as strings '
'referencing parameters on the class defining '
'the decorated method or as parameter instances. '
'Mixing of string specs and parameter instances '
'is not supported.')
elif string_specs and kw:
raise AssertionError('Supplying keywords to the decorated method '
'or function is not supported when referencing '
'parameters by name.')
if not string_specs and watch:
def cb(event):
args = (getattr(dep.owner, dep.name) for dep in dependencies)
dep_kwargs = {n: getattr(dep.owner, dep.name) for n, dep in kw.items()}
return func(*args, **dep_kwargs)
for dep in deps:
dep.owner.param.watch(cb, dep.name)
_dinfo = getattr(func, '_dinfo', {})
_dinfo.update({'dependencies': dependencies,
'kw': kw, 'watch': watch})
_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)):
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 what queued")
# 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__ = ['name','_internal_name','default','doc',
'precedence','instantiate','constant','readonly',
'pickle_default_value','allow_None', 'per_instance',
'watchers', 'owner', '_label']
# 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, name, and _internal_name are
# set.
def __init__(self,default=None,doc=None,label=None,precedence=None, # pylint: disable-msg=R0913
instantiate=False,constant=False,readonly=False,
pickle_default_value=True, allow_None=False,
per_instance=True):
"""
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).
per_instance defaults to True and controls whether a new
Parameter instance can be created for every Parameterized
instance. If False, all instances of a Parameterized class
will share the same parameter object, including all validation
attributes.
In rare cases where the default value should not be pickled,
set pickle_default_value=False (e.g. for file search paths).
"""
self.name = None
self._internal_name = None
self.owner = None
self._label = label
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 = {}
self.per_instance = per_instance
@property
def label(self):
if self.name and self._label is None:
return label_formatter(self.name)
else:
return self._label
@label.setter
def label(self, val):
self._label = val
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.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)
if not self.owner.param._BATCH_WATCH:
self.owner.param._batch_call_watchers()
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
@instance_descriptor
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).
"""
# ALERT: Deprecated Number set_hook called here to avoid duplicating
# setter, should be removed in 2.0
if hasattr(self, 'set_hook'):
val = self.set_hook(obj,val)
self._validate(val)
_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.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.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
self._post_setter(obj, val)
if obj is None:
watchers = self.watchers.get("value",[])
else:
watchers = getattr(obj,"_param_watchers",{}).get(self.name,{}).get('value',self.watchers.get("value",[]))
event = Event(what='value',name=self.name,obj=obj,cls=self.owner,old=_old,new=val, type=None)
obj = self.owner if obj is None else obj
if obj is None:
return
for watcher in watchers:
obj.param._call_watcher(watcher, event)
if not obj.param._BATCH_WATCH:
obj.param._batch_call_watchers()
def _validate(self, val):
"""Implements validation for the parameter"""
def _post_setter(self, obj, val):
"""Called after the parameter value has been validated and set"""
def __delete__(self,obj):
raise TypeError("Cannot delete '%s': Parameters deletion not allowed." % self.name)
def _set_names(self, attrib_name):
if None not in (self.owner, self.name) and attrib_name != self.name:
raise AttributeError('The %s parameter %r has already been '
'assigned a name by the %s class, '
'could not assign new name %r. Parameters '
'may not be shared by multiple classes; '
'ensure that you create a new parameter '
'instance for each new class.'
% (type(self).__name__, self.name,
self.owner.name, attrib_name))
self.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__)
# Handle renamed slots introduced for instance params
if '_attrib_name' in state:
state['name'] = state.pop('_attrib_name')
if '_owner' in state:
state['owner'] = state.pop('_owner')
if 'watchers' not in state:
state['watchers'] = {}
if 'per_instance' not in state:
state['per_instance'] = False
if '_label' not in state:
state['_label'] = None
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']
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._validate(default)
def _validate(self, val):
if self.allow_None and val is None:
return
if not isinstance(val, basestring):
raise ValueError("String '%s' only takes a string value."%self.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.name,val,self.regex))
[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,
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
def __getitem__(self_, key):
"""
Returns the class or instance parameter
"""
inst = self_.self
parameters = self_.objects(False) if inst is None else inst.param.objects(False)
p = parameters[key]
if (inst is not None and p.per_instance and
not getattr(inst, '_disable_instance__params', False)):
if key not in inst._instance__params:
try:
# Do not copy watchers on class parameter
watchers = p.watchers
p.watchers = {}
p = copy.copy(p)
except:
raise
finally:
p.watchers = watchers
p.owner = inst
inst._instance__params[key] = p
else:
p = inst._instance__params[key]
return p
def __dir__(self_):
"""
Adds parameters to dir
"""
return super(Parameters, self_).__dir__() + list(self_)
def __iter__(self_):
"""
Iterates over the parameters on this object.
"""
for p in self_.objects(instance=False):
yield p
def __contains__(self_, param):
return param in list(self_)
def __getattr__(self_, attr):
"""
Extends attribute access to parameter objects.
"""
cls = self_.__dict__.get('cls')
if cls is None: # Class not initialized
raise AttributeError
try:
params = list(getattr(cls, '_%s__params' % cls.__name__))
except AttributeError:
params = [n for class_ in classlist(cls) for n, v in class_.__dict__.items()
if isinstance(v, Parameter)]
if attr in params:
return self_.__getitem__(attr)
elif self_.self is None:
raise AttributeError("type object '%s.param' has no attribute %r" %
(self_.cls.__name__, attr))
else:
raise AttributeError("'%s.param' object has no attribute %r" %
(self_.cls.__name__, attr))
@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):
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(name=args[0].__class__.__name__).log(
WARNING, 'Use method %r via param namespace ' % fn.__name__)
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.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.
"""
if self_.self is not None and self_.self._instance__params:
self_.warning('The Parameterized instance has instance '
'parameters created using new-style param '
'APIs, which are incompatible with .params. '
'Use the new more explicit APIs on the '
'.param accessor to query parameter instances.'
'To query all parameter instances use '
'.param.objects with the option to return '
'either class or instance parameter objects. '
'Alternatively use .param[name] indexing to '
'access a specific parameter object by name.')
pdict = self_.objects(instance='existing')
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.
"""
BATCH_WATCH = self_.self_or_cls.param._BATCH_WATCH
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:
self_.self_or_cls.param._BATCH_WATCH = False
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:
self_.self_or_cls.param._BATCH_WATCH = False
raise ValueError("'%s' is not a parameter of %s" % (k, self_or_cls.name))
try:
setattr(self_or_cls, k, v)
except:
self_.self_or_cls.param._BATCH_WATCH = False
raise
self_.self_or_cls.param._BATCH_WATCH = BATCH_WATCH
if not BATCH_WATCH:
self_._batch_call_watchers()
[docs] def objects(self_, instance=True):
"""
Returns the Parameters of this instance or class
If instance=True and called on a Parameterized instance it
will create instance parameters for all Parameters defined on
the class. To force class parameters to be returned use
instance=False. Since classes avoid creating instance
parameters unless necessary you may also request only existing
instance parameters to be returned by setting
instance='existing'.
"""
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 instance and self_.self is not None:
if instance == 'existing':
if self_.self._instance__params:
return dict(pdict, **self_.self._instance__params)
return pdict
else:
return {k: self_.self.param[k] for k in pdict}
return pdict
[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)
else:
event = self_._update_event_type(watcher, event, self_.self_or_cls.param._TRIGGER)
with batch_watch(self_.self_or_cls, enable=watcher.queued, run=False):
if watcher.mode == 'args':
watcher.fn(event)
else:
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.
"""
while self_.self_or_cls.param._events:
event_dict = OrderedDict([((event.name, event.what), event)
for event 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, watcher.what)],
self_.self_or_cls.param._TRIGGER)
for name in watcher.parameter_names
if (name, watcher.what) in event_dict]
with batch_watch(self_.self_or_cls, enable=watcher.queued, run=False):
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.objects('existing').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.objects('existing').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
[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.objects('existing').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.objects('existing').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.objects('existing').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 cls in classlist(self_.cls):
for name in dir(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
if isinstance(spec, Parameter):
inst = spec.owner if isinstance(spec.owner, Parameterized) else None
cls = spec.owner if inst is None else type(inst)
info = PInfo(inst=inst, cls=cls, name=spec.name,
pobj=spec, what='value')
return [info]
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:
dependencies += src.param._spec_to_obj(p)
return dependencies
elif attr in src.param:
what = what if what != '' else 'value'
info = PInfo(inst=inst, cls=cls, name=attr,
pobj=src.param[attr], what=what)
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'
parameter_names = watcher.parameter_names
for parameter_name in parameter_names:
if parameter_name not in self_.cls.param:
raise ValueError("%s parameter was not found in list of "
"parameters of class %s" %
(parameter_name, self_.cls.__name__))
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_[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, queued=False):
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,
what=what, queued=queued)
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, queued=False):
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, what='value',
queued=queued)
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.objects('existing').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(name=self_or_cls.name).isEnabledFor(level):
if dbprint_prefix and callable(dbprint_prefix):
msg = dbprint_prefix() + ": " + msg # pylint: disable-msg=E1102
get_logger(name=self_or_cls.name).log(level, msg, *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.
# 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._instance__params = {}
self._param_watchers = {}
self.param._generate_name()
self.param._setup_params(**params)
object_count += 1
# add watched dependencies
for cls in classlist(self.__class__):
if not issubclass(cls, Parameterized):
continue
for n, queued in cls.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, queued=queued)
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
if '_instance__params' not in state:
state['_instance__params'] = {}
if '_param_watchers' not in state:
state['_param_watchers'] = {}
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.
"""
try:
settings = ['%s=%s' % (name, repr(val))
for name,val in self.param.get_param_values()]
except RuntimeError: # Handle recursion in parameter depth
settings = []
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, []
parameters = self.param.objects('existing')
ordering = sorted(
sorted(changed_params), # alphanumeric tie-breaker
key=lambda k: (- float('inf') # No precedence is lowest possible precendence
if parameters[k].precedence is None else
parameters[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.objects('existing').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.objects('existing').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] @bothmethod
@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()
[docs]def print_all_param_defaults():
"""Print the default values for all imported Parameters."""
print("_______________________________________________________________________________")
print("")
print(" Parameter Default Values")
print("")
classes = descendents(Parameterized)
classes.sort(key=lambda x:x.__name__)
for c in classes:
c.print_param_defaults()
print("_______________________________________________________________________________")
# 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 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
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)
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 = list(self._overridden.param)
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)
label_formatter = default_label_formatter
# 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__)()
