"""
Parameters are a kind of class attribute allowing special behavior,
including dynamically generated parameter values, documentation
strings, constant and read-only parameters, and type or range checking
at assignment time.
Potentially useful for any large Python program that needs
user-modifiable object attributes; see the parameterized.Parameter and
parameterized.Parameterized classes for more information.
This file contains subclasses of Parameter, implementing specific
parameter types (e.g. Number).
"""
# CEBALERT: we need more documentation above, now that params is a
# separate package.
import os.path
from param.parameterized import Parameterized, Parameter, String, \
descendents, ParameterizedFunction, ParamOverrides
#: Top-level object to allow messaging not tied to a particular
#: Parameterized object, as in 'param.main.warning("Invalid option")'.
main=Parameterized(name="main")
# A global random seed available for controlling the behaviour of
# parameterized objects with random state.
random_seed = 42
# Set __version__ using versioneer.
#
# Encased in a try/except to guarantee this does not interfere with
# using param (i.e. __init__.py and parameterized.py remain the only
# two necessary files).
try:
from ._version import get_versions
__version__ = get_versions()['version']
del get_versions
if __version__=='unknown':
# Think 'unknown' is what versioneer returns if unable to
# determine version info
raise ValueError
except:
main.warning("""\
Unable to determine the version of this copy of param.
For an official release, the version is stored in param/_version.py.
For a development copy, the version information is requested from Git
by param/_version.py.
""")
__version__ = 'unknown'
[docs]def produce_value(value_obj):
"""
A helper function that produces an actual parameter from a stored
object: if the object is callable, call it, otherwise return the
object.
"""
if callable(value_obj):
return value_obj()
else:
return value_obj
[docs]class Infinity(object):
"""
An instance of this class represents an infinite value. Unlike
Python's float('inf') value, this object can be safely compared
with gmpy numeric types across different gmpy versions.
All operators on Infinity() return Infinity(), apart from the
comparison and equality operators. Equality works by checking
whether the two objects are both instances of this class.
"""
def __eq__ (self,other): return isinstance(other,self.__class__)
def __ne__ (self,other): return not self==other
def __lt__ (self,other): return False
def __le__ (self,other): return False
def __gt__ (self,other): return True
def __ge__ (self,other): return True
def __add__ (self,other): return self
def __radd__(self,other): return self
def __ladd__(self,other): return self
def __sub__ (self,other): return self
def __iadd_ (self,other): return self
def __isub__(self,other): return self
def __repr__(self): return "Infinity()"
def __str__ (self): return repr(self)
[docs]class Time(Parameterized):
"""
A callable object returning a number for the current time.
Here 'time' is an abstract concept that can be interpreted in any
useful way. For instance, in a simulation, it would be the
current simulation time, while in a turn-taking game it could be
the number of moves so far. The key intended usage is to allow
independent Parameterized objects with Dynamic parameters to
remain consistent with a global reference.
The time datatype (time_type) is configurable, but should
typically be an exact numeric type like an integer or a rational,
so that small floating-point errors do not accumulate as time is
incremented repeatedly.
When used as a context manager using the 'with' statement
(implemented by the __enter__ and __exit__ special methods), entry
into a context pushes the state of the Time object, allowing the
effect of changes to the time value to be explored by setting,
incrementing or decrementing time as desired. This allows the
state of time-dependent objects to be modified temporarily as a
function of time, within the context's block. For instance, you
could use the context manager to "see into the future" to collect
data over multiple times, without affecting the global time state
once exiting the context. Of course, you need to be careful not to
do anything while in context that would affect the lasting state
of your other objects, if you want things to return to their
starting state when exiting the context.
The starting time value of a new Time object is 0, converted to
the chosen time type. Here is an illustration of how time can be
manipulated using a Time object:
>>> time = Time(until=20, timestep=1)
>>> 'The initial time is %s' % time()
'The initial time is 0'
>>> 'Setting the time to %s' % time(5)
'Setting the time to 5'
>>> time += 5
>>> 'After incrementing by 5, the time is %s' % time()
'After incrementing by 5, the time is 10'
>>> with time as t: # Entering a context
... 'Time before iteration: %s' % t()
... 'Iteration: %s' % [val for val in t]
... 'Time after iteration: %s' % t()
... t += 2
... 'The until parameter may be exceeded outside iteration: %s' % t()
'Time before iteration: 10'
'Iteration: [10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]'
'Time after iteration: 20'
'The until parameter may be exceeded outside iteration: 22'
>>> 'After exiting the context the time is back to %s' % time()
'After exiting the context the time is back to 10'
"""
_infinitely_iterable = True
forever = Infinity()
time_type = Parameter(default=int, constant=True, doc="""
Callable that Time will use to convert user-specified time
values into the current time; all times will be of the resulting
numeric type.
By default, time is of integer type, but you can supply any
arbitrary-precision type like a fixed-point decimal or a
rational, to allow fractional times. Floating-point times are
also allowed, but are not recommended because they will suffer
from accumulated rounding errors. For instance, incrementing
a floating-point value 0.0 by 0.05, 20 times, will not reach
1.0 exactly. Instead, it will be slightly higher than 1.0,
because 0.05 cannot be represented exactly in a standard
floating point numeric type. Fixed-point or rational types
should be able to handle such computations exactly, avoiding
accumulation issues over long time intervals.
Some potentially useful exact number classes::
- int: Suitable if all times can be expressed as integers.
- Python's decimal.Decimal and fractions.Fraction classes:
widely available but slow and also awkward to specify times
(e.g. cannot simply type 0.05, but have to use a special
constructor or a string).
- fixedpoint.FixedPoint: Allows a natural representation of
times in decimal notation, but very slow and needs to be
installed separately.
- gmpy.mpq: Allows a natural representation of times in
decimal notation, and very fast because it uses the GNU
Multi-Precision library, but needs to be installed
separately and depends on a non-Python library. gmpy.mpq
is gmpy's rational type.
""")
timestep = Parameter(default=1.0,doc="""
Stepsize to be used with the iterator interface.
Time can be advanced or decremented by any value, not just
those corresponding to the stepsize, and so this value is only
a default.""")
until = Parameter(default=forever,doc="""
Declaration of an expected end to time values, if any. When
using the iterator interface, iteration will end before this
value is exceeded.""")
def __init__(self, **params):
super(Time, self).__init__(**params)
self._time = self.time_type(0)
self._exhausted = None
self._pushed_state = []
def __eq__(self, other):
if not isinstance(other, Time):
return False
self_params = (self.timestep,self.until)
other_params = (other.timestep,other.until)
if self_params != other_params:
return False
return True
def __ne__(self, other):
return not (self == other)
def __iter__(self): return self
def __next__(self):
timestep = self.time_type(self.timestep)
if self._exhausted is None:
self._exhausted = False
elif (self._time + timestep) <= self.until:
self._time += timestep
else:
self._exhausted = None
raise StopIteration
return self._time
# For Python 2 compatibility; can be removed for Python 3.
next = __next__
def __call__(self, val=None, time_type=None):
"""
When called with no arguments, returns the current time value.
When called with a specified val, sets the time to it.
When called with a specified time_type, changes the time_type
and sets the current time to the given val (which *must* be
specified) converted to that time type. To ensure that
the current state remains consistent, this is normally the only
way to change the time_type of an existing Time instance.
"""
if time_type and val is None:
raise Exception("Please specify a value for the new time_type.")
if time_type:
type_param = self.params('time_type')
type_param.constant = False
self.time_type = time_type
type_param.constant = True
if val is not None:
self._time = self.time_type(val)
return self._time
def __iadd__(self, other):
self._time = self._time + self.time_type(other)
return self
def __isub__(self, other):
self._time = self._time - self.time_type(other)
return self
def __enter__(self):
"""Enter the context and push the current state."""
self._pushed_state.append((self._time, self.timestep, self.until))
self.in_context = True
return self
def __exit__(self, exc, *args):
"""
Exit from the current context, restoring the previous state.
The StopIteration exception raised in context will force the
context to exit. Any other exception exc that is raised in the
block will not be caught.
"""
(self._time, self.timestep, self.until) = self._pushed_state.pop()
self.in_context = len(self._pushed_state) != 0
if exc is StopIteration:
return True
[docs]class Dynamic(Parameter):
"""
Parameter whose value can be generated dynamically by a callable
object.
If a Parameter is declared as Dynamic, it can be set a callable
object (such as a function or callable class), and getting the
parameter's value will call that callable.
Note that at present, the callable object must allow attributes
to be set on itself.
[Python 2.4 limitation: the callable object must be an instance of a
callable class, rather than a named function or a lambda function,
otherwise the object will not be picklable or deepcopyable.]
If set as time_dependent, setting the Dynamic.time_fn allows the
production of dynamic values to be controlled: a new value will be
produced only if the current value of time_fn is different from
what it was the last time the parameter value was requested.
By default, the Dynamic parameters are not time_dependent so that
new values are generated on every call regardless of the time. The
default time_fn used when time_dependent is a single Time instance
that allows general manipulations of time. It may be set to some
other callable as required so long as a number is returned on each
call.
"""
# CB: making Dynamic support iterators and generators is sf.net
# feature request 1864370. When working on that task, note that
# detection of a dynamic generator by 'callable' needs to be
# replaced by something that matches whatever Dynamic becomes
# capable of using.
time_fn = Time()
time_dependent = False
# CBENHANCEMENT: Add an 'epsilon' slot.
# See email 'Re: simulation-time-controlled Dynamic parameters'
# Dec 22, 2007 CB->JAB
def __init__(self,**params):
"""
Call the superclass's __init__ and set instantiate=True if the
default is dynamic.
"""
super(Dynamic,self).__init__(**params)
if callable(self.default):
self._set_instantiate(True)
self._initialize_generator(self.default)
def _initialize_generator(self,gen,obj=None):
"""
Add 'last time' and 'last value' attributes to the generator.
"""
# CEBALERT: use a dictionary to hold these things.
if hasattr(obj,"_Dynamic_time_fn"):
gen._Dynamic_time_fn = obj._Dynamic_time_fn
gen._Dynamic_last = None
# CEB: I'd use None for this, except can't compare a fixedpoint
# number with None (e.g. 1>None but FixedPoint(1)>None can't be done)
gen._Dynamic_time = -1
gen._saved_Dynamic_last = []
gen._saved_Dynamic_time = []
def __get__(self,obj,objtype):
"""
Call the superclass's __get__; if the result is not dynamic
return that result, otherwise ask that result to produce a
value and return it.
"""
gen = super(Dynamic,self).__get__(obj,objtype)
if not hasattr(gen,'_Dynamic_last'):
return gen
else:
return self._produce_value(gen)
def __set__(self,obj,val):
"""
Call the superclass's set and keep this parameter's
instantiate value up to date (dynamic parameters
must be instantiated).
If val is dynamic, initialize it as a generator.
"""
super(Dynamic,self).__set__(obj,val)
dynamic = callable(val)
if dynamic: self._initialize_generator(val,obj)
if not obj: self._set_instantiate(dynamic)
def _produce_value(self,gen,force=False):
"""
Return a value from gen.
If there is no time_fn, then a new value will be returned
(i.e. gen will be asked to produce a new value).
If force is True, or the value of time_fn() is different from
what it was was last time produce_value was called, a new
value will be produced and returned. Otherwise, the last value
gen produced will be returned.
"""
if hasattr(gen,"_Dynamic_time_fn"):
time_fn = gen._Dynamic_time_fn
else:
time_fn = self.time_fn
if (time_fn is None) or (not self.time_dependent):
value = produce_value(gen)
gen._Dynamic_last = value
else:
time = time_fn()
if force or time!=gen._Dynamic_time:
value = produce_value(gen)
gen._Dynamic_last = value
gen._Dynamic_time = time
else:
value = gen._Dynamic_last
return value
def _value_is_dynamic(self,obj,objtype=None):
"""
Return True if the parameter is actually dynamic (i.e. the
value is being generated).
"""
return hasattr(super(Dynamic,self).__get__(obj,objtype),'_Dynamic_last')
def _inspect(self,obj,objtype=None):
"""Return the last generated value for this parameter."""
gen=super(Dynamic,self).__get__(obj,objtype)
if hasattr(gen,'_Dynamic_last'):
return gen._Dynamic_last
else:
return gen
def _force(self,obj,objtype=None):
"""Force a new value to be generated, and return it."""
gen=super(Dynamic,self).__get__(obj,objtype)
if hasattr(gen,'_Dynamic_last'):
return self._produce_value(gen,force=True)
else:
return gen
import numbers
def _is_number(obj):
if isinstance(obj, numbers.Number): return True
# The extra check is for classes that behave like numbers, such as those
# found in numpy, gmpy, etc.
elif (hasattr(obj, '__int__') and hasattr(obj, '__add__')): return True
# This is for older versions of gmpy
elif hasattr(obj, 'qdiv'): return True
else: return False
def identity_hook(obj,val): return val
[docs]class Number(Dynamic):
"""
A numeric Dynamic Parameter, with a default value and optional bounds.
There are two types of bounds: ``bounds`` and
``softbounds``. ``bounds`` are hard bounds: the parameter must
have a value within the specified range. The default bounds are
(None,None), meaning there are actually no hard bounds. One or
both bounds can be set by specifying a value
(e.g. bounds=(None,10) means there is no lower bound, and an upper
bound of 10). Bounds are inclusive by default, but exclusivity
can be specified for each bound by setting inclusive_bounds
(e.g. inclusive_bounds=(True,False) specifies an exclusive upper
bound).
Number is also a type of Dynamic parameter, so its value
can be set to a callable to get a dynamically generated
number (see Dynamic).
When not being dynamically generated, bounds are checked when a
Number is created or set. Using a default value outside the hard
bounds, or one that is not numeric, results in an exception. When
being dynamically generated, bounds are checked when a the value
of a Number is requested. A generated value that is not numeric,
or is outside the hard bounds, results in an exception.
As a special case, if allow_None=True (which is true by default if
the parameter has a default of None when declared) then a value
of None is also allowed.
A separate function set_in_bounds() is provided that will
silently crop the given value into the legal range, for use
in, for instance, a GUI.
``softbounds`` are present to indicate the typical range of
the parameter, but are not enforced. Setting the soft bounds
allows, for instance, a GUI to know what values to display on
sliders for the Number.
Example of creating a Number::
AB = Number(default=0.5, bounds=(None,10), softbounds=(0,1), doc='Distance from A to B.')
"""
__slots__ = ['bounds','_softbounds','allow_None','inclusive_bounds','set_hook']
def __init__(self,default=0.0,bounds=None,softbounds=None,allow_None=False,inclusive_bounds=(True,True),**params):
"""
Initialize this parameter object and store the bounds.
Non-dynamic default values are checked against the bounds.
"""
super(Number,self).__init__(default=default,**params)
self.set_hook = identity_hook
self.bounds = bounds
self.inclusive_bounds = inclusive_bounds
self._softbounds = softbounds
self.allow_None = (default is None or allow_None)
if not callable(default): self._check_value(default)
def __get__(self,obj,objtype):
"""
Same as the superclass's __get__, but if the value was
dynamically generated, check the bounds.
"""
result = super(Number,self).__get__(obj,objtype)
# CEBALERT: results in extra lookups (_value_is_dynamic() is
# also looking up 'result' - should just pass it in). Note
# that this method is called often.
if self._value_is_dynamic(obj,objtype): self._check_value(result)
return result
def __set__(self,obj,val):
"""
Set to the given value raising an exception if out of bounds.
Also applies set_hook, providing support for conversions
and transformations of the value.
"""
val = self.set_hook(obj,val)
if not callable(val): self._check_value(val)
super(Number,self).__set__(obj,val)
def set_in_bounds(self,obj,val):
"""
Set to the given value, but cropped to be within the legal bounds.
All objects are accepted, and no exceptions will be raised. See
crop_to_bounds for details on how cropping is done.
"""
if not callable(val):
bounded_val = self.crop_to_bounds(val)
else:
bounded_val = val
super(Number,self).__set__(obj,bounded_val)
# CEBERRORALERT: doesn't take account of exclusive bounds. When
# the gui uses set_in_bounds(), expecting to get acceptable
# values, it actually gets an out-of-bounds error. When fixed,
# should remove hack in
# topo.tkgui.projectionpanel.UnitsPanel.sheet_change().
def crop_to_bounds(self,val):
"""
Return the given value cropped to be within the hard bounds
for this parameter.
If a numeric value is passed in, check it is within the hard
bounds. If it is larger than the high bound, return the high
bound. If it's smaller, return the low bound. In either case, the
returned value could be None. If a non-numeric value is passed
in, set to be the default value (which could be None). In no
case is an exception raised; all values are accepted.
"""
# Currently, values outside the bounds are silently cropped to
# be inside the bounds; it may be appropriate to add a warning
# in such cases.
if _is_number(val):
if self.bounds is None:
return val
vmin, vmax = self.bounds
if vmin is not None:
if val < vmin:
return vmin
if vmax is not None:
if val > vmax:
return vmax
elif self.allow_None and val is None:
return val
else:
# non-numeric value sent in: reverts to default value
return self.default
return val
def _checkBounds(self, val):
if self.bounds is not None:
vmin,vmax = self.bounds
incmin,incmax = self.inclusive_bounds
if vmax is not None:
if incmax is True:
if not val <= vmax:
raise ValueError("Parameter '%s' must be at most %s"%(self._attrib_name,vmax))
else:
if not val < vmax:
raise ValueError("Parameter '%s' must be less than %s"%(self._attrib_name,vmax))
if vmin is not None:
if incmin is True:
if not val >= vmin:
raise ValueError("Parameter '%s' must be at least %s"%(self._attrib_name,vmin))
else:
if not val > vmin:
raise ValueError("Parameter '%s' must be greater than %s"%(self._attrib_name,vmin))
## could consider simplifying the above to something like this untested code:
## too_low = False if vmin is None else
## (val < vmin if incmin else val <= vmin) and
## (val > vmin if incmin else val <= vmin)
## too_high = ...
## if too_low or too_high:
## raise ValueError("Parameter '%s' must be in the range %s" % (self._attrib_name,self.rangestr()))
## where self.rangestr() formats the range using the usual notation for
## indicating exclusivity, e.g. "[0,10)".
def _check_value(self,val):
"""
Checks that the value is numeric and that it is within the hard
bounds; if not, an exception is raised.
"""
if self.allow_None and val is None:
return
if not _is_number(val):
raise ValueError("Parameter '%s' only takes numeric values"%(self._attrib_name))
self._checkBounds(val)
def get_soft_bounds(self):
"""
For each soft bound (upper and lower), if there is a defined bound (not equal to None)
then it is returned, otherwise it defaults to the hard bound. The hard bound could still be None.
"""
if self.bounds is None:
hl,hu=(None,None)
else:
hl,hu=self.bounds
if self._softbounds is None:
sl,su=(None,None)
else:
sl,su=self._softbounds
if sl is None: l = hl
else: l = sl
if su is None: u = hu
else: u = su
return (l,u)
[docs]class Integer(Number):
"""Numeric Parameter required to be an Integer"""
def _check_value(self,val):
if self.allow_None and val is None:
return
if not isinstance(val,int):
raise ValueError("Parameter '%s' must be an integer."%self._attrib_name)
self._checkBounds(val)
[docs]class Magnitude(Number):
"""Numeric Parameter required to be in the range [0.0-1.0]."""
def __init__(self,default=1.0,softbounds=None,**params):
Number.__init__(self,default=default,bounds=(0.0,1.0),softbounds=softbounds,**params)
# JAB: Should this and other Parameters below be a Dynamic instead?
[docs]class Boolean(Parameter):
"""Binary or tristate Boolean Parameter."""
__slots__ = ['bounds','allow_None']
# CB: what does bounds=(0,1) mean/do for this Parameter? (Maybe we meant to inherit from
# Integer?)
def __init__(self,default=False,bounds=(0,1),allow_None=False,**params):
self.bounds = bounds
self.allow_None = (default is None or allow_None)
Parameter.__init__(self,default=default,**params)
def __set__(self,obj,val):
if self.allow_None:
if not isinstance(val,bool) and val is not None:
raise ValueError("Boolean '%s' only takes a Boolean value or None."
%self._attrib_name)
if val is not True and val is not False and val is not None:
raise ValueError("Boolean '%s' must be True, False, or None."%self._attrib_name)
else:
if not isinstance(val,bool):
raise ValueError("Boolean '%s' only takes a Boolean value."%self._attrib_name)
if val is not True and val is not False:
raise ValueError("Boolean '%s' must be True or False."%self._attrib_name)
super(Boolean,self).__set__(obj,val)
[docs]class NumericTuple(Parameter):
"""A numeric tuple Parameter (e.g. (4.5,7.6,3)) with a fixed tuple length."""
__slots__ = ['length']
def __init__(self,default=(0,0),length=None,**params):
"""
Initialize a numeric tuple parameter with a fixed length
(number of elements). The length is determined by the initial
default value, and is not allowed to change after
instantiation.
"""
if length is None:
self.length = len(default)
else:
self.length = length
self._check(default)
Parameter.__init__(self,default=default,**params)
def _check(self,val):
if not isinstance(val,tuple):
raise ValueError("NumericTuple '%s' only takes a tuple value."%self._attrib_name)
if not len(val)==self.length:
raise ValueError("%s: tuple is not of the correct length (%d instead of %d)." %
(self._attrib_name,len(val),self.length))
for n in val:
if not _is_number(n):
raise ValueError("%s: tuple element is not numeric: %s." % (self._attrib_name,str(n)))
def __set__(self,obj,val):
self._check(val)
super(NumericTuple,self).__set__(obj,val)
[docs]class XYCoordinates(NumericTuple):
"""A NumericTuple for an X,Y coordinate."""
def __init__(self,default=(0.0,0.0),**params):
super(XYCoordinates,self).__init__(default=default,length=2,**params)
[docs]class Callable(Parameter):
"""
Parameter holding a value that is a callable object, such as a function.
A keyword argument instantiate=True should be provided when a
function object is used that might have state. On the other hand,
regular standalone functions cannot be deepcopied as of Python
2.4, so instantiate must be False for those values.
"""
def __set__(self,obj,val):
if not callable(val):
raise ValueError("Callable '%s' only takes a callable object."%self._attrib_name)
super(Callable,self).__set__(obj,val)
# CBNOTE: python now has abstract base classes, so we could update
# this. At least if the check is in a method, all such checks could be
# changed at once.
def _is_abstract(class_):
try:
return class_.abstract
except AttributeError:
return False
# CEBALERT: this should be a method of ClassSelector.
[docs]def concrete_descendents(parentclass):
"""
Return a dictionary containing all subclasses of the specified
parentclass, including the parentclass. Only classes that are
defined in scripts that have been run or modules that have been
imported are included, so the caller will usually first do ``from
package import *``.
Only non-abstract classes will be included.
"""
return dict((c.__name__,c) for c in descendents(parentclass)
if not _is_abstract(c))
[docs]class Composite(Parameter):
"""
A Parameter that is a composite of a set of other attributes of the class.
The constructor argument 'attribs' takes a list of attribute
names, which may or may not be Parameters. Getting the parameter
returns a list of the values of the constituents of the composite,
in the order specified. Likewise, setting the parameter takes a
sequence of values and sets the value of the constituent
attributes.
"""
__slots__=['attribs','objtype']
def __init__(self,attribs=None,**kw):
if attribs is None:
attribs = []
super(Composite,self).__init__(default=None,**kw)
self.attribs = attribs
def __get__(self,obj,objtype):
"""
Return the values of all the attribs, as a list.
"""
if not obj:
return [getattr(objtype,a) for a in self.attribs]
else:
return [getattr(obj,a) for a in self.attribs]
def __set__(self,obj,val):
"""
Set the values of all the attribs.
"""
assert len(val) == len(self.attribs),"Compound parameter '%s' got the wrong number of values (needed %d, but got %d)." % (self._attrib_name,len(self.attribs),len(val))
if not obj:
for a,v in zip(self.attribs,val):
setattr(self.objtype,a,v)
else:
for a,v in zip(self.attribs,val):
setattr(obj,a,v)
[docs]class Selector(Parameter):
"""
Parameter whose value must be chosen from a list of possibilities.
Subclasses must implement get_range().
"""
__abstract = True
def get_range(self):
raise NotImplementedError("get_range() must be implemented in subclasses.")
[docs]class ObjectSelector(Selector):
"""
Parameter whose value must be one object from a list of possible objects.
check_on_set restricts the value to be among the current list of
objects. By default, if objects are initially supplied,
check_on_set is True, whereas if no objects are initially
supplied, check_on_set is False. This can be overridden by
explicitly specifying check_on_set initially.
If check_on_set is True (either because objects are supplied
initially, or because it is explicitly specified), the default
(initial) value must be among the list of objects (unless the
default value is None).
"""
__slots__ = ['objects','compute_default_fn','check_on_set']
# ObjectSelector is usually used to allow selection from a list of
# existing objects, therefore instantiate is False by default.
def __init__(self,default=None,objects=None,instantiate=False,
compute_default_fn=None,check_on_set=None,**params):
if objects is None:
objects = []
self.objects = objects
self.compute_default_fn = compute_default_fn
if check_on_set is not None:
self.check_on_set=check_on_set
elif len(objects)==0:
self.check_on_set=False
else:
self.check_on_set=True
if default is not None and self.check_on_set is True:
self._check_value(default)
super(ObjectSelector,self).__init__(default=default,instantiate=instantiate,**params)
# CBNOTE: if the list of objects is changed, the current value for
# this parameter in existing POs could be out of the new range.
def compute_default(self):
"""
If this parameter's compute_default_fn is callable, call it
and store the result in self.default.
Also removes None from the list of objects (if the default is
no longer None).
"""
if self.default is None and callable(self.compute_default_fn):
self.default=self.compute_default_fn()
if self.default not in self.objects:
self.objects.append(self.default)
def _check_value(self,val,obj=None):
"""
val must be None or one of the objects in self.objects.
"""
if not val in self.objects:
# CEBALERT: can be called before __init__ has called
# super's __init__, i.e. before attrib_name has been set.
try:
attrib_name = self._attrib_name
except AttributeError:
attrib_name = ""
raise ValueError("%s not in Parameter %s's list of possible objects" \
%(val,attrib_name))
# CBNOTE: I think it's not helpful to do a type check for the value of
# an ObjectSelector. If we did such type checking, any user
# of this Parameter would have to be sure to update the list of possible
# objects before setting the Parameter's value. As it is, only users who care about the
# correct list of objects being displayed need to update the list.
def __set__(self,obj,val):
if self.check_on_set:
self._check_value(val,obj)
super(ObjectSelector,self).__set__(obj,val)
# CebAlert; move some bits into superclass (same for clsselector)?
def get_range(self):
"""
Return the possible objects to which this parameter could be set.
(Returns the dictionary {object.name:object}.)
"""
# CEBHACKALERT: was written assuming it would only operate on
# Parameterized instances. Think this is an sf.net bug/feature
# request. Temporary fix: don't use obj.name if unavailable.
try:
d=dict((obj.name,obj) for obj in self.objects)
except AttributeError:
d=dict((obj,obj) for obj in self.objects)
return d
[docs]class ClassSelector(Selector):
"""Parameter whose value is an instance of the specified class."""
__slots__ = ['class_','allow_None']
def __init__(self,class_,default=None,instantiate=True,allow_None=False,**params):
self.class_ = class_
self.allow_None = (default is None or allow_None)
self._check_value(default)
super(ClassSelector,self).__init__(default=default,instantiate=instantiate,**params)
def _check_value(self,val,obj=None):
"""val must be None or an instance of self.class_"""
if not (isinstance(val,self.class_)) and not (val is None and self.allow_None):
raise ValueError(
"Parameter '%s' value must be an instance of %s, not '%s'" %
(self._attrib_name, self.class_.__name__, val))
def __set__(self,obj,val):
self._check_value(val,obj)
super(ClassSelector,self).__set__(obj,val)
def get_range(self):
"""
Return the possible types for this parameter's value.
(I.e. return {name: <class>} for all classes that are
concrete_descendents() of self.class_.)
Only classes from modules that have been imported are added
(see concrete_descendents()).
"""
classes = concrete_descendents(self.class_)
d=dict((name,class_) for name,class_ in classes.items())
if self.allow_None:
d['None']=None
return d
[docs]class List(Parameter):
"""
Parameter whose value is a list of objects, usually of a specified type.
The bounds allow a minimum and/or maximum length of
list to be enforced. If the class is non-None, all
items in the list are checked to be of that type.
"""
__slots__ = ['class_','bounds']
def __init__(self,default=[],class_=None,instantiate=True,
bounds=(0,None),**params):
self.class_ = class_
self.bounds = bounds
self._check_bounds(default)
Parameter.__init__(self,default=default,instantiate=instantiate,
**params)
# Could add range() method from ClassSelector, to allow
# list to be populated in the GUI
def __set__(self,obj,val):
"""Set to the given value, raising an exception if out of bounds."""
self._check_bounds(val)
super(List,self).__set__(obj,val)
def _check_bounds(self,val):
"""
Checks that the list is of the right length and has the right contents.
Otherwise, an exception is raised.
"""
if not (isinstance(val,list)):
raise ValueError("List '%s' must be a list."%(self._attrib_name))
if self.bounds is not None:
min_length,max_length = self.bounds
l=len(val)
if min_length is not None and max_length is not None:
if not (min_length <= l <= max_length):
raise ValueError("%s: list length must be between %s and %s (inclusive)"%(self._attrib_name,min_length,max_length))
elif min_length is not None:
if not min_length <= l:
raise ValueError("%s: list length must be at least %s."%(self._attrib_name,min_length))
elif max_length is not None:
if not l <= max_length:
raise ValueError("%s: list length must be at most %s."%(self._attrib_name,max_length))
self._check_type(val)
def _check_type(self,val):
if self.class_ is not None:
for v in val:
assert isinstance(v,self.class_),repr(self._attrib_name)+": "+repr(v)+" is not an instance of " + repr(self.class_) + "."
[docs]class HookList(List):
"""
Parameter whose value is a list of callable objects.
This type of List Parameter is typically used to provide a place
for users to register a set of commands to be called at a
specified place in some sequence of processing steps.
"""
__slots__ = ['class_','bounds']
def _check_type(self,val):
for v in val:
assert callable(v),repr(self._attrib_name)+": "+repr(v)+" is not callable."
[docs]class Dict(ClassSelector):
"""
Parameter whose value is a dictionary.
"""
def __init__(self,**params):
super(Dict,self).__init__(dict,**params)
[docs]class Array(ClassSelector):
"""
Parameter whose value is a numpy array.
"""
def __init__(self, **params):
# CEBALERT: instead use python array as default?
from numpy import ndarray
super(Array,self).__init__(ndarray, allow_None=True, **params)
# For portable code:
# - specify paths in unix (rather than Windows) style;
# - use resolve_file_path() for paths to existing files to be read,
# - use resolve_folder_path() for paths to existing folders to be read,
# and normalize_path() for paths to new files to be written.
[docs]class resolve_path(ParameterizedFunction):
"""
Find the path to an existing file, searching the paths specified
in the search_paths parameter if the filename is not absolute, and
converting a UNIX-style path to the current OS's format if
necessary.
To turn a supplied relative path into an absolute one, the path is
appended to paths in the search_paths parameter, in order, until
the file is found.
An IOError is raised if the file is not found.
Similar to Python's os.path.abspath(), except more search paths
than just os.getcwd() can be used, and the file must exist.
"""
search_paths = List(default=[os.getcwd()], pickle_default_value=False, doc="""
Prepended to a non-relative path, in order, until a file is
found.""")
path_to_file = Boolean(default=True, pickle_default_value=False, doc="""
String specifying whether the path refers to a 'File' or a 'Folder'.""")
def __call__(self, path, **params):
p = ParamOverrides(self, params)
path = os.path.normpath(path)
if os.path.isabs(path):
if p.path_to_file:
if os.path.isfile(path):
return path
else:
raise IOError("File '%s' not found." %path)
elif not p.path_to_file:
if os.path.isdir(path):
return path
else:
raise IOError("Folder '%s' not found." %path)
else:
raise IOError("Type '%s' not recognised." %p.path_type)
else:
paths_tried = []
for prefix in p.search_paths:
try_path = os.path.join(os.path.normpath(prefix), path)
if p.path_to_file:
if os.path.isfile(try_path):
return try_path
elif not p.path_to_file:
if os.path.isdir(try_path):
return try_path
else:
raise IOError("Type '%s' not recognised." %p.path_type)
paths_tried.append(try_path)
raise IOError(os.path.split(path)[1] + " was not found in the following place(s): " + str(paths_tried) + ".")
[docs]class normalize_path(ParameterizedFunction):
"""
Convert a UNIX-style path to the current OS's format,
typically for creating a new file or directory.
If the path is not already absolute, it will be made absolute
(using the prefix parameter).
Should do the same as Python's os.path.abspath(), except using
prefix rather than os.getcwd).
"""
prefix = String(default=os.getcwd(),pickle_default_value=False,doc="""
Prepended to the specified path, if that path is not
absolute.""")
def __call__(self,path="",**params):
p = ParamOverrides(self,params)
if not os.path.isabs(path):
path = os.path.join(os.path.normpath(p.prefix),path)
return os.path.normpath(path)
[docs]class Path(Parameter):
"""
Parameter that can be set to a string specifying the path of a file or folder.
The string should be specified in UNIX style, but it will be
returned in the format of the user's operating system. Please use
the Filename or Foldername classes if you require discrimination
between the two possibilities.
The specified path can be absolute, or relative to either:
* any of the paths specified in the search_paths attribute (if
search_paths is not None);
or
* any of the paths searched by resolve_path() (if search_paths
is None).
"""
__slots__ = ['search_paths']
def __init__(self, default=None, search_paths=None, **params):
if search_paths is None:
search_paths = []
self.search_paths = search_paths
super(Path,self).__init__(default,**params)
def _resolve(self, path):
if self.search_paths:
return resolve_path(path, search_paths=self.search_paths)
else:
return resolve_path(path)
def __set__(self, obj, val):
"""
Call Parameter's __set__, but warn if the file cannot be found.
"""
try:
self._resolve(val)
except IOError as e:
Parameterized(name="%s.%s"%(obj.name,self._attrib_name)).warning('%s'%(e.args[0]))
super(Path,self).__set__(obj,val)
def __get__(self, obj, objtype):
"""
Return an absolute, normalized path (see resolve_path).
"""
raw_path = super(Path,self).__get__(obj,objtype)
return self._resolve(raw_path)
def __getstate__(self):
# don't want to pickle the search_paths
state = super(Path,self).__getstate__()
if 'search_paths' in state:
state['search_paths'] = []
return state
[docs]class Filename(Path):
"""
Parameter that can be set to a string specifying the path of a file.
The string should be specified in UNIX style, but it will be
returned in the format of the user's operating system.
The specified path can be absolute, or relative to either:
* any of the paths specified in the search_paths attribute (if
search_paths is not None);
or
* any of the paths searched by resolve_path() (if search_paths
is None).
"""
def _resolve(self, path):
if self.search_paths:
return resolve_path(path, path_to_file=True, search_paths=self.search_paths)
else:
return resolve_path(path, path_to_file=True)
[docs]class Foldername(Path):
"""
Parameter that can be set to a string specifying the path of a folder.
The string should be specified in UNIX style, but it will be
returned in the format of the user's operating system.
The specified path can be absolute, or relative to either:
* any of the paths specified in the search_paths attribute (if
search_paths is not None);
or
* any of the paths searched by resolve_dir_path() (if search_paths
is None).
"""
def _resolve(self, path):
if self.search_paths:
return resolve_path(path, path_to_file=False, search_paths=self.search_paths)
else:
return resolve_path(path, path_to_file=False)
