"""
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 Parameter and Parameterized
classes for more information. If you do not want to add a dependency
on external code by importing from a separately installed param
package, you can simply save this file as param.py and copy it and
parameterized.py directly into your own package.
This file contains subclasses of Parameter, implementing specific
parameter types (e.g. Number), and also imports the definition of
Parameters and Parameterized classes.
"""
import os.path
import sys
import copy
import glob
import re
import datetime as dt
import collections
from .parameterized import (
Parameterized, Parameter, String, ParameterizedFunction, ParamOverrides,
descendents, get_logger, instance_descriptor, basestring)
from .parameterized import (batch_watch, depends, output, # noqa: api import
discard_events, edit_constant)
from .parameterized import logging_level # noqa: api import
from .parameterized import shared_parameters # noqa: api import
from collections import OrderedDict
from numbers import Real
# Determine up-to-date version information, if possible, but with a
# safe fallback to ensure that this file and parameterized.py are the
# only two required files.
try:
from .version import Version
__version__ = str(Version(fpath=__file__, archive_commit="$Format:%h$", reponame="param"))
except:
__version__ = "0.0.0+unknown"
dt_types = (dt.datetime, dt.date)
try:
import numpy as np
dt_types = dt_types + (np.datetime64,)
except:
pass
try:
import collections.abc as collections_abc
except ImportError:
collections_abc = collections
if sys.version_info[0] >= 3:
unicode = str
#: 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 (integer or rational) available for controlling
# the behaviour of parameterized objects with random state.
random_seed = 42
[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]def as_unicode(obj):
"""
Safely casts any object to unicode including regular string
(i.e. bytes) types in python 2.
"""
if sys.version_info.major < 3 and isinstance(obj, str):
obj = obj.decode('utf-8')
return unicode(obj)
[docs]def is_ordered_dict(d):
"""
Predicate checking for ordered dictionaries. OrderedDict is always
ordered, and vanilla Python dictionaries are ordered for Python 3.6+
"""
py3_ordered_dicts = (sys.version_info.major == 3) and (sys.version_info.minor >= 6)
vanilla_odicts = (sys.version_info.major > 3) or py3_ordered_dicts
return isinstance(d, (OrderedDict))or (vanilla_odicts and isinstance(d, dict))
[docs]def hashable(x):
"""
Return a hashable version of the given object x, with lists and
dictionaries converted to tuples. Allows mutable objects to be
used as a lookup key in cases where the object has not actually
been mutated. Lookup will fail (appropriately) in cases where some
part of the object has changed. Does not (currently) recursively
replace mutable subobjects.
"""
if isinstance(x, collections_abc.MutableSequence):
return tuple(x)
elif isinstance(x, collections_abc.MutableMapping):
return tuple([(k,v) for k,v in x.items()])
else:
return x
[docs]def named_objs(objlist, namesdict=None):
"""
Given a list of objects, returns a dictionary mapping from
string name for the object to the object itself. Accepts
an optional name,obj dictionary, which will override any other
name if that item is present in the dictionary.
"""
objs = OrderedDict()
if namesdict is not None:
objtoname = {hashable(v): k for k, v in namesdict.items()}
for obj in objlist:
if namesdict is not None and hashable(obj) in objtoname:
k = objtoname[hashable(obj)]
elif hasattr(obj, "name"):
k = obj.name
elif hasattr(obj, '__name__'):
k = obj.__name__
else:
k = as_unicode(obj)
objs[k] = obj
return objs
[docs]def param_union(*parameterizeds, **kwargs):
"""
Given a set of Parameterized objects, returns a dictionary
with the union of all param name,value pairs across them.
If warn is True (default), warns if the same parameter has
been given multiple values; otherwise uses the last value
"""
warn = kwargs.pop('warn', True)
if len(kwargs):
raise TypeError(
"param_union() got an unexpected keyword argument '{}'".format(
kwargs.popitem()[0]))
d = dict()
for o in parameterizeds:
for k in o.param:
if k != 'name':
if k in d and warn:
get_logger().warning("overwriting parameter {}".format(k))
d[k] = getattr(o, k)
return d
[docs]def guess_param_types(**kwargs):
"""
Given a set of keyword literals, promote to the appropriate
parameter type based on some simple heuristics.
"""
params = {}
for k, v in kwargs.items():
kws = dict(default=v, constant=True)
if isinstance(v, Parameter):
params[k] = v
elif isinstance(v, dt_types):
params[k] = Date(**kws)
elif isinstance(v, bool):
params[k] = Boolean(**kws)
elif isinstance(v, int):
params[k] = Integer(**kws)
elif isinstance(v, float):
params[k] = Number(**kws)
elif isinstance(v, str):
params[k] = String(**kws)
elif isinstance(v, dict):
params[k] = Dict(**kws)
elif isinstance(v, tuple):
if all(_is_number(el) for el in v):
params[k] = NumericTuple(**kws)
elif all(isinstance(el. dt_types) for el in v) and len(v)==2:
params[k] = DateRange(**kws)
else:
params[k] = Tuple(**kws)
elif isinstance(v, list):
params[k] = List(**kws)
elif isinstance(v, np.ndarray):
params[k] = Array(**kws)
else:
from pandas import DataFrame as pdDFrame
from pandas import Series as pdSeries
if isinstance(v, pdDFrame):
params[k] = DataFrame(**kws)
elif isinstance(v, pdSeries):
params[k] = Series(**kws)
else:
params[k] = Parameter(**kws)
return params
[docs]def parameterized_class(name, params, bases=Parameterized):
"""
Dynamically create a parameterized class with the given name and the
supplied parameters, inheriting from the specified base(s).
"""
if not (isinstance(bases, list) or isinstance(bases, tuple)):
bases=[bases]
return type(name, tuple(bases), params)
[docs]def guess_bounds(params, **overrides):
"""
Given a dictionary of Parameter instances, return a corresponding
set of copies with the bounds appropriately set.
If given a set of override keywords, use those numeric tuple bounds.
"""
guessed = {}
for name, p in params.items():
new_param = copy.copy(p)
if isinstance(p, (Integer, Number)):
if name in overrides:
minv,maxv = overrides[name]
else:
minv, maxv, _ = _get_min_max_value(None, None, value=p.default)
new_param.bounds = (minv, maxv)
guessed[name] = new_param
return guessed
def _get_min_max_value(min, max, value=None, step=None):
"""Return min, max, value given input values with possible None."""
# Either min and max need to be given, or value needs to be given
if value is None:
if min is None or max is None:
raise ValueError('unable to infer range, value '
'from: ({0}, {1}, {2})'.format(min, max, value))
diff = max - min
value = min + (diff / 2)
# Ensure that value has the same type as diff
if not isinstance(value, type(diff)):
value = min + (diff // 2)
else: # value is not None
if not isinstance(value, Real):
raise TypeError('expected a real number, got: %r' % value)
# Infer min/max from value
if value == 0:
# This gives (0, 1) of the correct type
vrange = (value, value + 1)
elif value > 0:
vrange = (-value, 3*value)
else:
vrange = (3*value, -value)
if min is None:
min = vrange[0]
if max is None:
max = vrange[1]
if step is not None:
# ensure value is on a step
tick = int((value - min) / step)
value = min + tick * step
if not min <= value <= max:
raise ValueError('value must be between min and max (min={0}, value={1}, max={2})'.format(min, value, max))
return min, max, value
[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()
label= String(default='Time', doc="""
The label given to the Time object. Can be used to convey
more specific notions of time as appropriate. For instance,
the label could be 'Simulation Time' or 'Duration'.""")
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.""")
unit = String(default=None, doc="""
The units of the time dimensions. The default of None is set
as the global time function may on an arbitrary time base.
Typical values for the parameter are 'seconds' (the SI unit
for time) or subdivisions thereof (e.g. 'milliseconds').""")
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.param.objects('existing').get('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 advance(self, val):
self += val
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.
"""
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)
@instance_descriptor
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 obj is None: 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 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','inclusive_bounds','set_hook', 'step']
def __init__(self,default=0.0,bounds=None,softbounds=None,
inclusive_bounds=(True,True), step=None, **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.step = step
self._validate(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._validate(result)
return result
# Allow softbounds to be used like a normal attribute, as it
# probably should have been already (not _softbounds)
@property
def softbounds(self): return self._softbounds
@softbounds.setter
def softbounds(self,value): self._softbounds = value
[docs] 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; see
# https://github.com/ioam/param/issues/80.
[docs] 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
# Could simplify: see https://github.com/ioam/param/issues/83
if vmax is not None:
if incmax is True:
if not val <= vmax:
raise ValueError("Parameter '%s' must be at most %s"%(self.name,vmax))
else:
if not val < vmax:
raise ValueError("Parameter '%s' must be less than %s"%(self.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.name,vmin))
else:
if not val > vmin:
raise ValueError("Parameter '%s' must be greater than %s"%(self.name,vmin))
def _validate(self, val):
"""
Checks that the value is numeric and that it is within the hard
bounds; if not, an exception is raised.
"""
if callable(val):
return val
if self.allow_None and val is None:
return
if not _is_number(val):
raise ValueError("Parameter '%s' only takes numeric values"%(self.name))
if self.step is not None and not _is_number(self.step):
raise ValueError("Step parameter can only be None or a numeric value")
self._checkBounds(val)
[docs] 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)
def __setstate__(self,state):
if 'step' not in state:
state['step'] = None
super(Number,self).__setstate__(state)
[docs]class Integer(Number):
"""Numeric Parameter required to be an Integer"""
def __init__(self,default=0,**params):
Number.__init__(self,default=default,**params)
def _validate(self, val):
if callable(val): return
if self.allow_None and val is None:
return
if not isinstance(val,int):
raise ValueError("Parameter '%s' must be an integer."%self.name)
if self.step is not None and not isinstance(self.step, int):
raise ValueError("Step parameter can only be None or an integer value")
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)
[docs]class Boolean(Parameter):
"""Binary or tristate Boolean Parameter."""
__slots__ = ['bounds']
# CB: bounds have no effect; see https://github.com/ioam/param/issues/82
def __init__(self,default=False,bounds=(0,1),**params):
self.bounds = bounds
super(Boolean, self).__init__(default=default,**params)
def _validate(self, 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.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.name)
else:
if not isinstance(val,bool):
raise ValueError("Boolean '%s' only takes a Boolean value."%self.name)
if val is not True and val is not False:
raise ValueError("Boolean '%s' must be True or False."%self.name)
super(Boolean, self)._validate(val)
[docs]class Tuple(Parameter):
"""A tuple Parameter (e.g. ('a',7.6,[3,5])) with a fixed tuple length."""
__slots__ = ['length']
def __init__(self,default=(0,0),length=None,**params):
"""
Initialize a tuple parameter with a fixed length (number of
elements). The length is determined by the initial default
value, if any, and must be supplied explicitly otherwise. The
length is not allowed to change after instantiation.
"""
super(Tuple,self).__init__(default=default,**params)
if length is None and default is not None:
self.length = len(default)
elif length is None and default is None:
raise ValueError("%s: length must be specified if no default is supplied." %
(self.name))
else:
self.length = length
self._validate(default)
def _validate(self, val):
if val is None and self.allow_None:
return
if not isinstance(val,tuple):
raise ValueError("Tuple '%s' only takes a tuple value."%self.name)
if not len(val)==self.length:
raise ValueError("%s: tuple is not of the correct length (%d instead of %d)." %
(self.name,len(val),self.length))
[docs]class NumericTuple(Tuple):
"""A numeric tuple Parameter (e.g. (4.5,7.6,3)) with a fixed tuple length."""
def _validate(self, val):
super(NumericTuple, self)._validate(val)
if not (self.allow_None and val is None):
for n in val:
if not _is_number(n):
raise ValueError("%s: tuple element is not numeric: %s." %
(self.name,str(n)))
[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 _validate(self, val):
if not (self.allow_None and val is None) and (not callable(val)):
raise ValueError("Callable '%s' only takes a callable object."%self.name)
super(Callable, self)._validate(val)
[docs]class Action(Callable):
"""
A user-provided function that can be invoked like a class or object method using ().
In a GUI, this might be mapped to a button, but it can be invoked directly as well.
"""
# Currently same implementation as Callable, but kept separate to allow different handling in GUIs
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 obj is None:
return [getattr(objtype,a) for a in self.attribs]
else:
return [getattr(obj,a) for a in self.attribs]
def _validate(self, val):
assert len(val) == len(self.attribs),"Compound parameter '%s' got the wrong number of values (needed %d, but got %d)." % (self.name,len(self.attribs),len(val))
def _post_setter(self, obj, val):
if obj is None:
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 SelectorBase(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(SelectorBase):
"""
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).
The list of objects can be supplied as a list (appropriate for
selecting among a set of strings, or among a set of objects with a
"name" parameter), or as a (preferably ordered) dictionary from
names to objects. If a dictionary is supplied, the objects
will need to be hashable so that their names can be looked
up from the object value.
"""
__slots__ = ['objects','compute_default_fn','check_on_set','names']
# 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,allow_None=None,**params):
if objects is None:
objects = []
if isinstance(objects, collections_abc.Mapping):
self.names = objects
self.objects = list(objects.values())
else:
self.names = None
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
super(ObjectSelector,self).__init__(default=default,instantiate=instantiate,
**params)
# Required as Parameter sets allow_None=True if default is None
self.allow_None = allow_None
if default is not None and self.check_on_set is True:
self._validate(default)
# CBNOTE: if the list of objects is changed, the current value for
# this parameter in existing POs could be out of the new range.
[docs] 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 _validate(self, val):
"""
val must be None or one of the objects in self.objects.
"""
if not self.check_on_set:
self._ensure_value_is_in_objects(val)
return
if not (val in self.objects or (self.allow_None and val is None)):
# CEBALERT: can be called before __init__ has called
# super's __init__, i.e. before attrib_name has been set.
try:
attrib_name = self.name
except AttributeError:
attrib_name = ""
items = []
limiter = ']'
length = 0
for item in self.objects:
string = str(item)
length += len(string)
if length < 200:
items.append(string)
else:
limiter = ', ...]'
break
items = '[' + ', '.join(items) + limiter
raise ValueError("%s not in Parameter %s's list of possible objects, "
"valid options include %s"%(val,attrib_name, items))
def _ensure_value_is_in_objects(self,val):
"""
Make sure that the provided value is present on the objects list.
Subclasses can override if they support multiple items on a list,
to check each item instead.
"""
if not (val in self.objects):
self.objects.append(val)
[docs] def get_range(self):
"""
Return the possible objects to which this parameter could be set.
(Returns the dictionary {object.name:object}.)
"""
return named_objs(self.objects, self.names)
[docs]class Selector(ObjectSelector):
"""
A more user friendly ObjectSelector that picks the first object for
the default (by default) given an ordered data collection. As the
first argument is now objects, this can be passed in as a positional
argument which sufficient in many common use cases.
"""
def __init__(self,objects=None, default=None, instantiate=False,
compute_default_fn=None,check_on_set=None,allow_None=None,**params):
if is_ordered_dict(objects):
autodefault = list(objects.values())[0]
elif isinstance(objects, dict):
main.param.warning("Parameter default value is arbitrary due to "
"dictionaries prior to Python 3.6 not being "
"ordered; should use an ordered dict or "
"supply an explicit default value.")
autodefault = list(objects.values())[0]
elif isinstance(objects, list):
autodefault = objects[0]
else:
autodefault = None
default = autodefault if default is None else default
super(Selector,self).__init__(default=default, objects=objects,
instantiate=instantiate,
compute_default_fn=compute_default_fn,
check_on_set=check_on_set,
allow_None=allow_None, **params)
[docs]class ClassSelector(SelectorBase):
"""
Parameter whose value is a specified class or an instance of that class.
By default, requires an instance, but if is_instance=False, accepts a class instead.
Both class and instance values respect the instantiate slot, though it matters only
for is_instance=True.
"""
__slots__ = ['class_','is_instance']
def __init__(self,class_,default=None,instantiate=True,is_instance=True,**params):
self.class_ = class_
self.is_instance = is_instance
super(ClassSelector,self).__init__(default=default,instantiate=instantiate,**params)
self._validate(default)
def _validate(self,val):
"""val must be None, an instance of self.class_ if self.is_instance=True or a subclass of self_class if self.is_instance=False"""
if isinstance(self.class_, tuple):
class_name = ('(%s)' % ', '.join(cl.__name__ for cl in self.class_))
else:
class_name = self.class_.__name__
if self.is_instance:
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.name, class_name, val))
else:
if not (val is None and self.allow_None) and not (issubclass(val,self.class_)):
raise ValueError(
"Parameter '%s' must be a subclass of %s, not '%s'" %
(val.__name__, class_name, val.__class__.__name__))
[docs] 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=OrderedDict((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
Parameter.__init__(self,default=default,instantiate=instantiate,
**params)
self._validate(default)
def _validate(self, val):
"""
Checks that the list is of the right length and has the right contents.
Otherwise, an exception is raised.
"""
if self.allow_None and val is None:
return
if not isinstance(val, list):
raise ValueError("List '%s' must be a list."%(self.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.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.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.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.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.name)+": "+repr(v)+" is not callable."
[docs]class Dict(ClassSelector):
"""
Parameter whose value is a dictionary.
"""
def __init__(self, default=None, **params):
super(Dict,self).__init__(dict, default=default, **params)
[docs]class Array(ClassSelector):
"""
Parameter whose value is a numpy array.
"""
def __init__(self, default=None, **params):
# CEBALERT: instead use python array as default?
from numpy import ndarray
super(Array,self).__init__(ndarray, allow_None=True, default=default, **params)
[docs]class DataFrame(ClassSelector):
"""
Parameter whose value is a pandas DataFrame.
The structure of the DataFrame can be constrained by the rows and
columns arguments:
rows: If specified, may be a number or an integer bounds tuple to
constrain the allowable number of rows.
columns: If specified, may be a number, an integer bounds tuple, a
list or a set. If the argument is numeric, constrains the number of
columns using the same semantics as used for rows. If either a list
or set of strings, the column names will be validated. If a set is
used, the supplied DataFrame must contain the specified columns and
if a list is given, the supplied DataFrame must contain exactly the
same columns and in the same order and no other columns.
"""
__slots__ = ['rows','columns', 'ordered']
def __init__(self, default=None, rows=None, columns=None, ordered=None, **params):
from pandas import DataFrame as pdDFrame
self.rows = rows
self.columns = columns
self.ordered = ordered
super(DataFrame,self).__init__(pdDFrame, default=default, allow_None=True, **params)
self._validate(self.default)
def _length_bounds_check(self, bounds, length, name):
message = '{name} length {length} does not match declared bounds of {bounds}'
if not isinstance(bounds, tuple):
if (bounds != length):
raise ValueError(message.format(name=name, length=length, bounds=bounds))
else:
return
(lower, upper) = bounds
failure = ((lower is not None and (length < lower))
or (upper is not None and length > upper))
if failure:
raise ValueError(message.format(name=name,length=length, bounds=bounds))
def _validate(self, val):
super(DataFrame, self)._validate(val)
if isinstance(self.columns, set) and self.ordered is True:
raise ValueError('Columns cannot be ordered when specified as a set')
if self.columns is None:
pass
elif (isinstance(self.columns, tuple) and len(self.columns)==2
and all(isinstance(v, (type(None), numbers.Number)) for v in self.columns)): # Numeric bounds tuple
self._length_bounds_check(self.columns, len(val.columns), 'Columns')
elif isinstance(self.columns, (list, set)):
self.ordered = isinstance(self.columns, list) if self.ordered is None else self.ordered
difference = set(self.columns) - set([str(el) for el in val.columns])
if difference:
msg = 'Provided DataFrame columns {found} does not contain required columns {expected}'
raise ValueError(msg.format(found=list(val.columns), expected=sorted(self.columns)))
else:
self._length_bounds_check(self.columns, len(val.columns), 'Column')
if self.ordered:
if list(val.columns) != list(self.columns):
msg = 'Provided DataFrame columns {found} must exactly match {expected}'
raise ValueError(msg.format(found=list(val.columns), expected=self.columns))
if self.rows is not None:
self._length_bounds_check(self.rows, len(val), 'Row')
[docs]class Series(ClassSelector):
"""
Parameter whose value is a pandas Series.
The structure of the Series can be constrained by the rows argument
which may be a number or an integer bounds tuple to constrain the
allowable number of rows.
"""
__slots__ = ['rows']
def _length_bounds_check(self, bounds, length, name):
message = '{name} length {length} does not match declared bounds of {bounds}'
if not isinstance(bounds, tuple):
if (bounds != length):
raise ValueError(message.format(name=name, length=length, bounds=bounds))
else:
return
(lower, upper) = bounds
failure = ((lower is not None and (length < lower))
or (upper is not None and length > upper))
if failure:
raise ValueError(message.format(name=name,length=length, bounds=bounds))
def __init__(self, default=None, rows=None, **params):
from pandas import Series as pdSeries
self.rows = rows
super(Series,self).__init__(pdSeries, allow_None=True, default=default, **params)
self._validate(self.default)
def _validate(self, val):
super(Series, self)._validate(val)
if self.rows is not None:
self._length_bounds_check(self.rows, len(val), 'Row')
# 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 _validate(self, val):
if val is None:
if not self.allow_None:
Parameterized(name="%s.%s"%(self.owner.name,self.name)).warning('None is not allowed')
else:
try:
self._resolve(val)
except IOError as e:
Parameterized(name="%s.%s"%(self.owner.name,self.name)).warning('%s',e.args[0])
def __get__(self, obj, objtype):
"""
Return an absolute, normalized path (see resolve_path).
"""
raw_path = super(Path,self).__get__(obj,objtype)
return None if raw_path is None else 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)
[docs]def abbreviate_paths(pathspec,named_paths):
"""
Given a dict of (pathname,path) pairs, removes any prefix shared by all pathnames.
Helps keep menu items short yet unambiguous.
"""
from os.path import commonprefix, dirname, sep
prefix = commonprefix([dirname(name)+sep for name in named_paths.keys()]+[pathspec])
return OrderedDict([(name[len(prefix):],path) for name,path in named_paths.items()])
[docs]class FileSelector(ObjectSelector):
"""
Given a path glob, allows one file to be selected from those matching.
"""
__slots__ = ['path']
def __init__(self, default=None, path="", **kwargs):
super(FileSelector, self).__init__(default, **kwargs)
self.path = path
self.update()
def update(self):
self.objects = sorted(glob.glob(self.path))
if self.default in self.objects:
return
self.default = self.objects[0] if self.objects else None
[docs] def get_range(self):
return abbreviate_paths(self.path,super(FileSelector, self).get_range())
[docs]class ListSelector(ObjectSelector):
"""
Variant of ObjectSelector where the value can be multiple objects from
a list of possible objects.
"""
[docs] def compute_default(self):
if self.default is None and callable(self.compute_default_fn):
self.default = self.compute_default_fn()
for o in self.default:
if o not in self.objects:
self.objects.append(o)
def _validate(self, val):
for o in val:
super(ListSelector, self)._validate(o)
[docs]class MultiFileSelector(ListSelector):
"""
Given a path glob, allows multiple files to be selected from the list of matches.
"""
__slots__ = ['path']
def __init__(self, default=None, path="", **kwargs):
super(MultiFileSelector, self).__init__(default, **kwargs)
self.path = path
self.update()
def update(self):
self.objects = sorted(glob.glob(self.path))
if self.default and all([o in self.objects for o in self.default]):
return
self.default = self.objects
[docs] def get_range(self):
return abbreviate_paths(self.path,super(MultiFileSelector, self).get_range())
[docs]class Date(Number):
"""
Date parameter of datetime or date type.
"""
def __init__(self, default=None, **kwargs):
super(Date, self).__init__(default=default, **kwargs)
def _validate(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 isinstance(val, dt_types) and not (self.allow_None and val is None):
raise ValueError("Date '%s' only takes datetime and date types."%self.name)
if self.step is not None and not isinstance(self.step, dt_types):
raise ValueError("Step parameter can only be None, a datetime or datetime type")
self._checkBounds(val)
[docs]class CalendarDate(Number):
"""
CalendarDate parameter of date type.
"""
def __init__(self, default=None, **kwargs):
super(CalendarDate, self).__init__(default=default, **kwargs)
def _validate(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 isinstance(val, dt.date) and not (self.allow_None and val is None):
raise ValueError("CalendarDate '%s' only takes datetime types."%self.name)
if self.step is not None and not isinstance(self.step, dt.date):
raise ValueError("Step parameter can only be None or a date type")
self._checkBounds(val)
[docs]class Color(Parameter):
"""
Color parameter defined as a hex RGB string with an optional #
prefix.
"""
def __init__(self, default=None, allow_None=False, **kwargs):
super(Color, self).__init__(default=default, **kwargs)
self._validate(default)
def _validate(self, val):
if (self.allow_None and val is None):
return
if not isinstance(val, basestring):
raise ValueError("Color '%s' only takes a string value."%self.name)
if not re.match('^#?(([0-9a-fA-F]{2}){3}|([0-9a-fA-F]){3})$', val):
raise ValueError("Color '%s' only accepts valid RGB hex codes."
% self.name)
[docs]class Range(NumericTuple):
"A numeric range with optional bounds and softbounds"
__slots__ = ['bounds', 'inclusive_bounds', 'softbounds']
def __init__(self,default=None, bounds=None, softbounds=None,
inclusive_bounds=(True,True), **params):
self.bounds = bounds
self.inclusive_bounds = inclusive_bounds
self.softbounds = softbounds
super(Range,self).__init__(default=default,length=2,**params)
def _validate(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
super(Range, self)._validate(val)
self._checkBounds(val)
[docs] 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)
def rangestr(self):
vmin, vmax = self.bounds
incmin, incmax = self.inclusive_bounds
incmin = '[' if incmin else '('
incmax = ']' if incmax else ')'
return '%s%s, %s%s' % (incmin, vmin, vmax, incmax)
def _checkBounds(self, val):
if self.bounds is not None:
vmin,vmax = self.bounds
incmin,incmax = self.inclusive_bounds
for bound, v in zip(['lower', 'upper'], val):
too_low = (vmin is not None) and (v < vmin if incmin else v <= vmin)
too_high = (vmax is not None) and (v > vmax if incmax else v >= vmax)
if too_low or too_high:
raise ValueError("Parameter '%s' %s bound must be in range %s"
% (self.name, bound, self.rangestr()))
[docs]class DateRange(Range):
"""
A datetime or date range specified as (start, end).
Bounds must be specified as datetime or date types (see param.dt_types).
"""
def _validate(self, val):
if self.allow_None and val is None:
return
for n in val:
if not isinstance(n, dt_types):
raise ValueError("DateRange '%s' only takes datetime types: %s"%(self.name,val))
start, end = val
if not end >= start:
raise ValueError("DateRange '%s': end datetime %s is before start datetime %s."%(self.name,val[1],val[0]))
# Calling super(DateRange, self)._check(val) would also check
# values are numeric, which is redundant, so just call
# _checkBounds().
self._checkBounds(val)
[docs]class CalendarDateRange(Range):
"""
A date range specified as (start_date, end_date).
"""
def _validate(self, val):
if self.allow_None and val is None:
return
for n in val:
if not isinstance(n, dt.date):
raise ValueError("CalendarDateRange '%s' only takes date types: %s"%(self.name,val))
start, end = val
if not end >= start:
raise ValueError("CalendarDateRange '%s': end date %s is before start date %s."%(self.name,val[1],val[0]))
# Calling super(CalendarDateRange, self)._check(val) would also check
# values are numeric, which is redundant, so just call
# _checkBounds().
self._checkBounds(val)
