""" 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 collections import copy import datetime as dt import glob import inspect import numbers import os.path import pathlib import re import sys import typing import warnings from collections import OrderedDict from contextlib import contextmanager from .parameterized import ( Parameterized, Parameter, ParameterizedFunction, ParamOverrides, String, Undefined, get_logger, instance_descriptor, dt_types, _int_types, _identity_hook ) from ._utils import ( ParamDeprecationWarning as _ParamDeprecationWarning, _deprecate_positional_args, _deprecated, _dict_update, _validate_error_prefix, _deserialize_from_path, _named_objs, _produce_value, _get_min_max_value, _is_number, concrete_descendents, _abbreviate_paths, _to_datetime, ) #----------------------------------------------------------------------------- # Utilities #----------------------------------------------------------------------------- def param_union(*parameterizeds, warn=True): """ Given a set of Parameterized objects, returns a dictionary with the union of all param name,value pairs across them. Parameters ---------- warn : bool, optional Wether to warn if the same parameter have been given multiple values, otherwise use the last value, by default True Returns ------- dict Union of all param name,value pairs """ d = {} for o in parameterizeds: for k in o.param: if k != 'name': if k in d and warn: get_logger().warning(f"overwriting parameter {k}") d[k] = getattr(o, k) return d 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) else: if 'numpy' in sys.modules: from numpy import ndarray if isinstance(v, ndarray): params[k] = Array(**kws) continue if 'pandas' in sys.modules: from pandas import ( DataFrame as pdDFrame, Series as pdSeries ) if isinstance(v, pdDFrame): params[k] = DataFrame(**kws) continue elif isinstance(v, pdSeries): params[k] = Series(**kws) continue params[k] = Parameter(**kws) return params 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) 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_soft_bounds(bounds, softbounds): """ For each soft bound (upper and lower), if there is a defined bound (not equal to None) and does not exceed the hard bound, then it is returned. Otherwise it defaults to the hard bound. The hard bound could still be None. """ if bounds is None: hl, hu = (None, None) else: hl, hu = bounds if softbounds is None: sl, su = (None, None) else: sl, su = softbounds if sl is None or (hl is not None and slhu): u = hu else: u = su return (l, u) class Infinity: """ 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) 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().__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 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 #----------------------------------------------------------------------------- # Parameters #----------------------------------------------------------------------------- #----------------------------------------------------------------------------- # Dynamic/Number #----------------------------------------------------------------------------- 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. 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 @typing.overload def __init__( self, default=None, *, doc=None, label=None, precedence=None, instantiate=False, constant=False, readonly=False, pickle_default_value=True, allow_None=False, per_instance=True, allow_refs=False, nested_refs=False ): ... def __init__(self, default=Undefined, **params): """ Call the superclass's __init__ and set instantiate=True if the default is dynamic. """ super().__init__(default=default, **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. """ # Could 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 # Would have usede None for this, but 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().__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().__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().__get__(obj,objtype),'_Dynamic_last') def _inspect(self,obj,objtype=None): """Return the last generated value for this parameter.""" gen=super().__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().__get__(obj,objtype) if hasattr(gen,'_Dynamic_last'): return self._produce_value(gen,force=True) else: return gen class __compute_set_hook: """Remove when set_hook is removed""" def __call__(self, p): return _identity_hook def __repr__(self): return repr(self.sig) @property def sig(self): return None _compute_set_hook = __compute_set_hook() 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'] _slot_defaults = _dict_update( Dynamic._slot_defaults, default=0.0, bounds=None, softbounds=None, inclusive_bounds=(True,True), step=None, set_hook=_compute_set_hook, ) @typing.overload def __init__( self, default=0.0, *, bounds=None, softbounds=None, inclusive_bounds=(True,True), step=None, set_hook=None, allow_None=False, doc=None, label=None, precedence=None, instantiate=False, constant=False, readonly=False, pickle_default_value=True, per_instance=True, allow_refs=False, nested_refs=False ): ... @_deprecate_positional_args def __init__(self, default=Undefined, *, bounds=Undefined, softbounds=Undefined, inclusive_bounds=Undefined, step=Undefined, set_hook=Undefined, **params): """ Initialize this parameter object and store the bounds. Non-dynamic default values are checked against the bounds. """ super().__init__(default=default, **params) self.set_hook = set_hook self.bounds = bounds self.inclusive_bounds = inclusive_bounds self.softbounds = softbounds self.step = step self._validate(self.default) def __get__(self, obj, objtype): """ Same as the superclass's __get__, but if the value was dynamically generated, check the bounds. """ result = super().__get__(obj, objtype) # Should be able to optimize this commonly used method by # avoiding extra lookups (e.g. _value_is_dynamic() is also # looking up 'result' - should just pass it in). if self._value_is_dynamic(obj, objtype): self._validate(result) return result 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().__set__(obj, bounded_val) 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. As documented in https://github.com/holoviz/param/issues/80, currently does not respect exclusive bounds, which would strictly require setting to one less for integer values or an epsilon less for floats. """ # Values outside the bounds are silently cropped to # be inside the bounds. 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 _validate_bounds(self, val, bounds, inclusive_bounds): if bounds is None or (val is None and self.allow_None) or callable(val): return vmin, vmax = bounds incmin, incmax = inclusive_bounds if vmax is not None: if incmax is True: if not val <= vmax: raise ValueError( f"{_validate_error_prefix(self)} must be at most " f"{vmax}, not {val}." ) else: if not val < vmax: raise ValueError( f"{_validate_error_prefix(self)} must be less than " f"{vmax}, not {val}." ) if vmin is not None: if incmin is True: if not val >= vmin: raise ValueError( f"{_validate_error_prefix(self)} must be at least " f"{vmin}, not {val}." ) else: if not val > vmin: raise ValueError( f"{_validate_error_prefix(self)} must be greater than " f"{vmin}, not {val}." ) def _validate_value(self, val, allow_None): if (allow_None and val is None) or (callable(val) and not inspect.isgeneratorfunction(val)): return if not _is_number(val): raise ValueError( f"{_validate_error_prefix(self)} only takes numeric values, " f"not {type(val)}." ) def _validate_step(self, val, step): if step is not None and not _is_number(step): raise ValueError( f"{_validate_error_prefix(self, 'step')} can only be " f"None or a numeric value, not {type(step)}." ) def _validate(self, val): """ Checks that the value is numeric and that it is within the hard bounds; if not, an exception is raised. """ self._validate_value(val, self.allow_None) self._validate_step(val, self.step) self._validate_bounds(val, self.bounds, self.inclusive_bounds) def get_soft_bounds(self): return get_soft_bounds(self.bounds, self.softbounds) def __setstate__(self,state): if 'step' not in state: state['step'] = None super().__setstate__(state) class Integer(Number): """Numeric Parameter required to be an Integer""" _slot_defaults = _dict_update(Number._slot_defaults, default=0) def _validate_value(self, val, allow_None): if callable(val): return if allow_None and val is None: return if not isinstance(val, _int_types): raise ValueError( f"{_validate_error_prefix(self)} must be an integer, " f"not {type(val)}." ) def _validate_step(self, val, step): if step is not None and not isinstance(step, int): raise ValueError( f"{_validate_error_prefix(self, 'step')} can only be " f"None or an integer value, not {type(step)}." ) class Magnitude(Number): """Numeric Parameter required to be in the range [0.0-1.0].""" _slot_defaults = _dict_update(Number._slot_defaults, default=1.0, bounds=(0.0,1.0)) @typing.overload def __init__( self, default=1.0, *, bounds=(0.0, 1.0), softbounds=None, inclusive_bounds=(True,True), step=None, set_hook=None, allow_None=False, doc=None, label=None, precedence=None, instantiate=False, constant=False, readonly=False, pickle_default_value=True, per_instance=True, allow_refs=False, nested_refs=False ): ... def __init__(self, default=Undefined, *, bounds=Undefined, softbounds=Undefined, inclusive_bounds=Undefined, step=Undefined, set_hook=Undefined, **params): super().__init__( default=default, bounds=bounds, softbounds=softbounds, inclusive_bounds=inclusive_bounds, step=step, set_hook=set_hook, **params ) class Date(Number): """ Date parameter of datetime or date type. """ _slot_defaults = _dict_update(Number._slot_defaults, default=None) @typing.overload def __init__( self, default=None, *, bounds=None, softbounds=None, inclusive_bounds=(True,True), step=None, set_hook=None, doc=None, label=None, precedence=None, instantiate=False, constant=False, readonly=False, pickle_default_value=True, allow_None=False, per_instance=True, allow_refs=False, nested_refs=False ): ... def __init__(self, default=Undefined, **kwargs): super().__init__(default=default, **kwargs) def _validate_value(self, val, allow_None): """ 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 (allow_None and val is None): raise ValueError( f"{_validate_error_prefix(self)} only takes datetime and " f"date types, not {type(val)}." ) def _validate_step(self, val, step): if step is not None and not isinstance(step, dt_types): raise ValueError( f"{_validate_error_prefix(self, 'step')} can only be None, " f"a datetime or date type, not {type(step)}." ) def _validate_bounds(self, val, bounds, inclusive_bounds): val = _to_datetime(val) bounds = None if bounds is None else map(_to_datetime, bounds) return super()._validate_bounds(val, bounds, inclusive_bounds) @classmethod def serialize(cls, value): if value is None: return None if not isinstance(value, (dt.datetime, dt.date)): # i.e np.datetime64 value = value.astype(dt.datetime) return value.strftime("%Y-%m-%dT%H:%M:%S.%f") @classmethod def deserialize(cls, value): if value == 'null' or value is None: return None return dt.datetime.strptime(value, "%Y-%m-%dT%H:%M:%S.%f") class CalendarDate(Number): """ Parameter specifically allowing dates (not datetimes). """ _slot_defaults = _dict_update(Number._slot_defaults, default=None) @typing.overload def __init__( self, default=None, *, bounds=None, softbounds=None, inclusive_bounds=(True,True), step=None, set_hook=None, doc=None, label=None, precedence=None, instantiate=False, constant=False, readonly=False, pickle_default_value=True, allow_None=False, per_instance=True, allow_refs=False, nested_refs=False ): ... def __init__(self, default=Undefined, **kwargs): super().__init__(default=default, **kwargs) def _validate_value(self, val, allow_None): """ 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) or isinstance(val, dt.datetime)) and not (allow_None and val is None): raise ValueError( f"{_validate_error_prefix(self)} only takes date types." ) def _validate_step(self, val, step): if step is not None and not isinstance(step, dt.date): raise ValueError( f"{_validate_error_prefix(self, 'step')} can only be None or " f"a date type, not {type(step)}." ) @classmethod def serialize(cls, value): if value is None: return None return value.strftime("%Y-%m-%d") @classmethod def deserialize(cls, value): if value == 'null' or value is None: return None return dt.datetime.strptime(value, "%Y-%m-%d").date() #----------------------------------------------------------------------------- # Boolean #----------------------------------------------------------------------------- class Boolean(Parameter): """Binary or tristate Boolean Parameter.""" _slot_defaults = _dict_update(Parameter._slot_defaults, default=False) @typing.overload def __init__( self, default=False, *, allow_None=False, doc=None, label=None, precedence=None, instantiate=False, constant=False, readonly=False, pickle_default_value=True, per_instance=True, allow_refs=False, nested_refs=False ): ... @_deprecate_positional_args def __init__(self, default=Undefined, **params): super().__init__(default=default, **params) self._validate(self.default) def _validate_value(self, val, allow_None): if allow_None: if not isinstance(val, bool) and val is not None: raise ValueError( f"{_validate_error_prefix(self)} only takes a " f"boolean value or None, not {val!r}." ) elif not isinstance(val, bool): raise ValueError( f"{_validate_error_prefix(self)} must be True or False, " f"not {val!r}." ) def _validate(self, val): self._validate_value(val, self.allow_None) class Event(Boolean): """ An Event Parameter is one whose value is intimately linked to the triggering of events for watchers to consume. Event has a Boolean value, which when set to True triggers the associated watchers (as any Parameter does) and then is automatically set back to False. Conversely, if events are triggered directly via `.trigger`, the value is transiently set to True (so that it's clear which of many parameters being watched may have changed), then restored to False when the triggering completes. An Event parameter is thus like a momentary switch or pushbutton with a transient True value that serves only to launch some other action (e.g. via a param.depends decorator), rather than encapsulating the action itself as param.Action does. """ # _autotrigger_value specifies the value used to set the parameter # to when the parameter is supplied to the trigger method. This # value change is then what triggers the watcher callbacks. __slots__ = ['_autotrigger_value', '_mode', '_autotrigger_reset_value'] @typing.overload def __init__( self, default=False, *, allow_None=False, doc=None, label=None, precedence=None, instantiate=False, constant=False, readonly=False, pickle_default_value=True, per_instance=True, allow_refs=False, nested_refs=False ): ... @_deprecate_positional_args def __init__(self,default=False,**params): self._autotrigger_value = True self._autotrigger_reset_value = False self._mode = 'set-reset' # Mode can be one of 'set', 'set-reset' or 'reset' # 'set' is normal Boolean parameter behavior when set with a value. # 'set-reset' temporarily sets the parameter (which triggers # watching callbacks) but immediately resets the value back to # False. # 'reset' applies the reset from True to False without # triggering watched callbacks # This _mode attribute is one of the few places where a specific # parameter has a special behavior that is relied upon by the # core functionality implemented in # parameterized.py. Specifically, the set_param method # temporarily sets this attribute in order to disable resetting # back to False while triggered callbacks are executing super().__init__(default=default,**params) def _reset_event(self, obj, val): val = False if obj is None: self.default = val else: obj._param__private.values[self.name] = val self._post_setter(obj, val) @instance_descriptor def __set__(self, obj, val): if self._mode in ['set-reset', 'set']: super().__set__(obj, val) if self._mode in ['set-reset', 'reset']: self._reset_event(obj, val) #----------------------------------------------------------------------------- # Tuple #----------------------------------------------------------------------------- class __compute_length_of_default: def __call__(self, p): return len(p.default) def __repr__(self): return repr(self.sig) @property def sig(self): return None _compute_length_of_default = __compute_length_of_default() class Tuple(Parameter): """A tuple Parameter (e.g. ('a',7.6,[3,5])) with a fixed tuple length.""" __slots__ = ['length'] _slot_defaults = _dict_update(Parameter._slot_defaults, default=(0,0), length=_compute_length_of_default) @typing.overload def __init__( self, default=(0,0), *, length=None, doc=None, label=None, precedence=None, instantiate=False, constant=False, readonly=False, pickle_default_value=True, allow_None=False, per_instance=True, allow_refs=False, nested_refs=False ): ... @_deprecate_positional_args def __init__(self, default=Undefined, *, length=Undefined, **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().__init__(default=default, **params) if length is Undefined and self.default is None: raise ValueError( f"{_validate_error_prefix(self, 'length')} must be " "specified if no default is supplied." ) elif default is not Undefined and default: self.length = len(default) else: self.length = length self._validate(self.default) def _validate_value(self, val, allow_None): if val is None and allow_None: return if not isinstance(val, tuple): raise ValueError( f"{_validate_error_prefix(self)} only takes a tuple value, " f"not {type(val)}." ) def _validate_length(self, val, length): if val is None and self.allow_None: return if not len(val) == length: raise ValueError( f"{_validate_error_prefix(self, 'length')} is not " f"of the correct length ({len(val)} instead of {length})." ) def _validate(self, val): self._validate_value(val, self.allow_None) self._validate_length(val, self.length) @classmethod def serialize(cls, value): if value is None: return None return list(value) # As JSON has no tuple representation @classmethod def deserialize(cls, value): if value == 'null' or value is None: return None return tuple(value) # As JSON has no tuple representation class NumericTuple(Tuple): """A numeric tuple Parameter (e.g. (4.5,7.6,3)) with a fixed tuple length.""" def _validate_value(self, val, allow_None): super()._validate_value(val, allow_None) if allow_None and val is None: return for n in val: if _is_number(n): continue raise ValueError( f"{_validate_error_prefix(self)} only takes numeric " f"values, not {type(n)}." ) class XYCoordinates(NumericTuple): """A NumericTuple for an X,Y coordinate.""" _slot_defaults = _dict_update(NumericTuple._slot_defaults, default=(0.0, 0.0)) @typing.overload def __init__( self, default=(0.0, 0.0), *, length=None, allow_None=False, doc=None, label=None, precedence=None, instantiate=False, constant=False, readonly=False, pickle_default_value=True, per_instance=True, allow_refs=False, nested_refs=False ): ... def __init__(self, default=Undefined, **params): super().__init__(default=default, length=2, **params) class Range(NumericTuple): """ A numeric range with optional bounds and softbounds. """ __slots__ = ['bounds', 'inclusive_bounds', 'softbounds', 'step'] _slot_defaults = _dict_update( NumericTuple._slot_defaults, default=None, bounds=None, inclusive_bounds=(True,True), softbounds=None, step=None ) @typing.overload def __init__( self, default=None, *, bounds=None, softbounds=None, inclusive_bounds=(True,True), step=None, length=None, doc=None, label=None, precedence=None, instantiate=False, constant=False, readonly=False, pickle_default_value=True, allow_None=False, per_instance=True, allow_refs=False, nested_refs=False ): ... @_deprecate_positional_args def __init__(self, default=Undefined, *, bounds=Undefined, softbounds=Undefined, inclusive_bounds=Undefined, step=Undefined, **params): self.bounds = bounds self.inclusive_bounds = inclusive_bounds self.softbounds = softbounds self.step = step super().__init__(default=default,length=2,**params) def _validate(self, val): super()._validate(val) self._validate_bounds(val, self.bounds, self.inclusive_bounds, 'bound') self._validate_bounds(val, self.softbounds, self.inclusive_bounds, 'softbound') self._validate_step(val, self.step) self._validate_order(val, self.step, allow_None=self.allow_None) def _validate_step(self, val, step): if step is not None: if not _is_number(step): raise ValueError( f"{_validate_error_prefix(self, 'step')} can only be None " f"or a numeric value, not {type(step)}." ) elif step == 0: raise ValueError( f"{_validate_error_prefix(self, 'step')} cannot be 0." ) def _validate_order(self, val, step, allow_None): if val is None and allow_None: return elif val is not None and (val[0] is None or val[1] is None): return start, end = val if step is not None and step > 0 and not start <= end: raise ValueError( f"{_validate_error_prefix(self)} end {end} is less than its " f"start {start} with positive step {step}." ) elif step is not None and step < 0 and not start >= end: raise ValueError( f"{_validate_error_prefix(self)} start {start} is less than its " f"start {end} with negative step {step}." ) def _validate_bound_type(self, value, position, kind): if not _is_number(value): raise ValueError( f"{_validate_error_prefix(self)} {position} {kind} can only be " f"None or a numerical value, not {type(value)}." ) def _validate_bounds(self, val, bounds, inclusive_bounds, kind): if bounds is not None: for pos, v in zip(['lower', 'upper'], bounds): if v is None: continue self._validate_bound_type(v, pos, kind) if kind == 'softbound': return if bounds is None or (val is None and self.allow_None): return vmin, vmax = bounds incmin, incmax = 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( f"{_validate_error_prefix(self)} {bound} bound must be in " f"range {self.rangestr()}, not {v}." ) def get_soft_bounds(self): return get_soft_bounds(self.bounds, self.softbounds) def rangestr(self): vmin, vmax = self.bounds incmin, incmax = self.inclusive_bounds incmin = '[' if incmin else '(' incmax = ']' if incmax else ')' return f'{incmin}{vmin}, {vmax}{incmax}' 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_bound_type(self, value, position, kind): if not isinstance(value, dt_types): raise ValueError( f"{_validate_error_prefix(self)} {position} {kind} can only be " f"None or a date/datetime value, not {type(value)}." ) def _validate_bounds(self, val, bounds, inclusive_bounds, kind): val = None if val is None else tuple(map(_to_datetime, val)) bounds = None if bounds is None else tuple(map(_to_datetime, bounds)) super()._validate_bounds(val, bounds, inclusive_bounds, kind) def _validate_value(self, val, allow_None): # Cannot use super()._validate_value as DateRange inherits from # NumericTuple which check that the tuple values are numbers and # datetime objects aren't numbers. if allow_None and val is None: return if not isinstance(val, tuple): raise ValueError( f"{_validate_error_prefix(self)} only takes a tuple value, " f"not {type(val)}." ) for n in val: if isinstance(n, dt_types): continue raise ValueError( f"{_validate_error_prefix(self)} only takes date/datetime " f"values, not {type(n)}." ) start, end = val if not end >= start: raise ValueError( f"{_validate_error_prefix(self)} end datetime {val[1]} " f"is before start datetime {val[0]}." ) @classmethod def serialize(cls, value): if value is None: return None # List as JSON has no tuple representation serialized = [] for v in value: if not isinstance(v, (dt.datetime, dt.date)): # i.e np.datetime64 v = v.astype(dt.datetime) # Separate date and datetime to deserialize to the right type. if type(v) == dt.date: v = v.strftime("%Y-%m-%d") else: v = v.strftime("%Y-%m-%dT%H:%M:%S.%f") serialized.append(v) return serialized def deserialize(cls, value): if value == 'null' or value is None: return None deserialized = [] for v in value: # Date if len(v) == 10: v = dt.datetime.strptime(v, "%Y-%m-%d").date() # Datetime else: v = dt.datetime.strptime(v, "%Y-%m-%dT%H:%M:%S.%f") deserialized.append(v) # As JSON has no tuple representation return tuple(deserialized) class CalendarDateRange(Range): """ A date range specified as (start_date, end_date). """ def _validate_value(self, val, allow_None): if allow_None and val is None: return for n in val: if not isinstance(n, dt.date): raise ValueError( f"{_validate_error_prefix(self)} only takes date types, " f"not {val}." ) start, end = val if not end >= start: raise ValueError( f"{_validate_error_prefix(self)} end date {val[1]} is before " f"start date {val[0]}." ) def _validate_bound_type(self, value, position, kind): if not isinstance(value, dt.date): raise ValueError( f"{_validate_error_prefix(self)} {position} {kind} can only be " f"None or a date value, not {type(value)}." ) @classmethod def serialize(cls, value): if value is None: return None # As JSON has no tuple representation return [v.strftime("%Y-%m-%d") for v in value] @classmethod def deserialize(cls, value): if value == 'null' or value is None: return None # As JSON has no tuple representation return tuple([dt.datetime.strptime(v, "%Y-%m-%d").date() for v in value]) #----------------------------------------------------------------------------- # Callable #----------------------------------------------------------------------------- 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. """ @typing.overload def __init__( self, default=None, *, allow_None=False, doc=None, label=None, precedence=None, instantiate=False, constant=False, readonly=False, pickle_default_value=True, per_instance=True, allow_refs=False, nested_refs=False ): ... @_deprecate_positional_args def __init__(self, default=Undefined, **params): super().__init__(default=default, **params) self._validate(self.default) def _validate_value(self, val, allow_None): if (allow_None and val is None) or callable(val): return raise ValueError( f"{_validate_error_prefix(self)} only takes a callable object, " f"not objects of {type(val)}." ) def _validate(self, val): self._validate_value(val, self.allow_None) 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 #----------------------------------------------------------------------------- # Composite #----------------------------------------------------------------------------- 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. This Parameter type has not been tested with watchers and dependencies, and may not support them properly. """ __slots__ = ['attribs', 'objtype'] @typing.overload def __init__( self, *, attribs=None, allow_None=False, doc=None, label=None, precedence=None, instantiate=False, constant=False, readonly=False, pickle_default_value=True, per_instance=True, allow_refs=False, nested_refs=False ): ... @_deprecate_positional_args def __init__(self, *, attribs=Undefined, **kw): if attribs is Undefined: attribs = [] super().__init__(default=Undefined, **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_attribs(self, val, attribs): if len(val) == len(attribs): return raise ValueError( f"{_validate_error_prefix(self)} got the wrong number " f"of values (needed {len(attribs)}, but got {len(val)})." ) def _validate(self, val): self._validate_attribs(val, self.attribs) 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) #----------------------------------------------------------------------------- # Selector #----------------------------------------------------------------------------- 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.") class ListProxy(list): """ Container that supports both list-style and dictionary-style updates. Useful for replacing code that originally accepted lists but needs to support dictionary access (typically for naming items). """ def __init__(self, iterable, parameter=None): super().__init__(iterable) self._parameter = parameter def _warn(self, method): clsname = type(self._parameter).__name__ get_logger().warning( '{clsname}.objects{method} is deprecated if objects attribute ' 'was declared as a dictionary. Use `{clsname}.objects[label] ' '= value` instead.'.format(clsname=clsname, method=method) ) @contextmanager def _trigger(self, trigger=True): trigger = 'objects' in self._parameter.watchers and trigger old = dict(self._parameter.names) or list(self._parameter._objects) yield if trigger: value = self._parameter.names or self._parameter._objects self._parameter._trigger_event('objects', old, value) def __getitem__(self, index): if self._parameter.names: return self._parameter.names[index] return super().__getitem__(index) def __setitem__(self, index, object, trigger=True): if isinstance(index, (int, slice)): if self._parameter.names: self._warn('[index] = object') with self._trigger(): super().__setitem__(index, object) self._parameter._objects[index] = object return if self and not self._parameter.names: self._parameter.names = _named_objs(self) with self._trigger(trigger): if index in self._parameter.names: old = self._parameter.names[index] idx = self.index(old) super().__setitem__(idx, object) self._parameter._objects[idx] = object else: super().append(object) self._parameter._objects.append(object) self._parameter.names[index] = object def __eq__(self, other): eq = super().__eq__(other) if self._parameter.names and eq is NotImplemented: return dict(zip(self._parameter.names, self)) == other return eq def __ne__(self, other): return not self.__eq__(other) def append(self, object): if self._parameter.names: self._warn('.append') with self._trigger(): super().append(object) self._parameter._objects.append(object) def copy(self): if self._parameter.names: return self._parameter.names.copy() return list(self) def clear(self): with self._trigger(): super().clear() self._parameter._objects.clear() self._parameter.names.clear() def extend(self, objects): if self._parameter.names: self._warn('.append') with self._trigger(): super().extend(objects) self._parameter._objects.extend(objects) def get(self, key, default=None): if self._parameter.names: return self._parameter.names.get(key, default) return _named_objs(self).get(key, default) def insert(self, index, object): if self._parameter.names: self._warn('.insert') with self._trigger(): super().insert(index, object) self._parameter._objects.insert(index, object) def items(self): if self._parameter.names: return self._parameter.names.items() return _named_objs(self).items() def keys(self): if self._parameter.names: return self._parameter.names.keys() return _named_objs(self).keys() def pop(self, *args): index = args[0] if args else -1 if isinstance(index, int): with self._trigger(): super().pop(index) object = self._parameter._objects.pop(index) if self._parameter.names: self._parameter.names = { k: v for k, v in self._parameter.names.items() if v is object } return if self and not self._parameter.names: raise ValueError( 'Cannot pop an object from {clsname}.objects if ' 'objects was not declared as a dictionary.' ) with self._trigger(): object = self._parameter.names.pop(*args) super().remove(object) self._parameter._objects.remove(object) return object def remove(self, object): with self._trigger(): super().remove(object) self._parameter._objects.remove(object) if self._parameter.names: copy = self._parameter.names.copy() self._parameter.names.clear() self._parameter.names.update({ k: v for k, v in copy.items() if v is not object }) def update(self, objects, **items): if not self._parameter.names: self._parameter.names = _named_objs(self) objects = objects.items() if isinstance(objects, dict) else objects with self._trigger(): for i, o in enumerate(objects): if not isinstance(o, collections.abc.Sequence): raise TypeError( f'cannot convert dictionary update sequence element #{i} to a sequence' ) o = tuple(o) n = len(o) if n != 2: raise ValueError( f'dictionary update sequence element #{i} has length {n}; 2 is required' ) k, v = o self.__setitem__(k, v, trigger=False) for k, v in items.items(): self.__setitem__(k, v, trigger=False) def values(self): if self._parameter.names: return self._parameter.names.values() return _named_objs(self).values() class __compute_selector_default: """ Using a function instead of setting default to [] in _slot_defaults, as if it were modified in place later, which would happen with check_on_set set to False, then the object in _slot_defaults would itself be updated and the next Selector instance created wouldn't have [] as the default but a populated list. """ def __call__(self, p): return [] def __repr__(self): return repr(self.sig) @property def sig(self): return [] _compute_selector_default = __compute_selector_default() class __compute_selector_checking_default: def __call__(self, p): return len(p.objects) != 0 def __repr__(self): return repr(self.sig) @property def sig(self): return None _compute_selector_checking_default = __compute_selector_checking_default() class _SignatureSelector(Parameter): # Needs docstring; why is this a separate mixin? _slot_defaults = _dict_update( SelectorBase._slot_defaults, _objects=_compute_selector_default, compute_default_fn=None, check_on_set=_compute_selector_checking_default, allow_None=None, instantiate=False, default=None, ) @classmethod def _modified_slots_defaults(cls): defaults = super()._modified_slots_defaults() defaults['objects'] = defaults.pop('_objects') return defaults class Selector(SelectorBase, _SignatureSelector): """ Parameter whose value must be one object from a list of possible objects. By default, if no default is specified, picks the first object from the provided set of objects, as long as the objects are in an ordered data collection. 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. empty_default is an internal argument that does not have a slot. """ __slots__ = ['_objects', 'compute_default_fn', 'check_on_set', 'names'] @typing.overload def __init__( self, *, objects=[], default=None, instantiate=False, compute_default_fn=None, check_on_set=None, allow_None=None, empty_default=False, doc=None, label=None, precedence=None, constant=False, readonly=False, pickle_default_value=True, per_instance=True, allow_refs=False, nested_refs=False ): ... # Selector is usually used to allow selection from a list of # existing objects, therefore instantiate is False by default. @_deprecate_positional_args def __init__(self, *, objects=Undefined, default=Undefined, instantiate=Undefined, compute_default_fn=Undefined, check_on_set=Undefined, allow_None=Undefined, empty_default=False, **params): autodefault = Undefined if objects is not Undefined and objects: if isinstance(objects, dict): autodefault = list(objects.values())[0] elif isinstance(objects, list): autodefault = objects[0] default = autodefault if (not empty_default and default is Undefined) else default self.objects = objects self.compute_default_fn = compute_default_fn self.check_on_set = check_on_set super().__init__( default=default, instantiate=instantiate, **params) # Required as Parameter sets allow_None=True if default is None if allow_None is Undefined: self.allow_None = self._slot_defaults['allow_None'] else: self.allow_None = allow_None if self.default is not None: self._validate_value(self.default) self._update_state() def _update_state(self): if self.check_on_set is False and self.default is not None: self._ensure_value_is_in_objects(self.default) @property def objects(self): return ListProxy(self._objects, self) @objects.setter def objects(self, objects): if isinstance(objects, collections.abc.Mapping): self.names = objects self._objects = list(objects.values()) else: self.names = {} self._objects = objects # Note that if the list of objects is changed, the current value for # this parameter in existing POs could be outside 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() self._ensure_value_is_in_objects(self.default) def _validate(self, val): if not self.check_on_set: self._ensure_value_is_in_objects(val) return self._validate_value(val) def _validate_value(self, val): if self.check_on_set and not (self.allow_None and val is None) and val not in self.objects: 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( f"{_validate_error_prefix(self)} does not accept {val!r}; " f"valid options include: {items!r}" ) 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) 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) class ObjectSelector(Selector): """ Deprecated. Same as Selector, but with a different constructor for historical reasons. """ @typing.overload def __init__( self, default=None, *, objects=[], instantiate=False, compute_default_fn=None, check_on_set=None, allow_None=None, empty_default=False, doc=None, label=None, precedence=None, constant=False, readonly=False, pickle_default_value=True, per_instance=True, allow_refs=False, nested_refs=False ): ... @_deprecate_positional_args def __init__(self, default=Undefined, *, objects=Undefined, **kwargs): super().__init__(objects=objects, default=default, empty_default=True, **kwargs) class FileSelector(Selector): """ Given a path glob, allows one file to be selected from those matching. """ __slots__ = ['path'] _slot_defaults = _dict_update( Selector._slot_defaults, path="", ) @typing.overload def __init__( self, default=None, *, path="", objects=[], instantiate=False, compute_default_fn=None, check_on_set=None, allow_None=None, empty_default=False, doc=None, label=None, precedence=None, constant=False, readonly=False, pickle_default_value=True, per_instance=True, allow_refs=False, nested_refs=False ): ... @_deprecate_positional_args def __init__(self, default=Undefined, *, path=Undefined, **kwargs): self.default = default self.path = path self.update(path=path) if default is not Undefined: self.default = default super().__init__(default=self.default, objects=self._objects, **kwargs) def _on_set(self, attribute, old, new): super()._on_set(attribute, new, old) if attribute == 'path': self.update(path=new) def update(self, path=Undefined): if path is Undefined: path = self.path if path == "": self.objects = [] else: # Convert using os.fspath and pathlib.Path to handle ensure # the path separators are consistent (on Windows in particular) pathpattern = os.fspath(pathlib.Path(path)) self.objects = sorted(glob.glob(pathpattern)) if self.default in self.objects: return self.default = self.objects[0] if self.objects else None def get_range(self): return _abbreviate_paths(self.path,super().get_range()) class ListSelector(Selector): """ Variant of Selector where the value can be multiple objects from a list of possible objects. """ @typing.overload def __init__( self, default=None, *, objects=[], instantiate=False, compute_default_fn=None, check_on_set=None, allow_None=None, empty_default=False, doc=None, label=None, precedence=None, constant=False, readonly=False, pickle_default_value=True, per_instance=True, allow_refs=False, nested_refs=False ): ... @_deprecate_positional_args def __init__(self, default=Undefined, *, objects=Undefined, **kwargs): super().__init__( objects=objects, default=default, empty_default=True, **kwargs) 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): if (val is None and self.allow_None): return self._validate_type(val) if self.check_on_set: self._validate_value(val) else: for v in val: self._ensure_value_is_in_objects(v) def _validate_type(self, val): if not isinstance(val, list): raise ValueError( f"{_validate_error_prefix(self)} only takes list types, " f"not {val!r}." ) def _validate_value(self, val): self._validate_type(val) if val is not None: for o in val: super()._validate_value(o) def _update_state(self): if self.check_on_set is False and self.default is not None: for o in self.default: self._ensure_value_is_in_objects(o) class MultiFileSelector(ListSelector): """ Given a path glob, allows multiple files to be selected from the list of matches. """ __slots__ = ['path'] _slot_defaults = _dict_update( Selector._slot_defaults, path="", ) @typing.overload def __init__( self, default=None, *, path="", objects=[], compute_default_fn=None, check_on_set=None, allow_None=None, empty_default=False, doc=None, label=None, precedence=None, instantiate=False, constant=False, readonly=False, pickle_default_value=True, per_instance=True, allow_refs=False, nested_refs=False ): ... @_deprecate_positional_args def __init__(self, default=Undefined, *, path=Undefined, **kwargs): self.default = default self.path = path self.update(path=path) super().__init__(default=default, objects=self._objects, **kwargs) def _on_set(self, attribute, old, new): super()._on_set(attribute, new, old) if attribute == 'path': self.update(path=new) def update(self, path=Undefined): if path is Undefined: path = self.path self.objects = sorted(glob.glob(path)) if self.default and all([o in self.objects for o in self.default]): return elif not self.default: return self.default = self.objects def get_range(self): return _abbreviate_paths(self.path,super().get_range()) class ClassSelector(SelectorBase): """ Parameter allowing selection of either a subclass or an instance of a class or tuple of classes. 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'] _slot_defaults = _dict_update(SelectorBase._slot_defaults, instantiate=True, is_instance=True) @typing.overload def __init__( self, *, class_, default=None, instantiate=True, is_instance=True, allow_None=False, doc=None, label=None, precedence=None, constant=False, readonly=False, pickle_default_value=True, per_instance=True, allow_refs=False, nested_refs=False ): ... @_deprecate_positional_args def __init__(self, *, class_, default=Undefined, instantiate=Undefined, is_instance=Undefined, **params): self.class_ = class_ self.is_instance = is_instance super().__init__(default=default,instantiate=instantiate,**params) self._validate(self.default) def _validate(self, val): super()._validate(val) self._validate_class_(val, self.class_, self.is_instance) def _validate_class_(self, val, class_, is_instance): if (val is None and self.allow_None): return if isinstance(class_, tuple): class_name = ('(%s)' % ', '.join(cl.__name__ for cl in class_)) else: class_name = class_.__name__ if is_instance: if not (isinstance(val, class_)): raise ValueError( f"{_validate_error_prefix(self)} value must be an instance of {class_name}, not {val!r}.") else: if not (issubclass(val, class_)): raise ValueError( f"{_validate_error_prefix(self)} value must be a subclass of {class_name}, not {val}.") def get_range(self): """ Return the possible types for this parameter's value. (I.e. return `{name: }` for all classes that are concrete_descendents() of `self.class_`.) Only classes from modules that have been imported are added (see concrete_descendents()). """ classes = self.class_ if isinstance(self.class_, tuple) else (self.class_,) all_classes = {} for cls in classes: all_classes.update(concrete_descendents(cls)) d = OrderedDict((name, class_) for name,class_ in all_classes.items()) if self.allow_None: d['None'] = None return d class Dict(ClassSelector): """ Parameter whose value is a dictionary. """ @typing.overload def __init__( self, default=None, *, is_instance=True, allow_None=False, doc=None, label=None, precedence=None, instantiate=True, constant=False, readonly=False, pickle_default_value=True, per_instance=True, allow_refs=False, nested_refs=False ): ... def __init__(self, default=Undefined, **params): super().__init__(default=default, class_=dict, **params) class Array(ClassSelector): """ Parameter whose value is a numpy array. """ @typing.overload def __init__( self, default=None, *, is_instance=True, allow_None=False, doc=None, label=None, precedence=None, instantiate=True, constant=False, readonly=False, pickle_default_value=True, per_instance=True, allow_refs=False, nested_refs=False ): ... def __init__(self, default=Undefined, **params): from numpy import ndarray super().__init__(default=default, class_=ndarray, **params) @classmethod def serialize(cls, value): if value is None: return None return value.tolist() @classmethod def deserialize(cls, value): if value == 'null' or value is None: return None import numpy if isinstance(value, str): return _deserialize_from_path( {'.npy': numpy.load, '.txt': lambda x: numpy.loadtxt(str(x))}, value, 'Array' ) else: return numpy.asarray(value) 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'] _slot_defaults = _dict_update( ClassSelector._slot_defaults, rows=None, columns=None, ordered=None ) @typing.overload def __init__( self, default=None, *, rows=None, columns=None, ordered=None, is_instance=True, allow_None=False, doc=None, label=None, precedence=None, instantiate=True, constant=False, readonly=False, pickle_default_value=True, per_instance=True, allow_refs=False, nested_refs=False ): ... @_deprecate_positional_args def __init__(self, default=Undefined, *, rows=Undefined, columns=Undefined, ordered=Undefined, **params): from pandas import DataFrame as pdDFrame self.rows = rows self.columns = columns self.ordered = ordered super().__init__(default=default, class_=pdDFrame, **params) self._validate(self.default) def _length_bounds_check(self, bounds, length, name): message = f'{name} length {length} does not match declared bounds of {bounds}' if not isinstance(bounds, tuple): if (bounds != length): raise ValueError(f"{_validate_error_prefix(self)}: {message}") 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(f"{_validate_error_prefix(self)}: {message}") def _validate(self, val): super()._validate(val) if isinstance(self.columns, set) and self.ordered is True: raise ValueError( f'{_validate_error_prefix(self)}: columns cannot be ordered ' f'when specified as a set' ) if self.allow_None and val is None: return 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) - {str(el) for el in val.columns} if difference: raise ValueError( f"{_validate_error_prefix(self)}: provided columns " f"{list(val.columns)} does not contain required " f"columns {sorted(self.columns)}" ) else: self._length_bounds_check(self.columns, len(val.columns), 'column') if self.ordered: if list(val.columns) != list(self.columns): raise ValueError( f"{_validate_error_prefix(self)}: provided columns " f"{list(val.columns)} must exactly match {self.columns}" ) if self.rows is not None: self._length_bounds_check(self.rows, len(val), 'row') @classmethod def serialize(cls, value): if value is None: return None return value.to_dict('records') @classmethod def deserialize(cls, value): if value == 'null' or value is None: return None import pandas if isinstance(value, str): return _deserialize_from_path( { '.csv': pandas.read_csv, '.dta': pandas.read_stata, '.feather': pandas.read_feather, '.h5': pandas.read_hdf, '.hdf5': pandas.read_hdf, '.json': pandas.read_json, '.ods': pandas.read_excel, '.parquet': pandas.read_parquet, '.pkl': pandas.read_pickle, '.tsv': lambda x: pandas.read_csv(x, sep='\t'), '.xlsm': pandas.read_excel, '.xlsx': pandas.read_excel, }, value, 'DataFrame') else: return pandas.DataFrame(value) 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'] _slot_defaults = _dict_update( ClassSelector._slot_defaults, rows=None, allow_None=False ) @typing.overload def __init__( self, default=None, *, rows=None, allow_None=False, is_instance=True, doc=None, label=None, precedence=None, instantiate=True, constant=False, readonly=False, pickle_default_value=True, per_instance=True, allow_refs=False, nested_refs=False ): ... @_deprecate_positional_args def __init__(self, default=Undefined, *, rows=Undefined, allow_None=Undefined, **params): from pandas import Series as pdSeries self.rows = rows super().__init__(default=default, class_=pdSeries, allow_None=allow_None, **params) self._validate(self.default) def _length_bounds_check(self, bounds, length, name): message = f'{name} length {length} does not match declared bounds of {bounds}' if not isinstance(bounds, tuple): if (bounds != length): raise ValueError(f"{_validate_error_prefix(self)}: {message}") 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(f"{_validate_error_prefix(self)}: {message}") def _validate(self, val): super()._validate(val) if self.allow_None and val is None: return if self.rows is not None: self._length_bounds_check(self.rows, len(val), 'row') #----------------------------------------------------------------------------- # List #----------------------------------------------------------------------------- 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 item_type is non-None, all items in the list are checked to be of that type. `class_` is accepted as an alias for `item_type`, but is deprecated due to conflict with how the `class_` slot is used in Selector classes. """ __slots__ = ['bounds', 'item_type', 'class_'] _slot_defaults = _dict_update( Parameter._slot_defaults, class_=None, item_type=None, bounds=(0, None), instantiate=True, default=[], ) @typing.overload def __init__( self, default=[], *, class_=None, item_type=None, instantiate=True, bounds=(0, None), allow_None=False, doc=None, label=None, precedence=None, constant=False, readonly=False, pickle_default_value=True, per_instance=True, allow_refs=False, nested_refs=False ): ... @_deprecate_positional_args def __init__(self, default=Undefined, *, class_=Undefined, item_type=Undefined, instantiate=Undefined, bounds=Undefined, **params): if class_ is not Undefined: # PARAM3_DEPRECATION warnings.warn( message="The 'class_' attribute on 'List' is deprecated. Use instead 'item_type'", category=_ParamDeprecationWarning, stacklevel=3, ) if item_type is not Undefined and class_ is not Undefined: self.item_type = item_type elif item_type is Undefined or item_type is None: self.item_type = class_ else: self.item_type = item_type self.class_ = self.item_type self.bounds = bounds Parameter.__init__(self, default=default, instantiate=instantiate, **params) self._validate(self.default) def _validate(self, val): """ Checks that the value is numeric and that it is within the hard bounds; if not, an exception is raised. """ self._validate_value(val, self.allow_None) self._validate_bounds(val, self.bounds) self._validate_item_type(val, self.item_type) def _validate_bounds(self, val, bounds): "Checks that the list is of the right length and has the right contents." if bounds is None or (val is None and self.allow_None): return min_length, max_length = 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( f"{_validate_error_prefix(self)} length must be between " f"{min_length} and {max_length} (inclusive), not {l}." ) elif min_length is not None: if not min_length <= l: raise ValueError( f"{_validate_error_prefix(self)} length must be at " f"least {min_length}, not {l}." ) elif max_length is not None: if not l <= max_length: raise ValueError( f"{_validate_error_prefix(self)} length must be at " f"most {max_length}, not {l}." ) def _validate_value(self, val, allow_None): if allow_None and val is None: return if not isinstance(val, list): raise ValueError( f"{_validate_error_prefix(self)} must be a list, not an " f"object of {type(val)}." ) def _validate_item_type(self, val, item_type): if item_type is None or (self.allow_None and val is None): return for v in val: if isinstance(v, item_type): continue raise TypeError( f"{_validate_error_prefix(self)} items must be instances " f"of {item_type!r}, not {type(v)}." ) 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 _validate_value(self, val, allow_None): super()._validate_value(val, allow_None) if allow_None and val is None: return for v in val: if callable(v): continue raise ValueError( f"{_validate_error_prefix(self)} items must be callable, " f"not {v!r}." ) #----------------------------------------------------------------------------- # Path #----------------------------------------------------------------------------- # For portable code: # - specify paths in unix (rather than Windows) style; # - use resolve_path(path_to_file=True) for paths to existing files to be read, # - use resolve_path(path_to_file=False) for paths to existing folders to be read, # and normalize_path() for paths to new files to be written. 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, allow_None=True, doc=""" String specifying whether the path refers to a 'File' or a 'Folder'. If None, the path may point to *either* a 'File' *or* a 'Folder'.""") def __call__(self, path, **params): p = ParamOverrides(self, params) path = os.path.normpath(path) ftype = "File" if p.path_to_file is True \ else "Folder" if p.path_to_file is False else "Path" if not p.search_paths: p.search_paths = [os.getcwd()] if os.path.isabs(path): if ((p.path_to_file is None and os.path.exists(path)) or (p.path_to_file is True and os.path.isfile(path)) or (p.path_to_file is False and os.path.isdir( path))): return path raise OSError(f"{ftype} '{path}' not found.") else: paths_tried = [] for prefix in p.search_paths: try_path = os.path.join(os.path.normpath(prefix), path) if ((p.path_to_file is None and os.path.exists(try_path)) or (p.path_to_file is True and os.path.isfile(try_path)) or (p.path_to_file is False and os.path.isdir( try_path))): return try_path paths_tried.append(try_path) raise OSError(ftype + " " + os.path.split(path)[1] + " was not found in the following place(s): " + str(paths_tried) + ".") # PARAM3_DEPRECATION @_deprecated() 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) 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 Parameters 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). Parameters ---------- search_paths : list, default=[os.getcwd()] List of paths to search the path from check_exists: boolean, default=True If True (default) the path must exist on instantiation and set, otherwise the path can optionally exist. """ __slots__ = ['search_paths', 'check_exists'] _slot_defaults = _dict_update( Parameter._slot_defaults, check_exists=True, ) @typing.overload def __init__( self, default=None, *, search_paths=None, check_exists=True, allow_None=False, doc=None, label=None, precedence=None, instantiate=False, constant=False, readonly=False, pickle_default_value=True, per_instance=True, allow_refs=False, nested_refs=False ): ... @_deprecate_positional_args def __init__(self, default=Undefined, *, search_paths=Undefined, check_exists=Undefined, **params): if search_paths is Undefined: search_paths = [] self.search_paths = search_paths if check_exists is not Undefined and not isinstance(check_exists, bool): raise ValueError("'check_exists' attribute value must be a boolean") self.check_exists = check_exists super().__init__(default,**params) self._validate(self.default) def _resolve(self, path): return resolve_path(path, path_to_file=None, search_paths=self.search_paths) def _validate(self, val): if val is None: if not self.allow_None: raise ValueError(f'{_validate_error_prefix(self)} does not accept None') else: if not isinstance(val, (str, pathlib.Path)): raise ValueError(f'{_validate_error_prefix(self)} only take str or pathlib.Path types') try: self._resolve(val) except OSError as e: if self.check_exists: raise OSError(e.args[0]) from None def __get__(self, obj, objtype): """ Return an absolute, normalized path (see resolve_path). """ raw_path = super().__get__(obj,objtype) if raw_path is None: path = None else: try: path = self._resolve(raw_path) except OSError: if self.check_exists: raise else: path = raw_path return path def __getstate__(self): # don't want to pickle the search_paths state = super().__getstate__() if 'search_paths' in state: state['search_paths'] = [] return state 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): return resolve_path(path, path_to_file=True, search_paths=self.search_paths) 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): return resolve_path(path, path_to_file=False, search_paths=self.search_paths) #----------------------------------------------------------------------------- # Color #----------------------------------------------------------------------------- class Color(Parameter): """ Color parameter defined as a hex RGB string with an optional # prefix or (optionally) as a CSS3 color name. """ # CSS3 color specification https://www.w3.org/TR/css-color-3/#svg-color _named_colors = [ 'aliceblue', 'antiquewhite', 'aqua', 'aquamarine', 'azure', 'beige', 'bisque', 'black', 'blanchedalmond', 'blue', 'blueviolet', 'brown', 'burlywood', 'cadetblue', 'chartreuse', 'chocolate', 'coral', 'cornflowerblue', 'cornsilk', 'crimson', 'cyan', 'darkblue', 'darkcyan', 'darkgoldenrod', 'darkgray', 'darkgrey', 'darkgreen', 'darkkhaki', 'darkmagenta', 'darkolivegreen', 'darkorange', 'darkorchid', 'darkred', 'darksalmon', 'darkseagreen', 'darkslateblue', 'darkslategray', 'darkslategrey', 'darkturquoise', 'darkviolet', 'deeppink', 'deepskyblue', 'dimgray', 'dimgrey', 'dodgerblue', 'firebrick', 'floralwhite', 'forestgreen', 'fuchsia', 'gainsboro', 'ghostwhite', 'gold', 'goldenrod', 'gray', 'grey', 'green', 'greenyellow', 'honeydew', 'hotpink', 'indianred', 'indigo', 'ivory', 'khaki', 'lavender', 'lavenderblush', 'lawngreen', 'lemonchiffon', 'lightblue', 'lightcoral', 'lightcyan', 'lightgoldenrodyellow', 'lightgray', 'lightgrey', 'lightgreen', 'lightpink', 'lightsalmon', 'lightseagreen', 'lightskyblue', 'lightslategray', 'lightslategrey', 'lightsteelblue', 'lightyellow', 'lime', 'limegreen', 'linen', 'magenta', 'maroon', 'mediumaquamarine', 'mediumblue', 'mediumorchid', 'mediumpurple', 'mediumseagreen', 'mediumslateblue', 'mediumspringgreen', 'mediumturquoise', 'mediumvioletred', 'midnightblue', 'mintcream', 'mistyrose', 'moccasin', 'navajowhite', 'navy', 'oldlace', 'olive', 'olivedrab', 'orange', 'orangered', 'orchid', 'palegoldenrod', 'palegreen', 'paleturquoise', 'palevioletred', 'papayawhip', 'peachpuff', 'peru', 'pink', 'plum', 'powderblue', 'purple', 'red', 'rosybrown', 'royalblue', 'saddlebrown', 'salmon', 'sandybrown', 'seagreen', 'seashell', 'sienna', 'silver', 'skyblue', 'slateblue', 'slategray', 'slategrey', 'snow', 'springgreen', 'steelblue', 'tan', 'teal', 'thistle', 'tomato', 'turquoise', 'violet', 'wheat', 'white', 'whitesmoke', 'yellow', 'yellowgreen'] __slots__ = ['allow_named'] _slot_defaults = _dict_update(Parameter._slot_defaults, allow_named=True) @typing.overload def __init__( self, default=None, *, allow_named=True, allow_None=False, doc=None, label=None, precedence=None, instantiate=False, constant=False, readonly=False, pickle_default_value=True, per_instance=True, allow_refs=False, nested_refs=False ): ... @_deprecate_positional_args def __init__(self, default=Undefined, *, allow_named=Undefined, **kwargs): super().__init__(default=default, **kwargs) self.allow_named = allow_named self._validate(self.default) def _validate(self, val): self._validate_value(val, self.allow_None) self._validate_allow_named(val, self.allow_named) def _validate_value(self, val, allow_None): if (allow_None and val is None): return if not isinstance(val, str): raise ValueError( f"{_validate_error_prefix(self)} expects a string value, " f"not an object of {type(val)}." ) def _validate_allow_named(self, val, allow_named): if (val is None and self.allow_None): return is_hex = re.match('^#?(([0-9a-fA-F]{2}){3}|([0-9a-fA-F]){3})$', val) if self.allow_named: if not is_hex and val.lower() not in self._named_colors: raise ValueError( f"{_validate_error_prefix(self)} only takes RGB hex codes " f"or named colors, received '{val}'." ) elif not is_hex: raise ValueError( f"{_validate_error_prefix(self)} only accepts valid RGB hex " f"codes, received {val!r}." ) #----------------------------------------------------------------------------- # Bytes #----------------------------------------------------------------------------- class Bytes(Parameter): """ A Bytes Parameter, with a default value and optional regular expression (regex) matching. Similar to the String parameter, but instead of type string this parameter only allows objects of type bytes (e.g. b'bytes'). """ __slots__ = ['regex'] _slot_defaults = _dict_update( Parameter._slot_defaults, default=b"", regex=None, allow_None=False, ) @typing.overload def __init__( self, default=b"", *, regex=None, allow_None=False, doc=None, label=None, precedence=None, instantiate=False, constant=False, readonly=False, pickle_default_value=True, per_instance=True, allow_refs=False, nested_refs=False ): ... @_deprecate_positional_args def __init__(self, default=Undefined, *, regex=Undefined, allow_None=Undefined, **kwargs): super().__init__(default=default, **kwargs) self.regex = regex self._validate(self.default) def _validate_regex(self, val, regex): if (val is None and self.allow_None): return if regex is not None and re.match(regex, val) is None: raise ValueError( f"{_validate_error_prefix(self)} value {val!r} " f"does not match regex {regex!r}." ) def _validate_value(self, val, allow_None): if allow_None and val is None: return if not isinstance(val, bytes): raise ValueError( f"{_validate_error_prefix(self)} only takes a byte string value, " f"not value of {type(val)}." ) def _validate(self, val): self._validate_value(val, self.allow_None) self._validate_regex(val, self.regex)