Source code for andes.core.service

#  [ANDES] (C)2015-2021 Hantao Cui
#
#  This program is free software; you can redistribute it and/or modify
#  it under the terms of the GNU General Public License as published by
#  the Free Software Foundation; either version 3 of the License, or
#  (at your option) any later version.
#
#  File name: service.py
#  Last modified: 8/16/20, 7:28 PM

import logging
import numpy as np

from collections import OrderedDict
from typing import Optional, Union, Callable, Type

from andes.core.param import BaseParam
from andes.utils.func import list_flatten
from andes.core.common import dummify
from andes.utils.tab import Tab

logger = logging.getLogger(__name__)


[docs]class BaseService: """ Base class for Service. Service is a v-provider type for holding internal and temporary values. Subclasses need to implement ``v`` as a member attribute or using a property decorator. Parameters ---------- name : str Instance name Attributes ---------- owner : Model The hosting/owner model instance """ def __init__(self, name: str = None, tex_name: str = None, info: str = None, vtype: Type = None): self.name = name self.tex_name = tex_name if tex_name else name self.info = info self.vtype = vtype if vtype is not None else float # type for `v` self.owner = None
[docs] def get_names(self): """ Return `name` in a list Returns ------- list A list only containing the name of the service variable """ return [self.name]
[docs] def assign_memory(self, n): """ Assign memory for ``self.v`` and set the array to zero. Parameters ---------- n : int Number of elements of the value array. Provided by caller (Model.list2array). """ self.v = np.zeros(n, dtype=self.vtype)
@property def class_name(self): """ Return the class name """ return self.__class__.__name__ @property def n(self): """ Return the count of values in ``self.v``. Needs to be overloaded if ``v`` of subclasses is not a 1-dimensional array. Returns ------- int The count of elements in this variable """ if isinstance(self.v, (list, np.ndarray)): return len(self.v) else: return 1 def __repr__(self): val_str = '' if (1 <= self.n <= 20) and hasattr(self, 'v'): val_str = f', v={self.v}' return f'{self.class_name}: {self.owner.class_name}.{self.name}{val_str}'
[docs]class ConstService(BaseService): """ A type of Service that stays constant once initialized. ConstService are usually constants calculated from parameters. They are only evaluated once in the initialization phase before variables are initialized. Therefore, uninitialized variables must not be used in `v_str``. Parameters ---------- name : str Name of the ConstService v_str : str An equation string to calculate the variable value. v_numeric : Callable, optional A callable which returns the value of the ConstService Attributes ---------- v : array-like or a scalar ConstService value """ def __init__(self, v_str: Optional[str] = None, v_numeric: Optional[Callable] = None, vtype: Optional[type] = None, name: Optional[str] = None, tex_name=None, info=None): super().__init__(name=name, vtype=vtype, tex_name=tex_name, info=info) self.v_str = v_str self.v_numeric = v_numeric self.v: Union[float, int, np.ndarray] = np.array([0.])
[docs]class VarService(ConstService): """ Variable service that gets updated in each step/loop as variables change. This class is useful when one has non-differentiable algebraic equations, which make use of `abs()`, `re` and `im`. Instead of creating `Algeb`, one can put the equation in `VarService`, which will be updated before solving algebraic equations. Examples -------- In ESST3A model, the voltage and current sensors (vd + jvq), (Id + jIq) estimate the sensed VE using equation .. math :: VE = | K_{PC}*(v_d + 1j v_q) + 1j (K_I + K_{PC}*X_L)*(I_d + 1j I_q)| One can use `VarService` to implement this equation :: self.VE = VarService( tex_name='V_E', info='VE', v_str='Abs(KPC*(vd + 1j*vq) + 1j*(KI + KPC*XL)*(Id + 1j*Iq))', ) Warnings -------- `VarService` is not solved with other algebraic equations, meaning that there is one step "delay" between the algebraic variables and `VarService`. Use an algebraic variable whenever possible. """ pass
[docs]class EventFlag(VarService): """ Service to flag events when the input value changes. The typical input is a `v-provider` with binary values. Implemented by providing `self.check(**kwargs)` as `v_numeric`. `EventFlag.v` stores the values of the input variable in the most recent iteration/step. After the evaluation of `self.check()`, `self.v` will be updated. """ def __init__(self, u, vtype: Optional[type] = None, name: Optional[str] = None, tex_name=None, info=None): VarService.__init__(self, v_numeric=self.check, vtype=vtype, name=name, tex_name=tex_name, info=info) self.u = dummify(u)
[docs] def check(self, **kwargs): """ Check status and set event flags. Input values are compared with values in the memory. """ if not np.all(self.v == self.u.v): self.owner.system.TDS.custom_event = True logger.debug(f"Event flag set at t={self.owner.system.dae.t:.6f} sec.") return self.u.v
[docs]class VarHold(VarService): """ Service for holding the input when the hold signal is on. Parameters ---------- hold : v-provider, binary Hold signal array with length equal to the input. For elements that are 1, the corresponding inputs are held until the hold signal returns to 0. """ def __init__(self, u, hold, vtype=None, name=None, tex_name=None, info=None): VarService.__init__(self, v_numeric=self.check, vtype=vtype, name=name, tex_name=tex_name, info=info, ) self.u = dummify(u) self.hold = dummify(hold) self._init = False
[docs] def check(self, **kwargs): """ Custom `v_numeric` function for checking the hold signal and calculating outputs. """ if not np.all(self.hold.v == 0.0): hold_idx = np.where(self.hold.v == 1) ret = self.u.v.copy() ret[hold_idx] = self.v[hold_idx] return ret else: return self.u.v
[docs]class ExtendedEvent(VarService): """ Service for indicating an event for an extended, predefined period of time following the event disappearance. The triggering of an event, whether the rise or fall edge, is specified through `trig`. For example, if `trig = rise`, the change of the input from 0 to 1 will be considered as an input, whereas the subsequent change back to 0 will be considered as the event end. `ExtendedEvent.v` stores the flags whether the extended time has completed. Outputs will become 1 once the event starts and return to 0 when the extended time ends. Warnings -------- The performance of this class needs to be optimized. Parameters ---------- u : v-provider Triggering signal where the values are 0 or 1. trig : str in ("rise", "fall") Triggering edge for the beginning of an event. `rise` by default. enable : bool or v-provider If disabled, the output will be `v_disabled` extend_only : bool Only output during the extended period, not the event period. """ def __init__(self, u, t_ext: Union[int, float, BaseParam, BaseService] = 0.0, trig: str = 'rise', enable=True, v_disabled=0, extend_only=False, vtype: Optional[type] = None, name: Optional[str] = None, tex_name=None, info=None): VarService.__init__(self, v_numeric=self.check, vtype=vtype, name=name, tex_name=tex_name, info=info) self.u = dummify(u) self.t_ext = dummify(t_ext) self.enable = dummify(enable) self.v_disabled = v_disabled self.extend_only = extend_only self.t_final = None self.trig = trig self.v_event = None self.u_last = None self.z = None # if is in an extended event (from event start to extension end) self.n_ext = 0 # number of extended events
[docs] def assign_memory(self, n): """ Assign memory for internal data. """ VarService.assign_memory(self, n) self.t_final = np.zeros_like(self.v) self.v_event = np.zeros_like(self.v) self.u_last = np.zeros_like(self.v) self.z = np.zeros_like(self.v) if isinstance(self.t_ext.v, (int, float)): self.t_ext.v = np.ones_like(self.u.v) * self.t_ext.v
[docs] def check(self, **kwargs): """ Check if an extended event is in place. Supplied as a ``v_numeric`` to ``VarService``. """ dae_t = self.owner.system.dae.t if dae_t == 0.0: self.u_last[:] = self.u.v self.v_event[:] = self.u.v # when any input signal changes if not np.all(self.u.v == self.u_last): diff = self.u.v - self.u_last # detect the actual ending of an event if self.trig == 'rise': starting = np.where(diff == 1)[0] ending = np.where(diff == -1)[0] else: starting = np.where(diff == -1)[0] ending = np.where(diff == 1)[0] if len(starting): self.z[starting] = 1 if not self.extend_only: self.v_event[starting] = self.u.v[starting] if len(ending): if self.extend_only: self.v_event[ending] = self.u_last[ending] final_times = dae_t + self.t_ext.v[ending] self.t_final[ending] = final_times self.n_ext += len(ending) # TODO: insert extended event end times to a model-level list logger.debug(f"Extended Event ending time set at t={final_times} sec.") # final time of the extended event if self.n_ext and np.any(self.t_final <= dae_t): self.z[np.where(self.t_final <= dae_t)] = 0 self.n_ext = np.count_nonzero(self.z) self.u_last[:] = self.u.v return self.enable.v * (self.u.v * (1 - self.z) + self.v_event * self.z) + \ (1-self.enable.v) * self.v_disabled
[docs]class PostInitService(ConstService): """ Constant service that gets stored once after init. This service is useful when one need to store initialization values stored in variables. Examples -------- In ESST3A model, the `vf` variable is initialized followed by other variables. One can store the initial `vf` into `vf0` so that equation ``vf - vf0 = 0`` will hold. :: self.vref0 = PostInitService(info='Initial reference voltage input', tex_name='V_{ref0}', v_str='vref', ) Since all `ConstService` are evaluated before equation evaluation, without using PostInitService, one will need to create lots of `ConstService` to store values in the initialization path towards `vf0`, in order to correctly initialize `vf`. """ pass
[docs]class BackRef(BaseService): """ A special type of reference collector. `BackRef` is used for collecting device indices of other models referencing the parent model of the `BackRef`. The `v``field will be a list of lists, each containing the `idx` of other models referencing each device of the parent model. BackRef can be passed as indexer for params and vars, or shape for `NumReduce` and `NumRepeat`. See examples for illustration. Examples -------- A Bus device has an `IdxParam` of `area`, storing the `idx` of area to which the bus device belongs. In ``Bus.__init__()``, one has :: self.area = IdxParam(model='Area') Suppose `Bus` has the following data ==== ==== ==== idx area Vn ---- ---- ---- 1 1 110 2 2 220 3 1 345 4 1 500 ==== ==== ==== The Area model wants to collect the indices of Bus devices which points to the corresponding Area device. In ``Area.__init__``, one defines :: self.Bus = BackRef() where the member attribute name `Bus` needs to match exactly model name that `Area` wants to collect `idx` for. Similarly, one can define ``self.ACTopology = BackRef()`` to collect devices in the `ACTopology` group that references Area. The collection of `idx` happens in :py:func:`andes.system.System._collect_ref_param`. It has to be noted that the specific `Area` entry must exist to collect model idx-dx referencing it. For example, if `Area` has the following data :: idx 1 Then, only Bus 1, 3, and 4 will be collected into `self.Bus.v`, namely, ``self.Bus.v == [ [1, 3, 4] ]``. If `Area` has data :: idx 1 2 Then, `self.Bus.v` will end up with ``[ [1, 3, 4], [2] ]``. See Also -------- andes.core.service.NumReduce : A more complete example using BackRef to build the COI model """ def __init__(self, **kwargs): super().__init__(**kwargs) self.export = False self.v = list()
[docs]class ExtService(BaseService): """ Service constants whose value is from an external model or group. Parameters ---------- src : str Variable or parameter name in the source model or group model : str A model name or a group name indexer : IdxParam or BaseParam An "Indexer" instance whose ``v`` field contains the ``idx`` of devices in the model or group. Examples -------- A synchronous generator needs to retrieve the ``p`` and ``q`` values from static generators for initialization. ``ExtService`` is used for this purpose. In a synchronous generator, one can define the following to retrieve ``StaticGen.p`` as ``p0``:: class GENCLSModel(Model): def __init__(...): ... self.p0 = ExtService(src='p', model='StaticGen', indexer=self.gen, tex_name='P_0') """ def __init__(self, model: str, src: str, indexer: Union[BaseParam, BaseService], attr: str = 'v', allow_none: bool = False, default=0, name: str = None, tex_name: str = None, vtype=None, info: str = None, ): super().__init__(name=name, tex_name=tex_name, info=info, vtype=vtype) self.model = model self.src = src self.indexer = indexer self.attr = attr self.allow_none = allow_none self.default = default self.v = np.array([0.])
[docs]class DataSelect(BaseService): """ Class for selecting values for optional DataParam or NumParam. This service is a v-provider that uses optional DataParam if available with a fallback. DataParam will be tested for `None`, and NumParam will be tested with `np.isnan()`. Notes ----- An use case of DataSelect is remote bus. One can do :: self.buss = DataSelect(option=self.busr, fallback=self.bus) Then, pass ``self.buss`` instead of ``self.bus`` as indexer to retrieve voltages. Another use case is to allow an optional turbine rating. One can do :: self.Tn = NumParam(default=None) self.Sg = ExtParam(...) self.Sn = DataSelect(Tn, Sg) """ def __init__(self, optional, fallback, name: Optional[str] = None, tex_name: Optional[str] = None, info: Optional[str] = None, ): super().__init__(name=name, tex_name=tex_name, info=info, ) self.optional = optional self.fallback = fallback self._v = None @property def v(self): if self._v is None: self._v = [v1 if v1 is not None and not np.isnan(v1) else v2 for v1, v2 in zip(self.optional.v, self.fallback.v)] return self._v
[docs]class DeviceFinder(BaseService): """ Service for finding indices of optionally linked devices. If not provided, `DeviceFinder` will add devices at the beginning of `System.setup`. Examples -------- IEEEST stabilizer takes an optional `busf` (IdxParam) for specifying the connected BusFreq, which is needed for mode 6. To avoid reimplementing `BusFreq` within IEEEST, one can do .. code-block :: python self.busfreq = DeviceFinder(self.busf, link=self.buss, idx_name='bus') where `self.busf` is the optional input, `self.buss` is the bus indices that `busf` should measure, and `idx_name` is the name of a BusFreq parameter through which the measured bus indices are specified. For each `None` values in `self.busf`, a `BusFreq` is created to measure the corresponding bus in `self.buss`. That is, ``BusFreq.[idx_name].v = [link]``. `DeviceFinder` will find / create `BusFreq` devices so that the returned list of `BusFreq` indices are connected to `self.buss`, respectively. """ def __init__(self, u, link, idx_name, name=None, tex_name=None, info=None): super().__init__(name=name, tex_name=tex_name, info=info) self.u = u self.model = u.model self.idx_name = idx_name if self.model is None: raise ValueError(f'{u.owner.class_name}.{u.name} must contain "model".') self.link = link
[docs] def find_or_add(self, system): """ Find or add devices. Points `self.u.v` to the found or newly added devices. Find devices one by one. Devices previously added in this function can be used later without duplication. """ mdl = system.models[self.model] added = False for ii, link_to in enumerate(self.link.v): idx = mdl.find_idx(self.idx_name, (link_to, ), allow_none=True, default=None)[0] if idx is None: added = True new_idx = system.add(self.model, {self.idx_name: link_to}) self.u.v[ii] = new_idx logger.info(f"{self.owner.class_name} <{self.owner.idx.v[ii]}> " f"added {self.model} <{new_idx}> " f"linked to {self.idx_name} <{link_to}>") else: self.u.v[ii] = idx if added: mdl.list2array() mdl.refresh_inputs() system.link_ext_param({mdl.name: mdl})
@property def v(self): return self.u.v
[docs]class OperationService(BaseService): """ Base class for a type of Service which performs specific operations. OperationService may not use the `assign_memory` from `BaseService`, because it can have a different size. This class cannot be used by itself. See Also -------- NumReduce : Service for Reducing linearly stored 2-D services into 1-D NumRepeat : Service for repeating 1-D NumParam/ v-array following a sub-pattern IdxRepeat : Service for repeating 1-D IdxParam/ v-list following a sub-pattern """ def __init__(self, name=None, tex_name=None, info=None, ): self._v = None super().__init__(name=name, tex_name=tex_name, info=info, ) self.v_str = None @property def v(self): """ Return values stored in `self._v`. May be overloaded by subclasses. """ return self._v @v.setter def v(self, value): self._v = value
[docs]class NumReduce(OperationService): """ A helper Service type which reduces a linearly stored 2-D ExtParam into 1-D Service. NumReduce works with ExtParam whose `v` field is a list of lists. A reduce function which takes an array-like and returns a scalar need to be supplied. NumReduce calls the reduce function on each of the lists and return all the scalars in an array. Parameters ---------- u : ExtParam Input ExtParam whose ``v`` contains linearly stored 2-dimensional values ref : BackRef The BackRef whose 2-dimensional shapes are used for indexing fun : Callable The callable for converting a 1-D array-like to a scalar Examples -------- Suppose one wants to calculate the mean value of the ``Vn`` in one Area. In the ``Area`` class, one defines :: class AreaModel(...): def __init__(...): ... # backward reference from `Bus` self.Bus = BackRef() # collect the Vn in an 1-D array self.Vn = ExtParam(model='Bus', src='Vn', indexer=self.Bus) self.Vn_mean = NumReduce(u=self.Vn, fun=np.mean, ref=self.Bus) Suppose we define two areas, 1 and 2, the Bus data looks like === ===== ==== idx area Vn --- ----- ---- 1 1 110 2 2 220 3 1 345 4 1 500 === ===== ==== Then, `self.Bus.v` is a list of two lists ``[ [1, 3, 4], [2] ]``. `self.Vn.v` will be retrieved and linearly stored as ``[110, 345, 500, 220]``. Based on the shape from `self.Bus`, :py:func:`numpy.mean` will be called on ``[110, 345, 500]`` and ``[220]`` respectively. Thus, `self.Vn_mean.v` will become ``[318.33, 220]``. """ def __init__(self, u, ref: BackRef, fun: Callable, name=None, tex_name=None, info=None, cache=True, ): super().__init__(name=name, tex_name=tex_name, info=info) self.u = u self.ref = ref self.fun = fun self.cache = cache @property def v(self): """ Return the reduced values from the reduction function in an array Returns ------- The array ``self._v`` storing the reduced values """ if self._v is not None and self.cache is True: return self._v if self._v is None: self._v = np.zeros(len(self.ref.v)) idx = 0 for i, v in enumerate(self.ref.v): self._v[i] = self.fun(self.u.v[idx:idx + len(v)]) idx += len(v) return self._v
[docs]class NumRepeat(OperationService): r""" A helper Service type which repeats a v-provider's value based on the shape from a BackRef Examples -------- NumRepeat was originally designed for computing the inertia-weighted average rotor speed (center of inertia speed). COI speed is computed with .. math :: \omega_{COI} = \frac{ \sum{M_i * \omega_i} } {\sum{M_i}} The numerator can be calculated with a mix of BackRef, ExtParam and ExtState. The denominator needs to be calculated with NumReduce and Service Repeat. That is, use NumReduce to calculate the sum, and use NumRepeat to repeat the summed value for each device. In the COI class, one would have .. code-block :: python class COIModel(...): def __init__(...): ... self.SynGen = BackRef() self.SynGenIdx = RefFlatten(ref=self.SynGen) self.M = ExtParam(model='SynGen', src='M', indexer=self.SynGenIdx) self.wgen = ExtState(model='SynGen', src='omega', indexer=self.SynGenIdx) self.Mt = NumReduce(u=self.M, fun=np.sum, ref=self.SynGen) self.Mtr = NumRepeat(u=self.Mt, ref=self.SynGen) self.pidx = IdxRepeat(u=self.idx,ref=self.SynGen) Finally, one would define the center of inertia speed as .. code-block :: python self.wcoi = Algeb(v_str='1', e_str='-wcoi') self.wcoi_sub = ExtAlgeb(model='COI', src='wcoi', e_str='M * wgen / Mtr', v_str='M / Mtr', indexer=self.pidx, ) It is very worth noting that the implementation uses a trick to separate the average weighted sum into `n` sub-equations, each calculating the :math:`(M_i * \omega_i) / (\sum{M_i})`. Since all the variables are preserved in the sub-equation, the derivatives can be calculated correctly. """ def __init__(self, u, ref, **kwargs): super().__init__(**kwargs) self.u = u self.ref = ref @property def v(self): """ Return the values of the repeated values in a sequential 1-D array Returns ------- The array, ``self._v`` storing the repeated values """ if self._v is None: self._v = np.zeros(len(list_flatten(self.ref.v))) idx = 0 for i, v in enumerate(self.ref.v): self._v[idx:idx + len(v)] = self.u.v[i] idx += len(v) return self._v else: return self._v
[docs]class IdxRepeat(OperationService): """ Helper class to repeat IdxParam. This class has the same functionality as :py:class:`andes.core.service.NumRepeat` but only operates on IdxParam, DataParam or NumParam. """ def __init__(self, u, ref, **kwargs): super().__init__(**kwargs) self.u = u self.ref = ref @property def v(self): if self._v is None: self._v = [''] * len(list_flatten(self.ref.v)) idx = 0 for i, v in enumerate(self.ref.v): for jj in range(idx, idx + len(v)): self._v[jj] = self.u.v[i] idx += len(v) return self._v else: return self._v
[docs]class RefFlatten(OperationService): """ A service type for flattening :py:class:`andes.core.service.BackRef` into a 1-D list. Examples -------- This class is used when one wants to pass `BackRef` values as indexer. :py:class:`andes.models.coi.COI` collects referencing :py:class:`andes.models.group.SynGen` with .. code-block :: python self.SynGen = BackRef(info='SynGen idx lists', export=False) After collecting BackRefs, `self.SynGen.v` will become a two-level list of indices, where the first level correspond to each COI and the second level correspond to generators of the COI. Convert `self.SynGen` into 1-d as `self.SynGenIdx`, which can be passed as indexer for retrieving other parameters and variables .. code-block :: python self.SynGenIdx = RefFlatten(ref=self.SynGen) self.M = ExtParam(model='SynGen', src='M', indexer=self.SynGenIdx, export=False, ) """ def __init__(self, ref, **kwargs): super().__init__(**kwargs) self.ref = ref @property def v(self): return list_flatten(self.ref.v)
[docs]class NumSelect(OperationService): """ Class for selecting values for optional NumParam. NumSelect works with internal and external parameters. Notes ----- One use case is to allow an optional turbine rating. One can do :: self.Tn = NumParam(default=None) self.Sg = ExtParam(...) self.Sn = DataSelect(Tn, Sg) """ def __init__(self, optional, fallback, name: Optional[str] = None, tex_name: Optional[str] = None, info: Optional[str] = None, ): super().__init__(name=name, tex_name=tex_name, info=info) self.optional = optional self.fallback = fallback self._v = None @property def v(self): if self._v is None: self._v = [v1 if not np.isnan(v1) else v2 for v1, v2 in zip(self.optional.v, self.fallback.v)] self._v = np.array(self._v) return self._v
[docs]class InitChecker(OperationService): """ Class for checking init values against known typical values. Instances will be stored in `Model.services_post` and `Model.services_icheck`, which will be checked in `Model.post_init_check()` after initialization. Parameters ---------- u v-provider to be checked lower : float, BaseParam, BaseVar, BaseService lower bound upper : float, BaseParam, BaseVar, BaseService upper bound equal : float, BaseParam, BaseVar, BaseService values that the value from `v_str` should equal not_equal : float, BaseParam, BaseVar, BaseService values that should not equal enable : bool True to enable checking Examples -------- Let's say generator excitation voltages are known to be in the range of 1.6 - 3.0 per unit. One can add the following instance to `GENBase` :: self._vfc = InitChecker(u=self.vf, info='vf range', lower=1.8, upper=3.0, ) `lower` and `upper` can also take v-providers instead of float values. One can also pass float values from Config to make it adjustable as in our implementation of ``GENBase._vfc``. """ def __init__(self, u, lower=None, upper=None, equal=None, not_equal=None, enable=True, error_out=False, **kwargs): super().__init__(**kwargs) self.u = u self.lower = dummify(lower) if lower is not None else None self.upper = dummify(upper) if upper is not None else None self.equal = dummify(equal) if equal is not None else None self.not_equal = dummify(not_equal) if not_equal is not None else None self.enable = enable self.error_out = error_out self.checks = [(self.lower, np.less_equal, "out of typical lower limit", "limit"), (self.upper, np.greater_equal, "out of typical upper limit", "limit"), (self.equal, lambda a, b: np.logical_not(np.isclose(a, b)), 'should be equal', "expected"), (self.not_equal, np.equal, 'should not be equal', "not expected") ]
[docs] def check(self): """ Check the bounds and equality conditions. """ if not self.enable: return if self._v is None: self._v = np.zeros_like(self.u.v) for check in self.checks: limit = check[0] func = check[1] text = check[2] text2 = check[3] if limit is None: continue self.v[:] = np.logical_or(self.v, func(self.u.v, limit.v)) pos = np.argwhere(func(self.u.v, limit.v)).ravel() if len(pos) == 0: continue idx = [self.owner.idx.v[i] for i in pos] lim_v = limit.v * np.ones(self.n) title = f'{self.owner.class_name} {self.info} {text}.' err_dict = OrderedDict([('idx', idx), ('values', self.u.v[pos]), (f'{text2}', lim_v[pos]), ]) data = list(map(list, zip(*err_dict.values()))) tab = Tab(title=title, data=data, header=list(err_dict.keys())) if self.error_out: logger.error(tab.draw()) else: logger.warning(tab.draw()) self.v[:] = np.logical_not(self.v)
[docs]class FlagValue(BaseService): """ Class for flagging values that equal to the given value. By default, values that equal to `value` will be flagged as `0`. Non-matching values will be flagged as `1`. Parameters ---------- u Input parameter value Value to flag. Can be None, string, or a number. flag : 0 by default, only 0 or 1 is accepted. The flag for the matched ones Warnings -------- `FlagNotNone` can only be applied to `BaseParam` with `cache=True`. Applying to `Service` will fail unless `cache` is False (at a performance cost). """ def __init__(self, u, value, flag=0, name=None, tex_name=None, info=None, cache=True): BaseService.__init__(self, name=name, tex_name=tex_name, info=info) if flag != 0.0 and flag != 1.0: raise ValueError(f"flag must be 0 or 1. The given flag = {flag}.") self.u = u self.value = value self.flag = flag self.flag_neg = 1 - flag self.cache = cache self._v = None @property def v(self): new = False if self._v is None: self._v = np.zeros_like(self.u.v, dtype=float) new = True if not self.cache or new: # need to do it element-wise since `self.u.v` can be a list self._v[:] = np.array([self.flag if i == self.value else self.flag_neg for i in self.u.v]) return self._v
[docs]class ApplyFunc(BaseService): """ Class for applying a numerical function on a parameter.. Warnings -------- This class is not ready. Parameters ---------- u Input parameter func A condition function that returns True or False. """ def __init__(self, u, func, name=None, tex_name=None, info=None, cache=True): BaseService.__init__(self, name=name, tex_name=tex_name, info=info) self.u = u self.func = func self.cache = cache self._v = None self._eval = False # has been evaluated previously @property def v(self): if not self._eval: self._v = np.zeros_like(self.u.v, dtype=float) if not self.cache or (not self._eval): self._v[:] = self.func(self.u.v) self._eval = True return self._v
[docs]class FlagCondition(BaseService): """ Class for flagging values based on a condition function. By default, values whose condition function output equal that equal to True/1 will be flagged as `1`. `0` otherwise. Parameters ---------- u Input parameter func A condition function that returns True or False. flag : 1 by default, only 0 or 1 is accepted. The flag for the inputs whose condition output is True. Warnings -------- This class is not ready. `FlagCondition` can only be applied to `BaseParam` with `cache=True`. Applying to `Service` will fail unless `cache` is False (at a performance cost). """ def __init__(self, u, func, flag=1, name=None, tex_name=None, info=None, cache=True): BaseService.__init__(self, name=name, tex_name=tex_name, info=info) if flag != 0.0 and flag != 1.0: raise ValueError(f"flag must be 0 or 1. The given flag = {flag}.") self.u = u self.func = func self.flag = flag self.flag_neg = 1 - flag self.cache = cache self._v = None self._eval = False # has been evaluated previously @property def v(self): if not self._eval: self._v = np.zeros_like(self.u.v, dtype=float) if not self.cache or (not self._eval): cond_out = self.func(self.u.v) self._v[:] = np.array([self.flag if i == 1 else self.flag_neg for i in cond_out]) self._eval = True return self._v
[docs]class FlagLessThan(FlagCondition): """ Service for flagging parameters < or <= the given value element-wise. Parameters that satisfy the comparison (u < or <= value) will flagged as `flag` (1 by default). """ def __init__(self, u, value=0.0, flag=1, equal=False, name=None, tex_name=None, info=None, cache=True): self.value = dummify(value) self.equal = equal if self.equal is True: self.func = lambda x: np.less_equal(x, self.value.v) else: self.func = lambda x: np.less(x, self.value.v) FlagCondition.__init__(self, u, func=self.func, flag=flag, name=name, tex_name=tex_name, info=info, cache=cache, )
[docs]class FlagGreaterThan(FlagCondition): """ Service for flagging parameters > or >= the given value element-wise. Parameters that satisfy the comparison (u > or >= value) will flagged as `flag` (1 by default). """ def __init__(self, u, value=0.0, flag=1, equal=False, name=None, tex_name=None, info=None, cache=True): self.value = dummify(value) self.equal = equal if self.equal is True: self.func = lambda x: np.greater_equal(x, self.value.v) else: self.func = lambda x: np.greater(x, self.value.v) FlagCondition.__init__(self, u, func=self.func, flag=flag, name=name, tex_name=tex_name, info=info, cache=cache, )
[docs]class CurrentSign(ConstService): """ Service for computing the sign of the current flowing through a series device. With a given line connecting `bus1` and `bus2`, one can compute the current flow using ``(v1*exp(1j*a1) - v2*exp(1j*a2)) / (r + jx)`` whose value is the outflow on `bus1`. `CurrentSign` can be used to compute the sign to be multiplied depending on the observing bus. For each value in `bus`, the sign will be ``+1`` if it appears in `bus1` or ``-1`` otherwise. :: bus1 bus2 *------>>-----* bus(+) bus(-) """ def __init__(self, bus, bus1, bus2, name=None, tex_name=None, info=None): ConstService.__init__(self, v_numeric=self.check, name=name, tex_name=tex_name, info=info) self.bus = bus self.bus1 = bus1 self.bus2 = bus2
[docs] def check(self, **kwargs): out = np.zeros_like(self.v) for idx, (bus, bus1, bus2) in enumerate(zip(self.bus.v, self.bus1.v, self.bus2.v)): if bus == bus1: out[idx] = 1 elif bus == bus2: out[idx] = -1 else: raise ValueError(f"bus {bus} is not terminal of the line connecting {bus1} and {bus2}. " f"Check the data of {self.bus.owner.class_name}.{self.bus.name}") return out
[docs]class Replace(BaseService): """ Replace parameters with new values if the function returns True """ def __init__(self, old_val, flt, new_val, name=None, tex_name=None, info=None, cache=True): BaseService.__init__(self, name=name, tex_name=tex_name, info=info) self.cache = cache self.filter = flt # function self.old_val = old_val self.new_val = dummify(new_val) self._v = None @property def v(self): new = False if self._v is None or not self.cache: self._v = np.zeros_like(self.old_val.v, dtype=float) new = True if not self.cache or new: new_v = self.new_val.v * np.ones_like(self.old_val.v) flt = self.filter(self.old_val.v) self._v[:] = new_v * flt + self.old_val.v * (1 - flt) return self._v
[docs]class ParamCalc(BaseService): """ Parameter calculation service. Useful to create parameters calculated instantly from existing ones. """ def __init__(self, param1, param2, func, name=None, tex_name=None, info=None, cache=True): BaseService.__init__(self, name=name, tex_name=tex_name, info=info) self.param1 = param1 self.param2 = param2 self.func = func self.cache = cache self._v = None @property def v(self): new = False if self._v is None: new = True self._v = np.zeros_like(self.param1.v, dtype=float) if not self.cache or new: self._v[:] = self.func(self.param1.v, self.param2.v) return self._v
[docs]class RandomService(BaseService): """ A service type for generating random numbers. Parameters ---------- name : str Name func : Callable A callable for generating the random variable. Warnings -------- The value will be randomized every time it is accessed. Do not use it if the value needs to be stable for each simulation step. """ def __init__(self, func=np.random.rand, **kwargs): super(RandomService, self).__init__(**kwargs) self.func = func @property def v(self): """ This class has `v` wrapped by a property decorator. Returns ------- array-like Randomly generated service variables """ return np.random.rand(self.n)
[docs]class SwBlock(OperationService): """ Service type for switched shunt blocks. """ def __init__(self, *, init, ns, blocks, ext_sel=None, name=None, tex_name=None, info=None): OperationService.__init__(self, name=name, tex_name=tex_name, info=info) self.init = init self.ns = ns self.bs = blocks self.sel = None self.bcs = None self.maxsel = None self.ext_sel = ext_sel @property def v(self): self.check_data() # allocate memory if self._v is not None: return self._v # initialize n_dev = len(self.init.v) self._v = np.array(self.init.v) # effective value self.sel = np.zeros(n_dev, dtype=int) # the index of capacity in use self.bcs = [0] * n_dev # cumulative sums of `bs` self.maxsel = np.zeros(n_dev, dtype=int) # max index into `bs` for idx in range(n_dev): item = self.ns.v[idx] self.maxsel[idx] = sum(item) # repeat each value in `bs` by `ns` times for idx in range(n_dev): # disabled - use default `b` if self.maxsel[idx] == 0: self.bcs[idx] = np.array([self.init.v[idx]]) continue # calculate cumulative sum b_rep = list([0]) for nn, bb in zip(self.ns.v[idx], self.bs.v[idx]): b_rep += [bb] * nn self.bcs[idx] = np.cumsum(b_rep) if self.ext_sel is None: # use internal selector - for shunt's b attribute self.find_sel() else: # use external selector - for shunt's g attribute self.sel = self.ext_sel.sel # modify reference self.set_v() return self._v
[docs] def check_data(self): """ Check data consistency. """ model = self.ns.owner.class_name for idx in range(len(self.ns.v)): device = self.ns.owner.idx.v[idx] bs_name = self.bs.name ns_name = self.ns.name if isinstance(self.ns.v[idx], (list, np.ndarray)): if len(self.ns.v[idx]) == 0: continue if len(self.ns.v[idx]) == 1 and self.ns.v[idx][0] == 0: continue if isinstance(self.bs.v[idx], (int, float, str)): raise ValueError("<%s>: idx=%s, `%s` parameter should be list literal, got %s" % (model, device, bs_name, self.bs.v[idx])) if len(self.ns.v[idx]) != len(self.bs.v[idx]): raise ValueError("<%s>: idx=%s, `%s` and `%s` lengths do not match" % (model, device, bs_name, ns_name))
[docs] def adjust(self, amount): """ Adjust capacitor banks by an amount. """ if self.ext_sel is None: self.sel[:] += amount self.set_v()
[docs] def set_v(self): """ Set values to `_v` based on `sel`. """ for idx in range(len(self._v)): self._v[idx] = self.bcs[idx][self.sel[idx]]
[docs] def find_sel(self): """ Determine the initial shunt selection level. """ for idx in range(len(self._v)): binit = self.init.v[idx] if binit > self.bcs[idx][-1]: # out of maximum b logger.warning("<%s> idx=%s, initial %s=%g is greater than max=%g", self.init.owner.class_name, self.init.owner.idx.v[idx], self.init.name, binit, self.bcs[idx][-1]) self.sel[idx] = self.maxsel[idx] elif binit == self.bcs[idx][-1]: self.sel[idx] = self.maxsel[idx] elif binit < 0: logger.warning("<%s> idx=%s, initial %s=%g is less than zero", self.init.owner.class_name, self.init.owner.idx.v[idx], self.init.name, binit) self.sel[idx] = 0 else: for pos in range(self.maxsel[idx]): blo = self.bcs[idx][pos] bup = self.bcs[idx][pos + 1] if binit == blo: self.sel[idx] = pos break if binit == bup: self.sel[idx] = pos + 1 break if blo < binit < bup: self.sel[idx] = pos + 1 break