# -*- coding: utf-8 -*-

"""

Definition of main class to run Modelica simulations - ModelicaSystem.

"""

__license__ = """

This file is part of OpenModelica.

Copyright (c) 1998-CurrentYear, Open Source Modelica Consortium (OSMC),

c/o Linköpings universitet, Department of Computer and Information Science,

SE-58183 Linköping, Sweden.

All rights reserved.

THIS PROGRAM IS PROVIDED UNDER THE TERMS OF THE BSD NEW LICENSE OR THE

GPL VERSION 3 LICENSE OR THE OSMC PUBLIC LICENSE (OSMC-PL) VERSION 1.2.

ANY USE, REPRODUCTION OR DISTRIBUTION OF THIS PROGRAM CONSTITUTES

RECIPIENT'S ACCEPTANCE OF THE OSMC PUBLIC LICENSE OR THE GPL VERSION 3,

ACCORDING TO RECIPIENTS CHOICE.

The OpenModelica software and the OSMC (Open Source Modelica Consortium)

Public License (OSMC-PL) are obtained from OSMC, either from the above

address, from the URLs: http://www.openmodelica.org or

http://www.ida.liu.se/projects/OpenModelica, and in the OpenModelica

distribution. GNU version 3 is obtained from:

http://www.gnu.org/copyleft/gpl.html. The New BSD License is obtained from:

http://www.opensource.org/licenses/BSD-3-Clause.

This program is distributed WITHOUT ANY WARRANTY; without even the implied

warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE, EXCEPT AS

EXPRESSLY SET FORTH IN THE BY RECIPIENT SELECTED SUBSIDIARY LICENSE

CONDITIONS OF OSMC-PL.

"""

import csv

from dataclasses import dataclass

import importlib

import logging

import numbers

import numpy as np

import os

import pathlib

import platform

import re

import subprocess

import tempfile

import textwrap

from typing import Optional, Any

import warnings

import xml.etree.ElementTree as ET

from OMPython.OMCSession import OMCSessionException, OMCSessionZMQ, OMCProcessLocal

# define logger using the current module name as ID

logger = logging.getLogger(__name__)

class ModelicaSystemError(Exception):

"""

Exception used in ModelicaSystem and ModelicaSystemCmd classes.

"""

@dataclass

class LinearizationResult:

"""Modelica model linearization results.

Attributes:

n: number of states

m: number of inputs

p: number of outputs

A: state matrix (n x n)

B: input matrix (n x m)

C: output matrix (p x n)

D: feedthrough matrix (p x m)

x0: fixed point

u0: input corresponding to the fixed point

stateVars: names of state variables

inputVars: names of inputs

outputVars: names of outputs

"""

n: int

m: int

p: int

A: list

B: list

C: list

D: list

x0: list[float]

u0: list[float]

stateVars: list[str]

inputVars: list[str]

outputVars: list[str]

def __iter__(self):

"""Allow unpacking A, B, C, D = result."""

yield self.A

yield self.B

yield self.C

yield self.D

def __getitem__(self, index: int):

"""Allow accessing A, B, C, D via result[0] through result[3].

This is needed for backwards compatibility, because

ModelicaSystem.linearize() used to return [A, B, C, D].

"""

return {0: self.A, 1: self.B, 2: self.C, 3: self.D}[index]

class ModelicaSystemCmd:

"""A compiled model executable."""

def __init__(self, runpath: pathlib.Path, modelname: str, timeout: Optional[float] = None) -> None:

self._runpath = pathlib.Path(runpath).resolve().absolute()

self._model_name = modelname

self._timeout = timeout

self._args: dict[str, str | None] = {}

self._arg_override: dict[str, str] = {}

def arg_set(self, key: str, val: Optional[str | dict] = None) -> None:

"""

Set one argument for the executable model.

Parameters

----------

key : str

val : str, None

"""

if not isinstance(key, str):

raise ModelicaSystemError(f"Invalid argument key: {repr(key)} (type: {type(key)})")

key = key.strip()

if val is None:

argval = None

elif isinstance(val, str):

argval = val.strip()

elif isinstance(val, numbers.Number):

argval = str(val)

elif key == 'override' and isinstance(val, dict):

for okey in val:

if not isinstance(okey, str) or not isinstance(val[okey], (str, numbers.Number)):

raise ModelicaSystemError("Invalid argument for 'override': "

f"{repr(okey)} = {repr(val[okey])}")

self._arg_override[okey] = val[okey]

argval = ','.join([f"{okey}={str(self._arg_override[okey])}" for okey in self._arg_override])

else:

raise ModelicaSystemError(f"Invalid argument value for {repr(key)}: {repr(val)} (type: {type(val)})")

if key in self._args:

logger.warning(f"Overwrite model executable argument: {repr(key)} = {repr(argval)} "

f"(was: {repr(self._args[key])})")

self._args[key] = argval

def arg_get(self, key: str) -> Optional[str | dict]:

"""

Return the value for the given key

"""

if key in self._args:

return self._args[key]

return None

def args_set(self, args: dict[str, Optional[str | dict[str, str]]]) -> None:

"""

Define arguments for the model executable.

Parameters

----------

args : dict[str, Optional[str | dict[str, str]]]

"""

for arg in args:

self.arg_set(key=arg, val=args[arg])

def get_exe(self) -> pathlib.Path:

"""Get the path to the compiled model executable."""

if platform.system() == "Windows":

path_exe = self._runpath / f"{self._model_name}.exe"

else:

path_exe = self._runpath / self._model_name

if not path_exe.exists():

raise ModelicaSystemError(f"Application file path not found: {path_exe}")

return path_exe

def get_cmd(self) -> list:

"""Get a list with the path to the executable and all command line args.

This can later be used as an argument for subprocess.run().

"""

path_exe = self.get_exe()

cmdl = [path_exe.as_posix()]

for key in self._args:

if self._args[key] is None:

cmdl.append(f"-{key}")

else:

cmdl.append(f"-{key}={self._args[key]}")

return cmdl

def run(self) -> int:

"""Run the requested simulation.

Returns

-------

Subprocess return code (0 on success).

"""

cmdl: list = self.get_cmd()

logger.debug("Run OM command %s in %s", repr(cmdl), self._runpath.as_posix())

if platform.system() == "Windows":

path_dll = ""

# set the process environment from the generated .bat file in windows which should have all the dependencies

path_bat = self._runpath / f"{self._model_name}.bat"

if not path_bat.exists():

raise ModelicaSystemError("Batch file (*.bat) does not exist " + str(path_bat))

with open(file=path_bat, mode='r', encoding='utf-8') as fh:

for line in fh:

match = re.match(r"^SET PATH=([^%]*)", line, re.IGNORECASE)

if match:

path_dll = match.group(1).strip(';') # Remove any trailing semicolons

my_env = os.environ.copy()

my_env["PATH"] = path_dll + os.pathsep + my_env["PATH"]

else:

# TODO: how to handle path to resources of external libraries for any system not Windows?

my_env = None

try:

cmdres = subprocess.run(cmdl, capture_output=True, text=True, env=my_env, cwd=self._runpath,

timeout=self._timeout, check=True)

stdout = cmdres.stdout.strip()

stderr = cmdres.stderr.strip()

returncode = cmdres.returncode

logger.debug("OM output for command %s:\n%s", repr(cmdl), stdout)

if stderr:

raise ModelicaSystemError(f"Error running command {repr(cmdl)}: {stderr}")

except subprocess.TimeoutExpired as ex:

raise ModelicaSystemError(f"Timeout running command {repr(cmdl)}") from ex

except subprocess.CalledProcessError as ex:

raise ModelicaSystemError(f"Error running command {repr(cmdl)}") from ex

return returncode

@staticmethod

def parse_simflags(simflags: str) -> dict[str, Optional[str | dict[str, str]]]:

"""

Parse a simflag definition; this is deprecated!

The return data can be used as input for self.args_set().

"""

warnings.warn("The argument 'simflags' is depreciated and will be removed in future versions; "

"please use 'simargs' instead", DeprecationWarning, stacklevel=2)

simargs: dict[str, Optional[str | dict[str, str]]] = {}

args = [s for s in simflags.split(' ') if s]

for arg in args:

if arg[0] != '-':

raise ModelicaSystemError(f"Invalid simulation flag: {arg}")

arg = arg[1:]

parts = arg.split('=')

if len(parts) == 1:

simargs[parts[0]] = None

elif parts[0] == 'override':

override = '='.join(parts[1:])

override_dict = {}

for item in override.split(','):

kv = item.split('=')

if not 0 < len(kv) < 3:

raise ModelicaSystemError(f"Invalid value for '-override': {override}")

if kv[0]:

try:

override_dict[kv[0]] = kv[1]

except (KeyError, IndexError) as ex:

raise ModelicaSystemError(f"Invalid value for '-override': {override}") from ex

simargs[parts[0]] = override_dict

return simargs

class ModelicaSystem:

def __init__(

self,

fileName: Optional[str | os.PathLike | pathlib.Path] = None,

modelName: Optional[str] = None,

lmodel: Optional[list[str | tuple[str, str]]] = None,

commandLineOptions: Optional[str] = None,

variableFilter: Optional[str] = None,

customBuildDirectory: Optional[str | os.PathLike | pathlib.Path] = None,

omhome: Optional[str] = None,

omc_process: Optional[OMCProcessLocal] = None,

build: bool = True,

) -> None:

"""Initialize, load and build a model.

The constructor loads the model file and builds it, generating exe and

xml files, etc.

Args:

fileName: Path to the model file. Either absolute or relative to

the current working directory.

modelName: The name of the model class. If it is contained within

a package, "PackageName.ModelName" should be used.

lmodel: List of libraries to be loaded before the model itself is

loaded. Two formats are supported for the list elements:

lmodel=["Modelica"] for just the library name

and lmodel=[("Modelica","3.2.3")] for specifying both the name

and the version.

commandLineOptions: String with extra command line options to be

provided to omc via setCommandLineOptions().

variableFilter: A regular expression. Only variables fully

matching the regexp will be stored in the result file.

Leaving it unspecified is equivalent to ".*".

customBuildDirectory: Path to a directory to be used for temporary

files like the model executable. If left unspecified, a tmp

directory will be created.

omhome: OPENMODELICAHOME value to be used when creating the OMC

session.

omc_process: definition of a (local) OMC process to be used. If

unspecified, a new local session will be created.

build: Boolean controlling whether or not the model should be

built when constructor is called. If False, the constructor

simply loads the model without compiling.

Examples:

mod = ModelicaSystem("ModelicaModel.mo", "modelName")

mod = ModelicaSystem("ModelicaModel.mo", "modelName", ["Modelica"])

mod = ModelicaSystem("ModelicaModel.mo", "modelName", [("Modelica","3.2.3"), "PowerSystems"])

"""

if fileName is None and modelName is None and not lmodel: # all None

raise ModelicaSystemError("Cannot create ModelicaSystem object without any arguments")

if modelName is None:

raise ModelicaSystemError("A modelname must be provided (argument modelName)!")

self._quantities: list[dict[str, Any]] = []

self._params: dict[str, str] = {} # even numerical values are stored as str

self._inputs: dict[str, list | None] = {}

# _outputs values are str before simulate(), but they can be

# np.float64 after simulate().

self._outputs: dict[str, Any] = {}

# same for _continuous

self._continuous: dict[str, Any] = {}

self._simulate_options: dict[str, str] = {}

self._override_variables: dict[str, str] = {}

self._simulate_options_override: dict[str, str] = {}

self._linearization_options = {'startTime': 0.0, 'stopTime': 1.0, 'stepSize': 0.002, 'tolerance': 1e-8}

self._optimization_options = {'startTime': 0.0, 'stopTime': 1.0, 'numberOfIntervals': 500, 'stepSize': 0.002,

'tolerance': 1e-8}

self._linearized_inputs: list[str] = [] # linearization input list

self._linearized_outputs: list[str] = [] # linearization output list

self._linearized_states: list[str] = [] # linearization states list

if omc_process is not None:

if not isinstance(omc_process, OMCProcessLocal):

raise ModelicaSystemError("Invalid (local) omc process definition provided!")

self._getconn = OMCSessionZMQ(omc_process=omc_process)

else:

self._getconn = OMCSessionZMQ(omhome=omhome)

# set commandLineOptions if provided by users

self.setCommandLineOptions(commandLineOptions=commandLineOptions)

if lmodel is None:

lmodel = []

if not isinstance(lmodel, list):

raise ModelicaSystemError(f"Invalid input type for lmodel: {type(lmodel)} - list expected!")

self._xml_file = None

self._lmodel = lmodel # may be needed if model is derived from other model

self._model_name = modelName # Model class name

self._file_name = pathlib.Path(fileName).resolve() if fileName is not None else None # Model file/package name

self._has_inputs = False # for model with input quantity

self._simulated = False # True if the model has already been simulated

self._result_file: Optional[pathlib.Path] = None # for storing result file

self._variable_filter = variableFilter

if self._file_name is not None and not self._file_name.is_file(): # if file does not exist

raise IOError(f"{self._file_name} does not exist!")

# set default command Line Options for linearization as

# linearize() will use the simulation executable and runtime

# flag -l to perform linearization

self.setCommandLineOptions("--linearizationDumpLanguage=python")

self.setCommandLineOptions("--generateSymbolicLinearization")

self._tempdir = self.setTempDirectory(customBuildDirectory)

if self._file_name is not None:

self._loadLibrary(lmodel=self._lmodel)

self._loadFile(fileName=self._file_name)

# allow directly loading models from MSL without fileName

elif fileName is None and modelName is not None:

self._loadLibrary(lmodel=self._lmodel)

if build:

self.buildModel(variableFilter)

def setCommandLineOptions(self, commandLineOptions: Optional[str] = None):

# set commandLineOptions if provided by users

if commandLineOptions is None:

return

exp = f'setCommandLineOptions("{commandLineOptions}")'

self.sendExpression(exp)

def _loadFile(self, fileName: pathlib.Path):

# load file

self.sendExpression(f'loadFile("{fileName.as_posix()}")')

# for loading file/package, loading model and building model

def _loadLibrary(self, lmodel: list):

# load Modelica standard libraries or Modelica files if needed

for element in lmodel:

if element is not None:

if isinstance(element, str):

if element.endswith(".mo"):

apiCall = "loadFile"

else:

apiCall = "loadModel"

self._requestApi(apiCall, element)

elif isinstance(element, tuple):

if not element[1]:

expr_load_lib = f"loadModel({element[0]})"

else:

expr_load_lib = f'loadModel({element[0]}, {{"{element[1]}"}})'

self.sendExpression(expr_load_lib)

else:

raise ModelicaSystemError("loadLibrary() failed, Unknown type detected: "

f"{element} is of type {type(element)}, "

"The following patterns are supported:\n"

'1)["Modelica"]\n'

'2)[("Modelica","3.2.3"), "PowerSystems"]\n')

def setTempDirectory(self, customBuildDirectory: Optional[str | os.PathLike | pathlib.Path] = None) -> pathlib.Path:

# create a unique temp directory for each session and build the model in that directory

if customBuildDirectory is not None:

if not os.path.exists(customBuildDirectory):

raise IOError(f"{customBuildDirectory} does not exist")

tempdir = pathlib.Path(customBuildDirectory).absolute()

else:

tempdir = pathlib.Path(tempfile.mkdtemp()).absolute()

if not tempdir.is_dir():

raise IOError(f"{tempdir} could not be created")

logger.info("Define tempdir as %s", tempdir)

exp = f'cd("{tempdir.as_posix()}")'

self.sendExpression(exp)

return tempdir

def getWorkDirectory(self) -> pathlib.Path:

return self._tempdir

def buildModel(self, variableFilter: Optional[str] = None):

if variableFilter is not None:

self._variable_filter = variableFilter

if self._variable_filter is not None:

varFilter = f'variableFilter="{self._variable_filter}"'

else:

varFilter = 'variableFilter=".*"'

buildModelResult = self._requestApi("buildModel", self._model_name, properties=varFilter)

logger.debug("OM model build result: %s", buildModelResult)

self._xml_file = pathlib.Path(buildModelResult[0]).parent / buildModelResult[1]

self._xmlparse()

def sendExpression(self, expr: str, parsed: bool = True):

try:

retval = self._getconn.sendExpression(expr, parsed)

except OMCSessionException as ex:

raise ModelicaSystemError(f"Error executing {repr(expr)}") from ex

logger.debug(f"Result of executing {repr(expr)}: {textwrap.shorten(repr(retval), width=100)}")

return retval

# request to OMC

def _requestApi(self, apiName, entity=None, properties=None): # 2

if entity is not None and properties is not None:

exp = f'{apiName}({entity}, {properties})'

elif entity is not None and properties is None:

if apiName in ("loadFile", "importFMU"):

exp = f'{apiName}("{entity}")'

else:

exp = f'{apiName}({entity})'

else:

exp = f'{apiName}()'

return self.sendExpression(exp)

def _xmlparse(self):

if not self._xml_file.is_file():

raise ModelicaSystemError(f"XML file not generated: {self._xml_file}")

tree = ET.parse(self._xml_file)

rootCQ = tree.getroot()

for attr in rootCQ.iter('DefaultExperiment'):

for key in ("startTime", "stopTime", "stepSize", "tolerance",

"solver", "outputFormat"):

self._simulate_options[key] = attr.get(key)

for sv in rootCQ.iter('ScalarVariable'):

scalar = {}

for key in ("name", "description", "variability", "causality", "alias"):

scalar[key] = sv.get(key)

scalar["changeable"] = sv.get('isValueChangeable')

scalar["aliasvariable"] = sv.get('aliasVariable')

ch = list(sv)

for att in ch:

scalar["start"] = att.get('start')

scalar["min"] = att.get('min')

scalar["max"] = att.get('max')

scalar["unit"] = att.get('unit')

if scalar["variability"] == "parameter":

if scalar["name"] in self._override_variables:

self._params[scalar["name"]] = self._override_variables[scalar["name"]]

else:

self._params[scalar["name"]] = scalar["start"]

if scalar["variability"] == "continuous":

self._continuous[scalar["name"]] = scalar["start"]

if scalar["causality"] == "input":

self._inputs[scalar["name"]] = scalar["start"]

if scalar["causality"] == "output":

self._outputs[scalar["name"]] = scalar["start"]

self._quantities.append(scalar)

def getQuantities(self, names: Optional[str | list[str]] = None) -> list[dict]:

"""

This method returns list of dictionaries. It displays details of

quantities such as name, value, changeable, and description.

Examples:

>>> mod.getQuantities()

[

{

'alias': 'noAlias',

'aliasvariable': None,

'causality': 'local',

'changeable': 'true',

'description': None,

'max': None,

'min': None,

'name': 'x',

'start': '1.0',

'unit': None,

'variability': 'continuous',

},

{

'name': 'der(x)',

# ...

},

# ...

]

>>> getQuantities("y")

[{

'name': 'y', # ...

}]

>>> getQuantities(["y","x"])

[

{

'name': 'y', # ...

},

{

'name': 'x', # ...

}

]

"""

if names is None:

return self._quantities

if isinstance(names, str):

r = [x for x in self._quantities if x["name"] == names]

if r == []:

raise KeyError(names)

return r

if isinstance(names, list):

return [x for y in names for x in self._quantities if x["name"] == y]

raise ModelicaSystemError("Unhandled input for getQuantities()")

def getContinuous(self, names: Optional[str | list[str]] = None):

"""Get values of continuous signals.

If called before simulate(), the initial values are returned as

strings (or None). If called after simulate(), the final values (at

stopTime) are returned as numpy.float64.

Args:

names: Either None (default), a string with the continuous signal

name, or a list of signal name strings.

Returns:

If `names` is None, a dict in the format

{signal_name: signal_value} is returned.

If `names` is a string, a single element list [signal_value] is

returned.

If `names` is a list, a list with one value for each signal name

in names is returned: [signal1_value, signal2_value, ...].

Examples:

Before simulate():

>>> mod.getContinuous()

{'x': '1.0', 'der(x)': None, 'y': '-0.4'}

>>> mod.getContinuous("y")

['-0.4']

>>> mod.getContinuous(["y","x"])

['-0.4', '1.0']

After simulate():

>>> mod.getContinuous()

{'x': np.float64(0.68), 'der(x)': np.float64(-0.24), 'y': np.float64(-0.24)}

>>> mod.getContinuous("x")

[np.float64(0.68)]

>>> mod.getOutputs(["y","x"])

[np.float64(-0.24), np.float64(0.68)]

"""

if not self._simulated:

if names is None:

return self._continuous

if isinstance(names, str):

return [self._continuous[names]]

if isinstance(names, list):

return [self._continuous[x] for x in names]

else:

if names is None:

for i in self._continuous:

try:

value = self.getSolutions(i)

self._continuous[i] = value[0][-1]

except (OMCSessionException, ModelicaSystemError) as ex:

raise ModelicaSystemError(f"{i} could not be computed") from ex

return self._continuous

if isinstance(names, str):

if names in self._continuous:

value = self.getSolutions(names)

self._continuous[names] = value[0][-1]

return [self._continuous[names]]

else:

raise ModelicaSystemError(f"{names} is not continuous")

if isinstance(names, list):

valuelist = []

for i in names:

if i in self._continuous:

value = self.getSolutions(i)

self._continuous[i] = value[0][-1]

valuelist.append(value[0][-1])

else:

raise ModelicaSystemError(f"{i} is not continuous")

return valuelist

raise ModelicaSystemError("Unhandled input for getContinous()")

def getParameters(self, names: Optional[str | list[str]] = None) -> dict[str, str] | list[str]: # 5

"""Get parameter values.

Args:

names: Either None (default), a string with the parameter name,

or a list of parameter name strings.

Returns:

If `names` is None, a dict in the format

{parameter_name: parameter_value} is returned.

If `names` is a string, a single element list is returned.

If `names` is a list, a list with one value for each parameter name

in names is returned.

In all cases, parameter values are returned as strings.

Examples:

>>> mod.getParameters()

{'Name1': '1.23', 'Name2': '4.56'}

>>> mod.getParameters("Name1")

['1.23']

>>> mod.getParameters(["Name1","Name2"])

['1.23', '4.56']

"""

if names is None:

return self._params

elif isinstance(names, str):

return [self._params[names]]

elif isinstance(names, list):

return [self._params[x] for x in names]

raise ModelicaSystemError("Unhandled input for getParameters()")

def getInputs(self, names: Optional[str | list[str]] = None) -> dict | list: # 6

"""Get values of input signals.

Args:

names: Either None (default), a string with the input name,

or a list of input name strings.

Returns:

If `names` is None, a dict in the format

{input_name: input_value} is returned.

If `names` is a string, a single element list [input_value] is

returned.

If `names` is a list, a list with one value for each input name

in names is returned: [input1_values, input2_values, ...].

In all cases, input values are returned as a list of tuples,

where the first element in the tuple is the time and the second

element is the input value.

Examples:

>>> mod.getInputs()

{'Name1': [(0.0, 0.0), (1.0, 1.0)], 'Name2': None}

>>> mod.getInputs("Name1")

[[(0.0, 0.0), (1.0, 1.0)]]

>>> mod.getInputs(["Name1","Name2"])

[[(0.0, 0.0), (1.0, 1.0)], None]

"""

if names is None:

return self._inputs

elif isinstance(names, str):

return [self._inputs[names]]

elif isinstance(names, list):

return [self._inputs[x] for x in names]

raise ModelicaSystemError("Unhandled input for getInputs()")

def getOutputs(self, names: Optional[str | list[str]] = None): # 7

"""Get values of output signals.

If called before simulate(), the initial values are returned as

strings. If called after simulate(), the final values (at stopTime)

are returned as numpy.float64.

Args:

names: Either None (default), a string with the output name,

or a list of output name strings.

Returns:

If `names` is None, a dict in the format

{output_name: output_value} is returned.

If `names` is a string, a single element list [output_value] is

returned.

If `names` is a list, a list with one value for each output name

in names is returned: [output1_value, output2_value, ...].

Examples:

Before simulate():

>>> mod.getOutputs()

{'out1': '-0.4', 'out2': '1.2'}

>>> mod.getOutputs("out1")

['-0.4']

>>> mod.getOutputs(["out1","out2"])

['-0.4', '1.2']

After simulate():

>>> mod.getOutputs()

{'out1': np.float64(-0.1234), 'out2': np.float64(2.1)}

>>> mod.getOutputs("out1")

[np.float64(-0.1234)]

>>> mod.getOutputs(["out1","out2"])

[np.float64(-0.1234), np.float64(2.1)]

"""

if not self._simulated:

if names is None:

return self._outputs

elif isinstance(names, str):

return [self._outputs[names]]

else:

return [self._outputs[x] for x in names]

else:

if names is None:

for i in self._outputs:

value = self.getSolutions(i)

self._outputs[i] = value[0][-1]

return self._outputs

elif isinstance(names, str):

if names in self._outputs:

value = self.getSolutions(names)

self._outputs[names] = value[0][-1]

return [self._outputs[names]]

else:

raise KeyError(names)

elif isinstance(names, list):

valuelist = []

for i in names:

if i in self._outputs:

value = self.getSolutions(i)

self._outputs[i] = value[0][-1]

valuelist.append(value[0][-1])

else:

raise KeyError(i)

return valuelist

raise ModelicaSystemError("Unhandled input for getOutputs()")

def getSimulationOptions(self, names: Optional[str | list[str]] = None) -> dict[str, str] | list[str]:

"""Get simulation options such as stopTime and tolerance.

Args:

names: Either None (default), a string with the simulation option

name, or a list of option name strings.

Returns:

If `names` is None, a dict in the format

{option_name: option_value} is returned.

If `names` is a string, a single element list [option_value] is

returned.

If `names` is a list, a list with one value for each option name

in names is returned: [option1_value, option2_value, ...].

Option values are always returned as strings.

Examples:

>>> mod.getSimulationOptions()

{'startTime': '0', 'stopTime': '1.234', 'stepSize': '0.002', 'tolerance': '1.1e-08', 'solver': 'dassl', 'outputFormat': 'mat'}

>>> mod.getSimulationOptions("stopTime")

['1.234']

>>> mod.getSimulationOptions(["tolerance", "stopTime"])

['1.1e-08', '1.234']

"""

if names is None:

return self._simulate_options

elif isinstance(names, str):

return [self._simulate_options[names]]

elif isinstance(names, list):

return [self._simulate_options[x] for x in names]

raise ModelicaSystemError("Unhandled input for getSimulationOptions()")

def getLinearizationOptions(self, names: Optional[str | list[str]] = None) -> dict | list:

"""Get simulation options used for linearization.

Args:

names: Either None (default), a string with the linearization option

name, or a list of option name strings.

Returns:

If `names` is None, a dict in the format

{option_name: option_value} is returned.

If `names` is a string, a single element list [option_value] is

returned.

If `names` is a list, a list with one value for each option name

in names is returned: [option1_value, option2_value, ...].

Some option values are returned as float when first initialized,

but always as strings after setLinearizationOptions is used to

change them.

Examples:

>>> mod.getLinearizationOptions()

{'startTime': 0.0, 'stopTime': 1.0, 'stepSize': 0.002, 'tolerance': 1e-08}

>>> mod.getLinearizationOptions("stopTime")

[1.0]

>>> mod.getLinearizationOptions(["tolerance", "stopTime"])

[1e-08, 1.0]

"""

if names is None:

return self._linearization_options

elif isinstance(names, str):

return [self._linearization_options[names]]

elif isinstance(names, list):

return [self._linearization_options[x] for x in names]

raise ModelicaSystemError("Unhandled input for getLinearizationOptions()")

def getOptimizationOptions(self, names: Optional[str | list[str]] = None) -> dict | list:

"""Get simulation options used for optimization.

Args:

names: Either None (default), a string with the optimization option

name, or a list of option name strings.

Returns:

If `names` is None, a dict in the format

{option_name: option_value} is returned.

If `names` is a string, a single element list [option_value] is

returned.

If `names` is a list, a list with one value for each option name

in names is returned: [option1_value, option2_value, ...].

Some option values are returned as float when first initialized,

but always as strings after setOptimizationOptions is used to

change them.

Examples:

>>> mod.getOptimizationOptions()

{'startTime': 0.0, 'stopTime': 1.0, 'numberOfIntervals': 500, 'stepSize': 0.002, 'tolerance': 1e-08}

>>> mod.getOptimizationOptions("stopTime")

[1.0]

>>> mod.getOptimizationOptions(["tolerance", "stopTime"])

[1e-08, 1.0]

"""

if names is None:

return self._optimization_options

elif isinstance(names, str):

return [self._optimization_options[names]]

elif isinstance(names, list):

return [self._optimization_options[x] for x in names]

raise ModelicaSystemError("Unhandled input for getOptimizationOptions()")

def simulate_cmd(

self,

result_file: pathlib.Path,

simflags: Optional[str] = None,

simargs: Optional[dict[str, Optional[str | dict[str, str]]]] = None,

timeout: Optional[float] = None,

) -> ModelicaSystemCmd:

"""

This method prepares the simulates model according to the simulation options. It returns an instance of

ModelicaSystemCmd which can be used to run the simulation.

Due to the tempdir being unique for the ModelicaSystem instance, *NEVER* use this to create several simulations

with the same instance of ModelicaSystem! Restart each simulation process with a new instance of ModelicaSystem.

However, if only non-structural parameters are used, it is possible to reuse an existing instance of

ModelicaSystem to create several version ModelicaSystemCmd to run the model using different settings.

Parameters

----------

result_file

simflags

simargs

timeout

Returns

-------

An instance if ModelicaSystemCmd to run the requested simulation.

"""

om_cmd = ModelicaSystemCmd(runpath=self._tempdir, modelname=self._model_name, timeout=timeout)

# always define the result file to use

om_cmd.arg_set(key="r", val=result_file.as_posix())

# allow runtime simulation flags from user input

if simflags is not None:

om_cmd.args_set(args=om_cmd.parse_simflags(simflags=simflags))

if simargs:

om_cmd.args_set(args=simargs)

overrideFile = self._tempdir / f"{self._model_name}_override.txt"

if self._override_variables or self._simulate_options_override:

tmpdict = self._override_variables.copy()

tmpdict.update(self._simulate_options_override)

# write to override file

with open(file=overrideFile, mode="w", encoding="utf-8") as fh:

for key, value in tmpdict.items():

fh.write(f"{key}={value}\n")

om_cmd.arg_set(key="overrideFile", val=overrideFile.as_posix())

if self._has_inputs: # if model has input quantities

# csvfile is based on name used for result file

csvfile = result_file.parent / f"{result_file.stem}.csv"

for i in self._inputs:

val = self._inputs[i]

if val is None:

val = [(float(self._simulate_options["startTime"]), 0.0),

(float(self._simulate_options["stopTime"]), 0.0)]

self._inputs[i] = [(float(self._simulate_options["startTime"]), 0.0),

(float(self._simulate_options["stopTime"]), 0.0)]

if float(self._simulate_options["startTime"]) != val[0][0]:

raise ModelicaSystemError(f"startTime not matched for Input {i}!")

if float(self._simulate_options["stopTime"]) != val[-1][0]:

raise ModelicaSystemError(f"stopTime not matched for Input {i}!")

# write csv file and store the name

csvfile = self._createCSVData(csvfile=csvfile)

om_cmd.arg_set(key="csvInput", val=csvfile.as_posix())

return om_cmd

def simulate(

self,

resultfile: Optional[str] = None,

simflags: Optional[str] = None,

simargs: Optional[dict[str, Optional[str | dict[str, str]]]] = None,

timeout: Optional[float] = None,

) -> None:

"""Simulate the model according to simulation options.