# -*- 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
# 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:
"""
Execute a simulation by running the compiled model.
"""
def __init__(self, runpath: pathlib.Path, modelname: str, timeout: Optional[int] = None) -> None:
"""
Initialisation
Parameters
----------
runpath : pathlib.Path
modelname : str
timeout : Optional[int], None
"""
self._runpath = pathlib.Path(runpath).resolve().absolute()
self._modelname = 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 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 executable / complied model.
Returns
-------
pathlib.Path
"""
if platform.system() == "Windows":
path_exe = self._runpath / f"{self._modelname}.exe"
else:
path_exe = self._runpath / self._modelname
if not path_exe.exists():
raise ModelicaSystemError(f"Application file path not found: {path_exe}")
return path_exe
def get_cmd(self) -> list:
"""
Run the requested simulation
Returns
-------
list
"""
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
-------
int
"""
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._modelname}.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 depreciated!
The return data can be used as input for self.args_set().
Parameters
----------
simflags : str
Returns
-------
dict
"""
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,
session: Optional[OMCSessionZMQ] = 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.
session: OMC session to be used. If unspecified, a new 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.quantitiesList: list[dict[str, Any]] = []
self.paramlist: dict[str, str] = {} # even numerical values are stored as str
self.inputlist: dict[str, list | None] = {}
# outputlist values are str before simulate(), but they can be
# np.float64 after simulate().
self.outputlist: dict[str, Any] = {}
# same for continuouslist
self.continuouslist: dict[str, Any] = {}
self.simulateOptions: dict[str, str] = {}
self.overridevariables: dict[str, str] = {}
self.simoptionsoverride: dict[str, str] = {}
self.linearOptions = {'startTime': 0.0, 'stopTime': 1.0, 'stepSize': 0.002, 'tolerance': 1e-8}
self.optimizeOptions = {'startTime': 0.0, 'stopTime': 1.0, 'numberOfIntervals': 500, 'stepSize': 0.002,
'tolerance': 1e-8}
self.linearinputs: list[str] = [] # linearization input list
self.linearoutputs: list[str] = [] # linearization output list
self.linearstates: list[str] = [] # linearization states list
if session is not None:
if not isinstance(session, OMCSessionZMQ):
raise ModelicaSystemError("Invalid session data provided!")
self.getconn = session
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.xmlFile = None
self.lmodel = lmodel # may be needed if model is derived from other model
self.modelName = modelName # Model class name
self.fileName = pathlib.Path(fileName).resolve() if fileName is not None else None # Model file/package name
self.inputFlag = False # for model with input quantity
self.simulationFlag = False # if the model is simulated?
self.outputFlag = False
self.csvFile: Optional[pathlib.Path] = None # for storing inputs condition
self.resultfile: Optional[pathlib.Path] = None # for storing result file
self.variableFilter = variableFilter
if self.fileName is not None and not self.fileName.is_file(): # if file does not exist
raise IOError(f"{self.fileName} 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.fileName is not None:
self.loadLibrary(lmodel=self.lmodel)
self.loadFile(fileName=self.fileName)
# 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)
else:
tempdir = pathlib.Path(tempfile.mkdtemp())
if not tempdir.is_dir():
raise IOError(f"{tempdir} could not be created")
logger.info("Define tempdir as %s", tempdir)
exp = f'cd("{tempdir.absolute().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.variableFilter = variableFilter
if self.variableFilter is not None:
varFilter = f'variableFilter="{self.variableFilter}"'
else:
varFilter = 'variableFilter=".*"'
buildModelResult = self.requestApi("buildModel", self.modelName, properties=varFilter)
logger.debug("OM model build result: %s", buildModelResult)
self.xmlFile = 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.xmlFile.is_file():
raise ModelicaSystemError(f"XML file not generated: {self.xmlFile}")
tree = ET.parse(self.xmlFile)
rootCQ = tree.getroot()
for attr in rootCQ.iter('DefaultExperiment'):
for key in ("startTime", "stopTime", "stepSize", "tolerance",
"solver", "outputFormat"):
self.simulateOptions[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.overridevariables:
self.paramlist[scalar["name"]] = self.overridevariables[scalar["name"]]
else:
self.paramlist[scalar["name"]] = scalar["start"]
if scalar["variability"] == "continuous":
self.continuouslist[scalar["name"]] = scalar["start"]
if scalar["causality"] == "input":
self.inputlist[scalar["name"]] = scalar["start"]
if scalar["causality"] == "output":
self.outputlist[scalar["name"]] = scalar["start"]
self.quantitiesList.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.quantitiesList
if isinstance(names, str):
r = [x for x in self.quantitiesList 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.quantitiesList if x["name"] == y]
raise ModelicaSystemError("Unhandled input for getQuantities()")
def getContinuous(self, names=None): # 4
"""
This method returns dict. The key is continuous names and value is corresponding continuous value.
usage:
>>> getContinuous()
>>> getContinuous("Name1")
>>> getContinuous(["Name1","Name2"])
"""
if not self.simulationFlag:
if names is None:
return self.continuouslist
if isinstance(names, str):
return [self.continuouslist[names]]
if isinstance(names, list):
return [self.continuouslist[x] for x in names]
else:
if names is None:
for i in self.continuouslist:
try:
value = self.getSolutions(i)
self.continuouslist[i] = value[0][-1]
except (OMCSessionException, ModelicaSystemError) as ex:
raise ModelicaSystemError(f"{i} could not be computed") from ex
return self.continuouslist
if isinstance(names, str):
if names in self.continuouslist:
value = self.getSolutions(names)
self.continuouslist[names] = value[0][-1]
return [self.continuouslist[names]]
else:
raise ModelicaSystemError(f"{names} is not continuous")
if isinstance(names, list):
valuelist = []
for i in names:
if i in self.continuouslist:
value = self.getSolutions(i)
self.continuouslist[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.paramlist
elif isinstance(names, str):
return [self.paramlist[names]]
elif isinstance(names, list):
return [self.paramlist[x] for x in names]
raise ModelicaSystemError("Unhandled input for getParameters()")
def getInputs(self, names: Optional[str | list[str]] = None) -> dict | list: # 6
"""Get input values.
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.inputlist
elif isinstance(names, str):
return [self.inputlist[names]]
elif isinstance(names, list):
return [self.inputlist[x] for x in names]
raise ModelicaSystemError("Unhandled input for getInputs()")
def getOutputs(self, names: Optional[str | list[str]] = None): # 7
"""Get output values.
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.simulationFlag:
if names is None:
return self.outputlist
elif isinstance(names, str):
return [self.outputlist[names]]
else:
return [self.outputlist[x] for x in names]
else:
if names is None:
for i in self.outputlist:
value = self.getSolutions(i)
self.outputlist[i] = value[0][-1]
return self.outputlist
elif isinstance(names, str):
if names in self.outputlist:
value = self.getSolutions(names)
self.outputlist[names] = value[0][-1]
return [self.outputlist[names]]
else:
raise KeyError(names)
elif isinstance(names, list):
valuelist = []
for i in names:
if i in self.outputlist:
value = self.getSolutions(i)
self.outputlist[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=None): # 8
"""
This method returns dict. The key is simulation option names and value is corresponding simulation option value.
If name is None then the function will return dict which contain all simulation option names as key and value as corresponding values. eg., getSimulationOptions()
usage:
>>> getSimulationOptions()
>>> getSimulationOptions("Name1")
>>> getSimulationOptions(["Name1","Name2"])
"""
if names is None:
return self.simulateOptions
elif isinstance(names, str):
return [self.simulateOptions[names]]
elif isinstance(names, list):
return [self.simulateOptions[x] for x in names]
raise ModelicaSystemError("Unhandled input for getSimulationOptions()")
def getLinearizationOptions(self, names=None): # 9
"""
This method returns dict. The key is linearize option names and value is corresponding linearize option value.
If name is None then the function will return dict which contain all linearize option names as key and value as corresponding values. eg., getLinearizationOptions()
usage:
>>> getLinearizationOptions()
>>> getLinearizationOptions("Name1")
>>> getLinearizationOptions(["Name1","Name2"])
"""
if names is None:
return self.linearOptions
elif isinstance(names, str):
return [self.linearOptions[names]]
elif isinstance(names, list):
return [self.linearOptions[x] for x in names]
raise ModelicaSystemError("Unhandled input for getLinearizationOptions()")
def getOptimizationOptions(self, names=None): # 10
"""
usage:
>>> getOptimizationOptions()
>>> getOptimizationOptions("Name1")
>>> getOptimizationOptions(["Name1","Name2"])
"""
if names is None:
return self.optimizeOptions
elif isinstance(names, str):
return [self.optimizeOptions[names]]
elif isinstance(names, list):
return [self.optimizeOptions[x] for x in names]
raise ModelicaSystemError("Unhandled input for getOptimizationOptions()")
def simulate(self, resultfile: Optional[str] = None, simflags: Optional[str] = None,
simargs: Optional[dict[str, Optional[str | dict[str, str]]]] = None,
timeout: Optional[int] = None): # 11
"""
This method simulates model according to the simulation options.
usage
>>> simulate()
>>> simulate(resultfile="a.mat")
>>> simulate(simflags="-noEventEmit -noRestart -override=e=0.3,g=10") # set runtime simulation flags
>>> simulate(simargs={"noEventEmit": None, "noRestart": None, "override": "e=0.3,g=10"}) # using simargs
"""
om_cmd = ModelicaSystemCmd(runpath=self.tempdir, modelname=self.modelName, timeout=timeout)
if resultfile is None:
# default result file generated by OM
self.resultfile = self.tempdir / f"{self.modelName}_res.mat"
elif os.path.exists(resultfile):
self.resultfile = pathlib.Path(resultfile)
else:
self.resultfile = self.tempdir / resultfile
# always define the resultfile to use
om_cmd.arg_set(key="r", val=self.resultfile.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.modelName}_override.txt"
if self.overridevariables or self.simoptionsoverride:
tmpdict = self.overridevariables.copy()
tmpdict.update(self.simoptionsoverride)
# 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.inputFlag: # if model has input quantities
for i in self.inputlist:
val = self.inputlist[i]
if val is None:
val = [(float(self.simulateOptions["startTime"]), 0.0),
(float(self.simulateOptions["stopTime"]), 0.0)]
self.inputlist[i] = [(float(self.simulateOptions["startTime"]), 0.0),
(float(self.simulateOptions["stopTime"]), 0.0)]
if float(self.simulateOptions["startTime"]) != val[0][0]:
raise ModelicaSystemError(f"startTime not matched for Input {i}!")
if float(self.simulateOptions["stopTime"]) != val[-1][0]:
raise ModelicaSystemError(f"stopTime not matched for Input {i}!")
self.csvFile = self.createCSVData() # create csv file
om_cmd.arg_set(key="csvInput", val=self.csvFile.as_posix())
# delete resultfile ...
if self.resultfile.is_file():
self.resultfile.unlink()
# ... run simulation ...
returncode = om_cmd.run()
# and check returncode *AND* resultfile
if returncode != 0 and self.resultfile.is_file():
logger.warning(f"Return code = {returncode} but result file exists!")
self.simulationFlag = True
# to extract simulation results
def getSolutions(self, varList=None, resultfile=None): # 12
"""
This method returns tuple of numpy arrays. It can be called:
•with a list of quantities name in string format as argument: it returns the simulation results of the corresponding names in the same order. Here it supports Python unpacking depending upon the number of variables assigned.
usage:
>>> getSolutions()
>>> getSolutions("Name1")
>>> getSolutions(["Name1","Name2"])
>>> getSolutions(resultfile="c:/a.mat")
>>> getSolutions("Name1",resultfile=""c:/a.mat"")
>>> getSolutions(["Name1","Name2"],resultfile=""c:/a.mat"")
"""
if resultfile is None:
resFile = self.resultfile.as_posix()
else:
resFile = resultfile
# check for result file exits
if not os.path.exists(resFile):
raise ModelicaSystemError(f"Result file does not exist {resFile}")
resultVars = self.sendExpression(f'readSimulationResultVars("{resFile}")')
self.sendExpression("closeSimulationResultFile()")
if varList is None:
return resultVars
if isinstance(varList, str):
if varList not in resultVars and varList != "time":
raise ModelicaSystemError(f"Requested data {repr(varList)} does not exist")
res = self.sendExpression(f'readSimulationResult("{resFile}", {{{varList}}})')
npRes = np.array(res)
self.sendExpression("closeSimulationResultFile()")
return npRes
if isinstance(varList, list):
for var in varList:
if var == "time":
continue
if var not in resultVars:
raise ModelicaSystemError(f"Requested data {repr(var)} does not exist")
variables = ",".join(varList)
res = self.sendExpression(f'readSimulationResult("{resFile}",{{{variables}}})')
npRes = np.array(res)
self.sendExpression("closeSimulationResultFile()")
return npRes
raise ModelicaSystemError("Unhandled input for getSolutions()")
@staticmethod
def _strip_space(name):
if isinstance(name, str):
return name.replace(" ", "")
if isinstance(name, list):
return [x.replace(" ", "") for x in name]
raise ModelicaSystemError("Unhandled input for strip_space()")
def setMethodHelper(self, args1, args2, args3, args4=None):
"""
Helper function for setParameter(),setContinuous(),setSimulationOptions(),setLinearizationOption(),setOptimizationOption()
args1 - string or list of string given by user
args2 - dict() containing the values of different variables(eg:, parameter,continuous,simulation parameters)
args3 - function name (eg; continuous, parameter, simulation, linearization,optimization)
args4 - dict() which stores the new override variables list,
"""
def apply_single(args1):
args1 = self._strip_space(args1)
value = args1.split("=")
if value[0] in args2:
if args3 == "parameter" and self.isParameterChangeable(value[0], value[1]):
args2[value[0]] = value[1]
if args4 is not None:
args4[value[0]] = value[1]
elif args3 != "parameter":
args2[value[0]] = value[1]
if args4 is not None:
args4[value[0]] = value[1]
return True
else:
raise ModelicaSystemError("Unhandled case in setMethodHelper.apply_single() - "
f"{repr(value[0])} is not a {repr(args3)} variable")
result = []
if isinstance(args1, str):
result = [apply_single(args1)]
elif isinstance(args1, list):
result = []
args1 = self._strip_space(args1)
for var in args1:
result.append(apply_single(var))
return all(result)
def setContinuous(self, cvals): # 13
"""
This method is used to set continuous values. It can be called:
with a sequence of continuous name and assigning corresponding values as arguments as show in the example below:
usage
>>> setContinuous("Name=value")
>>> setContinuous(["Name1=value1","Name2=value2"])
"""
return self.setMethodHelper(cvals, self.continuouslist, "continuous", self.overridevariables)
def setParameters(self, pvals): # 14
"""
This method is used to set parameter values. It can be called:
with a sequence of parameter name and assigning corresponding value as arguments as show in the example below:
usage
>>> setParameters("Name=value")
>>> setParameters(["Name1=value1","Name2=value2"])
"""
return self.setMethodHelper(pvals, self.paramlist, "parameter", self.overridevariables)
def isParameterChangeable(self, name, value):
q = self.getQuantities(name)
if q[0]["changeable"] == "false":
logger.verbose(f"setParameters() failed : It is not possible to set the following signal {repr(name)}. "
"It seems to be structural, final, protected or evaluated or has a non-constant binding, "
f"use sendExpression(\"setParameterValue({self.modelName}, {name}, {value})\") "
"and rebuild the model using buildModel() API")
return False
return True
def setSimulationOptions(self, simOptions): # 16
"""
This method is used to set simulation options. It can be called:
with a sequence of simulation options name and assigning corresponding values as arguments as show in the example below:
usage
>>> setSimulationOptions("Name=value")
>>> setSimulationOptions(["Name1=value1","Name2=value2"])
"""
return self.setMethodHelper(simOptions, self.simulateOptions, "simulation-option", self.simoptionsoverride)
def setLinearizationOptions(self, linearizationOptions): # 18
"""
This method is used to set linearization options. It can be called:
with a sequence of linearization options name and assigning corresponding value as arguments as show in the example below
usage
>>> setLinearizationOptions("Name=value")
>>> setLinearizationOptions(["Name1=value1","Name2=value2"])
"""
return self.setMethodHelper(linearizationOptions, self.linearOptions, "Linearization-option", None)
def setOptimizationOptions(self, optimizationOptions): # 17
"""
This method is used to set optimization options. It can be called:
with a sequence of optimization options name and assigning corresponding values as arguments as show in the example below:
usage
>>> setOptimizationOptions("Name=value")
>>> setOptimizationOptions(["Name1=value1","Name2=value2"])
"""
return self.setMethodHelper(optimizationOptions, self.optimizeOptions, "optimization-option", None)
def setInputs(self, name): # 15
"""
This method is used to set input values. It can be called:
with a sequence of input name and assigning corresponding values as arguments as show in the example below:
usage
>>> setInputs("Name=value")
>>> setInputs(["Name1=value1","Name2=value2"])
"""
if isinstance(name, str):
name = self._strip_space(name)
value = name.split("=")
if value[0] in self.inputlist:
tmpvalue = eval(value[1])
if isinstance(tmpvalue, (int, float)):
self.inputlist[value[0]] = [(float(self.simulateOptions["startTime"]), float(value[1])),
(float(self.simulateOptions["stopTime"]), float(value[1]))]
elif isinstance(tmpvalue, list):
self.checkValidInputs(tmpvalue)
self.inputlist[value[0]] = tmpvalue
self.inputFlag = True
else:
raise ModelicaSystemError(f"{value[0]} is not an input")
elif isinstance(name, list):
name = self._strip_space(name)
for var in name:
value = var.split("=")
if value[0] in self.inputlist:
tmpvalue = eval(value[1])
if isinstance(tmpvalue, (int, float)):
self.inputlist[value[0]] = [(float(self.simulateOptions["startTime"]), float(value[1])),
(float(self.simulateOptions["stopTime"]), float(value[1]))]
elif isinstance(tmpvalue, list):
self.checkValidInputs(tmpvalue)
self.inputlist[value[0]] = tmpvalue
self.inputFlag = True
else:
raise ModelicaSystemError(f"{value[0]} is not an input!")
def checkValidInputs(self, name):
if name != sorted(name, key=lambda x: x[0]):
raise ModelicaSystemError('Time value should be in increasing order')
for l in name:
if isinstance(l, tuple):
# if l[0] < float(self.simValuesList[0]):
if l[0] < float(self.simulateOptions["startTime"]):
raise ModelicaSystemError('Input time value is less than simulation startTime')
if len(l) != 2:
raise ModelicaSystemError(f'Value for {l} is in incorrect format!')
else:
raise ModelicaSystemError('Error!!! Value must be in tuple format')
def createCSVData(self) -> pathlib.Path:
start_time: float = float(self.simulateOptions["startTime"])
stop_time: float = float(self.simulateOptions["stopTime"])
# Replace None inputs with a default constant zero signal
inputs: dict[str, list[tuple[float, float]]] = {}
for input_name, input_signal in self.inputlist.items():
if input_signal is None:
inputs[input_name] = [(start_time, 0.0), (stop_time, 0.0)]
else:
inputs[input_name] = input_signal
# Collect all unique timestamps across all input signals
all_times = np.array(
sorted({t for signal in inputs.values() for t, _ in signal}),
dtype=float
)
# Interpolate missing values
interpolated_inputs: dict[str, np.ndarray] = {}
for signal_name, signal_values in inputs.items():
signal = np.array(signal_values)
interpolated_inputs[signal_name] = np.interp(
all_times,
signal[:, 0], # times
signal[:, 1] # values
)
# Write CSV file
input_names = list(interpolated_inputs.keys())
header = ['time'] + input_names + ['end']
csv_rows = [header]
for i, t in enumerate(all_times):
row = [
t, # time
*(interpolated_inputs[name][i] for name in input_names), # input values
0 # trailing 'end' column
]
csv_rows.append(row)
csvFile = self.tempdir / f'{self.modelName}.csv'
with open(file=csvFile, mode="w", encoding="utf-8", newline="") as fh:
writer = csv.writer(fh)
writer.writerows(csv_rows)
return csvFile
# to convert Modelica model to FMU
def convertMo2Fmu(self, version="2.0", fmuType="me_cs", fileNamePrefix="