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1161 lines (1018 loc) · 47.9 KB
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# -*- 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
import logging
import os
import platform
import re
import subprocess
import tempfile
import xml.etree.ElementTree as ET
import numpy as np
import importlib
import pathlib
from dataclasses import dataclass
from typing import Optional
from OMPython.OMCSession import OMCSessionBase, OMCSessionZMQ
# define logger using the current module name as ID
logger = logging.getLogger(__name__)
class ModelicaSystemError(Exception):
pass
@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 ModelicaSystem:
def __init__(
self,
fileName: Optional[str | os.PathLike] = 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] = None,
verbose: bool = True,
raiseerrors: bool = False,
omhome: Optional[str] = None,
session: Optional[OMCSessionBase] = 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.
verbose: If True, enable verbose logging.
raiseerrors: If True, raise exceptions instead of just logging
OpenModelica errors.
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.
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 Exception("Cannot create ModelicaSystem object without any arguments")
self.quantitiesList = []
self.paramlist = {}
self.inputlist = {}
self.outputlist = {}
self.continuouslist = {}
self.simulateOptions = {}
self.overridevariables = {}
self.simoptionsoverride = {}
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 = [] # linearization input list
self.linearoutputs = [] # linearization output list
self.linearstates = [] # linearization states list
self.tempdir = ""
self._verbose = verbose
if session is not None:
self.getconn = session
else:
self.getconn = OMCSessionZMQ(omhome=omhome)
# needed for properly deleting the session
self._omc_log_file = self.getconn._omc_log_file
self._omc_process = self.getconn._omc_process
# set commandLineOptions if provided by users
self.setCommandLineOptions(commandLineOptions=commandLineOptions)
if lmodel is None:
lmodel = []
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 = '' # for storing inputs condition
self.resultfile = "" # for storing result file
self.variableFilter = variableFilter
self._raiseerrors = raiseerrors
if fileName is not None and not self.fileName.is_file(): # if file does not exist
raise IOError(f"File Error: {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.setTempDirectory(customBuildDirectory)
if fileName is not None:
self.loadLibrary()
self.loadFile()
# allow directly loading models from MSL without fileName
if fileName is None and modelName is not None:
self.loadLibrary()
self.buildModel(variableFilter)
def setCommandLineOptions(self, commandLineOptions: str):
# set commandLineOptions if provided by users
if commandLineOptions is None:
return
exp = f'setCommandLineOptions("{commandLineOptions}")'
if not self.sendExpression(exp):
self._check_error()
def loadFile(self):
# load file
loadMsg = self.sendExpression(f'loadFile("{self.fileName.as_posix()}")')
# Show notification or warnings to the user when verbose=True OR if some error occurred i.e., not result
if self._verbose or not loadMsg:
self._check_error()
# for loading file/package, loading model and building model
def loadLibrary(self):
# load Modelica standard libraries or Modelica files if needed
for element in self.lmodel:
if element is not None:
if isinstance(element, str):
if element.endswith(".mo"):
apiCall = "loadFile"
else:
apiCall = "loadModel"
result = self.requestApi(apiCall, element)
elif isinstance(element, tuple):
if not element[1]:
libname = f"loadModel({element[0]})"
else:
libname = f'loadModel({element[0]}, {{"{element[1]}"}})'
result = self.sendExpression(libname)
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')
# Show notification or warnings to the user when verbose=True OR if some error occurred i.e., not result
if self._verbose or not result:
self._check_error()
def setTempDirectory(self, customBuildDirectory):
# 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(customBuildDirectory, " does not exist")
self.tempdir = customBuildDirectory
else:
self.tempdir = tempfile.mkdtemp()
if not os.path.exists(self.tempdir):
raise IOError(self.tempdir, " cannot be created")
logger.info("Define tempdir as %s", self.tempdir)
exp = f'cd("{pathlib.Path(self.tempdir).as_posix()}")'
self.sendExpression(exp)
def getWorkDirectory(self):
return self.tempdir
def _run_cmd(self, cmd: list):
logger.debug("Run OM command %s in %s", cmd, self.tempdir)
if platform.system() == "Windows":
dllPath = ""
# set the process environment from the generated .bat file in windows which should have all the dependencies
batFilePath = pathlib.Path(self.tempdir) / f"{self.modelName}.bat"
if not batFilePath.exists():
ModelicaSystemError("Batch file (*.bat) does not exist " + batFilePath)
with open(batFilePath, 'r') as file:
for line in file:
match = re.match(r"^SET PATH=([^%]*)", line, re.IGNORECASE)
if match:
dllPath = match.group(1).strip(';') # Remove any trailing semicolons
my_env = os.environ.copy()
my_env["PATH"] = dllPath + 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:
p = subprocess.Popen(cmd, env=my_env, stdout=subprocess.PIPE,
stderr=subprocess.PIPE, cwd=self.tempdir)
stdout, stderr = p.communicate()
stdout = stdout.decode('ascii').strip()
stderr = stderr.decode('ascii').strip()
if stderr:
raise ModelicaSystemError(f"Error running command {cmd}: {stderr}")
if self._verbose and stdout:
logger.info("OM output for command %s:\n%s", cmd, stdout)
p.wait()
p.terminate()
except Exception as e:
raise ModelicaSystemError(f"Exception {type(e)} running command {cmd}: {e}")
def _check_error(self):
errstr = self.sendExpression("getErrorString()")
if not errstr:
return
self._raise_error(errstr=errstr)
def _raise_error(self, errstr: str):
if self._raiseerrors:
raise ModelicaSystemError(f"OM error: {errstr}")
else:
logger.error(errstr)
def buildModel(self, variableFilter=None):
if variableFilter is not None:
self.variableFilter = variableFilter
if self.variableFilter is not None:
varFilter = f'variableFilter="{self.variableFilter}"'
else:
varFilter = 'variableFilter=".*"'
logger.debug("varFilter=%s", varFilter)
buildModelResult = self.requestApi("buildModel", self.modelName, properties=varFilter)
if self._verbose:
logger.info("OM model build result: %s", buildModelResult)
self._check_error()
self.xmlFile = pathlib.Path(buildModelResult[0]).parent / buildModelResult[1]
self.xmlparse()
def sendExpression(self, expr, parsed=True):
logger.debug("sendExpression(%r, %r)", expr, parsed)
return self.getconn.sendExpression(expr, parsed)
# 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}()'
try:
res = self.sendExpression(exp)
except Exception as e:
self._raise_error(errstr=f"Exception {type(e)} raised: {e}")
res = None
return res
def xmlparse(self):
if not self.xmlFile.exists():
self._raise_error(errstr=f"XML file not generated: {self.xmlFile}")
return
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)
start = None
min = None
max = None
unit = None
for att in ch:
start = att.get('start')
min = att.get('min')
max = att.get('max')
unit = att.get('unit')
scalar["start"] = start
scalar["min"] = min
scalar["max"] = max
scalar["unit"] = 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=None): # 3
"""
This method returns list of dictionaries. It displays details of quantities such as name, value, changeable, and description, where changeable means if value for corresponding quantity name is changeable or not. It can be called :
usage:
>>> getQuantities()
>>> getQuantities("Name1")
>>> getQuantities(["Name1","Name2"])
"""
if names is None:
return self.quantitiesList
elif isinstance(names, str):
return [x for x in self.quantitiesList if x["name"] == names]
elif isinstance(names, list):
return [x for y in names for x in self.quantitiesList if x["name"] == y]
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
elif isinstance(names, str):
return [self.continuouslist.get(names, "NotExist")]
elif isinstance(names, list):
return [self.continuouslist.get(x, "NotExist") 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 Exception:
raise ModelicaSystemError(f"OM error: {i} could not be computed")
return self.continuouslist
elif isinstance(names, str):
if names in self.continuouslist:
value = self.getSolutions(names)
self.continuouslist[names] = value[0][-1]
return [self.continuouslist.get(names)]
else:
raise ModelicaSystemError(f"OM error: {names} is not continuous")
elif 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"OM error: {i} is not continuous")
return valuelist
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.get(names, "NotExist")]
elif isinstance(names, list):
return ([self.paramlist.get(x, "NotExist") for x in names])
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]
>>> mod.getInputs("ThisInputDoesNotExist")
['NotExist']
"""
if names is None:
return self.inputlist
elif isinstance(names, str):
return [self.inputlist.get(names, "NotExist")]
elif isinstance(names, list):
return ([self.inputlist.get(x, "NotExist") for x in names])
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']
>>> mod.getOutputs("ThisOutputDoesNotExist")
['NotExist']
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.get(names, "NotExist")]
else:
return ([self.outputlist.get(x, "NotExist") 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.get(names)]
else:
return (names, " is not Output")
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:
return (i, "is not Output")
return valuelist
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.get(names, "NotExist")]
elif isinstance(names, list):
return ([self.simulateOptions.get(x, "NotExist") for x in names])
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.get(names, "NotExist")]
elif isinstance(names, list):
return ([self.linearOptions.get(x, "NotExist") for x in names])
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.get(names, "NotExist")]
elif isinstance(names, list):
return ([self.optimizeOptions.get(x, "NotExist") for x in names])
def get_exe_file(self) -> pathlib.Path:
"""Get path to model executable."""
if platform.system() == "Windows":
return pathlib.Path(self.tempdir) / f"{self.modelName}.exe"
else:
return pathlib.Path(self.tempdir) / self.modelName
def simulate(self, resultfile=None, simflags=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
"""
if resultfile is None:
r = ""
self.resultfile = (pathlib.Path(self.tempdir) / f"{self.modelName}_res.mat").as_posix()
else:
if os.path.exists(resultfile):
self.resultfile = resultfile
else:
self.resultfile = (pathlib.Path(self.tempdir) / resultfile).as_posix()
r = " -r=" + self.resultfile
# allow runtime simulation flags from user input
if simflags is None:
simflags = ""
else:
simflags = " " + simflags
overrideFile = pathlib.Path(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(overrideFile, "w") as file:
for key, value in tmpdict.items():
file.write(f"{key}={value}\n")
override = " -overrideFile=" + overrideFile.as_posix()
else:
override = ""
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]:
errstr = f"!!! startTime not matched for Input {i}"
self._raise_error(errstr=errstr)
return
if float(self.simulateOptions["stopTime"]) != val[-1][0]:
errstr = f"!!! stopTime not matched for Input {i}"
self._raise_error(errstr=errstr)
return
self.createCSVData() # create csv file
csvinput = " -csvInput=" + self.csvFile
else:
csvinput = ""
exe_file = self.get_exe_file()
if not exe_file.exists():
raise Exception(f"Error: Application file path not found: {exe_file}")
cmd = exe_file.as_posix() + override + csvinput + r + simflags
cmd = cmd.split(" ")
self._run_cmd(cmd=cmd)
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
else:
resFile = resultfile
# check for result file exits
if not os.path.exists(resFile):
errstr = f"Error: Result file does not exist {resFile}"
self._raise_error(errstr=errstr)
return
resultVars = self.sendExpression(f'readSimulationResultVars("{resFile}")')
self.sendExpression("closeSimulationResultFile()")
if varList is None:
return resultVars
elif isinstance(varList, str):
if varList not in resultVars and varList != "time":
self._raise_error(errstr=f'!!! {varList} does not exist')
return
res = self.sendExpression(f'readSimulationResult("{resFile}", {{{varList}}})')
npRes = np.array(res)
self.sendExpression("closeSimulationResultFile()")
return npRes
elif isinstance(varList, list):
# varList, = varList
for v in varList:
if v == "time":
continue
if v not in resultVars:
self._raise_error(errstr=f'!!! {v} does not exist')
return
variables = ",".join(varList)
res = self.sendExpression(f'readSimulationResult("{resFile}",{{{variables}}})')
npRes = np.array(res)
self.sendExpression("closeSimulationResultFile()")
return npRes
def strip_space(self, name):
if isinstance(name, str):
return name.replace(" ", "")
elif isinstance(name, list):
return [x.replace(" ", "") for x in name]
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:
self._raise_error(errstr=f'"{value[0]}" is not a {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"):
if self._verbose:
logger.info("setParameters() failed : It is not possible to set "
f'the following signal "{name}", It seems to be structural, final, '
"protected or evaluated or has a non-constant binding, use sendExpression("
f"setParameterValue({self.modelName}, {name}, {value}), "
"parsed=false) 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) or isinstance(tmpvalue, 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:
errstr = value[0] + " is not an input"
self._raise_error(errstr=errstr)
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) or isinstance(tmpvalue, 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:
errstr = value[0] + " is not an input"
self._raise_error(errstr=errstr)
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"]):
ModelicaSystemError('Input time value is less than simulation startTime')
if len(l) != 2:
ModelicaSystemError(f'Value for {l} is in incorrect format!')
else:
ModelicaSystemError('Error!!! Value must be in tuple format')
def createCSVData(self) -> None:
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)
self.csvFile: str = (pathlib.Path(self.tempdir) / f'{self.modelName}.csv').as_posix()
with open(self.csvFile, "w", newline="") as f:
writer = csv.writer(f)
writer.writerows(csv_rows)
# to convert Modelica model to FMU
def convertMo2Fmu(self, version="2.0", fmuType="me_cs", fileNamePrefix="<default>", includeResources=True): # 19
"""
This method is used to generate FMU from the given Modelica model. It creates "modelName.fmu" in the current working directory. It can be called:
with no arguments
with arguments of https://build.openmodelica.org/Documentation/OpenModelica.Scripting.translateModelFMU.html
usage
>>> convertMo2Fmu()
>>> convertMo2Fmu(version="2.0", fmuType="me|cs|me_cs", fileNamePrefix="<default>", includeResources=True)
"""