Merge branch 'ModelicaSystem_rewrite_set_functions' into remove_depreciated_functionality

Author: syntron

OMPython/ModelicaSystem.py

@@ -32,6 +32,7 @@
  CONDITIONS OF OSMC-PL.
 """
 
+import ast
 import csv
 from dataclasses import dataclass
 import importlib
@@ -944,164 +945,252 @@ def getSolutions(self, varList=None, resultfile=None):  # 12
         return np_res
 
     @staticmethod
-    def _strip_space(name):
-        if isinstance(name, str):
-            return name.replace(" ", "")
+    def _prepare_input_data(
+            raw_input: str | list[str] | dict[str, Any],
+    ) -> dict[str, str]:
+        """
+        Convert raw input to a structured dictionary {'key1': 'value1', 'key2': 'value2'}.
+        """
+
+        def prepare_str(str_in: str) -> dict[str, str]:
+            str_in = str_in.replace(" ", "")
+            key_val_list: list[str] = str_in.split("=")
+            if len(key_val_list) != 2:
+                raise ModelicaSystemError(f"Invalid 'key=value' pair: {str_in}")
+
+            input_data_from_str: dict[str, str] = {key_val_list[0]: key_val_list[1]}
+
+            return input_data_from_str
+
+        input_data: dict[str, str] = {}
+
+        if isinstance(raw_input, str):
+            warnings.warn(message="The definition of values to set should use a dictionary, "
+                                  "i.e. {'key1': 'val1', 'key2': 'val2', ...}. Please convert all cases which "
+                                  "use a string ('key=val') or list ['key1=val1', 'key2=val2', ...]",
+                          category=DeprecationWarning,
+                          stacklevel=3)
+            return prepare_str(raw_input)
+
+        if isinstance(raw_input, list):
+            warnings.warn(message="The definition of values to set should use a dictionary, "
+                                  "i.e. {'key1': 'val1', 'key2': 'val2', ...}. Please convert all cases which "
+                                  "use a string ('key=val') or list ['key1=val1', 'key2=val2', ...]",
+                          category=DeprecationWarning,
+                          stacklevel=3)
+
+            for item in raw_input:
+                input_data |= prepare_str(item)
 
-        if isinstance(name, list):
-            return [x.replace(" ", "") for x in name]
+            return input_data
 
-        raise ModelicaSystemError("Unhandled input for strip_space()")
+        if isinstance(raw_input, dict):
+            for key, val in raw_input.items():
+                # convert all values to strings to align it on one type: dict[str, str]
+                # spaces have to be removed as setInput() could take list of tuples as input and spaces would
+                str_val = str(val).replace(' ', '')
+                if ' ' in key or ' ' in str_val:
+                    raise ModelicaSystemError(f"Spaces not allowed in key/value pairs: {repr(key)} = {repr(val)}!")
+                input_data[key] = str_val
 
-    def setMethodHelper(self, args1, args2, args3, args4=None):
+            return input_data
+
+        raise ModelicaSystemError(f"Invalid type of input: {type(raw_input)}")
+
+    def _set_method_helper(
+            self,
+            inputdata: dict[str, str],
+            classdata: dict[str, Any],
+            datatype: str,
+            overwritedata: Optional[dict[str, str]] = None,
+    ) -> bool:
         """
-        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,
+        Helper function for:
+        * setParameter()
+        * setContinuous()
+        * setSimulationOptions()
+        * setLinearizationOption()
+        * setOptimizationOption()
+        * setInputs()
+
+        Parameters
+        ----------
+        inputdata
+            string or list of string given by user
+        classdata
+            dict() containing the values of different variables (eg: parameter, continuous, simulation parameters)
+        datatype
+            type identifier (eg; continuous, parameter, simulation, linearization, optimization)
+        overwritedata
+            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:
+        inputdata_status: dict[str, bool] = {}
+        for key, val in inputdata.items():
+            if key not in classdata:
                 raise ModelicaSystemError("Unhandled case in setMethodHelper.apply_single() - "
-                                          f"{repr(value[0])} is not a {repr(args3)} variable")
+                                          f"{repr(key)} is not a {repr(datatype)} variable")
+
+            status = False
+            if datatype == "parameter" and not self.isParameterChangeable(key):
+                logger.debug(f"It is not possible to set the parameter {repr(key)}. It seems to be "
+                             "structural, final, protected, evaluated or has a non-constant binding. "
+                             "Use sendExpression(...) and rebuild the model using buildModel() API; example: "
+                             "sendExpression(\"setParameterValue("
+                             f"{self.modelName}, {key}, {val if val is not None else '<?value?>'}"
+                             ")\") ")
+            else:
+                classdata[key] = val
+                if overwritedata is not None:
+                    overwritedata[key] = val
+                status = True
 
-        result = []
-        if isinstance(args1, str):
-            result = [apply_single(args1)]
+            inputdata_status[key] = status
 
-        elif isinstance(args1, list):
-            result = []
-            args1 = self._strip_space(args1)
-            for var in args1:
-                result.append(apply_single(var))
+        return all(inputdata_status.values())
 
-        return all(result)
+    def isParameterChangeable(
+            self,
+            name: str,
+    ) -> bool:
+        q = self.getQuantities(name)
+        if q[0]["changeable"] == "false":
+            return False
+        return True
 
-    def setContinuous(self, cvals):  # 13
+    def setContinuous(self, cvals: str | list[str] | dict[str, Any]) -> bool:
         """
         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"])
+        >>> setContinuous("Name=value")  # depreciated
+        >>> setContinuous(["Name1=value1","Name2=value2"])  # depreciated
+        >>> setContinuous(cvals={"Name1": "value1", "Name2": "value2"})
         """
-        return self.setMethodHelper(cvals, self.continuouslist, "continuous", self.overridevariables)
+        inputdata = self._prepare_input_data(raw_input=cvals)
+
+        return self._set_method_helper(
+            inputdata=inputdata,
+            classdata=self.continuouslist,
+            datatype="continuous",
+            overwritedata=self.overridevariables)
 
-    def setParameters(self, pvals):  # 14
+    def setParameters(self, pvals: str | list[str] | dict[str, Any]) -> bool:
         """
         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"])
+        >>> setParameters("Name=value")  # depreciated
+        >>> setParameters(["Name1=value1","Name2=value2"])  # depreciated
+        >>> setParameters(pvals={"Name1": "value1", "Name2": "value2"})
         """
-        return self.setMethodHelper(pvals, self.paramlist, "parameter", self.overridevariables)
+        inputdata = self._prepare_input_data(raw_input=pvals)
 
-    def isParameterChangeable(self, name, value):
-        q = self.getQuantities(name)
-        if q[0]["changeable"] == "false":
-            logger.debug(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
+        return self._set_method_helper(
+            inputdata=inputdata,
+            classdata=self.paramlist,
+            datatype="parameter",
+            overwritedata=self.overridevariables)
 
-    def setSimulationOptions(self, simOptions):  # 16
+    def setSimulationOptions(self, simOptions: str | list[str] | dict[str, Any]) -> bool:
         """
         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"])
+        >>> setSimulationOptions("Name=value")  # depreciated
+        >>> setSimulationOptions(["Name1=value1","Name2=value2"])  # depreciated
+        >>> setSimulationOptions(simOptions={"Name1": "value1", "Name2": "value2"})
         """
-        return self.setMethodHelper(simOptions, self.simulateOptions, "simulation-option", self.simoptionsoverride)
+        inputdata = self._prepare_input_data(raw_input=simOptions)
 
-    def setLinearizationOptions(self, linearizationOptions):  # 18
+        return self._set_method_helper(
+            inputdata=inputdata,
+            classdata=self.simulateOptions,
+            datatype="simulation-option",
+            overwritedata=self.simoptionsoverride)
+
+    def setLinearizationOptions(self, linearizationOptions: str | list[str] | dict[str, Any]) -> bool:
         """
         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"])
+        >>> setLinearizationOptions("Name=value")  # depreciated
+        >>> setLinearizationOptions(["Name1=value1","Name2=value2"])  # depreciated
+        >>> setLinearizationOptions(linearizationOtions={"Name1": "value1", "Name2": "value2"})
         """
-        return self.setMethodHelper(linearizationOptions, self.linearOptions, "Linearization-option", None)
+        inputdata = self._prepare_input_data(raw_input=linearizationOptions)
+
+        return self._set_method_helper(
+            inputdata=inputdata,
+            classdata=self.linearOptions,
+            datatype="Linearization-option",
+            overwritedata=None)
 
-    def setOptimizationOptions(self, optimizationOptions):  # 17
+    def setOptimizationOptions(self, optimizationOptions: str | list[str] | dict[str, Any]) -> bool:
         """
         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"])
+        >>> setOptimizationOptions("Name=value")  # depreciated
+        >>> setOptimizationOptions(["Name1=value1","Name2=value2"])  # depreciated
+        >>> setOptimizationOptions(optimizationOptions={"Name1": "value1", "Name2": "value2"})
         """
-        return self.setMethodHelper(optimizationOptions, self.optimizeOptions, "optimization-option", None)
+        inputdata = self._prepare_input_data(raw_input=optimizationOptions)
+
+        return self._set_method_helper(
+            inputdata=inputdata,
+            classdata=self.optimizeOptions,
+            datatype="optimization-option",
+            overwritedata=None)
 
-    def setInputs(self, name):  # 15
+    def setInputs(self, name: str | list[str] | dict[str, Any]) -> bool:
         """
-        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"])
+        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. Compared to other set*() methods this is a
+        special case as value could be a list of tuples - these are converted to a string in _prepare_input_data()
+        and restored here via ast.literal_eval().
+
+        >>> setInputs("Name=value")  # depreciated
+        >>> setInputs(["Name1=value1","Name2=value2"])  # depreciated
+        >>> setInputs(name={"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
+        inputdata = self._prepare_input_data(raw_input=name)
+
+        for key, val in inputdata.items():
+            if key not in self.inputlist:
+                raise ModelicaSystemError(f"{key} is not an input")
+
+            if not isinstance(val, str):
+                raise ModelicaSystemError(f"Invalid data in input for {repr(key)}: {repr(val)}")
+
+            val_evaluated = ast.literal_eval(val)
+
+            if isinstance(val_evaluated, (int, float)):
+                self.inputlist[key] = [(float(self.simulateOptions["startTime"]), float(val)),
+                                       (float(self.simulateOptions["stopTime"]), float(val))]
+            elif isinstance(val_evaluated, list):
+                if not all([isinstance(item, tuple) for item in val_evaluated]):
+                    raise ModelicaSystemError("Value for setInput() must be in tuple format; "
+                                              f"got {repr(val_evaluated)}")
+                if val_evaluated != sorted(val_evaluated, key=lambda x: x[0]):
+                    raise ModelicaSystemError("Time value should be in increasing order; "
+                                              f"got {repr(val_evaluated)}")
+
+                for item in val_evaluated:
+                    if item[0] < float(self.simulateOptions["startTime"]):
+                        raise ModelicaSystemError(f"Time value in {repr(item)} of {repr(val_evaluated)} is less "
+                                                  "than the simulation start time")
+                    if len(item) != 2:
+                        raise ModelicaSystemError(f"Value {repr(item)} of {repr(val_evaluated)} "
+                                                  "is in incorrect format!")
+
+                self.inputlist[key] = val_evaluated
             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')
+                raise ModelicaSystemError(f"Data cannot be evaluated for {repr(key)}: {repr(val)}")
+
+            self.inputFlag = True
+
+        return True
 
     def createCSVData(self) -> pathlib.Path:
         start_time: float = float(self.simulateOptions["startTime"])