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diff --git a/opendc-experiments/opendc-experiments-m3sa/src/main/python/models/Model.py b/opendc-experiments/opendc-experiments-m3sa/src/main/python/models/Model.py
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+"""
+A model is the output of simulator. It contains the data the simulator output, under a certain topology, seed,
+workload, datacenter configuration, etc. A model is further used in the analyzer as part of the MultiModel class,
+and further in the MetaModel class.
+
+:param sim: the simulation data of the model
+"""
+import json
+from dataclasses import dataclass, field
+
+@dataclass
+class Model:
+    """
+    Represents a single simulation output containing various data metrics collected under specific simulation conditions.
+    A Model object stores raw and processed simulation data and is designed to interact with higher-level structures like
+    MultiModel and MetaModel for complex data analysis.
+
+    Attributes:
+        raw_sim_data (list): Initial raw data from the simulator output.
+        processed_sim_data (list): Data derived from raw_sim_data after applying certain processing operations like aggregation or smoothing.
+        cumulative_time_series_values (list): Stores cumulative data values useful for time series analysis.
+        id (int): Unique identifier for the model, typically used for tracking and referencing within analysis tools.
+        path (str): Base path for storing or accessing related data files.
+        cumulated (float): Cumulative sum of processed data, useful for quick summaries and statistical analysis.
+        experiment_name (str): A descriptive name for the experiment associated with this model, potentially extracted from external metadata.
+        margins_of_error (list): Stores error margins associated with the data, useful for uncertainty analysis.
+        topologies (list): Describes the network or system topologies used during the simulation.
+        workloads (list): Lists the types of workloads applied during the simulation, affecting the simulation's applicability and scope.
+        allocation_policies (list): Details the resource allocation policies used, which influence the simulation outcomes.
+        carbon_trace_paths (list): Paths to data files containing carbon output or usage data, important for environmental impact studies.
+
+    Methods:
+        parse_trackr(): Reads additional configuration and metadata from a JSON file named 'trackr.json', enhancing the model with detailed context information.
+
+    Usage:
+        Model objects are typically instantiated with raw data from simulation outputs and an identifier. After instantiation,
+        the 'parse_trackr' method can be called to load additional experimental details from a corresponding JSON file.
+    """
+
+    path: str
+    raw_sim_data: list
+    id: int
+    processed_sim_data: list = field(default_factory=list)
+    cumulative_time_series_values: list = field(default_factory=list)
+    cumulated: float = 0.0
+    experiment_name: str = ""
+    margins_of_error: list = field(default_factory=list)
+    topologies: list = field(default_factory=list)
+    workloads: list = field(default_factory=list)
+    allocation_policies: list = field(default_factory=list)
+    carbon_trace_paths: list = field(default_factory=list)
+
+    def parse_trackr(self):
+        """
+        Parses the 'trackr.json' file located in the model's base path to extract and store detailed experimental metadata.
+        This method enhances the model with comprehensive contextual information about the simulation environment.
+
+        :return: None
+        :side effect: Updates model attributes with data from the 'trackr.json' file, such as experiment names, topologies, and policies.
+        :raises FileNotFoundError: If the 'trackr.json' file does not exist at the specified path.
+        :raises json.JSONDecodeError: If there is an error parsing the JSON data.
+        """
+        trackr_path = self.path + "/trackr.json"
+        with open(trackr_path) as f:
+            trackr = json.load(f)
+            self.experiment_name = trackr.get(self.id, {}).get('name', "")
+            self.topologies = trackr.get(self.id, {}).get('topologies', [])
+            self.workloads = trackr.get(self.id, {}).get('workloads', [])
+            self.allocation_policies = trackr.get(self.id, {}).get('allocationPolicies', [])
+            self.carbon_trace_paths = trackr.get(self.id, {}).get('carbonTracePaths', [])