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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
deleted file mode 100644
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--- a/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', [])