diff --git a/baler/baler.py b/baler/baler.py
index e37c275d..d90c514f 100644
--- a/baler/baler.py
+++ b/baler/baler.py
@@ -65,6 +65,7 @@ def main():
     elif mode == "train":
         check_enabled_profilers(
             perform_training,
+            output_path,
             pytorch_profile,
             energy_profile,
             output_path,
@@ -92,13 +93,14 @@ def main():
 
 
 def check_enabled_profilers(
-    f, pytorchProfile=False, energyProfile=False, *args, **kwargs
+    f, output_path="/", pytorchProfile=False, energyProfile=False, *args, **kwargs
 ):
     """
     Conditionally apply profiling based on the given boolean flags.
 
     Args:
         f (callable): The function to be potentially profiled.
+        output_path (str): The path where the profiling logs and reports are to be saved.
         pytorchProfile (bool): Whether to apply PyTorch profiling.
         energyProfile (bool): Whether to apply energy profiling.
 
@@ -108,10 +110,10 @@ def check_enabled_profilers(
     if pytorchProfile and not energyProfile:
         return pytorch_profile(f, *args, **kwargs)
     elif energyProfile and not pytorchProfile:
-        return energy_profiling(f, "baler_training", 1, *args, **kwargs)
+        return energy_profiling(f, output_path, "baler_training", 1, *args, **kwargs)
     elif pytorchProfile and energyProfile:
         return pytorch_profile(
-            energy_profiling, f, "baler_training", 1, *args, **kwargs
+            energy_profiling, f, output_path, "baler_training", 1, *args, **kwargs
         )
     else:
         return f(*args, **kwargs)
diff --git a/baler/modules/profile_plotting.py b/baler/modules/profile_plotting.py
new file mode 100644
index 00000000..97e5124e
--- /dev/null
+++ b/baler/modules/profile_plotting.py
@@ -0,0 +1,252 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+from matplotlib.dates import date2num
+import seaborn as sns
+import os
+
+
+def plot(profiling_path, func):
+    """
+    Visualizes the data that is generated from .CSV logs
+    of codecarbon by plotting graphs .The codecarbon package
+    is utilized for tracking amount of electricity that is
+    consumed by the given function and the amount of CO2 emission.
+
+    Args:
+        profiling_path (str): The path where the .CSV logs are generated
+        func (callable): The function to be profiled.
+
+    Returns:
+        Void. The plots are stored in the `profiling_path` location
+    """
+
+    # Load CSV data into a DataFrame
+    emission_csv_path = os.path.join(profiling_path, "emissions.csv")
+    data = pd.read_csv(emission_csv_path)
+
+    # Define the scaling factor (adjust this value according to your needs)
+    scaling_factor = 10**6
+
+    # List of columns to scale up (you can modify this as per your requirement)
+    columns_to_scale = [
+        "duration",
+        "emissions",
+        "emissions_rate",
+        "cpu_power",
+        "gpu_power",
+        "ram_power",
+        "cpu_energy",
+        "gpu_energy",
+        "ram_energy",
+        "energy_consumed",
+    ]
+
+    # Scale up the values in the selected columns
+    data[columns_to_scale] *= scaling_factor
+
+    # Convert the 'timestamp' column to datetime
+    data["timestamp"] = pd.to_datetime(data["timestamp"], errors="coerce")
+
+    # Plot 1: Time series graph for 'timestamp' vs 'duration'
+    plt.figure(figsize=(10, 6))
+    plt.plot(data["timestamp"], data["duration"], marker="o")
+    plt.xlabel("Timestamp")
+    plt.ylabel("Duration (in seconds) x 1e-6")
+    plt.title("Time Series " + f"{func.__name__}" + " : Duration vs Timestamp")
+    plt.xticks(rotation=45)
+    plt.grid(True)
+    plt.tight_layout()
+    plt.savefig(os.path.join(profiling_path, f"{func.__name__}" + "_" + "plot1.png"))
+    plt.close()
+    # plt.show()
+
+    # Plot 2 and 3: Time series graph for 'timestamp' vs 'emissions' and 'emissions_rate'
+    fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 10), sharex=True)
+
+    ax1.plot(data["timestamp"], data["emissions"], marker="o")
+    ax1.set_ylabel("Emissions in CO₂eq (in kg) x 1e-6")
+    ax1.set_title("Time Series " + f"{func.__name__}" + " : Emissions vs Timestamp")
+    ax1.grid(True)
+
+    ax2.plot(data["timestamp"], data["emissions_rate"], marker="o")
+    ax2.set_xlabel("Timestamp")
+    ax2.set_ylabel("Emissions Rate in CO₂eq in (kg/second) x 1e-6")
+    ax2.set_title(
+        "Time Series " + f"{func.__name__}" + " : Emissions Rate vs Timestamp"
+    )
+    ax2.grid(True)
+
+    plt.xticks(rotation=45)
+    plt.tight_layout()
+    plt.savefig(os.path.join(profiling_path, f"{func.__name__}" + "_" + "plot2.png"))
+    plt.close()
+    # plt.show()
+
+    # Plot 4, 5, 6, and 7: Time series graph for 'timestamp' vs 'ram_power', 'cpu_energy', 'ram_energy', and 'energy_consumed'
+    fig, axs = plt.subplots(2, 2, figsize=(12, 10), sharex=True)
+
+    axs[0, 0].plot(data["timestamp"], data["ram_power"], marker="o")
+    axs[0, 0].set_ylabel("RAM Power (in watt)")
+    axs[0, 0].set_title(
+        "Time Series " + f"{func.__name__}" + " : RAM Power vs Timestamp"
+    )
+    axs[0, 0].grid(True)
+
+    axs[0, 1].plot(data["timestamp"], data["cpu_energy"], marker="o")
+    axs[0, 1].set_ylabel("CPU Energy in (kilo-watt) x 1e-6")
+    axs[0, 1].set_title(
+        "Time Series " + f"{func.__name__}" + " : CPU Energy vs Timestamp"
+    )
+    axs[0, 1].grid(True)
+
+    axs[1, 0].plot(data["timestamp"], data["ram_energy"], marker="o")
+    axs[1, 0].set_xlabel("Timestamp")
+    axs[1, 0].set_ylabel("RAM Energy (in kilo-watt) x 1e-6")
+    axs[1, 0].set_title(
+        "Time Series " + f"{func.__name__}" + " : RAM Energy vs Timestamp"
+    )
+    axs[1, 0].grid(True)
+
+    axs[1, 1].plot(data["timestamp"], data["energy_consumed"], marker="o")
+    axs[1, 1].set_xlabel("Timestamp")
+    axs[1, 1].set_ylabel("Energy Consumed (in kilo-watt) x 1e-6")
+    axs[1, 1].set_title(
+        "Time Series " + f"{func.__name__}" + " : Energy Consumed vs Timestamp"
+    )
+    axs[1, 1].grid(True)
+
+    plt.xticks(rotation=45)
+    plt.tight_layout()
+    plt.savefig(os.path.join(profiling_path, f"{func.__name__}" + "_" + "plot3.png"))
+    plt.close()
+    # plt.show()
+
+    # Measure central tendencies of the data for each metric
+    emissions_central = data["emissions"].describe()
+    emissions_rate_central = data["emissions_rate"].describe()
+    ram_power_central = data["ram_power"].describe()
+    cpu_energy_central = data["cpu_energy"].describe()
+    ram_energy_central = data["ram_energy"].describe()
+    energy_consumed_central = data["energy_consumed"].describe()
+
+    # Plot histograms for each metric with central tendencies
+    plt.figure(figsize=(15, 10))
+
+    # Emissions
+    plt.subplot(2, 3, 1)
+    sns.histplot(data["emissions"], bins=20, kde=True, color="skyblue")
+    plt.axvline(emissions_central["mean"], color="red", linestyle="--", label="Mean")
+    plt.axvline(
+        emissions_central["50%"], color="orange", linestyle="--", label="Median (50%)"
+    )
+    plt.axvline(
+        emissions_central["25%"], color="green", linestyle="--", label="Q1 (25%)"
+    )
+    plt.axvline(
+        emissions_central["75%"], color="blue", linestyle="--", label="Q3 (75%)"
+    )
+    plt.xlabel("Emissions")
+    plt.title("Histogram " + f"{func.__name__}" + " : Emissions")
+    plt.legend()
+
+    # Emissions Rate
+    plt.subplot(2, 3, 2)
+    sns.histplot(data["emissions_rate"], bins=20, kde=True, color="salmon")
+    plt.axvline(
+        emissions_rate_central["mean"], color="red", linestyle="--", label="Mean"
+    )
+    plt.axvline(
+        emissions_rate_central["50%"],
+        color="orange",
+        linestyle="--",
+        label="Median (50%)",
+    )
+    plt.axvline(
+        emissions_rate_central["25%"], color="green", linestyle="--", label="Q1 (25%)"
+    )
+    plt.axvline(
+        emissions_rate_central["75%"], color="blue", linestyle="--", label="Q3 (75%)"
+    )
+    plt.xlabel("Emissions Rate")
+    plt.title("Histogram " + f"{func.__name__}" + " Emissions Rate")
+    plt.legend()
+
+    # RAM Power
+    plt.subplot(2, 3, 3)
+    sns.histplot(data["ram_power"], bins=20, kde=True, color="lightgreen")
+    plt.axvline(ram_power_central["mean"], color="red", linestyle="--", label="Mean")
+    plt.axvline(
+        ram_power_central["50%"], color="orange", linestyle="--", label="Median (50%)"
+    )
+    plt.axvline(
+        ram_power_central["25%"], color="green", linestyle="--", label="Q1 (25%)"
+    )
+    plt.axvline(
+        ram_power_central["75%"], color="blue", linestyle="--", label="Q3 (75%)"
+    )
+    plt.xlabel("RAM Power")
+    plt.title("Histogram " + f"{func.__name__}" + " : RAM Power")
+    plt.legend()
+
+    # CPU Energy
+    plt.subplot(2, 3, 4)
+    sns.histplot(data["cpu_energy"], bins=20, kde=True, color="lightcoral")
+    plt.axvline(cpu_energy_central["mean"], color="red", linestyle="--", label="Mean")
+    plt.axvline(
+        cpu_energy_central["50%"], color="orange", linestyle="--", label="Median (50%)"
+    )
+    plt.axvline(
+        cpu_energy_central["25%"], color="green", linestyle="--", label="Q1 (25%)"
+    )
+    plt.axvline(
+        cpu_energy_central["75%"], color="blue", linestyle="--", label="Q3 (75%)"
+    )
+    plt.xlabel("CPU Energy")
+    plt.title("Histogram " + f"{func.__name__}" + " : CPU Energy")
+    plt.legend()
+
+    # RAM Energy
+    plt.subplot(2, 3, 5)
+    sns.histplot(data["ram_energy"], bins=20, kde=True, color="lightblue")
+    plt.axvline(ram_energy_central["mean"], color="red", linestyle="--", label="Mean")
+    plt.axvline(
+        ram_energy_central["50%"], color="orange", linestyle="--", label="Median (50%)"
+    )
+    plt.axvline(
+        ram_energy_central["25%"], color="green", linestyle="--", label="Q1 (25%)"
+    )
+    plt.axvline(
+        ram_energy_central["75%"], color="blue", linestyle="--", label="Q3 (75%)"
+    )
+    plt.xlabel("RAM Energy")
+    plt.title("Histogram " + f"{func.__name__}" + " : RAM Energy")
+    plt.legend()
+
+    # Energy Consumed
+    plt.subplot(2, 3, 6)
+    sns.histplot(data["energy_consumed"], bins=20, kde=True, color="lightyellow")
+    plt.axvline(
+        energy_consumed_central["mean"], color="red", linestyle="--", label="Mean"
+    )
+    plt.axvline(
+        energy_consumed_central["50%"],
+        color="orange",
+        linestyle="--",
+        label="Median (50%)",
+    )
+    plt.axvline(
+        energy_consumed_central["25%"], color="green", linestyle="--", label="Q1 (25%)"
+    )
+    plt.axvline(
+        energy_consumed_central["75%"], color="blue", linestyle="--", label="Q3 (75%)"
+    )
+    plt.xlabel("Energy Consumed")
+    plt.title("Histogram " + f"{func.__name__}" + " : Energy Consumed")
+    plt.legend()
+
+    plt.tight_layout()
+    plt.savefig(os.path.join(profiling_path, f"{func.__name__}" + "_" + "plot4.png"))
+    plt.close()
+    # plt.show()
+
+    print(f"Your codecarbon profiling plots are available at {profiling_path}")
diff --git a/baler/modules/profiling.py b/baler/modules/profiling.py
index 4613537e..bcbbfe2a 100644
--- a/baler/modules/profiling.py
+++ b/baler/modules/profiling.py
@@ -1,10 +1,12 @@
 import io
+import os
 import pstats
 import cProfile
 from pstats import SortKey
 import torch
 from torch.profiler import profile, record_function, ProfilerActivity
 import codecarbon
+from ..modules import profile_plotting
 
 
 def pytorch_profile(f, *args, **kwargs):
@@ -54,7 +56,7 @@ def pytorch_profile(f, *args, **kwargs):
     return result
 
 
-def energy_profiling(f, project_name, measure_power_secs, *args, **kwargs):
+def energy_profiling(f, output_path, project_name, measure_power_secs, *args, **kwargs):
     """
     Energy Profiling measures the amount of electricity that
     was consumed by the given function f and the amount of CO2 emission.
@@ -62,29 +64,62 @@ def energy_profiling(f, project_name, measure_power_secs, *args, **kwargs):
 
     Args:
         f (callable): The function to be profiled.
+        output_path (str): The path where the profiling logs and reports are to be saved.
         project_name (str): The name of the project.
         measure_power_secs (int): The number of seconds to measure power.
 
     Returns:
         result: The result of the function `f` execution.
+        profile_plotting.plot(profiling_path, f): Subsequently called to generate plots from the codecarbon log files.
     """
 
+    profiling_path = os.path.join(output_path, "profiling")
     tracker = codecarbon.EmissionsTracker(
-        project_name=project_name, measure_power_secs=measure_power_secs
+        project_name=project_name,
+        measure_power_secs=measure_power_secs,
+        save_to_file=True,
+        output_dir=profiling_path,
+        co2_signal_api_token="script-overwrite",
+        experiment_id="235b1da5-aaaa-aaaa-aaaa-893681599d2c",
+        log_level="DEBUG",
+        tracking_mode="process",
     )
     tracker.start_task(f"{f.__name__}")
 
     # Execute the function and get its result
     result = f(*args, **kwargs)
 
-    emissions = tracker.stop_task()
-    print("CO2 emission [kg]: ", emissions.emissions)
-    print("CO2 emission rate [kg/h]: ", 3600 * emissions.emissions_rate)
-    print("CPU energy consumed [kWh]: ", emissions.cpu_energy)
-    print("GPU energy consumed [kWh]: ", emissions.gpu_energy)
-    print("RAM energy consumed [kWh]: ", emissions.ram_energy)
+    tracker.stop_task()
+    emissions = tracker.stop()
 
-    return result
+    print(
+        "----------------------------------Energy Profile-----------------------------------------------"
+    )
+    print(
+        "-----------------------------------------------------------------------------------------------"
+    )
+    print(f"Emissions : {1000 * emissions} g CO₂")
+    for task_name, task in tracker._tasks.items():
+        print(
+            f"Emissions : {1000 * task.emissions_data.emissions} g CO₂ for task {task_name} \nEmission Rate : {3600*task.emissions_data.emissions_rate} Kg/h"
+        )
+        print(
+            "-----------------------------------------------------------------------------------------------"
+        )
+        print("Energy Consumption")
+        print(
+            f"CPU : {1000 * task.emissions_data.cpu_energy} Wh \nGPU : {1000 * task.emissions_data.gpu_energy} Wh \nRAM : {1000 * task.emissions_data.ram_energy} Wh"
+        )
+        print(
+            "-----------------------------------------------------------------------------------------------"
+        )
+        print("Power Consumption")
+        print(
+            f"CPU : { task.emissions_data.cpu_power} W \nGPU : { task.emissions_data.gpu_power} W \nRAM : { task.emissions_data.ram_power} W"
+            + f"\nduring {task.emissions_data.duration} seconds."
+        )
+
+    return result, profile_plotting.plot(profiling_path, f)
 
 
 def c_profile(func, *args, **kwargs):
diff --git a/poetry.lock b/poetry.lock
index 0a557f31..029ac158 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand.
 
 [[package]]
 name = "absl-py"
@@ -1088,6 +1088,16 @@ files = [
     {file = "MarkupSafe-2.1.3-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:5bbe06f8eeafd38e5d0a4894ffec89378b6c6a625ff57e3028921f8ff59318ac"},
     {file = "MarkupSafe-2.1.3-cp311-cp311-win32.whl", hash = "sha256:dd15ff04ffd7e05ffcb7fe79f1b98041b8ea30ae9234aed2a9168b5797c3effb"},
     {file = "MarkupSafe-2.1.3-cp311-cp311-win_amd64.whl", hash = "sha256:134da1eca9ec0ae528110ccc9e48041e0828d79f24121a1a146161103c76e686"},
+    {file = "MarkupSafe-2.1.3-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:f698de3fd0c4e6972b92290a45bd9b1536bffe8c6759c62471efaa8acb4c37bc"},
+    {file = "MarkupSafe-2.1.3-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:aa57bd9cf8ae831a362185ee444e15a93ecb2e344c8e52e4d721ea3ab6ef1823"},
+    {file = "MarkupSafe-2.1.3-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ffcc3f7c66b5f5b7931a5aa68fc9cecc51e685ef90282f4a82f0f5e9b704ad11"},
+    {file = "MarkupSafe-2.1.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:47d4f1c5f80fc62fdd7777d0d40a2e9dda0a05883ab11374334f6c4de38adffd"},
+    {file = "MarkupSafe-2.1.3-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1f67c7038d560d92149c060157d623c542173016c4babc0c1913cca0564b9939"},
+    {file = "MarkupSafe-2.1.3-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:9aad3c1755095ce347e26488214ef77e0485a3c34a50c5a5e2471dff60b9dd9c"},
+    {file = "MarkupSafe-2.1.3-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:14ff806850827afd6b07a5f32bd917fb7f45b046ba40c57abdb636674a8b559c"},
+    {file = "MarkupSafe-2.1.3-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:8f9293864fe09b8149f0cc42ce56e3f0e54de883a9de90cd427f191c346eb2e1"},
+    {file = "MarkupSafe-2.1.3-cp312-cp312-win32.whl", hash = "sha256:715d3562f79d540f251b99ebd6d8baa547118974341db04f5ad06d5ea3eb8007"},
+    {file = "MarkupSafe-2.1.3-cp312-cp312-win_amd64.whl", hash = "sha256:1b8dd8c3fd14349433c79fa8abeb573a55fc0fdd769133baac1f5e07abf54aeb"},
     {file = "MarkupSafe-2.1.3-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:8e254ae696c88d98da6555f5ace2279cf7cd5b3f52be2b5cf97feafe883b58d2"},
     {file = "MarkupSafe-2.1.3-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:cb0932dc158471523c9637e807d9bfb93e06a95cbf010f1a38b98623b929ef2b"},
     {file = "MarkupSafe-2.1.3-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9402b03f1a1b4dc4c19845e5c749e3ab82d5078d16a2a4c2cd2df62d57bb0707"},
@@ -2119,6 +2129,11 @@ files = [
     {file = "scikit_learn-1.3.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f66eddfda9d45dd6cadcd706b65669ce1df84b8549875691b1f403730bdef217"},
     {file = "scikit_learn-1.3.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c6448c37741145b241eeac617028ba6ec2119e1339b1385c9720dae31367f2be"},
     {file = "scikit_learn-1.3.1-cp311-cp311-win_amd64.whl", hash = "sha256:c413c2c850241998168bbb3bd1bb59ff03b1195a53864f0b80ab092071af6028"},
+    {file = "scikit_learn-1.3.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:ef540e09873e31569bc8b02c8a9f745ee04d8e1263255a15c9969f6f5caa627f"},
+    {file = "scikit_learn-1.3.1-cp312-cp312-macosx_12_0_arm64.whl", hash = "sha256:9147a3a4df4d401e618713880be023e36109c85d8569b3bf5377e6cd3fecdeac"},
+    {file = "scikit_learn-1.3.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d2cd3634695ad192bf71645702b3df498bd1e246fc2d529effdb45a06ab028b4"},
+    {file = "scikit_learn-1.3.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0c275a06c5190c5ce00af0acbb61c06374087949f643ef32d355ece12c4db043"},
+    {file = "scikit_learn-1.3.1-cp312-cp312-win_amd64.whl", hash = "sha256:0e1aa8f206d0de814b81b41d60c1ce31f7f2c7354597af38fae46d9c47c45122"},
     {file = "scikit_learn-1.3.1-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:52b77cc08bd555969ec5150788ed50276f5ef83abb72e6f469c5b91a0009bbca"},
     {file = "scikit_learn-1.3.1-cp38-cp38-macosx_12_0_arm64.whl", hash = "sha256:a683394bc3f80b7c312c27f9b14ebea7766b1f0a34faf1a2e9158d80e860ec26"},
     {file = "scikit_learn-1.3.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a15d964d9eb181c79c190d3dbc2fff7338786bf017e9039571418a1d53dab236"},
@@ -2181,6 +2196,27 @@ dev = ["click", "doit (>=0.36.0)", "flake8", "mypy", "pycodestyle", "pydevtool",
 doc = ["matplotlib (>2)", "numpydoc", "pydata-sphinx-theme (==0.9.0)", "sphinx (!=4.1.0)", "sphinx-design (>=0.2.0)"]
 test = ["asv", "gmpy2", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
 
+[[package]]
+name = "seaborn"
+version = "0.13.0"
+description = "Statistical data visualization"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "seaborn-0.13.0-py3-none-any.whl", hash = "sha256:70d740828c48de0f402bb17234e475eda687e3c65f4383ea25d0cc4728f7772e"},
+    {file = "seaborn-0.13.0.tar.gz", hash = "sha256:0e76abd2ec291c655b516703c6a022f0fd5afed26c8e714e8baef48150f73598"},
+]
+
+[package.dependencies]
+matplotlib = ">=3.3,<3.6.1 || >3.6.1"
+numpy = ">=1.20,<1.24.0 || >1.24.0"
+pandas = ">=1.2"
+
+[package.extras]
+dev = ["flake8", "flit", "mypy", "pandas-stubs", "pre-commit", "pytest", "pytest-cov", "pytest-xdist"]
+docs = ["ipykernel", "nbconvert", "numpydoc", "pydata_sphinx_theme (==0.10.0rc2)", "pyyaml", "sphinx (<6.0.0)", "sphinx-copybutton", "sphinx-design", "sphinx-issues"]
+stats = ["scipy (>=1.7)", "statsmodels (>=0.12)"]
+
 [[package]]
 name = "setuptools"
 version = "68.2.2"
@@ -2729,4 +2765,4 @@ hls4ml = ["hls4ml", "tensorflow"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8, <3.11"
-content-hash = "3a093254d6705133fc660cfebfdc53b22d34e07ec514003bd2d89cd3df23abdf"
+content-hash = "38aca329ba09c34ebe82ac5aeffd91a92fd2103d047371b2d88d517a12d180bd"
diff --git a/pyproject.toml b/pyproject.toml
index f70d7c18..dc658694 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -18,6 +18,7 @@ hls4ml = { version = "^0.7.1", optional = true }
 tensorflow = { version = "^2.12.0", optional = true }
 numpy = "1.23.5"
 codecarbon = "2.3.1"
+seaborn = "^0.13.0"
 
 [tool.poetry.group.dev.dependencies]
 pytest = "^7.2.1"
diff --git a/workspaces/CFD_workspace/CFD_project_animation/output/profiling/.gitkeep b/workspaces/CFD_workspace/CFD_project_animation/output/profiling/.gitkeep
new file mode 100644
index 00000000..8b137891
--- /dev/null
+++ b/workspaces/CFD_workspace/CFD_project_animation/output/profiling/.gitkeep
@@ -0,0 +1 @@
+