diff --git a/.gitignore b/.gitignore
index 68bc17f..b8aa775 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,4 +1,6 @@
 # Byte-compiled / optimized / DLL files
+.DS_Store
+
 __pycache__/
 *.py[cod]
 *$py.class
diff --git a/README.md b/README.md
index d15bb72..5e6bfe1 100644
--- a/README.md
+++ b/README.md
@@ -1 +1,14 @@
-# zooms_classifier
\ No newline at end of file
+# ZooMS Classifier Documentation
+
+## Model Training and Explainability Guide
+
+> **Note:** The project Google Drive access is essential for using this Colab notebook.
+
+[![Run on Google Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1Ljos45IErs819W3ynY5-u9ach85y9J9G?usp=sharing)
+
+### Local Usage Instructions
+
+To run the notebook locally, either:
+
+- Download the `.ipynb` file directly from Google Colab
+- Obtain the notebook from the GitHub repository at [ZooMS Classifier on GitHub](https://github.com/mlcolab/zooms_classifier/blob/vadims_branch/ZooMS_1DCNN_model_explainability.ipynb)
diff --git a/ZooMS_1DCNN_model_explainability.ipynb b/ZooMS_1DCNN_model_explainability.ipynb
new file mode 100644
index 0000000..36f43e8
--- /dev/null
+++ b/ZooMS_1DCNN_model_explainability.ipynb
@@ -0,0 +1,12648 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "OGgpVwqwBNZL"
+      },
+      "outputs": [],
+      "source": [
+        "# Data storage:\n",
+        "\n",
+        "# BisonYak\n",
+        "# /content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/BisonYak/CSV\n",
+        "\n",
+        "# Canidae\n",
+        "# /content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Canidae/CSV\n",
+        "\n",
+        "# Capra\n",
+        "# /content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Capra/CSV\n",
+        "\n",
+        "# ....\n",
+        "# 4079"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "RSoScnf6FxO_"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "\n",
+        "import torch\n",
+        "from torch.utils.data import Dataset, DataLoader\n",
+        "from torch.utils.data import random_split\n",
+        "\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "from multiprocessing import Pool"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "6TYC9sArEFvc"
+      },
+      "outputs": [],
+      "source": [
+        "species_all = [\n",
+        "    \"BisonYak\",\n",
+        "    \"Hominins\",\n",
+        "    \"Leporidae\",\n",
+        "    \"Canidae\",\n",
+        "    \"Ovis\",\n",
+        "    \"Capra\",\n",
+        "    \"Cervidae\",\n",
+        "    \"Rangifer tarandus\",\n",
+        "    \"CervidaeGazellaSaiga\",\n",
+        "    \"CrocutaPanthera\",\n",
+        "    \"Rhinocerotidae\",\n",
+        "    \"Elephantidae\",\n",
+        "    \"Equidae\",\n",
+        "    \"Ursidae\",\n",
+        "    \"Felidae\",\n",
+        "    \"Vulpes vulpes\"\n",
+        "]\n",
+        "\n",
+        "final_selected = [\n",
+        "        \"Vulpes vulpes\",\n",
+        "        \"Canidae\",\n",
+        "        \"Ursidae\",\n",
+        "        \"CrocutaPanthera\",\n",
+        "        \"Elephantidae\",\n",
+        "        \"Equidae\",\n",
+        "        \"Rhinocerotidae\",\n",
+        "        \"Rangifer tarandus\",\n",
+        "        \"Cervidae\",\n",
+        "        \"CervidaeGazellaSaiga\",\n",
+        "        \"BisonYak\",\n",
+        "        \"Capra\",\n",
+        "        \"Ovis\",\n",
+        "        \"Hominins\",\n",
+        "        \"Other\"\n",
+        "]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "7K2VJ-LuZ8_o",
+        "outputId": "6dfcfc26-7989-4b96-b4bd-e46f16637b5a"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "16"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 7
+        }
+      ],
+      "source": [
+        "len(species_all)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "\n",
+        "\n",
+        "file_paths = []\n",
+        "species_file_count = {}\n",
+        "total_files = 0\n",
+        "total_size = 0\n",
+        "sample_size = 10  # Number of files to sample\n",
+        "sample_shapes = []\n",
+        "\n",
+        "\n",
+        "for i_sp in species_all:\n",
+        "    directory_path = f\"/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/{i_sp}/CSV\"\n",
+        "    print(directory_path)\n",
+        "    try:\n",
+        "        file_count = 0\n",
+        "        for root, dirs, files in os.walk(directory_path):\n",
+        "            for file in files:\n",
+        "                if file.endswith('.csv'):\n",
+        "                    file_path = os.path.join(root, file)\n",
+        "                    file_paths.append(file_path)\n",
+        "                    file_count += 1\n",
+        "                    total_files += 1\n",
+        "                    total_size += os.path.getsize(file_path)\n",
+        "        species_file_count[i_sp] = file_count\n",
+        "    except:\n",
+        "        print('CANNOT FIND ANYTHING')\n",
+        "\n",
+        "print(f\"Total number of files: {total_files}\")\n",
+        "print(f\"Total size of files: {total_size} bytes\")\n",
+        "print(f\"Number of files per species: {species_file_count}\")\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "PKbP_GcdZM--",
+        "outputId": "77e2ab4e-4bec-4aba-a56b-95376274e1b9"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/BisonYak/CSV\n",
+            "/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Hominins/CSV\n",
+            "/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Leporidae/CSV\n",
+            "/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Canidae/CSV\n",
+            "/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Ovis/CSV\n",
+            "/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Capra/CSV\n",
+            "/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Cervidae/CSV\n",
+            "/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Rangifer tarandus/CSV\n",
+            "/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CervidaeGazellaSaiga/CSV\n",
+            "/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CrocutaPanthera/CSV\n",
+            "/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Rhinocerotidae/CSV\n",
+            "/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Elephantidae/CSV\n",
+            "/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Equidae/CSV\n",
+            "/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Ursidae/CSV\n",
+            "/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Felidae/CSV\n",
+            "/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Vulpes vulpes/CSV\n",
+            "Total number of files: 4079\n",
+            "Total size of files: 11947368592 bytes\n",
+            "Number of files per species: {'BisonYak': 1184, 'Hominins': 16, 'Leporidae': 2, 'Canidae': 156, 'Ovis': 35, 'Capra': 296, 'Cervidae': 16, 'Rangifer tarandus': 7, 'CervidaeGazellaSaiga': 1065, 'CrocutaPanthera': 131, 'Rhinocerotidae': 73, 'Elephantidae': 228, 'Equidae': 761, 'Ursidae': 93, 'Felidae': 7, 'Vulpes vulpes': 9}\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "random_sample_files = random.sample(file_paths, min(sample_size, len(file_paths)))\n",
+        "\n",
+        "for sample_file in random_sample_files:\n",
+        "    df = pd.read_csv(sample_file)\n",
+        "    sample_shapes.append(df.shape)\n",
+        "\n",
+        "average_shape = tuple(map(lambda y: sum(y) // len(y), zip(*sample_shapes)))\n"
+      ],
+      "metadata": {
+        "id": "V_Hs-UksZNIn"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(f\"Average shape of sampled CSV files: {average_shape}\")\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "46bzKy3mZYty",
+        "outputId": "819e26dc-bfd6-4f81-ea07-e60c750ddb1f"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Average shape of sampled CSV files: (106343, 2)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "plt.figure(figsize=(15, 8))\n",
+        "plt.bar(species_file_count.keys(), species_file_count.values(), color='purple')\n",
+        "plt.xlabel('Species', fontsize=14)\n",
+        "plt.ylabel('Number of Samples', fontsize=14)\n",
+        "plt.title('Number of Samples per Species', fontsize=16)\n",
+        "plt.xticks(rotation=45, ha='right', fontsize=12)\n",
+        "plt.yticks(fontsize=12)\n",
+        "plt.grid(axis='y')\n",
+        "\n",
+        "# Annotate bars with the actual values\n",
+        "for i, (key, value) in enumerate(species_file_count.items()):\n",
+        "    plt.text(i, value, str(value), ha='center', va='bottom', fontsize=12)\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 642
+        },
+        "id": "N-KFG0zgZYwr",
+        "outputId": "92b2d0b8-73c6-4044-8622-493e31621c1d"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1500x800 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "species_all = [\n",
+        "    \"BisonYak\",\n",
+        "    \"Hominins\",\n",
+        "    \"Leporidae\",\n",
+        "    \"Canidae\",\n",
+        "    \"Ovis\",\n",
+        "    \"Capra\",\n",
+        "    \"Cervidae\",\n",
+        "    \"Rangifer tarandus\",\n",
+        "    \"CervidaeGazellaSaiga\",\n",
+        "    \"CrocutaPanthera\",\n",
+        "    \"Rhinocerotidae\",\n",
+        "    \"Elephantidae\",\n",
+        "    \"Equidae\",\n",
+        "    \"Ursidae\",\n",
+        "    \"Felidae\",\n",
+        "    \"Vulpes vulpes\"\n",
+        "]"
+      ],
+      "metadata": {
+        "id": "6qM8Z23Zqbxv"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# final_selected = [\n",
+        "#         \"Vulpes vulpes\",\n",
+        "#         \"Canidae\",\n",
+        "#         \"Ursidae\",\n",
+        "#         \"CrocutaPanthera\",\n",
+        "#         \"Elephantidae\",\n",
+        "#         \"Equidae\",\n",
+        "#         \"Rhinocerotidae\",\n",
+        "#         \"Rangifer tarandus\",\n",
+        "#         \"Cervidae\",\n",
+        "#         \"CervidaeGazellaSaiga\",\n",
+        "#         \"BisonYak\",\n",
+        "#         \"Capra\",\n",
+        "#         \"Ovis\",\n",
+        "#         \"Hominins\",\n",
+        "#         \"Other\"\n",
+        "# ]"
+      ],
+      "metadata": {
+        "id": "b3nyT_1WZYzl"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "set(species_all) ^ set(final_selected)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ODzxNy_SrZgq",
+        "outputId": "7779cfa9-41bd-44fe-cafa-44e33301d510"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "{'Felidae', 'Leporidae', 'Other'}"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 10
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "len(final_selected)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "qQr-yC0siok4",
+        "outputId": "21bb475d-7f05-4946-8cca-b9bd9643c09c"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "15"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 18
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "jAAmcELEE6Bt",
+        "outputId": "f2db09e8-efe9-416b-a89c-33b09ef53560"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/BisonYak/CSV\n",
+            "/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Hominins/CSV\n",
+            "/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Leporidae/CSV\n",
+            "/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Canidae/CSV\n",
+            "/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Ovis/CSV\n",
+            "/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Capra/CSV\n",
+            "/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Cervidae/CSV\n",
+            "/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Rangifer tarandus/CSV\n",
+            "/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CervidaeGazellaSaiga/CSV\n",
+            "/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CrocutaPanthera/CSV\n",
+            "/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Rhinocerotidae/CSV\n",
+            "/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Elephantidae/CSV\n",
+            "/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Equidae/CSV\n",
+            "/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Ursidae/CSV\n",
+            "/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Felidae/CSV\n",
+            "/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Vulpes vulpes/CSV\n"
+          ]
+        }
+      ],
+      "source": [
+        "import os\n",
+        "\n",
+        "file_paths = []\n",
+        "\n",
+        "for i_sp in species_all:\n",
+        "    directory_path = f\"/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/{i_sp}/CSV\"\n",
+        "    print(directory_path)\n",
+        "    try:\n",
+        "        for root, dirs, files in os.walk(directory_path):\n",
+        "            for file in files:\n",
+        "                if file.endswith('.csv'):  # Check if the file has a .csv extension\n",
+        "                    file_paths.append(os.path.join(root, file))\n",
+        "    except:\n",
+        "        print('CANNOT FIND ANYTHING')\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "gKUVx2tWtyN8"
+      },
+      "outputs": [],
+      "source": [
+        "# import os\n",
+        "# from collections import defaultdict\n",
+        "\n",
+        "# file_paths = []\n",
+        "# species_count = defaultdict(int)  # Dictionary to count the number of samples for each species\n",
+        "\n",
+        "# # Count the number of samples for each species\n",
+        "# for i_sp in species_all:\n",
+        "#     directory_path = f\"/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/{i_sp}/CSV\"\n",
+        "#     try:\n",
+        "#         for root, dirs, files in os.walk(directory_path):\n",
+        "#             for file in files:\n",
+        "#                 if file.endswith('.csv'):\n",
+        "#                     species_count[i_sp] += 1\n",
+        "#     except:\n",
+        "#         print('CANNOT FIND ANYTHING')\n",
+        "\n",
+        "# # Filter species based on sample count and always include \"Hominins\"\n",
+        "# selected_species = [sp for sp in species_all if species_count[sp] > 100 or sp == \"Hominins\"]\n",
+        "\n",
+        "# # Collect file paths for selected species\n",
+        "# for i_sp in selected_species:\n",
+        "#     directory_path = f\"/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/{i_sp}/CSV\"\n",
+        "#     try:\n",
+        "#         for root, dirs, files in os.walk(directory_path):\n",
+        "#             for file in files:\n",
+        "#                 if file.endswith('.csv'):\n",
+        "#                     file_paths.append(os.path.join(root, file))\n",
+        "#     except:\n",
+        "#         print('CANNOT FIND ANYTHING')\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "WF7-Wg38tyRS",
+        "outputId": "9110231d-c6cb-4cad-88d1-87bcd6c391e9"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "['BisonYak',\n",
+              " 'Hominins',\n",
+              " 'Canidae',\n",
+              " 'Capra',\n",
+              " 'CervidaeGazellaSaiga',\n",
+              " 'CrocutaPanthera',\n",
+              " 'Elephantidae',\n",
+              " 'Equidae']"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 11
+        }
+      ],
+      "source": [
+        "selected_species"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "9OaoS9-aFFmR",
+        "outputId": "8365f273-ae7c-44e9-b84f-3b4973205ac4"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "4079"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 12
+        }
+      ],
+      "source": [
+        "len(file_paths)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 35
+        },
+        "id": "OfzYc52QGCX6",
+        "outputId": "0262f2a2-53c7-4e69-fba2-dfc31c03069a"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Vulpes vulpes/CSV/FNR_DC9111_2.csv'"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            }
+          },
+          "metadata": {},
+          "execution_count": 15
+        }
+      ],
+      "source": [
+        "file_paths[-1]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "A6w2RjOYGERO"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "XzFwcSd1GPJv",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "eb05ef93-5075-49d7-b1f5-b4111e2f956d"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(176981, 2)\n",
+            "2500\n"
+          ]
+        }
+      ],
+      "source": [
+        "temp_df = pd.read_csv(file_paths[29])\n",
+        "print(temp_df.shape)\n",
+        "temp_list = []\n",
+        "for i_value in range(1000, 3500): # Adjust the range to match the actual values\n",
+        "  lower_bound = i_value - 0.5\n",
+        "  upper_bound = i_value + 0.5\n",
+        "  int_mean_value = temp_df[(lower_bound < temp_df.mass) & (temp_df.mass < upper_bound)]['intensity'].mean()\n",
+        "  temp_list.append(int_mean_value)\n",
+        "print(len(temp_list))\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.figure(figsize=(12, 6))\n",
+        "plt.plot(temp_df['intensity'].values)\n",
+        "plt.figure(figsize=(12, 6))\n",
+        "plt.plot(temp_list)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "vsIkEy8L3Ba-",
+        "outputId": "a576cacd-47a8-4f54-f68a-42a545badb51"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[<matplotlib.lines.Line2D at 0x7b6167253430>]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 18
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "2NFh3PvE5oBy"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.figure(figsize=(12, 6))\n",
+        "plt.plot(temp_list)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 539
+        },
+        "id": "rQHfRDUr13Wf",
+        "outputId": "105a2399-c9a4-4ab6-b6d2-3ed864fb3350"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[<matplotlib.lines.Line2D at 0x7b6167129360>]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 19
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "QA_Xt-WUpXah"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 416
+        },
+        "id": "vX9Rk2OgSgy4",
+        "outputId": "dfa8dc33-3faa-4e96-80d2-3cd895b836f5"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 2000x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "plt.figure(figsize=(20, 6))\n",
+        "#plt.scatter(temp_df['mass'], temp_df['intensity'], c='blue', s=100, marker='o',linewidth=1)\n",
+        "plt.plot(temp_df['mass'], temp_df['intensity'], linestyle='--', linewidth=1, color='red')\n",
+        "\n",
+        "plt.xlabel('Mass', fontsize=14)\n",
+        "plt.ylabel('Intensity', fontsize=14)\n",
+        "plt.title('Mass vs Intensity', fontsize=16)\n",
+        "\n",
+        "plt.xticks(fontsize=12)\n",
+        "plt.yticks(fontsize=12)\n",
+        "plt.grid(True)\n",
+        "\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "X8hsJba-5QPh"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "B3SJpY8ZetBy"
+      },
+      "outputs": [],
+      "source": [
+        "# import numpy as np\n",
+        "# import pandas as pd\n",
+        "# from torch.utils.data import Dataset, DataLoader\n",
+        "# import torch\n",
+        "\n",
+        "# species = selected_species\n",
+        "\n",
+        "# species_to_label = {species_name: index for index, species_name in enumerate(species)}\n",
+        "\n",
+        "# from multiprocessing import Pool\n",
+        "# from tqdm import tqdm\n",
+        "\n",
+        "\n",
+        "# from torch.utils.data import Dataset\n",
+        "# from multiprocessing import Pool\n",
+        "# import pandas as pd\n",
+        "# import numpy as np\n",
+        "# import torch\n",
+        "\n",
+        "# class CustomDataset(Dataset):\n",
+        "#     def __init__(self, file_paths):\n",
+        "#         self.file_paths = file_paths\n",
+        "#         with Pool() as pool:\n",
+        "#             self.cached_data = list(pool.imap(self.process_file, self.file_paths))\n",
+        "\n",
+        "#     def extract_species(self, file_path):\n",
+        "#         return file_path.split('/')[-3]\n",
+        "\n",
+        "#     def mean_intensity(self, temp_df, i_values):\n",
+        "#         bins = np.arange(999.9, 3500, 0.5)\n",
+        "#         temp_df['bin'] = pd.cut(temp_df['mass'], bins=bins)\n",
+        "#         return temp_df.groupby('bin')['intensity'].mean().values\n",
+        "\n",
+        "#     def process_file(self, file_path):\n",
+        "#         temp_df = pd.read_csv(file_path)\n",
+        "#         species_name = self.extract_species(file_path)\n",
+        "#         i_values = np.arange(1000, 3500, 0.5)\n",
+        "#         intensities = self.mean_intensity(temp_df, i_values)\n",
+        "#         return file_path, species_name, intensities\n",
+        "\n",
+        "#     def __len__(self):\n",
+        "#         return len(self.file_paths)\n",
+        "\n",
+        "#     def __getitem__(self, idx):\n",
+        "#         file_path, species_name, intensities = self.cached_data[idx]\n",
+        "#         intensities = torch.tensor(intensities, dtype=torch.float32)\n",
+        "\n",
+        "#         intensities[torch.isnan(intensities)] = 0\n",
+        "#         mean = intensities.mean()\n",
+        "#         std = intensities.std()\n",
+        "#         intensities = (intensities - mean) / (std + torch.finfo(torch.float32).eps)\n",
+        "\n",
+        "#         return species_to_label[species_name], intensities, file_path\n",
+        "\n",
+        "\n",
+        "\n",
+        "# # Creating an instance of the custom dataset\n",
+        "# dataset = CustomDataset(file_paths)\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "species_all = [\n",
+        "    \"BisonYak\",\n",
+        "    \"Hominins\",\n",
+        "    \"Leporidae\",\n",
+        "    \"Canidae\",\n",
+        "    \"Ovis\",\n",
+        "    \"Capra\",\n",
+        "    \"Cervidae\",\n",
+        "    \"Rangifer tarandus\",\n",
+        "    \"CervidaeGazellaSaiga\",\n",
+        "    \"CrocutaPanthera\",\n",
+        "    \"Rhinocerotidae\",\n",
+        "    \"Elephantidae\",\n",
+        "    \"Equidae\",\n",
+        "    \"Ursidae\",\n",
+        "    \"Felidae\",\n",
+        "    \"Vulpes vulpes\"\n",
+        "]\n",
+        "\n",
+        "merged_group = {\"Felidae\": \"Others\", \"Leporidae\": \"Others\"}\n",
+        "final_selected = [merged_group.get(species, species) for species in species_all]\n",
+        "\n",
+        "# Removing duplicates if any, and then creating labels\n",
+        "final_selected = list(set(final_selected))\n",
+        "species_to_label = {species_name: index for index, species_name in enumerate(final_selected)}\n"
+      ],
+      "metadata": {
+        "id": "v21s8Qln1s3d"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "species_all = [\n",
+        "    \"BisonYak\",\n",
+        "    \"Hominins\",\n",
+        "    \"Leporidae\",\n",
+        "    \"Canidae\",\n",
+        "    \"Ovis\",\n",
+        "    \"Capra\",\n",
+        "    \"Cervidae\",\n",
+        "    \"Rangifer tarandus\",\n",
+        "    \"CervidaeGazellaSaiga\",\n",
+        "    \"CrocutaPanthera\",\n",
+        "    \"Rhinocerotidae\",\n",
+        "    \"Elephantidae\",\n",
+        "    \"Equidae\",\n",
+        "    \"Ursidae\",\n",
+        "    \"Felidae\",\n",
+        "    \"Vulpes vulpes\"\n",
+        "]\n",
+        "\n",
+        "# Create the list with 'Others' replacing 'Felidae' and 'Leporidae'\n",
+        "final_selected = ['Others' if species in ['Felidae', 'Leporidae'] else species for species in species_all]\n",
+        "\n",
+        "# Create species_to_label by enumerating unique species names\n",
+        "final_unique_selected = list(set(final_selected))\n",
+        "species_to_label = {species_name: index for index, species_name in enumerate(final_unique_selected)}\n",
+        "\n",
+        "# Adding specific mappings for 'Felidae' and 'Leporidae' to the label of 'Others'\n",
+        "species_to_label['Felidae'] = species_to_label['Others']\n",
+        "species_to_label['Leporidae'] = species_to_label['Others']\n"
+      ],
+      "metadata": {
+        "id": "4jjiacwl2CNr"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "species_to_label[\"Felidae\"]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "q48NM3eb1umf",
+        "outputId": "955e46d1-da5a-4ea8-992e-77f367cde09b"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "11"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 17
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "species_to_label"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Ej8v3zVL2E8B",
+        "outputId": "0921e332-9ee5-4be2-c2ce-2a7bed29776d"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "{'Canidae': 0,\n",
+              " 'Cervidae': 1,\n",
+              " 'CervidaeGazellaSaiga': 2,\n",
+              " 'Ovis': 3,\n",
+              " 'Equidae': 4,\n",
+              " 'CrocutaPanthera': 5,\n",
+              " 'BisonYak': 6,\n",
+              " 'Capra': 7,\n",
+              " 'Ursidae': 8,\n",
+              " 'Vulpes vulpes': 9,\n",
+              " 'Elephantidae': 10,\n",
+              " 'Others': 11,\n",
+              " 'Rhinocerotidae': 12,\n",
+              " 'Rangifer tarandus': 13,\n",
+              " 'Hominins': 14,\n",
+              " 'Felidae': 11,\n",
+              " 'Leporidae': 11}"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 18
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "\n",
+        "# species = final_selected\n",
+        "# species_to_label = {species_name: index for index, species_name in enumerate(species)}\n",
+        "\n",
+        "class CustomDataset(Dataset):\n",
+        "    def __init__(self, file_paths, bin_resolution=0.5, preprocess=True):\n",
+        "        self.file_paths = file_paths\n",
+        "        self.bin_resolution = bin_resolution\n",
+        "        self.preprocess = preprocess\n",
+        "        with Pool() as pool:\n",
+        "            self.cached_data = list(pool.imap(self.process_file, self.file_paths))\n",
+        "\n",
+        "    def extract_species(self, file_path):\n",
+        "        return file_path.split('/')[-3]\n",
+        "\n",
+        "    def mean_intensity(self, temp_df):\n",
+        "        bins = np.arange(899.9, 3500, self.bin_resolution)\n",
+        "        temp_df['bin'] = pd.cut(temp_df['mass'], bins=bins)\n",
+        "        return temp_df.groupby('bin')['intensity'].mean().values\n",
+        "\n",
+        "    def process_file(self, file_path):\n",
+        "        temp_df = pd.read_csv(file_path)\n",
+        "        species_name = self.extract_species(file_path)\n",
+        "        if self.preprocess:\n",
+        "            intensities = self.mean_intensity(temp_df)\n",
+        "        else:\n",
+        "            intensities = temp_df['intensity'].values\n",
+        "        return file_path, species_name, intensities\n",
+        "\n",
+        "    def __len__(self):\n",
+        "        return len(self.file_paths)\n",
+        "\n",
+        "    def __getitem__(self, idx):\n",
+        "        file_path, species_name, intensities = self.cached_data[idx]\n",
+        "        intensities = torch.tensor(intensities, dtype=torch.float32)\n",
+        "\n",
+        "        intensities[torch.isnan(intensities)] = 0\n",
+        "        mean = intensities.mean()\n",
+        "        std = intensities.std()\n",
+        "        intensities = (intensities - mean) / (std + torch.finfo(torch.float32).eps)\n",
+        "\n",
+        "        return species_to_label[species_name], intensities, file_path\n",
+        "\n",
+        "# Example usage\n",
+        "dataset = CustomDataset(file_paths, bin_resolution=0.5, preprocess=True)\n"
+      ],
+      "metadata": {
+        "id": "BvePtz99Ib0P"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "3PcHEf8cXHOq"
+      },
+      "outputs": [],
+      "source": [
+        "# class_counts = {}\n",
+        "# for label, _ in dataset:\n",
+        "#     if label in class_counts:\n",
+        "#         class_counts[label] += 1\n",
+        "#     else:\n",
+        "#         class_counts[label] = 1\n",
+        "# num_samples = len(dataset)\n",
+        "# class_weights = [num_samples/class_counts[i] for i in sorted(class_counts)]\n",
+        "# class_weights\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "oq7qW_PU0BCq"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "5_CCuF0Y0BN0"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "vjrdnYMF0BQl"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "KNkflq6X0BXs"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "kGVzkXxTXeXJ"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "FxUmF149etFd"
+      },
+      "outputs": [],
+      "source": [
+        "# batch_size = 256\n",
+        "\n",
+        "# train_size = int(0.81 * len(dataset))\n",
+        "# test_size = len(dataset) - train_size\n",
+        "# train_dataset, test_dataset = random_split(dataset, [train_size, test_size])\n",
+        "\n",
+        "# train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=4)\n",
+        "# test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=4)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from torch.utils.data import DataLoader, Subset\n",
+        "from sklearn.model_selection import StratifiedShuffleSplit\n",
+        "\n",
+        "# Get the labels for each data point in your dataset\n",
+        "labels = [species_to_label[item[1]] for item in dataset.cached_data]\n",
+        "\n",
+        "# Initialize StratifiedShuffleSplit\n",
+        "sss = StratifiedShuffleSplit(n_splits=1, test_size=0.19, random_state=0)\n",
+        "for train_index, test_index in sss.split(dataset, labels):\n",
+        "    train_indices, test_indices = train_index, test_index\n",
+        "\n",
+        "# Create train and test datasets\n",
+        "train_subset = Subset(dataset, train_indices)\n",
+        "test_subset = Subset(dataset, test_indices)\n",
+        "\n",
+        "# Create DataLoader instances\n",
+        "train_loader = DataLoader(train_subset, batch_size=256, shuffle=True, num_workers=4)\n",
+        "test_loader = DataLoader(test_subset, batch_size=256, shuffle=False, num_workers=4)\n"
+      ],
+      "metadata": {
+        "id": "nF022VZF-0yG"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "PLCU7UXS-05c"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# from torch.utils.data import DataLoader, random_split\n",
+        "# import torch\n",
+        "\n",
+        "# def collate_fn(batch):\n",
+        "#     labels, intensities, file_paths = zip(*batch)\n",
+        "\n",
+        "#     intensities = [torch.nn.functional.pad(x, (0, 111000 - x.numel())) for x in intensities]\n",
+        "\n",
+        "#     #labels = torch.stack(labels)\n",
+        "#     intensities = torch.stack(intensities)\n",
+        "#     return torch.Tensor(labels), intensities, file_paths\n",
+        "\n",
+        "# batch_size = 32\n",
+        "\n",
+        "# # Splitting the dataset into 80% training and 20% testing\n",
+        "# train_size = int(0.8 * len(dataset))\n",
+        "# test_size = len(dataset) - train_size\n",
+        "# train_dataset, test_dataset = random_split(dataset, [train_size, test_size])\n",
+        "\n",
+        "# # Using custom collate function for padding\n",
+        "# train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=4, collate_fn=collate_fn)\n",
+        "# test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=4, collate_fn=collate_fn)\n"
+      ],
+      "metadata": {
+        "id": "5VaS9iGYLjmm"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 35
+        },
+        "id": "FK50prNbetIE",
+        "outputId": "7b5c4d0c-6487-4f0b-d8ea-b0b77dce8dff"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'READY'"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            }
+          },
+          "metadata": {},
+          "execution_count": 24
+        }
+      ],
+      "source": [
+        "\"READY\""
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Wa74z-QEetKg",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "40675c4a-7540-4fe7-9505-c1a502f93af8"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(6,\n",
+              " tensor([-0.0397,  1.3817, -0.0557,  ..., -0.1509, -0.1253, -0.1253]),\n",
+              " '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/BisonYak/CSV/DC1818.csv')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 25
+        }
+      ],
+      "source": [
+        "test_dataset[0]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "slb7-vD3Zx_O"
+      },
+      "outputs": [],
+      "source": [
+        "# import torch.nn as nn\n",
+        "\n",
+        "# class CNN1D(nn.Module):\n",
+        "#     def __init__(self, input_size, num_classes):\n",
+        "#         super(CNN1D, self).__init__()\n",
+        "#         self.conv1 = nn.Conv1d(1, 16, kernel_size=5, stride=1)\n",
+        "#         self.conv2 = nn.Conv1d(16, 32, kernel_size=5, stride=1)\n",
+        "#         self.pool = nn.MaxPool1d(kernel_size=2)\n",
+        "#         self.fc1 = nn.Linear(32 * ((input_size - 4) // 2 - 4) // 2, 128)\n",
+        "#         self.fc2 = nn.Linear(128, num_classes)\n",
+        "#         self.relu = nn.ReLU()\n",
+        "\n",
+        "#     def forward(self, x):\n",
+        "#         x = self.pool(self.relu(self.conv1(x)))\n",
+        "#         x = self.pool(self.relu(self.conv2(x)))\n",
+        "#         x = x.view(x.size(0), -1)\n",
+        "#         x = self.relu(self.fc1(x))\n",
+        "#         x = self.fc2(x)\n",
+        "#         return x\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "30myg7SNdqic"
+      },
+      "outputs": [],
+      "source": [
+        "import torch.nn as nn\n",
+        "\n",
+        "class CNN1D(nn.Module):\n",
+        "    def __init__(self, input_size, num_classes):\n",
+        "        super(CNN1D, self).__init__()\n",
+        "\n",
+        "        self.conv1 = nn.Conv1d(1, 32, kernel_size=5, stride=1)\n",
+        "\n",
+        "        self.conv2 = nn.Conv1d(32, 64, kernel_size=5, stride=1)\n",
+        "        #self.bn2 = nn.BatchNorm1d(32)\n",
+        "\n",
+        "        self.pool = nn.AvgPool1d(kernel_size=3)\n",
+        "\n",
+        "        output_size = (input_size - 5 + 1) // 3  # After conv1 and pool\n",
+        "        output_size = (output_size - 5 + 1) // 3  # After conv2 and pool\n",
+        "\n",
+        "        self.fc1 = nn.Linear(64 * output_size, 128)\n",
+        "        self.dropout1 = nn.Dropout(0.25)\n",
+        "\n",
+        "        self.fc2 = nn.Linear(128, num_classes)\n",
+        "\n",
+        "        self.relu = nn.ReLU()\n",
+        "\n",
+        "    def forward(self, x):\n",
+        "        x = self.pool(self.relu(self.conv1(x)))\n",
+        "        x = self.pool(self.relu(self.conv2(x)))\n",
+        "\n",
+        "        x = x.view(x.size(0), -1)\n",
+        "\n",
+        "        x = self.relu(self.fc1(x))\n",
+        "        x = self.dropout1(x)\n",
+        "\n",
+        "        x = self.fc2(x)\n",
+        "        return x\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "S_nWnRWrcdQZ"
+      },
+      "outputs": [],
+      "source": [
+        "# import torch\n",
+        "# import torch.nn as nn\n",
+        "\n",
+        "# class DNN(nn.Module):\n",
+        "#     def __init__(self, input_size, num_classes):\n",
+        "#         super(DNN, self).__init__()\n",
+        "#         self.fc1 = nn.Linear(input_size, 512)\n",
+        "#         self.fc2 = nn.Linear(512, 64)\n",
+        "#         self.fc3 = nn.Linear(64, num_classes)\n",
+        "#         self.relu = nn.ReLU()\n",
+        "\n",
+        "#     def forward(self, x):\n",
+        "#         x = x.view(x.size(0), -1)\n",
+        "#         x = self.relu(self.fc1(x))\n",
+        "#         x = self.relu(self.fc2(x))\n",
+        "#         x = self.fc3(x)\n",
+        "#         return x"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "uPNU6RxoZyB2"
+      },
+      "outputs": [],
+      "source": [
+        "input_size = 5200\n",
+        "num_classes = 15# len(selected_species)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "JPpI8Ijoa8Ow",
+        "outputId": "99e23afb-22dc-4972-b657-2bd6018d1217"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(['Canidae',\n",
+              "  'Cervidae',\n",
+              "  'CervidaeGazellaSaiga',\n",
+              "  'Ovis',\n",
+              "  'Equidae',\n",
+              "  'CrocutaPanthera',\n",
+              "  'BisonYak',\n",
+              "  'Capra',\n",
+              "  'Ursidae',\n",
+              "  'Vulpes vulpes',\n",
+              "  'Elephantidae',\n",
+              "  'Others',\n",
+              "  'Rhinocerotidae',\n",
+              "  'Rangifer tarandus',\n",
+              "  'Hominins'],\n",
+              " 15)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 133
+        }
+      ],
+      "source": [
+        "selected_species, num_classes"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "DDoVTQZZZyEi"
+      },
+      "outputs": [],
+      "source": [
+        "#species_to_label = {species_name: index for index, species_name in enumerate(selected_species)}\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "YEgr_dAPi7hN",
+        "outputId": "92539135-244d-4184-9391-dd3dba18a984"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "torch.Size([8, 1, 5200])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 135
+        }
+      ],
+      "source": [
+        "intensities.shape\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 834,
+          "referenced_widgets": [
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+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 9/10 | Training Loss: 0.264307344189057\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Epoch 9/10 Testing:   0%|          | 0/4 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "5d88a49abb844737a7a36f0415357ce7"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 9/10 | Test Loss: 0.43032338470220566 | Test Accuracy: 89.17525773195877%\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Epoch 10/10 Training:   0%|          | 0/13 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "5da99cc50ea74e12a30fe83cca65415b"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 10/10 | Training Loss: 0.2606851813884882\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Epoch 10/10 Testing:   0%|          | 0/4 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "3f3a81847bba4f518ce8f89efb455c2e"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 10/10 | Test Loss: 0.4433029294013977 | Test Accuracy: 88.4020618556701%\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "from tqdm.notebook import tqdm\n",
+        "from torch.optim import AdamW\n",
+        "import torch.nn as nn\n",
+        "\n",
+        "learning_rate = 0.001\n",
+        "epochs = 10\n",
+        "weight_decay_rate = 0.3\n",
+        "\n",
+        "model = CNN1D(input_size, num_classes).cuda()\n",
+        "\n",
+        "loss_function = nn.CrossEntropyLoss()\n",
+        "optimizer = AdamW(model.parameters(), lr=learning_rate, weight_decay=weight_decay_rate)\n",
+        "\n",
+        "train_losses = []\n",
+        "test_losses = []\n",
+        "\n",
+        "\n",
+        "\n",
+        "# Training\n",
+        "for epoch in range(epochs):\n",
+        "    model.train()\n",
+        "    train_loss = 0.0\n",
+        "    train_progress_bar = tqdm(train_loader, desc=f\"Epoch {epoch + 1}/{epochs} Training\", leave=False)\n",
+        "    for species, intensities, _ in train_progress_bar:\n",
+        "        intensities = intensities.unsqueeze(1).cuda()\n",
+        "        labels = species.cuda()\n",
+        "\n",
+        "        predictions = model(intensities)\n",
+        "        loss = loss_function(predictions, labels)\n",
+        "\n",
+        "        train_loss += loss.item()\n",
+        "\n",
+        "        optimizer.zero_grad()\n",
+        "        loss.backward()\n",
+        "        torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n",
+        "        optimizer.step()\n",
+        "\n",
+        "        train_progress_bar.set_postfix(loss=loss.item())\n",
+        "\n",
+        "    train_loss /= len(train_loader)\n",
+        "    train_losses.append(train_loss)\n",
+        "    print(f\"Epoch {epoch + 1}/{epochs} | Training Loss: {train_loss}\")\n",
+        "\n",
+        "    # Testing\n",
+        "    model.eval()\n",
+        "    test_loss = 0.0\n",
+        "    correct = 0\n",
+        "\n",
+        "    true_labels = []\n",
+        "    pred_labels = []\n",
+        "\n",
+        "    test_progress_bar = tqdm(test_loader, desc=f\"Epoch {epoch + 1}/{epochs} Testing\", leave=False)\n",
+        "    with torch.no_grad():\n",
+        "        for species, intensities, _ in test_progress_bar:\n",
+        "            intensities = intensities.unsqueeze(1).cuda()\n",
+        "            labels = species.cuda()\n",
+        "            predictions = model(intensities)\n",
+        "            test_loss += loss_function(predictions, labels).item()\n",
+        "\n",
+        "            _, predicted = torch.max(predictions.data, 1)\n",
+        "            correct += (predicted == labels).sum().item()\n",
+        "\n",
+        "            true_labels.extend(labels.cpu().numpy())\n",
+        "            pred_labels.extend(predicted.cpu().numpy())\n",
+        "\n",
+        "    test_loss /= len(test_loader)\n",
+        "    test_losses.append(test_loss)\n",
+        "    accuracy = 100 * correct / test_size\n",
+        "    print(f\"Epoch {epoch + 1}/{epochs} | Test Loss: {test_loss} | Test Accuracy: {accuracy}%\")\n",
+        "\n",
+        "# Plotting the losses\n",
+        "plt.figure(figsize=(10, 5))\n",
+        "plt.plot(train_losses, label='Training Loss')\n",
+        "plt.plot(test_losses, label='Testing Loss')\n",
+        "plt.xlabel('Epochs')\n",
+        "plt.ylabel('Loss')\n",
+        "plt.title('Training and Testing Losses')\n",
+        "plt.legend()\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "torch.save(model, './model.pth')\n"
+      ],
+      "metadata": {
+        "id": "XrSbT_JowuRZ"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "RXn4rvD-hxv-",
+        "outputId": "3ffd9378-6818-4efe-dd49-37a829ef7f9a"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "{'Canidae': 0,\n",
+              " 'Cervidae': 1,\n",
+              " 'CervidaeGazellaSaiga': 2,\n",
+              " 'Ovis': 3,\n",
+              " 'Equidae': 4,\n",
+              " 'CrocutaPanthera': 5,\n",
+              " 'BisonYak': 6,\n",
+              " 'Capra': 7,\n",
+              " 'Ursidae': 8,\n",
+              " 'Vulpes vulpes': 9,\n",
+              " 'Elephantidae': 10,\n",
+              " 'Others': 11,\n",
+              " 'Rhinocerotidae': 12,\n",
+              " 'Rangifer tarandus': 13,\n",
+              " 'Hominins': 14,\n",
+              " 'Felidae': 11,\n",
+              " 'Leporidae': 11}"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 138
+        }
+      ],
+      "source": [
+        "species_to_label"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sorted_keys = [k for k, v in sorted(species_to_label.items(), key=lambda item: item[1])]\n",
+        "sorted_keys.remove('Felidae')\n",
+        "sorted_keys.remove('Leporidae')\n",
+        "sorted_keys"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "YDn9U67W5Vdu",
+        "outputId": "b52253c4-8874-4d02-89db-3e33a4f3a2d0"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "['Canidae',\n",
+              " 'Cervidae',\n",
+              " 'CervidaeGazellaSaiga',\n",
+              " 'Ovis',\n",
+              " 'Equidae',\n",
+              " 'CrocutaPanthera',\n",
+              " 'BisonYak',\n",
+              " 'Capra',\n",
+              " 'Ursidae',\n",
+              " 'Vulpes vulpes',\n",
+              " 'Elephantidae',\n",
+              " 'Others',\n",
+              " 'Rhinocerotidae',\n",
+              " 'Rangifer tarandus',\n",
+              " 'Hominins']"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 139
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "selected_species = sorted_keys"
+      ],
+      "metadata": {
+        "id": "aresA4hq5ZWG"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "A8tgIwEKhxz2",
+        "outputId": "1e3f7647-d8fc-4c8a-a58b-195a3de314dd"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "15"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 141
+        }
+      ],
+      "source": [
+        "len(selected_species)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "6dQMErhPhx4V",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "9b0643e3-db0b-4e22-9ab2-1f09fa287cdf"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "15"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 142
+        }
+      ],
+      "source": [
+        "len(set(true_labels))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "TK-wNLYjgUwx",
+        "outputId": "37eff4c2-4e68-4cdf-fb8e-e5c6ce206c6c"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Final Classification Report:\n",
+            "                      precision    recall  f1-score   support\n",
+            "\n",
+            "             Canidae       0.88      0.77      0.82        30\n",
+            "            Cervidae       0.00      0.00      0.00         3\n",
+            "CervidaeGazellaSaiga       0.87      0.89      0.88       202\n",
+            "                Ovis       0.75      0.43      0.55         7\n",
+            "             Equidae       0.90      0.88      0.89       145\n",
+            "     CrocutaPanthera       0.95      0.80      0.87        25\n",
+            "            BisonYak       0.92      0.97      0.95       225\n",
+            "               Capra       0.76      0.79      0.77        56\n",
+            "             Ursidae       0.84      0.89      0.86        18\n",
+            "       Vulpes vulpes       1.00      1.00      1.00         2\n",
+            "        Elephantidae       0.86      0.84      0.85        43\n",
+            "              Others       0.00      0.00      0.00         2\n",
+            "      Rhinocerotidae       0.93      0.93      0.93        14\n",
+            "   Rangifer tarandus       0.00      0.00      0.00         1\n",
+            "            Hominins       1.00      1.00      1.00         3\n",
+            "\n",
+            "            accuracy                           0.88       776\n",
+            "           macro avg       0.71      0.68      0.69       776\n",
+            "        weighted avg       0.88      0.88      0.88       776\n",
+            "\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+            "  _warn_prf(average, modifier, msg_start, len(result))\n",
+            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+            "  _warn_prf(average, modifier, msg_start, len(result))\n",
+            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
+            "  _warn_prf(average, modifier, msg_start, len(result))\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x800 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "from sklearn.metrics import classification_report, confusion_matrix\n",
+        "import seaborn as sns\n",
+        "\n",
+        "# Generate the classification report\n",
+        "print(\"Final Classification Report:\")\n",
+        "print(classification_report(true_labels, pred_labels, target_names=selected_species))\n",
+        "\n",
+        "# Generate the confusion matrix\n",
+        "cm = confusion_matrix(true_labels, pred_labels)\n",
+        "\n",
+        "# Plot the confusion matrix\n",
+        "plt.figure(figsize=(10, 8))\n",
+        "sns.heatmap(cm, annot=True, fmt='g', cmap='Blues', xticklabels=selected_species, yticklabels=selected_species)\n",
+        "plt.xlabel('Predicted Labels')\n",
+        "plt.ylabel('True Labels')\n",
+        "plt.title('Confusion Matrix')\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "6RJodxr7h0e0",
+        "outputId": "07f6a3ba-9c03-46ae-898a-7562ba803be3"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Final Classification Report:\n",
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.90      0.94      0.92       218\n",
+            "           1       1.00      1.00      1.00         1\n",
+            "           2       0.00      0.00      0.00         1\n",
+            "           3       0.86      0.84      0.85        38\n",
+            "           4       0.80      0.80      0.80         5\n",
+            "           5       0.89      0.74      0.81        65\n",
+            "           6       1.00      1.00      1.00         2\n",
+            "           8       0.83      0.93      0.88       205\n",
+            "           9       0.87      0.79      0.83        33\n",
+            "          10       1.00      0.84      0.91        19\n",
+            "          11       0.97      0.79      0.87        48\n",
+            "          12       0.91      0.89      0.90       162\n",
+            "          13       1.00      0.88      0.94        17\n",
+            "          14       0.00      0.00      0.00         1\n",
+            "          15       1.00      0.50      0.67         2\n",
+            "\n",
+            "    accuracy                           0.88       817\n",
+            "   macro avg       0.80      0.73      0.76       817\n",
+            "weighted avg       0.89      0.88      0.88       817\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 1.0\n",
+        "from sklearn.metrics import classification_report\n",
+        "print(\"Final Classification Report:\")\n",
+        "print(classification_report(true_labels, pred_labels))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "qE74948HmjUp"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "thN0JU1ZmjYR",
+        "outputId": "cb92ea54-e552-4b8e-fa67-3b25c9c36275"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "['BisonYak',\n",
+              " 'Hominins',\n",
+              " 'Canidae',\n",
+              " 'Capra',\n",
+              " 'CervidaeGazellaSaiga',\n",
+              " 'CrocutaPanthera',\n",
+              " 'Elephantidae',\n",
+              " 'Equidae']"
+            ]
+          },
+          "execution_count": 71,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "selected_species"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XeIf5QwWmpZO"
+      },
+      "source": [
+        "# Explainability"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "h1RyOimymjcP",
+        "outputId": "b8a5b5c0-b030-423d-a1b4-6e3c4599b67e"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 164
+        }
+      ],
+      "source": [
+        "target, input_tensor, _ = dataset.__getitem__(11+400)\n",
+        "target"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.plot(attributions.view(-1).detach().cpu().numpy())"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 447
+        },
+        "id": "yv06tY3rJrZd",
+        "outputId": "167eae86-88ad-404f-c00e-a8f0361d18e0"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[<matplotlib.lines.Line2D at 0x7ded4ebfc2e0>]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 174
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# !pip install captum"
+      ],
+      "metadata": {
+        "id": "VVv-Vij9PhHw"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "sQjieRCdlw0p"
+      },
+      "outputs": [],
+      "source": [
+        "from captum.attr import IntegratedGradients"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "torch.rand?"
+      ],
+      "metadata": {
+        "id": "QtvHc0KtKDFB"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "urFL_PoDlw2q"
+      },
+      "outputs": [],
+      "source": [
+        "\n",
+        "ig = IntegratedGradients(model)\n",
+        "\n",
+        "baseline = torch.rand(1, 1, input_size).cuda()*0.1  # Baseline (reference) is set to all zeros\n",
+        "attributions = ig.attribute(input_tensor.cuda(), baseline, target=target)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 586
+        },
+        "id": "95z_5Ocilw4Y",
+        "outputId": "ff510f3c-883c-4325-8908-771833aabb65"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1800x800 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Convert tensors to numpy arrays and remove singleton dimensions\n",
+        "attributions_np = attributions.detach().cpu().numpy().squeeze()\n",
+        "input_tensor_np = input_tensor.detach().cpu().numpy().squeeze()\n",
+        "\n",
+        "# Create the plot\n",
+        "plt.figure(figsize=(18, 8))\n",
+        "\n",
+        "# Plot attributions\n",
+        "plt.plot(attributions_np, label='Attributions', linewidth=2, color='b')\n",
+        "\n",
+        "# Plot input tensor\n",
+        "plt.plot(input_tensor_np, label='Input Tensor', linewidth=2, linestyle='dashed', color='r', alpha=0.5)\n",
+        "\n",
+        "# Add title and labels\n",
+        "plt.title('Explanations', fontsize=16)\n",
+        "plt.xlabel('Feature Index', fontsize=14)\n",
+        "plt.ylabel('Value', fontsize=14)\n",
+        "\n",
+        "# Add a legend\n",
+        "plt.legend(fontsize=12)\n",
+        "\n",
+        "# Add grid lines\n",
+        "plt.grid(True, linestyle='--', alpha=0.7)\n",
+        "\n",
+        "# Show the plot\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "qCTzfuN-lw7v"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 537
+        },
+        "id": "6f2rLR-CgU_X",
+        "outputId": "a74b41ed-e1fe-43fd-bb9c-d613d9e1bb53"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1800x800 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "\n",
+        "# Convert tensors to numpy arrays and remove singleton dimensions\n",
+        "attributions_np = attributions.detach().cpu().numpy().squeeze()\n",
+        "input_tensor_np = input_tensor.detach().cpu().numpy().squeeze()\n",
+        "\n",
+        "# Create the plot\n",
+        "fig, ax1 = plt.subplots(figsize=(18, 8))\n",
+        "\n",
+        "# Plot attributions\n",
+        "color = 'tab:blue'\n",
+        "ax1.set_xlabel('Feature Index', fontsize=16)\n",
+        "ax1.set_ylabel('Attributions', color=color, fontsize=16)\n",
+        "ax1.plot(attributions_np, label='Attributions', linewidth=2, color=color)\n",
+        "ax1.tick_params(axis='y', labelcolor=color)\n",
+        "ax1.grid(True, linestyle='--', alpha=0.5)\n",
+        "\n",
+        "# Highlight important regions in attributions\n",
+        "threshold = np.percentile(np.abs(attributions_np), 99.5)  # 99.9th percentile\n",
+        "important_regions = np.where(np.abs(attributions_np) > threshold)\n",
+        "ax1.scatter(important_regions, attributions_np[important_regions], color='red', zorder=5, label='Important Features')\n",
+        "\n",
+        "# Add a second y-axis to plot input tensor\n",
+        "ax2 = ax1.twinx()\n",
+        "color = 'tab:orange'\n",
+        "ax2.set_ylabel('Input Tensor', color=color, fontsize=16)\n",
+        "ax2.plot(input_tensor_np, label='Input Tensor', linewidth=2, linestyle='dashed', color=color)\n",
+        "ax2.tick_params(axis='y', labelcolor=color)\n",
+        "\n",
+        "# Add title and legend\n",
+        "plt.title('Feature Attributions vs Input Tensor', fontsize=18)\n",
+        "fig.tight_layout()\n",
+        "fig.legend(loc=\"upper right\", bbox_to_anchor=(0.9,0.9), fontsize=14)\n",
+        "\n",
+        "# Show the plot\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "BTFDTcMygVCM"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 537
+        },
+        "id": "iFe4mjyVgVFw",
+        "outputId": "94b513f4-f654-4407-bc26-521ff3bf8c4a"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1800x800 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "\n",
+        "# Convert tensors to numpy arrays and remove singleton dimensions\n",
+        "attributions_np = attributions.detach().cpu().numpy().squeeze()\n",
+        "input_tensor_np = input_tensor.detach().cpu().numpy().squeeze()\n",
+        "\n",
+        "# Create the plot\n",
+        "fig, ax1 = plt.subplots(figsize=(18, 8))\n",
+        "\n",
+        "# Plot input tensor\n",
+        "color = 'tab:blue'\n",
+        "ax1.set_xlabel('Feature Index', fontsize=16)\n",
+        "ax1.set_ylabel('Attributions & Input Tensor', fontsize=16)\n",
+        "ax1.plot(input_tensor_np, label='Input Tensor', linewidth=2, linestyle='dashed', color=color, zorder=2)\n",
+        "ax1.tick_params(axis='y', labelcolor=color)\n",
+        "ax1.grid(True, linestyle='--', alpha=0.5)\n",
+        "\n",
+        "normalized_attributions = (attributions_np - np.min(attributions_np)) / (np.max(attributions_np) - np.min(attributions_np))\n",
+        "enhanced_attributions = normalized_attributions ** 2  # Cubing to enhance contrast\n",
+        "\n",
+        "for i, val in enumerate(enhanced_attributions):\n",
+        "    ax1.add_patch(plt.Rectangle((i-0.5, np.min(input_tensor_np)), 1, np.max(input_tensor_np) - np.min(input_tensor_np), color=(1, 0, 0, val), zorder=1))\n",
+        "\n",
+        "# Add title and legend\n",
+        "plt.title('Input Tensor with Heatmap of Feature Attributions', fontsize=18)\n",
+        "fig.tight_layout()\n",
+        "fig.legend(loc=\"upper right\", bbox_to_anchor=(0.9, 0.9), fontsize=14)\n",
+        "\n",
+        "# Show the plot\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "icF_OViKKOh8"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "stsKkSxcKOmE"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "tLqcPhaZKOoW"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dDHpqJqdKQLv"
+      },
+      "source": [
+        "# TSNE plot"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Kd9wp4C8KOqt",
+        "outputId": "1d876099-6430-44f5-c3b2-fbb55d88de6a"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(3072, 5000) (3072,)\n"
+          ]
+        }
+      ],
+      "source": [
+        "from sklearn.manifold import TSNE\n",
+        "import matplotlib.pyplot as plt\n",
+        "import torch\n",
+        "import numpy as np\n",
+        "\n",
+        "# Initialize empty lists to hold features and labels\n",
+        "all_features = []\n",
+        "all_labels = []\n",
+        "\n",
+        "\n",
+        "\n",
+        "for batch_labels, batch_features, _ in train_loader:\n",
+        "    all_features.append(batch_features.reshape(batch_features.size(0), -1))  # Flatten each batch to 2D\n",
+        "    all_labels.append(batch_labels)\n",
+        "\n",
+        "\n",
+        "all_features = torch.cat(all_features).numpy()\n",
+        "all_labels = torch.cat(all_labels).numpy()\n",
+        "print(all_features.shape, all_labels.shape)\n",
+        "\n",
+        "# 3. Apply t-SNE\n",
+        "tsne = TSNE(n_components=2, random_state=0)\n",
+        "tsne_results = tsne.fit_transform(all_features)\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 762
+        },
+        "id": "lf0n5PqLKOt8",
+        "outputId": "5b0df78b-2e88-444c-b153-8e04ceaebf3b"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "<ipython-input-86-7057cc22bff6>:8: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
+            "  cmap = plt.cm.get_cmap('tab10')\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1600x800 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "\n",
+        "# 4. Plot\n",
+        "plt.figure(figsize=(16, 8))\n",
+        "\n",
+        "# Custom colormap\n",
+        "cmap = plt.cm.get_cmap('tab10')\n",
+        "\n",
+        "# Scatter plot with high-quality markers\n",
+        "scatter = plt.scatter(tsne_results[:, 0], tsne_results[:, 1], c=all_labels, cmap=cmap, s=100, alpha=0.6, edgecolors='w', linewidths=0.5)\n",
+        "\n",
+        "# Colorbar with custom ticks and labels\n",
+        "cbar = plt.colorbar(scatter, ticks=range(len(np.unique(all_labels))))\n",
+        "cbar.set_label('Species Label', rotation=270, labelpad=20, fontsize=14)\n",
+        "cbar.ax.tick_params(labelsize=12)\n",
+        "\n",
+        "# Annotate cluster centers\n",
+        "species_names = selected_species\n",
+        "unique_labels = np.unique(all_labels)\n",
+        "for i, label in enumerate(unique_labels):\n",
+        "    cluster_center = np.mean(tsne_results[all_labels == label], axis=0)\n",
+        "    plt.text(cluster_center[0], cluster_center[1], species_names[i], fontsize=16, ha='center', va='center', color='black', bbox=dict(facecolor='white', edgecolor='black', boxstyle='round,pad=0.5'))\n",
+        "\n",
+        "# Title and Labels with custom font sizes\n",
+        "plt.title('t-SNE Plot of Ancient Species', fontsize=20)\n",
+        "plt.xlabel('Dimension 1', fontsize=16)\n",
+        "plt.ylabel('Dimension 2', fontsize=16)\n",
+        "\n",
+        "# Custom grid\n",
+        "plt.grid(True, linestyle='--', alpha=0.7)\n",
+        "\n",
+        "# Custom tick marks for better readability\n",
+        "plt.tick_params(axis='both', which='major', labelsize=14)\n",
+        "\n",
+        "# Show the plot\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "xFVCaNwNaFh1"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "VMaFjFpG0J7q"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "zN06fWcq0KFf"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "VVy-JOJZ0K7t"
+      },
+      "source": [
+        "# Find \"difficult\" sampels"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "PhrPN5EU2rnP"
+      },
+      "outputs": [],
+      "source": [
+        "# # Initialize CustomDataset\n",
+        "# dataset = CustomDataset(file_paths)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "k-9aWiUu0KR9",
+        "outputId": "83d9d358-f876-4bc0-c398-acae98bba074"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Test Accuracy: 90.46793760831889%\n",
+            "Test Accuracy: 91.16117850953206%\n",
+            "Test Accuracy: 91.33448873483536%\n",
+            "Test Accuracy: 89.60138648180242%\n",
+            "Test Accuracy: 91.50779896013864%\n",
+            "Test Accuracy: 90.64124783362219%\n",
+            "Test Accuracy: 89.60138648180242%\n",
+            "Test Accuracy: 89.25476603119584%\n",
+            "Test Accuracy: 88.21490467937609%\n",
+            "Test Accuracy: 88.38821490467937%\n",
+            "Test Accuracy: 89.77469670710572%\n",
+            "Test Accuracy: 90.81455805892547%\n",
+            "Test Accuracy: 90.81455805892547%\n",
+            "Test Accuracy: 89.77469670710572%\n",
+            "Test Accuracy: 89.25476603119584%\n",
+            "Test Accuracy: 89.94800693240902%\n",
+            "Test Accuracy: 89.94800693240902%\n",
+            "Test Accuracy: 89.42807625649914%\n",
+            "Test Accuracy: 90.12131715771231%\n",
+            "Test Accuracy: 90.2946273830156%\n",
+            "Test Accuracy: 89.60138648180242%\n",
+            "Test Accuracy: 89.08145580589255%\n",
+            "Test Accuracy: 91.16117850953206%\n",
+            "Test Accuracy: 91.16117850953206%\n",
+            "Test Accuracy: 88.73483535528597%\n",
+            "Test Accuracy: 89.42807625649914%\n",
+            "Test Accuracy: 90.46793760831889%\n",
+            "Test Accuracy: 90.81455805892547%\n",
+            "Test Accuracy: 89.42807625649914%\n",
+            "Test Accuracy: 89.60138648180242%\n",
+            "Test Accuracy: 89.42807625649914%\n",
+            "Test Accuracy: 89.60138648180242%\n",
+            "Test Accuracy: 90.98786828422877%\n",
+            "Test Accuracy: 90.81455805892547%\n",
+            "Test Accuracy: 89.60138648180242%\n",
+            "Test Accuracy: 90.98786828422877%\n",
+            "Test Accuracy: 88.90814558058925%\n",
+            "Test Accuracy: 90.12131715771231%\n",
+            "Test Accuracy: 92.20103986135182%\n",
+            "Test Accuracy: 90.98786828422877%\n",
+            "Test Accuracy: 91.16117850953206%\n",
+            "Test Accuracy: 90.2946273830156%\n",
+            "Test Accuracy: 90.46793760831889%\n",
+            "Test Accuracy: 90.64124783362219%\n",
+            "Test Accuracy: 90.81455805892547%\n",
+            "Test Accuracy: 90.81455805892547%\n",
+            "Test Accuracy: 89.60138648180242%\n",
+            "Test Accuracy: 88.56152512998267%\n",
+            "Test Accuracy: 89.77469670710572%\n",
+            "Test Accuracy: 91.50779896013864%\n",
+            "Test Accuracy: 90.12131715771231%\n",
+            "Test Accuracy: 91.50779896013864%\n",
+            "Test Accuracy: 90.12131715771231%\n",
+            "Test Accuracy: 90.46793760831889%\n",
+            "Test Accuracy: 90.2946273830156%\n",
+            "Test Accuracy: 91.85441941074524%\n",
+            "Test Accuracy: 89.77469670710572%\n",
+            "Test Accuracy: 89.42807625649914%\n",
+            "Test Accuracy: 90.12131715771231%\n",
+            "Test Accuracy: 90.81455805892547%\n",
+            "Test Accuracy: 91.16117850953206%\n",
+            "Test Accuracy: 89.42807625649914%\n",
+            "Test Accuracy: 88.73483535528597%\n",
+            "Test Accuracy: 89.60138648180242%\n",
+            "Test Accuracy: 89.94800693240902%\n",
+            "Test Accuracy: 87.8682842287695%\n",
+            "Test Accuracy: 90.98786828422877%\n",
+            "Test Accuracy: 89.08145580589255%\n",
+            "Test Accuracy: 88.73483535528597%\n",
+            "Test Accuracy: 90.2946273830156%\n",
+            "Test Accuracy: 90.2946273830156%\n",
+            "Test Accuracy: 90.98786828422877%\n",
+            "Test Accuracy: 90.46793760831889%\n",
+            "Test Accuracy: 91.68110918544194%\n",
+            "Test Accuracy: 89.08145580589255%\n",
+            "Test Accuracy: 88.90814558058925%\n",
+            "Test Accuracy: 88.21490467937609%\n",
+            "Test Accuracy: 89.42807625649914%\n",
+            "Test Accuracy: 90.2946273830156%\n",
+            "Test Accuracy: 90.12131715771231%\n",
+            "Test Accuracy: 91.85441941074524%\n",
+            "Test Accuracy: 92.20103986135182%\n",
+            "Test Accuracy: 90.81455805892547%\n",
+            "Test Accuracy: 89.08145580589255%\n",
+            "Test Accuracy: 88.90814558058925%\n",
+            "Test Accuracy: 90.98786828422877%\n",
+            "Test Accuracy: 89.77469670710572%\n",
+            "Test Accuracy: 89.60138648180242%\n",
+            "Test Accuracy: 89.25476603119584%\n",
+            "Test Accuracy: 90.2946273830156%\n",
+            "Test Accuracy: 89.08145580589255%\n",
+            "Test Accuracy: 88.38821490467937%\n",
+            "Test Accuracy: 89.77469670710572%\n",
+            "Test Accuracy: 89.25476603119584%\n",
+            "Test Accuracy: 90.2946273830156%\n",
+            "Test Accuracy: 90.46793760831889%\n",
+            "Test Accuracy: 90.2946273830156%\n",
+            "Test Accuracy: 91.68110918544194%\n",
+            "Test Accuracy: 88.56152512998267%\n",
+            "Test Accuracy: 89.42807625649914%\n",
+            "Test Accuracy: 89.94800693240902%\n",
+            "Test Accuracy: 89.42807625649914%\n",
+            "Test Accuracy: 90.64124783362219%\n",
+            "Test Accuracy: 90.98786828422877%\n",
+            "Test Accuracy: 90.12131715771231%\n",
+            "Test Accuracy: 90.12131715771231%\n",
+            "Test Accuracy: 89.77469670710572%\n",
+            "Test Accuracy: 88.90814558058925%\n",
+            "Test Accuracy: 90.46793760831889%\n",
+            "Test Accuracy: 89.42807625649914%\n",
+            "Test Accuracy: 90.46793760831889%\n",
+            "Test Accuracy: 89.94800693240902%\n",
+            "Test Accuracy: 91.33448873483536%\n",
+            "Test Accuracy: 90.64124783362219%\n",
+            "Test Accuracy: 89.60138648180242%\n",
+            "Test Accuracy: 90.46793760831889%\n",
+            "Test Accuracy: 90.12131715771231%\n",
+            "Test Accuracy: 89.08145580589255%\n",
+            "Test Accuracy: 90.12131715771231%\n",
+            "Test Accuracy: 91.16117850953206%\n",
+            "Test Accuracy: 90.12131715771231%\n",
+            "Test Accuracy: 88.73483535528597%\n",
+            "Test Accuracy: 90.2946273830156%\n",
+            "Test Accuracy: 87.8682842287695%\n",
+            "Test Accuracy: 90.2946273830156%\n",
+            "Test Accuracy: 90.46793760831889%\n",
+            "Test Accuracy: 91.68110918544194%\n",
+            "Test Accuracy: 92.02772963604853%\n",
+            "Test Accuracy: 91.33448873483536%\n",
+            "Test Accuracy: 90.81455805892547%\n",
+            "Test Accuracy: 91.16117850953206%\n",
+            "Test Accuracy: 90.2946273830156%\n",
+            "Test Accuracy: 88.73483535528597%\n",
+            "Test Accuracy: 90.64124783362219%\n",
+            "Test Accuracy: 89.60138648180242%\n",
+            "Test Accuracy: 92.89428076256499%\n",
+            "Test Accuracy: 88.56152512998267%\n",
+            "Test Accuracy: 89.60138648180242%\n",
+            "Test Accuracy: 90.46793760831889%\n",
+            "Test Accuracy: 91.16117850953206%\n",
+            "Test Accuracy: 89.42807625649914%\n",
+            "Test Accuracy: 90.98786828422877%\n",
+            "Test Accuracy: 89.60138648180242%\n",
+            "Test Accuracy: 90.64124783362219%\n",
+            "Test Accuracy: 90.46793760831889%\n",
+            "Test Accuracy: 91.68110918544194%\n",
+            "Test Accuracy: 90.12131715771231%\n",
+            "Test Accuracy: 92.20103986135182%\n",
+            "Test Accuracy: 90.64124783362219%\n",
+            "Test Accuracy: 89.42807625649914%\n",
+            "Test Accuracy: 90.64124783362219%\n",
+            "Test Accuracy: 89.08145580589255%\n",
+            "Test Accuracy: 90.2946273830156%\n",
+            "Test Accuracy: 91.85441941074524%\n",
+            "Test Accuracy: 89.25476603119584%\n",
+            "Test Accuracy: 91.50779896013864%\n",
+            "Test Accuracy: 89.42807625649914%\n",
+            "Test Accuracy: 90.46793760831889%\n",
+            "Test Accuracy: 89.94800693240902%\n",
+            "Test Accuracy: 90.46793760831889%\n",
+            "Test Accuracy: 89.77469670710572%\n",
+            "Test Accuracy: 89.60138648180242%\n",
+            "Test Accuracy: 90.12131715771231%\n",
+            "Test Accuracy: 90.12131715771231%\n",
+            "Test Accuracy: 88.90814558058925%\n",
+            "Test Accuracy: 88.90814558058925%\n",
+            "Test Accuracy: 89.94800693240902%\n",
+            "Test Accuracy: 89.77469670710572%\n",
+            "Test Accuracy: 89.60138648180242%\n",
+            "Test Accuracy: 90.81455805892547%\n",
+            "Test Accuracy: 91.33448873483536%\n",
+            "Test Accuracy: 89.42807625649914%\n",
+            "Test Accuracy: 90.12131715771231%\n",
+            "Test Accuracy: 88.90814558058925%\n",
+            "Test Accuracy: 89.94800693240902%\n",
+            "Test Accuracy: 90.2946273830156%\n",
+            "Test Accuracy: 89.77469670710572%\n",
+            "Test Accuracy: 89.25476603119584%\n",
+            "Test Accuracy: 88.90814558058925%\n",
+            "Test Accuracy: 89.08145580589255%\n",
+            "Test Accuracy: 88.21490467937609%\n",
+            "Test Accuracy: 91.68110918544194%\n",
+            "Test Accuracy: 90.81455805892547%\n",
+            "Test Accuracy: 91.68110918544194%\n",
+            "Test Accuracy: 90.64124783362219%\n",
+            "Test Accuracy: 90.64124783362219%\n",
+            "Test Accuracy: 90.81455805892547%\n",
+            "Test Accuracy: 89.60138648180242%\n",
+            "Test Accuracy: 90.12131715771231%\n",
+            "Test Accuracy: 92.20103986135182%\n",
+            "Test Accuracy: 94.62738301559791%\n",
+            "Test Accuracy: 89.25476603119584%\n",
+            "Test Accuracy: 89.77469670710572%\n",
+            "Test Accuracy: 88.90814558058925%\n",
+            "Test Accuracy: 89.42807625649914%\n",
+            "Test Accuracy: 90.2946273830156%\n",
+            "Test Accuracy: 90.2946273830156%\n",
+            "Test Accuracy: 90.2946273830156%\n",
+            "Test Accuracy: 89.77469670710572%\n",
+            "Test Accuracy: 89.42807625649914%\n",
+            "Test Accuracy: 91.33448873483536%\n",
+            "Test Accuracy: 90.81455805892547%\n",
+            "Test Accuracy: 88.73483535528597%\n",
+            "Test Accuracy: 90.2946273830156%\n",
+            "Test Accuracy: 89.08145580589255%\n",
+            "Test Accuracy: 90.64124783362219%\n",
+            "Test Accuracy: 89.42807625649914%\n",
+            "Test Accuracy: 90.46793760831889%\n",
+            "Test Accuracy: 88.90814558058925%\n",
+            "Test Accuracy: 89.94800693240902%\n",
+            "Test Accuracy: 90.98786828422877%\n",
+            "Test Accuracy: 89.08145580589255%\n",
+            "Test Accuracy: 91.85441941074524%\n",
+            "Test Accuracy: 91.16117850953206%\n",
+            "Test Accuracy: 89.25476603119584%\n",
+            "Test Accuracy: 90.12131715771231%\n",
+            "Test Accuracy: 90.64124783362219%\n",
+            "Test Accuracy: 89.77469670710572%\n",
+            "Test Accuracy: 90.98786828422877%\n",
+            "Test Accuracy: 89.25476603119584%\n",
+            "Test Accuracy: 89.08145580589255%\n",
+            "Test Accuracy: 88.38821490467937%\n",
+            "Test Accuracy: 89.77469670710572%\n",
+            "Test Accuracy: 90.46793760831889%\n",
+            "Test Accuracy: 90.2946273830156%\n",
+            "Test Accuracy: 89.42807625649914%\n",
+            "Test Accuracy: 92.20103986135182%\n",
+            "Test Accuracy: 90.64124783362219%\n",
+            "Test Accuracy: 91.33448873483536%\n",
+            "Test Accuracy: 89.25476603119584%\n",
+            "Test Accuracy: 87.6949740034662%\n",
+            "Test Accuracy: 89.42807625649914%\n",
+            "Test Accuracy: 89.25476603119584%\n",
+            "Test Accuracy: 92.20103986135182%\n",
+            "Test Accuracy: 88.21490467937609%\n",
+            "Test Accuracy: 89.60138648180242%\n",
+            "Test Accuracy: 89.60138648180242%\n",
+            "Test Accuracy: 90.98786828422877%\n",
+            "Test Accuracy: 89.25476603119584%\n",
+            "Test Accuracy: 88.38821490467937%\n",
+            "Test Accuracy: 91.68110918544194%\n",
+            "Test Accuracy: 89.60138648180242%\n",
+            "Test Accuracy: 91.68110918544194%\n",
+            "Test Accuracy: 89.60138648180242%\n",
+            "Test Accuracy: 90.12131715771231%\n",
+            "Test Accuracy: 88.73483535528597%\n",
+            "Test Accuracy: 90.46793760831889%\n",
+            "Test Accuracy: 89.60138648180242%\n",
+            "Test Accuracy: 91.50779896013864%\n",
+            "Test Accuracy: 90.98786828422877%\n",
+            "Test Accuracy: 89.60138648180242%\n",
+            "Test Accuracy: 89.42807625649914%\n",
+            "Test Accuracy: 88.73483535528597%\n",
+            "Test Accuracy: 90.98786828422877%\n",
+            "Test Accuracy: 88.90814558058925%\n",
+            "Test Accuracy: 89.60138648180242%\n",
+            "Test Accuracy: 90.64124783362219%\n",
+            "Test Accuracy: 89.25476603119584%\n",
+            "Test Accuracy: 89.25476603119584%\n",
+            "Test Accuracy: 89.25476603119584%\n",
+            "Test Accuracy: 89.77469670710572%\n",
+            "Test Accuracy: 88.56152512998267%\n",
+            "Test Accuracy: 90.64124783362219%\n",
+            "Test Accuracy: 93.24090121317158%\n",
+            "Test Accuracy: 90.46793760831889%\n",
+            "Test Accuracy: 87.8682842287695%\n",
+            "Test Accuracy: 89.77469670710572%\n",
+            "Test Accuracy: 90.2946273830156%\n",
+            "Test Accuracy: 89.25476603119584%\n",
+            "Test Accuracy: 90.2946273830156%\n",
+            "Test Accuracy: 90.12131715771231%\n",
+            "Test Accuracy: 91.68110918544194%\n",
+            "Test Accuracy: 91.85441941074524%\n",
+            "Test Accuracy: 89.60138648180242%\n",
+            "Test Accuracy: 91.50779896013864%\n",
+            "Test Accuracy: 91.16117850953206%\n",
+            "Test Accuracy: 87.6949740034662%\n",
+            "Test Accuracy: 90.2946273830156%\n",
+            "Test Accuracy: 88.73483535528597%\n",
+            "Test Accuracy: 90.46793760831889%\n",
+            "Test Accuracy: 89.08145580589255%\n",
+            "Test Accuracy: 89.60138648180242%\n",
+            "Test Accuracy: 88.0415944540728%\n",
+            "Test Accuracy: 90.12131715771231%\n",
+            "Test Accuracy: 92.02772963604853%\n",
+            "Test Accuracy: 91.85441941074524%\n",
+            "Test Accuracy: 89.42807625649914%\n",
+            "Test Accuracy: 90.12131715771231%\n",
+            "Test Accuracy: 91.16117850953206%\n",
+            "Test Accuracy: 88.38821490467937%\n",
+            "Test Accuracy: 90.2946273830156%\n",
+            "Test Accuracy: 89.08145580589255%\n",
+            "Test Accuracy: 88.0415944540728%\n",
+            "Test Accuracy: 91.50779896013864%\n",
+            "Test Accuracy: 92.02772963604853%\n",
+            "Test Accuracy: 86.65511265164645%\n",
+            "Test Accuracy: 92.37435008665511%\n",
+            "Test Accuracy: 89.94800693240902%\n",
+            "Test Accuracy: 90.46793760831889%\n",
+            "Test Accuracy: 89.25476603119584%\n"
+          ]
+        }
+      ],
+      "source": [
+        "from sklearn.metrics import accuracy_score\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "# Splitting and Training N times\n",
+        "N = 300\n",
+        "difficult_samples = []\n",
+        "\n",
+        "for i in range(N):\n",
+        "    train_data, test_data = train_test_split(dataset, test_size=0.15, random_state=i)\n",
+        "    train_loader = torch.utils.data.DataLoader(train_data, batch_size=320, shuffle=True)\n",
+        "    test_loader = torch.utils.data.DataLoader(test_data, batch_size=320, shuffle=False)\n",
+        "\n",
+        "    # Initialize 1D CNN model, loss, and optimizer here\n",
+        "    model = CNN1D(5600, 8).cuda()\n",
+        "    criterion = nn.CrossEntropyLoss()\n",
+        "    optimizer = torch.optim.AdamW(model.parameters())\n",
+        "\n",
+        "    model.train()\n",
+        "    # Train the model\n",
+        "    for epoch in range(20):\n",
+        "        correct_train = 0\n",
+        "        total_train = 0\n",
+        "        for labels, inputs, _ in train_loader:\n",
+        "            optimizer.zero_grad()\n",
+        "            outputs = model(inputs.unsqueeze(1).cuda())\n",
+        "            loss = criterion(outputs, labels.cuda())\n",
+        "            loss.backward()\n",
+        "            optimizer.step()\n",
+        "\n",
+        "            _, predicted_train = torch.max(outputs, 1)\n",
+        "            correct_train += (predicted_train == labels.cuda()).sum().item()\n",
+        "            total_train += labels.size(0)\n",
+        "\n",
+        "        train_accuracy = 100 * correct_train / total_train\n",
+        "        #print(f\"Epoch {epoch+1}, Train Accuracy: {train_accuracy}%\")\n",
+        "\n",
+        "    # Test the model\n",
+        "    model.eval()\n",
+        "    correct_test = 0\n",
+        "    total_test = 0\n",
+        "    for labels, inputs, file_paths in test_loader:\n",
+        "        outputs = model(inputs.unsqueeze(1).cuda())\n",
+        "        _, predicted = torch.max(outputs, 1)\n",
+        "        incorrect_indices = (predicted != labels.cuda()).nonzero(as_tuple=True)[0]\n",
+        "\n",
+        "        correct_test += (predicted == labels.cuda()).sum().item()\n",
+        "        total_test += labels.size(0)\n",
+        "\n",
+        "        for idx in incorrect_indices:\n",
+        "            difficult_samples.append((labels[idx].item(), file_paths[idx]))\n",
+        "\n",
+        "    test_accuracy = 100 * correct_test / total_test\n",
+        "    print(f\"Test Accuracy: {test_accuracy}%\")\n",
+        "\n",
+        "# Store difficult_samples for analysis\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "PKivxCGu0KTi"
+      },
+      "outputs": [],
+      "source": [
+        "from collections import Counter\n",
+        "\n",
+        "counted = Counter(difficult_samples)\n",
+        "#print(counted)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "BfsOamqE2ozl",
+        "outputId": "cfa02a53-475a-4da9-ce11-6414ce5953b3"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(7, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Equidae/CSV/DC0533.csv')\n",
+            "(3, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Capra/CSV/DC1008.csv')\n",
+            "(6, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Elephantidae/CSV/DC0803.csv')\n",
+            "(7, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Equidae/CSV/DC0223.csv')\n",
+            "(6, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Elephantidae/CSV/DC0403.csv')\n",
+            "(2, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Canidae/CSV/DC4514.csv')\n",
+            "(7, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Equidae/CSV/DC0318.csv')\n",
+            "(0, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/BisonYak/CSV/DC0645.csv')\n",
+            "(7, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Equidae/CSV/DC0738.csv')\n",
+            "(3, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Capra/CSV/DC0762.csv')\n",
+            "(2, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Canidae/CSV/DC2214.csv')\n",
+            "(5, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CrocutaPanthera/CSV/DC2213.csv')\n",
+            "(7, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Equidae/CSV/DC0548.csv')\n",
+            "(0, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/BisonYak/CSV/DC2063.csv')\n"
+          ]
+        }
+      ],
+      "source": [
+        "repeated_tuples = [k for k,v in counted.items() if v>54]\n",
+        "for i in repeated_tuples:\n",
+        "  print(i)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "[(0, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/BisonYak/CSV/DC0234.csv'), (5, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CrocutaPanthera/CSV/DC0521.csv'), (7, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Equidae/CSV/DC0533.csv'), (0, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/BisonYak/CSV/DC0348.csv'), (3, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Capra/CSV/DC0561.csv'), (0, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/BisonYak/CSV/DC0341.csv'), (3, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Capra/CSV/DC1008.csv'), (4, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CervidaeGazellaSaiga/CSV/DC1837.csv'), (0, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/BisonYak/CSV/DC0821.csv'), (4, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CervidaeGazellaSaiga/CSV/DC0296.csv'), (6, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Elephantidae/CSV/DC0803.csv'), (0, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/BisonYak/CSV/DC0806.csv'), (6, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Elephantidae/CSV/DC0273.csv'), (0, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/BisonYak/CSV/DC0667.csv'), (0, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/BisonYak/CSV/DC1007.csv'), (7, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Equidae/CSV/DC0223.csv'), (5, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CrocutaPanthera/CSV/DC0441.csv'), (6, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Elephantidae/CSV/DC0403.csv'), (2, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Canidae/CSV/DC4514.csv'), (6, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Elephantidae/CSV/DC0963.csv'), (7, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Equidae/CSV/DC0318.csv'), (0, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/BisonYak/CSV/DC0645.csv'), (3, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Capra/CSV/DC0777.csv'), (3, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Capra/CSV/DC0321.csv'), (0, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/BisonYak/CSV/DC0822.csv'), (6, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Elephantidae/CSV/DC0359.csv'), (6, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Elephantidae/CSV/DC0764.csv'), (4, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CervidaeGazellaSaiga/CSV/DC0385.csv'), (7, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Equidae/CSV/DC0738.csv'), (3, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Capra/CSV/DC0762.csv'), (0, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/BisonYak/CSV/DC0843.csv'), (6, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Elephantidae/CSV/DC0947.csv'), (7, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Equidae/CSV/DC0719.csv'), (4, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CervidaeGazellaSaiga/CSV/DC0567.csv'), (3, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Capra/CSV/DC0491.csv'), (3, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Capra/CSV/DC0837.csv'), (6, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Elephantidae/CSV/DC0985.csv'), (4, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CervidaeGazellaSaiga/CSV/DC653.csv'), (2, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Canidae/CSV/DC2214.csv'), (3, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Capra/CSV/DC1015.csv'), (5, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CrocutaPanthera/CSV/DC2213.csv'), (3, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Capra/CSV/DC0516.csv'), (3, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Capra/CSV/DC0711.csv'), (4, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CervidaeGazellaSaiga/CSV/DC0331.csv'), (7, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Equidae/CSV/DC0548.csv'), (0, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/BisonYak/CSV/DC2063.csv'), (3, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Capra/CSV/DC0354.csv'), (5, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CrocutaPanthera/CSV/DC0552.csv'), (0, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/BisonYak/CSV/DC0414.csv'), (4, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CervidaeGazellaSaiga/CSV/DC4361.csv'), (4, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CervidaeGazellaSaiga/CSV/DC0500.csv')]\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "kAk8ktcLGQgI",
+        "outputId": "4d8dc84e-ff64-489e-d8f4-5b762f51d000"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[(0,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/BisonYak/CSV/DC0234.csv'),\n",
+              " (5,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CrocutaPanthera/CSV/DC0521.csv'),\n",
+              " (7,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Equidae/CSV/DC0533.csv'),\n",
+              " (0,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/BisonYak/CSV/DC0348.csv'),\n",
+              " (3,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Capra/CSV/DC0561.csv'),\n",
+              " (0,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/BisonYak/CSV/DC0341.csv'),\n",
+              " (3,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Capra/CSV/DC1008.csv'),\n",
+              " (4,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CervidaeGazellaSaiga/CSV/DC1837.csv'),\n",
+              " (0,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/BisonYak/CSV/DC0821.csv'),\n",
+              " (4,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CervidaeGazellaSaiga/CSV/DC0296.csv'),\n",
+              " (6,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Elephantidae/CSV/DC0803.csv'),\n",
+              " (0,\n",
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+              " (0,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/BisonYak/CSV/DC0667.csv'),\n",
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+              " (7,\n",
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+              " (5,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CrocutaPanthera/CSV/DC0441.csv'),\n",
+              " (6,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Elephantidae/CSV/DC0403.csv'),\n",
+              " (2,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Canidae/CSV/DC4514.csv'),\n",
+              " (6,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Elephantidae/CSV/DC0963.csv'),\n",
+              " (7,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Equidae/CSV/DC0318.csv'),\n",
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+              " (3,\n",
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+              " (6,\n",
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+              " (4,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CervidaeGazellaSaiga/CSV/DC0385.csv'),\n",
+              " (7,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Equidae/CSV/DC0738.csv'),\n",
+              " (3,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Capra/CSV/DC0762.csv'),\n",
+              " (0,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/BisonYak/CSV/DC0843.csv'),\n",
+              " (6,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Elephantidae/CSV/DC0947.csv'),\n",
+              " (7,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Equidae/CSV/DC0719.csv'),\n",
+              " (4,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CervidaeGazellaSaiga/CSV/DC0567.csv'),\n",
+              " (3,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Capra/CSV/DC0491.csv'),\n",
+              " (3,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Capra/CSV/DC0837.csv'),\n",
+              " (6,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Elephantidae/CSV/DC0985.csv'),\n",
+              " (4,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CervidaeGazellaSaiga/CSV/DC653.csv'),\n",
+              " (2,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Canidae/CSV/DC2214.csv'),\n",
+              " (3,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Capra/CSV/DC1015.csv'),\n",
+              " (5,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CrocutaPanthera/CSV/DC2213.csv'),\n",
+              " (3,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Capra/CSV/DC0516.csv'),\n",
+              " (3,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Capra/CSV/DC0711.csv'),\n",
+              " (4,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CervidaeGazellaSaiga/CSV/DC0331.csv'),\n",
+              " (7,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Equidae/CSV/DC0548.csv'),\n",
+              " (0,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/BisonYak/CSV/DC2063.csv'),\n",
+              " (3,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Capra/CSV/DC0354.csv'),\n",
+              " (5,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CrocutaPanthera/CSV/DC0552.csv'),\n",
+              " (0,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/BisonYak/CSV/DC0414.csv'),\n",
+              " (4,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CervidaeGazellaSaiga/CSV/DC4361.csv'),\n",
+              " (4,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CervidaeGazellaSaiga/CSV/DC0500.csv')]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 88
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Z1rjUoKbDwyU",
+        "outputId": "ad42330b-14da-4976-f312-f8b9f98ff81e"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "[(0,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/BisonYak/CSV/DC0300.csv'),\n",
+              " (6,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Elephantidae/CSV/DC0980.csv'),\n",
+              " (6,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Elephantidae/CSV/DC0996.csv'),\n",
+              " (4,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CervidaeGazellaSaiga/CSV/DC0535.csv'),\n",
+              " (0,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/BisonYak/CSV/DC0495.csv'),\n",
+              " (4,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CervidaeGazellaSaiga/CSV/DC0400.csv'),\n",
+              " (6,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Elephantidae/CSV/DC0275.csv')]"
+            ]
+          },
+          "execution_count": 106,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "[(0, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/BisonYak/CSV/DC0300.csv'), (6, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Elephantidae/CSV/DC0980.csv'), (6, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Elephantidae/CSV/DC0996.csv'), (4, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CervidaeGazellaSaiga/CSV/DC0535.csv'), (0, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/BisonYak/CSV/DC0495.csv'), (4, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CervidaeGazellaSaiga/CSV/DC0400.csv'), (6, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Elephantidae/CSV/DC0275.csv')]\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "B1sBd4lFDw0z",
+        "outputId": "f8f46132-dc45-4922-a13e-21b0feb718b3"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "[(6,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Elephantidae/CSV/DC0714.csv'),\n",
+              " (5,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CrocutaPanthera/CSV/DC0458.csv'),\n",
+              " (3,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Capra/CSV/DC0930.csv'),\n",
+              " (3,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Capra/CSV/DC0762.csv'),\n",
+              " (4,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CervidaeGazellaSaiga/CSV/DC0977.csv'),\n",
+              " (3,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Capra/CSV/DC0561.csv'),\n",
+              " (4,\n",
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+              " (4,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CervidaeGazellaSaiga/CSV/DC0500.csv'),\n",
+              " (6,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Elephantidae/CSV/DC1902.csv'),\n",
+              " (7,\n",
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+              " (5,\n",
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+              " (6,\n",
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+              " (4,\n",
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+              " (5,\n",
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+              " (4,\n",
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+              " (2,\n",
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+              " (6,\n",
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+              " (5,\n",
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+              " (3,\n",
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+              " (5,\n",
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+              " (6,\n",
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+              " (3,\n",
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+              " (2,\n",
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+              " (2,\n",
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+              " (0,\n",
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+              " (0,\n",
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+              " (3,\n",
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+              " (5,\n",
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+              " (7,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Equidae/CSV/DC0894.csv'),\n",
+              " (0,\n",
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+              " (5,\n",
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+              " (7,\n",
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+              " (4,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CervidaeGazellaSaiga/CSV/DC0770.csv'),\n",
+              " (6,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Elephantidae/CSV/DC0313.csv'),\n",
+              " (7,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Equidae/CSV/DC0572.csv'),\n",
+              " (6,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Elephantidae/CSV/DC0996.csv'),\n",
+              " (2,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Canidae/CSV/DC0570.csv'),\n",
+              " (3,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Capra/CSV/DC1903.csv'),\n",
+              " (7,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Equidae/CSV/DC2177.csv'),\n",
+              " (4,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CervidaeGazellaSaiga/CSV/DC4361.csv'),\n",
+              " (7,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Equidae/CSV/DC0793.csv'),\n",
+              " (4,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CervidaeGazellaSaiga/CSV/DC0450.csv'),\n",
+              " (3,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Capra/CSV/DC0681.csv'),\n",
+              " (4,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CervidaeGazellaSaiga/CSV/DC0432.csv'),\n",
+              " (4,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CervidaeGazellaSaiga/CSV/DC0535.csv'),\n",
+              " (7,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Equidae/CSV/DC0533.csv'),\n",
+              " (4,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CervidaeGazellaSaiga/CSV/DC0757.csv'),\n",
+              " (0,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/BisonYak/CSV/DC0495.csv'),\n",
+              " (7,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Equidae/CSV/DC1619.csv'),\n",
+              " (2,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Canidae/CSV/DC2214.csv'),\n",
+              " (5,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CrocutaPanthera/CSV/DC0718.csv'),\n",
+              " (3,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Capra/CSV/DC0560.csv'),\n",
+              " (0,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/BisonYak/CSV/DC1476.csv'),\n",
+              " (4,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CervidaeGazellaSaiga/CSV/DC0877.csv'),\n",
+              " (5,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CrocutaPanthera/CSV/DC0766.csv'),\n",
+              " (2,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Canidae/CSV/DC0407.csv'),\n",
+              " (3,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Capra/CSV/DC0992.csv'),\n",
+              " (4,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CervidaeGazellaSaiga/CSV/DC0400.csv'),\n",
+              " (4,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CervidaeGazellaSaiga/CSV/DC4791.csv'),\n",
+              " (5,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CrocutaPanthera/CSV/DC0483.csv'),\n",
+              " (2,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Canidae/CSV/DC0544.csv'),\n",
+              " (6,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Elephantidae/CSV/DC0985.csv'),\n",
+              " (6,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Elephantidae/CSV/DC0532.csv'),\n",
+              " (3,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Capra/CSV/DC0538.csv'),\n",
+              " (5,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CrocutaPanthera/CSV/DC0443.csv'),\n",
+              " (6,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Elephantidae/CSV/DC0730.csv'),\n",
+              " (2,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Canidae/CSV/DC0334.csv'),\n",
+              " (4,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CervidaeGazellaSaiga/CSV/DC0725.csv'),\n",
+              " (4,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CervidaeGazellaSaiga/CSV/DC1837.csv'),\n",
+              " (7,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Equidae/CSV/DC0952.csv'),\n",
+              " (7,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Equidae/CSV/DC0921.csv'),\n",
+              " (7,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Equidae/CSV/DC0627.csv'),\n",
+              " (0,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/BisonYak/CSV/DC0546.csv'),\n",
+              " (0,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/BisonYak/CSV/DC0349.csv'),\n",
+              " (7,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Equidae/CSV/DC0471.csv'),\n",
+              " (7,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Equidae/CSV/DC0640.csv'),\n",
+              " (3,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Capra/CSV/DC0553.csv'),\n",
+              " (4,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CervidaeGazellaSaiga/CSV/DC0445.csv'),\n",
+              " (0,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/BisonYak/CSV/DC0241.csv'),\n",
+              " (5,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CrocutaPanthera/CSV/DC0713.csv'),\n",
+              " (4,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CervidaeGazellaSaiga/CSV/DC2217.csv'),\n",
+              " (3,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Capra/CSV/DC0354.csv'),\n",
+              " (0,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/BisonYak/CSV/DC0469.csv'),\n",
+              " (3,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Capra/CSV/DC1015.csv'),\n",
+              " (7,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Equidae/CSV/DC0804.csv'),\n",
+              " (2,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Canidae/CSV/DC0700.csv'),\n",
+              " (5,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CrocutaPanthera/CSV/DC4764.csv'),\n",
+              " (7,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Equidae/CSV/DC0519.csv'),\n",
+              " (6,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Elephantidae/CSV/DC0254.csv'),\n",
+              " (6,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Elephantidae/CSV/DC0957.csv'),\n",
+              " (5,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CrocutaPanthera/CSV/DC0256.csv'),\n",
+              " (6,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Elephantidae/CSV/DC0275.csv'),\n",
+              " (7,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Equidae/CSV/DC0367.csv'),\n",
+              " (3,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Capra/CSV/DC0516.csv'),\n",
+              " (4,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CervidaeGazellaSaiga/CSV/DC0296.csv'),\n",
+              " (6,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Elephantidae/CSV/DC0569.csv'),\n",
+              " (3,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Capra/CSV/DC0376.csv'),\n",
+              " (0,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/BisonYak/CSV/DC0513.csv'),\n",
+              " (5,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CrocutaPanthera/CSV/DC0624.csv')]"
+            ]
+          },
+          "execution_count": 99,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Jgw-OeJLDxhk"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "_hAxQdr_Dxkm"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "dMLZK_CR2o5s",
+        "outputId": "f57b1f1f-0485-43d4-d1fa-25ec68b078e3"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "[(6,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Elephantidae/CSV/DC0714.csv'),\n",
+              " (4,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CervidaeGazellaSaiga/CSV/DC0977.csv'),\n",
+              " (4,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CervidaeGazellaSaiga/CSV/DC0448.csv'),\n",
+              " (0,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/BisonYak/CSV/DC0300.csv'),\n",
+              " (7,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Equidae/CSV/DC0299.csv'),\n",
+              " (6,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Elephantidae/CSV/DC0858.csv'),\n",
+              " (2,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Canidae/CSV/DC0492.csv'),\n",
+              " (0,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/BisonYak/CSV/DC1631.csv'),\n",
+              " (2,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Canidae/CSV/DC0578.csv'),\n",
+              " (5,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CrocutaPanthera/CSV/DC0486.csv'),\n",
+              " (3,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Capra/CSV/DC7800.csv'),\n",
+              " (0,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/BisonYak/CSV/DC0495.csv'),\n",
+              " (7,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Equidae/CSV/DC1619.csv'),\n",
+              " (3,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Capra/CSV/DC0992.csv'),\n",
+              " (6,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Elephantidae/CSV/DC0532.csv')]"
+            ]
+          },
+          "execution_count": 94,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "[(6, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Elephantidae/CSV/DC0714.csv'), (4, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CervidaeGazellaSaiga/CSV/DC0977.csv'), (4, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CervidaeGazellaSaiga/CSV/DC0448.csv'), (0, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/BisonYak/CSV/DC0300.csv'), (7, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Equidae/CSV/DC0299.csv'), (6, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Elephantidae/CSV/DC0858.csv'), (2, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Canidae/CSV/DC0492.csv'), (0, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/BisonYak/CSV/DC1631.csv'), (2, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Canidae/CSV/DC0578.csv'), (5, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CrocutaPanthera/CSV/DC0486.csv'), (3, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Capra/CSV/DC7800.csv'), (0, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/BisonYak/CSV/DC0495.csv'), (7, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Equidae/CSV/DC1619.csv'), (3, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Capra/CSV/DC0992.csv'), (6, '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Elephantidae/CSV/DC0532.csv')]\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "HOuHqke52o8Q"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "1KG_KvLZ2pAV",
+        "outputId": "1715d394-6cd2-4d29-b617-f4d181137d84"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "[(2,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Canidae/CSV/DC0492.csv'),\n",
+              " (7,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Equidae/CSV/DC0894.csv'),\n",
+              " (7,\n",
+              "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Equidae/CSV/DC0297.csv')]"
+            ]
+          },
+          "execution_count": 83,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "[(2,\n",
+        "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Canidae/CSV/DC0492.csv'),\n",
+        " (7,\n",
+        "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Equidae/CSV/DC0894.csv'),\n",
+        " (7,\n",
+        "  '/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Equidae/CSV/DC0297.csv')]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "cGEjvaXf2pB-"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "HQ5Df47f2pFR"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "M_77wThSaFke"
+      },
+      "outputs": [],
+      "source": [
+        "def compute_grad_hess(preds, labels, proxy_anchors, margin):\n",
+        "    num_data_points = len(labels)\n",
+        "    grad = np.zeros(num_data_points)\n",
+        "    hess = np.zeros(num_data_points)\n",
+        "\n",
+        "    for i in range(num_data_points):\n",
+        "        positive_anchor = proxy_anchors[int(labels[i])]\n",
+        "        negative_anchors = np.delete(proxy_anchors, int(labels[i]), axis=0)\n",
+        "\n",
+        "        positive_distance = np.linalg.norm(preds[i] - positive_anchor)\n",
+        "        negative_distances = np.linalg.norm(preds[i] - negative_anchors, axis=1)  # Define negative_distances\n",
+        "        negative_distance = np.min(negative_distances)  # Use the minimum negative distance\n",
+        "\n",
+        "        # Compute the gradient based on the specific formulation of the Proxy Anchor Loss\n",
+        "        # Here we use a simple placeholder\n",
+        "        grad[i] = 2 * (positive_distance - negative_distance)\n",
+        "\n",
+        "        # Compute the Hessian as a placeholder for the second derivative\n",
+        "        hess[i] = 2\n",
+        "\n",
+        "    return grad, hess\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "kQQ8d0FjaFnq"
+      },
+      "outputs": [],
+      "source": [
+        "def proxy_anchor_loss(preds, dtrain):\n",
+        "    labels = dtrain.get_label()\n",
+        "    num_data_points = len(labels)\n",
+        "    grad = np.zeros(num_data_points)\n",
+        "    hess = np.zeros(num_data_points)\n",
+        "\n",
+        "    for i in range(num_data_points):\n",
+        "        positive_anchor = proxy_anchors[int(labels[i])]\n",
+        "        negative_anchors = np.delete(proxy_anchors, int(labels[i]), axis=0)\n",
+        "\n",
+        "        # Compute distances to positive and negative anchors\n",
+        "        positive_distance = np.linalg.norm(preds[i] - positive_anchor)\n",
+        "        negative_distances = np.linalg.norm(preds[i] - negative_anchors, axis=0)\n",
+        "        negative_distance = np.min(negative_distances)\n",
+        "\n",
+        "        # Compute gradient and Hessian based on the specific formulation of the Proxy Anchor Loss\n",
+        "        grad[i] = 2 * (positive_distance - negative_distance) + margin\n",
+        "        hess[i] = 2\n",
+        "\n",
+        "    return grad, hess\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "prM0RIbE7v1X"
+      },
+      "outputs": [],
+      "source": [
+        "def angular_loss(preds, dtrain):\n",
+        "    labels = dtrain.get_label()\n",
+        "    num_data_points = len(labels)\n",
+        "    grad = np.zeros(num_data_points)\n",
+        "    hess = np.zeros(num_data_points)\n",
+        "\n",
+        "    for i in range(num_data_points):\n",
+        "        positive_anchor = proxy_anchors[int(labels[i])]\n",
+        "        negative_anchors = np.delete(proxy_anchors, int(labels[i]), axis=0)\n",
+        "\n",
+        "        # Compute cosine similarities to positive and negative anchors\n",
+        "        positive_similarity = np.dot(preds[i], positive_anchor) / (np.linalg.norm(preds[i]) * np.linalg.norm(positive_anchor))\n",
+        "        negative_similarity = np.max([np.dot(preds[i], neg) / (np.linalg.norm(preds[i]) * np.linalg.norm(neg)) for neg in negative_anchors])\n",
+        "\n",
+        "        # Compute the loss as the difference between positive and negative similarities\n",
+        "        loss = positive_similarity - negative_similarity\n",
+        "\n",
+        "        # Compute the gradient and Hessian (second derivative) of the loss\n",
+        "        grad[i] = -loss\n",
+        "        hess[i] = 1  # Placeholder for the second derivative\n",
+        "\n",
+        "    return grad, hess\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 488
+        },
+        "id": "p8MeoKCKZyG_",
+        "outputId": "c748bd6b-849e-42a9-c5d0-f59572a60e47"
+      },
+      "outputs": [
+        {
+          "ename": "ValueError",
+          "evalue": "ignored",
+          "output_type": "error",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+            "\u001b[0;31mTypeError\u001b[0m: only size-1 arrays can be converted to Python scalars",
+            "\nThe above exception was the direct cause of the following exception:\n",
+            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-26-942b1a5d3246>\u001b[0m in \u001b[0;36m<cell line: 21>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0mparams\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m'max_depth'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'eta'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;36m0.1\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0mdtrain\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mxgb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDMatrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m \u001b[0mbst\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mxgb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_boost_round\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mangular_loss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;31m# Make predictions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/xgboost/core.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    618\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    619\u001b[0m                 \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 620\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    621\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    622\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/xgboost/training.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(params, dtrain, num_boost_round, evals, obj, feval, maximize, early_stopping_rounds, evals_result, verbose_eval, xgb_model, callbacks, custom_metric)\u001b[0m\n\u001b[1;32m    183\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcb_container\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbefore_iteration\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbst\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    184\u001b[0m             \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 185\u001b[0;31m         \u001b[0mbst\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    186\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcb_container\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mafter_iteration\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbst\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    187\u001b[0m             \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/xgboost/core.py\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, dtrain, iteration, fobj)\u001b[0m\n\u001b[1;32m   1921\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1922\u001b[0m             \u001b[0mpred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput_margin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraining\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1923\u001b[0;31m             \u001b[0mgrad\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhess\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfobj\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtrain\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1924\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mboost\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhess\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1925\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m<ipython-input-25-52321ca2de9c>\u001b[0m in \u001b[0;36mangular_loss\u001b[0;34m(preds, dtrain)\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m         \u001b[0;31m# Compute the gradient and Hessian (second derivative) of the loss\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m         \u001b[0mgrad\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     20\u001b[0m         \u001b[0mhess\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m  \u001b[0;31m# Placeholder for the second derivative\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mValueError\u001b[0m: setting an array element with a sequence."
+          ]
+        }
+      ],
+      "source": [
+        "import xgboost as xgb\n",
+        "import numpy as np\n",
+        "from sklearn.datasets import make_classification\n",
+        "from sklearn.metrics import accuracy_score\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "\n",
+        "# Create a synthetic dataset\n",
+        "X, y = make_classification(n_samples=1000, n_features=20, n_informative=10, n_classes=5)\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+        "num_classes = len(np.unique(y))\n",
+        "num_features = X.shape[1]\n",
+        "margin = 1.0\n",
+        "\n",
+        "# Define Proxy Anchors (e.g., randomly or using mean vectors)\n",
+        "proxy_anchors = np.random.rand(num_classes, num_features)\n",
+        "\n",
+        "\n",
+        "# Train XGBoost model with custom objective\n",
+        "params = {'max_depth': 3, 'eta': 0.1}\n",
+        "dtrain = xgb.DMatrix(X_train, label=y_train)\n",
+        "bst = xgb.train(params, dtrain, num_boost_round=10, obj=angular_loss)\n",
+        "\n",
+        "# Make predictions\n",
+        "dtest = xgb.DMatrix(X_test)\n",
+        "preds = bst.predict(dtest)\n",
+        "\n",
+        "# Convert predictions to class labels\n",
+        "preds_labels = np.round(preds).astype(int)\n",
+        "\n",
+        "# Evaluate accuracy\n",
+        "accuracy = accuracy_score(y_test, preds_labels)\n",
+        "print(\"Accuracy:\", accuracy)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Ndxiwcv96D2F",
+        "outputId": "a5237e4d-e594-4dae-ee5d-9a461721ef23"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "array([-4, -3, -4, -3, -4, -3, -4, -4, -4, -4, -3, -4, -3, -4, -4, -3, -3,\n",
+              "       -3, -4, -4, -3, -3, -4, -4, -3, -4, -4, -4, -3, -4, -3, -4, -3, -4,\n",
+              "       -3, -3, -4, -4, -3, -3, -4, -3, -4, -4, -3, -3, -4, -4, -3, -3, -4,\n",
+              "       -4, -4, -3, -3, -4, -4, -3, -4, -3, -3, -3, -3, -3, -3, -4, -3, -4,\n",
+              "       -3, -4, -3, -3, -3, -3, -4, -4, -4, -3, -3, -3, -3, -4, -3, -4, -3,\n",
+              "       -4, -4, -4, -4, -4, -4, -4, -3, -3, -4, -4, -3, -3, -4, -3, -4, -4,\n",
+              "       -3, -3, -4, -4, -3, -3, -3, -4, -4, -3, -4, -3, -3, -3, -4, -3, -3,\n",
+              "       -4, -4, -3, -3, -4, -4, -3, -4, -3, -4, -4, -3, -4, -4, -4, -4, -3,\n",
+              "       -3, -3, -4, -4, -4, -3, -4, -3, -4, -3, -4, -4, -3, -4, -3, -3, -3,\n",
+              "       -4, -4, -3, -3, -4, -4, -3, -3, -3, -4, -3, -3, -4, -4, -4, -4, -4,\n",
+              "       -3, -4, -4, -4, -4, -4, -3, -4, -3, -3, -3, -4, -4, -3, -3, -4, -4,\n",
+              "       -4, -3, -4, -4, -3, -3, -3, -4, -4, -4, -3, -3, -4])"
+            ]
+          },
+          "execution_count": 16,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "preds_labels"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "5Kz9pADO6D5w"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "nt3JGf7P6D7p"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "nFszy1Fj6D-s"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "3zVkq6IY4u7I"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "bgxiymm14u-F"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "g5ChAxFA4vA4"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "PxIaHotoSg7Y",
+        "outputId": "b00cb357-3fa4-410f-df20-b5b7d026c75d"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Loading file: /content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Ursidae/CSV/FNR_DC9001_1.csv\n",
+            "Loading file: /content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Capra/CSV/DC6178.csv\n",
+            "Loading file: /content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/BisonYak/CSV/DC3897.csv\n",
+            "Loading file: /content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CervidaeGazellaSaiga/CSV/DC0607.csv\n",
+            "Loading file: /content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Equidae/CSV/DC4779.csv\n",
+            "Loading file: /content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/BisonYak/CSV/DC10168.csv\n",
+            "Loading file: /content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/CervidaeGazellaSaiga/CSV/DC1737.csv\n",
+            "Loading file: /content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/Equidae/CSV/DC8309.csv\n",
+            "('Ursidae', 'Capra', 'BisonYak', 'CervidaeGazellaSaiga', 'Equidae', 'BisonYak', 'CervidaeGazellaSaiga', 'Equidae')\n",
+            "tensor([[2.9421e-04, 3.2030e-04, 3.3259e-04,  ..., 0.0000e+00, 0.0000e+00,\n",
+            "         0.0000e+00],\n",
+            "        [6.6539e-04, 6.4100e-04, 7.6001e-04,  ..., 9.6620e-05, 0.0000e+00,\n",
+            "         0.0000e+00],\n",
+            "        [3.0021e-04, 3.0432e-04, 2.9134e-04,  ..., 2.9871e-04, 3.0471e-04,\n",
+            "         2.9516e-04],\n",
+            "        ...,\n",
+            "        [6.2988e-04, 6.5550e-04, 5.7734e-04,  ..., 1.1046e-04, 0.0000e+00,\n",
+            "         0.0000e+00],\n",
+            "        [2.3251e-04, 2.7858e-04, 2.1795e-04,  ..., 2.1126e-04, 1.2188e-04,\n",
+            "         9.7504e-05],\n",
+            "        [2.8419e-04, 2.9799e-04, 3.1090e-04,  ..., 0.0000e+00, 0.0000e+00,\n",
+            "         0.0000e+00]])\n"
+          ]
+        }
+      ],
+      "source": [
+        "from torch.utils.data import DataLoader\n",
+        "\n",
+        "# Creating an instance of the custom Dataset\n",
+        "dataset = CustomDataset(file_paths)\n",
+        "\n",
+        "# Creating a DataLoader\n",
+        "batch_size = 8\n",
+        "dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)\n",
+        "\n",
+        "# Example of iterating through the DataLoader\n",
+        "for species, intensities in dataloader:\n",
+        "    print(species)    # Batch of species names\n",
+        "    print(intensities) # Batch of intensity values\n",
+        "    break\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "-OOERpPVX_jS"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "PzL1aeIhX_l0"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "4EcTpqmkX_od"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "s72Bqj0TX_rH"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "tVE0sm8BX_tj"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "cRseQFJGX_v-"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 448
+        },
+        "id": "evwrySyJHqJQ",
+        "outputId": "1813831f-00a0-4592-b61b-761b3a39dd37"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "[<matplotlib.lines.Line2D at 0x7ef07a75d150>]"
+            ]
+          },
+          "execution_count": 21,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
+            "image/png": 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LF+PZZ58FANjZ2WH69Ol477330KFDB/j7+2Pu3Lnw9fXFQw89BADo3LkzRo0ahSlTpmDFihUoKCjAtGnTMHHiRLNGIBEREZFts3iAWbp0KebOnYsXXngBqamp8PX1xf/+9z/MmzdPKvP6668jOzsbU6dORXp6OgYPHozw8HC4uLhIZdauXYtp06ZhxIgRUKlUGD9+PJYsWWLp6hIREZECWXwemPqC88A0XCXzwKjsgAthY61cGyIiqgqrzQNDVF/YZDInIiIADDBERESkQAwwZLNs8+QoEREBDDBERESkQAwwREREpDgMMERERKQ4DDBERESkOAwwREREpDgMMERERKQ4DDBERESkOAwwREREpDgMMERERKQ4DDBERESkOAwwREREpDgMMERERKQ4DDBERESkOAwwREREpDgMMERERKQ4DDBERESkOAwwREREpDgMMERERKQ4DDBERESkOAwwREREpDgMMERERKQ4DDBERESkOAwwREREpDgMMERERKQ4DDBERESkOAwwREREpDgMMERERKQ4DDBERESkOAwwREREpDgMMERERKQ4DDBERESkOAwwREREpDgMMERERKQ4DDBERESkOAwwREREpDgMMERERKQ4DDBERESkOAwwREREpDgMMERERKQ4DDBERESkOAwwREREpDgMMERERKQ4tRJgrly5gn//+99o1qwZXF1d0b17dxw6dEhaLoTAvHnz4OPjA1dXVwQFBeHs2bOydaSlpSEkJARqtRoeHh6YPHkysrKyaqO6REREpDAWDzC3bt3CoEGD4OjoiL/++gsnT57EJ598gqZNm0plFi1ahCVLlmDFihWIiYlB48aNERwcjNzcXKlMSEgI4uPjERERgS1btiAqKgpTp061dHWJiIhIgeyEEMKSK5w1axb27t2L3bt3G10uhICvry9effVVvPbaawCAjIwMaDQarF69GhMnTsSpU6fQpUsXHDx4EP369QMAhIeHY8yYMbh8+TJ8fX0rrYdOp4O7uzsyMjKgVqst9wap3msz60/p74sLx1qxJkREVFXm/n5bvAXm999/R79+/fDYY4/By8sLvXv3xtdffy0tT0xMhFarRVBQkPSYu7s7AgICEB0dDQCIjo6Gh4eHFF4AICgoCCqVCjExMUZfNy8vDzqdTnYjIiIi22TxAHPhwgUsX74cHTp0wN9//43nn38eL730EtasWQMA0Gq1AACNRiN7nkajkZZptVp4eXnJljs4OMDT01MqU1ZYWBjc3d2lm5+fn6XfGhEREdUTFg8wer0effr0wQcffIDevXtj6tSpmDJlClasWGHpl5KZPXs2MjIypFtycnKtvh4RERFZj8UDjI+PD7p06SJ7rHPnzkhKSgIAeHt7AwBSUlJkZVJSUqRl3t7eSE1NlS0vLCxEWlqaVKYsZ2dnqNVq2Y2IiIhsk8UDzKBBg5CQkCB77MyZM2jdujUAwN/fH97e3oiMjJSW63Q6xMTEIDAwEAAQGBiI9PR0xMbGSmW2b98OvV6PgIAAS1eZiIiIFMbB0it85ZVXcM899+CDDz7A448/jgMHDmDlypVYuXIlAMDOzg7Tp0/He++9hw4dOsDf3x9z586Fr68vHnroIQDFLTajRo2STj0VFBRg2rRpmDhxolkjkIiIiMi2WTzA9O/fHxs3bsTs2bOxYMEC+Pv747PPPkNISIhU5vXXX0d2djamTp2K9PR0DB48GOHh4XBxcZHKrF27FtOmTcOIESOgUqkwfvx4LFmyxNLVJSIiIgWy+Dww9QXngWm4OA8MEZFyWW0eGCIiIqLaxgBDREREisMAQ0RERIrDAENERESKwwBDREREisMAQ0RERIrDAENERESKwwBDREREisMAQ0RERIrDAENERESKwwBDREREisMAQ0RERIrDAENERESKwwBDREREisMAQ0RERIrDAENERESKwwBDREREisMAQ0RERIrDAENERESKwwBDREREisMAQ0RERIrDAENERESKwwBDREREisMAQ0RERIrDAENERESKwwBDREREisMAQ0RERIrDAENERESKwwBDREREisMAQ0RERIrDAENERESKwwBDREREisMAQ0RERIrDAENERESKwwBDREREisMAQ0RERIrDAENERESKwwBDREREisMAQ0RERIrDAENERESKwwBDREREisMAQ0RERIrDAENERESKwwBDREREilPrAWbhwoWws7PD9OnTpcdyc3MRGhqKZs2aoUmTJhg/fjxSUlJkz0tKSsLYsWPRqFEjeHl5YebMmSgsLKzt6hIREZEC1GqAOXjwIL766iv06NFD9vgrr7yCP/74Axs2bMCuXbtw9epVPPLII9LyoqIijB07Fvn5+di3bx/WrFmD1atXY968ebVZXSIiIlKIWgswWVlZCAkJwddff42mTZtKj2dkZODbb7/F4sWLMXz4cPTt2xerVq3Cvn37sH//fgDAP//8g5MnT+LHH39Er169MHr0aLz77rtYtmwZ8vPza6vKRJUqLNJDrxfWrgYRUYNXawEmNDQUY8eORVBQkOzx2NhYFBQUyB7v1KkTWrVqhejoaABAdHQ0unfvDo1GI5UJDg6GTqdDfHx8bVWZqEIFRXrc+9FOPLBsj7WrQkTU4DnUxkrXr1+Pw4cP4+DBg+WWabVaODk5wcPDQ/a4RqOBVquVyhiGl5LlJcuMycvLQ15ennRfp9PV5C0QlZN4IxtX0m/jSvptCCFgZ2dn7SoRETVYFm+BSU5Oxssvv4y1a9fCxcXF0qs3KSwsDO7u7tLNz8+vzl6bGh7Bs0hERFZl8QATGxuL1NRU9OnTBw4ODnBwcMCuXbuwZMkSODg4QKPRID8/H+np6bLnpaSkwNvbGwDg7e1dblRSyf2SMmXNnj0bGRkZ0i05OdnSb41IwvxCRGRdFg8wI0aMwPHjxxEXFyfd+vXrh5CQEOlvR0dHREZGSs9JSEhAUlISAgMDAQCBgYE4fvw4UlNTpTIRERFQq9Xo0qWL0dd1dnaGWq2W3Yhqi2ATDBGRVVm8D4ybmxu6desme6xx48Zo1qyZ9PjkyZMxY8YMeHp6Qq1W48UXX0RgYCAGDhwIABg5ciS6dOmCp556CosWLYJWq8WcOXMQGhoKZ2dnS1eZqMoYX4iIrKtWOvFW5tNPP4VKpcL48eORl5eH4OBgfPnll9Jye3t7bNmyBc8//zwCAwPRuHFjTJo0CQsWLLBGdYnKYQMMEZF11UmA2blzp+y+i4sLli1bhmXLlpl8TuvWrbF169ZarhlR9Qi2wRARWRWvhURUDWyBISKyLgYYIiIiUhwGGKJqYAsMEZF1McAQVQP7wBARWRcDDJGZDFtd2AJDRGRdDDBE1cD8QkRkXQwwRNXAmXiJiKyLAYaoGhhfiIisiwGGqBrYAENEZF0MMETVwQBDRGRVDDBE1cBh1ERE1sUAQ1QNPIVERGRdDDBE1cD8QkRkXQwwRNXAYdRERNbFAENkJsN+L4wvRETWxQBDZCZeSoCIqP5ggCEykyzAsA2GiMiqGGCIzKSXJxgiIrIiBhiiamB+ISKyLgYYIjOxDwwRUf3BAENkJvkoJCYYIiJrYoAhMhNbYIiI6g8GGCIzCRN/ExFR3WOAITKT4ey7VZ2J90ZWHv6O16KwSG/pahHZjOS0HFzLuG3tapBCMMAQmUnWAlPFJpixS3bjfz/EYvW+i5asEpHNyM4rxJBFOxAYth16fd23cRbw4EJxGGCIzFSTfi8pujwAwD/xKRaqDZFtuZGVJ/1doK/bMDHr12Po+vbfuJrO1h8lYYAhMpvhKaTqrUHP3r9ERjnYl/4c5RfWbYBZfzAZ+YV6rGELqaIwwBCZyRKXEmB8ITLOUWUn/V1QZJ1vCr+fysIAQ2SmmvSBKX0ed5FElanrFpgS/H4qCwMMkZkMOxZWdzdnhb6JRIpg+NWwVoDh91NZGGCIzCRvgeEpJCJLMvxK5VtpRBAbYJSFAYbITBa5GDX3kERGGfYrs9aQZl4iRFkYYKhBuJmVh7FLdmP13sRqr0NYZBRStV+eyKYZfjeslfN5fKEsDDDUICyJPIv4qzrM/+Nk9VciTN4xG4dRExknm+naSi0h7MSrLAww1CDkFtS8Sdoyo5BqXA0im1QfLpbKr6eyMMBQg2BnV3mZyliiDwx3kESVs1ZLJQ8wlIUBhhoEiwQYC/SBYRM1kXF6UfPvV02xE6+yMMBQA1HzBGORmXi5fyQyyvC7Ya0WGHayVxYGGGoQLNMCY/C3KG5Nefq7Awhde7gK6+AeksgYYeLvOq0Dv56K4mDtChDVBQvkF/koCQEkpeUg6sx1AMCnhXo4OVR+PMAjPCLj5N8vjkKiyrEFhhoEi7fAQFTraI07SCLj6sM8MKQsDDDUINhZpA+M6U6G5p4a4n6ZyJTSb4e1Wio5T5OyMMAQmansvs2wVcfc/R73j0TG1YdOvKQsDDDUIKgsPQ+MkLfqmLvD5SkkIuMsMVFkjevAr6eiMMBQg2BngU4wZfvAGK6yyMw2b+4fiYyTHyDwm0KVs3iACQsLQ//+/eHm5gYvLy889NBDSEhIkJXJzc1FaGgomjVrhiZNmmD8+PFISUmRlUlKSsLYsWPRqFEjeHl5YebMmSgsLLR0dYnMVlEfGHPP2bNpnMg42UR2VqoDv53KYvEAs2vXLoSGhmL//v2IiIhAQUEBRo4ciezsbKnMK6+8gj/++AMbNmzArl27cPXqVTzyyCPS8qKiIowdOxb5+fnYt28f1qxZg9WrV2PevHmWri41EJYfhVS2D4y5p5BqXg8iW8Q+MFRVFp8HJjw8XHZ/9erV8PLyQmxsLIYOHYqMjAx8++23WLduHYYPHw4AWLVqFTp37oz9+/dj4MCB+Oeff3Dy5Els27YNGo0GvXr1wrvvvos33ngD8+fPh5OTk6WrTVSpsk3chqelzG2B4X6ZyDhLXKqjxnXg91NRar0PTEZGBgDA09MTABAbG4uCggIEBQVJZTp16oRWrVohOjoaABAdHY3u3btDo9FIZYKDg6HT6RAfH2/0dfLy8qDT6WQ3ohIqSzTBQN7EbbhGduIlqhm2wFBV1WqA0ev1mD59OgYNGoRu3boBALRaLZycnODh4SErq9FooNVqpTKG4aVkeckyY8LCwuDu7i7d/Pz8LPxuSMksMxOv/G/DXazZAcYC9SCyRWW/X1apQ5lvaG5BkXUqQmap1QATGhqKEydOYP369bX5MgCA2bNnIyMjQ7olJyfX+muScli6DwwgKuzUa3IdTDBERslOIdWDqB95KgWd5objm90XrF0VMqHWAsy0adOwZcsW7NixAy1btpQe9/b2Rn5+PtLT02XlU1JS4O3tLZUpOyqp5H5JmbKcnZ2hVqtlN6ISFhlGXbYFphpN3mwaJzJO9n3SW6sSpX++8nMcAOC9P09Zpy5UKYsHGCEEpk2bho0bN2L79u3w9/eXLe/bty8cHR0RGRkpPZaQkICkpCQEBgYCAAIDA3H8+HGkpqZKZSIiIqBWq9GlSxdLV5nILKJMHxhDZnfitVx1iGxKfbgatSFLHPRQ7bL4KKTQ0FCsW7cOmzdvhpubm9Rnxd3dHa6urnB3d8fkyZMxY8YMeHp6Qq1W48UXX0RgYCAGDhwIABg5ciS6dOmCp556CosWLYJWq8WcOXMQGhoKZ2dnS1eZamjbyRS4uTggoG0za1fFJEvsi8pebE5+xMhh1EQ1Ydg6aa2WSn49lcXiAWb58uUAgGHDhskeX7VqFf7zn/8AAD799FOoVCqMHz8eeXl5CA4OxpdffimVtbe3x5YtW/D8888jMDAQjRs3xqRJk7BgwQJLV5dq6FrGbfz3+0MAgIsLx1q5NsXs7Ixct8jiF3MU1Rz2yV0kkTH1oROvITbA1H8WDzDmDBN1cXHBsmXLsGzZMpNlWrduja1bt1qyalQLrmfmWbsKZrH0zkigun1gLFsPItshP0CwSg0MXpf5pf7jtZCoRgznV6nPc5zUm2HU9XgbEVmT/IDAevUowT4w9R8DDFmMuRc0VKqywzyF7Jy9uesgImNkfczq8Jti6qCC8aX+Y4ChGjFsgSmqx60LFpkHpswwCdldc08h2XjII6qu6hwQWII8OJGSMMBQjagMPkFWm7uhDGNZxRKXEhBldnTVafLmDpLIuOocEFjkdU21wLAJpt5jgKEaMRzdU69bYCywDvkOVv6I2cM+6+8mIrIqa41CKjs9QikmmPqOAYZqxPAopV73gbFIC4y8D4xe1gLDmXiJaqLs96vOXpdHFYrFAEM1Irsicz0JMLU1eqBsC0x1jhjrxxYiqn9ko/rq8HR02VPDJXgKqf5jgKEakbXA1OPWBYvsi8r2ganGKSS2wBAZV515lSz9uoaYX+o/BhiymPry41xrnXjLTLRVnU689aSRiqjeqehaY7XJcL/FeZqUhQGGakTWD6SejEIytgsyzC/V3UmVHW5ZrSNG7h+JjJJ3pq3LPjDG8RRS/ccAQzViuJ9Ryimk6raClJ8HpupHbuwwSGSc/Fpjdfe6shYYg8ctcf00ql0MMFQjsivI1pPzI4a7nfSc/HLLq3t0V34m3tJlPIVEVDPyS3PU4eua6gNjRn755WAyJnwVjbTs8vsZqn0MMFQjshaYevjrPPLTKADynZElWmDK7vTMDW88x05kQn24lIDBn+a0v7z+6zHEJKbhnT/iLV4vqhwDDNWI4Y6mvpxCMgwrqXeulm04tLq6nY0rGkbNmXiJakbWmlsPWmCqIiktp+YroSpjgKEakf2I18MWmBLyTrzVXEmZc+XV6gNTfzcRkVXJWzjr7osi7wNT+ndV5pNibxnrYIChGqlKJ966OsVUWec7y7TAVK8PDBEZV/5SHbb9ulRzDDBUI4ZhoKKAcuxyOrq9/Te+jrpQ63Uydv7cMNRYoAHmTgtMqfoyBw6RUsmvRm2dFhh+j5WFAYZqxNzpv2f/dhy3C4rw/tZTtV4nY+SdeKu3kzIMaEIAv8ZervE6iaiY6Ysq1i5TLalVmQemti5fQhVjgKEakbXAVLDXcVDV3Rfc2Ckkw0eW7TiHzXFXqrxeeUgR+GH/pdJ7Cskv3+y+gOEf70SKLtfaVSEqwzotIab63jCT1H8MMFQj5g6jVtVhgClr6veHZPe/2nUBL6+Pq3JHwbItMIas0QKTmVuAm1l5VXrOe3+ewoUb2fg04kwt1Yqoeqx1EGCJ0U/MOtbhYO0KkLKZe966LltgVCoARaX3/zmZAlcn+3LlbuUUwLOxk9nrLXspAVPL6kr3+f8AAI7NHwm1i2OVnptbUFR5IaI6ZK0+ZaZelzPx1n9sgaEaMfzyV9QCY1+HAcbeSNtvfmH5DjqZuQUAiod/X7qZXWmLTEWd/epqh5tXWISP/05A7KVb0mMXrmeb9dxkg7kqFHLGixoQa80DYzj9Q3X7wJB1MMBQjci+/PUlwBh5rUIjdcstKA417/wRj3s/2okfDfq0AMDt/CIUFJUGH8OAVvLcElU5HfXLwWSzywohZNv1m92J+GLHOYxfvk96zNwt+8uh0tetat4y9v7yCovww/5LSLopn8SrSC9wNDkdhUX15OqepAgVzXRdd3UwbIExH8OOdTDAULWk6nKx79wNeQtMBXsde1XdfdSMBRhjrUMlp1HWRBcHl4V/ncaO06m4kn4buQVFGPD+Ngz7aCeEEFgXk4TjVzKk52bnFcrWVZUjxtd/PSa7X1ikx5/HriEztwCpmbn4+O8EXL5VHApCvonBmCW78eP+S/g19jJOazPLrc/cnafhabSqtBidSclEwAeRsk7LALBi5wXM3XQCwz/ZKXt80d+n8eCyvXj7d06vTuaz1ikkDqNWLvaBaeCEENUaAjjrt+PYfjoVY3v4SI+VhIQTVzLg4qhCey83aZlhpkjQZqKjd+kyS1MZeT/GWmDyypxWys4vwjOrDwIAVj3TH5l5hcjMK8TGI1fw5sbjsrJZ5QJM8fpz8gux99xNDOnQHC6O5fvdGHP3nL+kADSgjScOXEzDn8evYfHjPbHv/E0AwJxNJ0w+39j7LfFr7GV4NnZC08ZOWBSeID1elf30nI0nkJqZh7mbTuCpga2lx/ecuw6geNtez8zDXW7OAIo7SQPA2pgkvP9wdwDFrTVCQNomsZfS8EP0Jbw5pjO81C7mV4ZslrWuEyafTbz0b8P9orH9pKXrm5FTALWrA4dkVwFbYBqwfedvoP/727Dl2NUKy2XnFWLP2Ruy0ynbT6cCAP48dk16TC8EbmXn4/6lexC0OMpkc2zwZ1FVuvjhoYtpUn8VcxhvgSl/OmPSdwdk/UIMPbPqoPT33nM3yy0v2wLz1wkt9HqBmRuOYcr3h8oFnrIOXUyT/jbcFAfuPJ54IxsPf7mv7NOM2nPuBo5fzsCH4aexNqa0leTSzWy8uuEonll9EA8t2yt7TmaZ+hvSZuTKOvkeMKirMNFPof/727DrzHWj6xNCYPjHu9D33QjkFRavd/zyaGyKu4oBH0Sa9R4PJ93Cksizss8g2Za6vixJYZEea2Mu4WxqVunrmthnGWvBNTwosoMdktNykFrN6QkOJ91CzwX/4K0KDlSoPAaYBmzq97G4kZWPaeuOmCxz8qoOvRdE4N/fxmDp9nMVrq9ID6zed1G6b9jCUfaoIuJUill1/O3wFTy6Ilrq8yGEwO38ikfQGAswxkLI7YIiDFm0o9I6pOfkl3vM8H0CxUHup4NJ+PP4NaneSTdzMG3d4XKnXgDg0RXR5UJQdS386zTGfbEHy3eex1sbT0h9T25UMMT60s1sFBTpMW/zCfwTr5UeP5eaiYFhkRi7ZLfRuWI6vPUXzl8v3uGXbW6f9N0Bo6+1/mAyrqTfRnZ+EX45dBm3suXb8/8MJgQ05ZEv92FxxBmsP5AEoPhzUNFIKo6yUh7ZtcXq4PV+P3oVb208gSkG0yyYalQx1oJbWFT6mC63AEMW7TA7kJf17e5EAMC6mKRqPb+hYoCxQVl5hZj/ezxiL6UZXV5QpMf30RfLnQYxZsyS3ci/84O4dv8lbD+dglGfRRktW6TX4/PIs9L9HIOgUTZT/O+HWISuO1xpM+zGI8UTzp1JKf7RDPvrNLq+HY4jScWjcC7eyC73g2hsxFFNRN5pbTKUYyRErd0v3/kM/WgHthy7hrkmjqoycwurfcRWkfZv/XWnZcl0U7STvQq/HErG99GXMPWHWOnxLXda1M5fz0bAB5H4bk+i7HmFeoH5v8cjRZeLI0np5dZ7IFH+mSss0mP2b6WtUXM3nUDvdyNkZV7bcNRkPZNu5qDNrD+l++fuHC2/ufE4uswLx4XrWeWeE3XmOjrNDceyHRUH7sok3czB7N+OI/FGxaO8Lt/KwaCF27Ey6nyNXq864q9mlPv8K5V8RlyBsymZyLhtfstrVSWklO9PViQE9p27gdNanezrY6xvTKFBq+55g89hdfY/ateqTYNAxRhgrMzYkWJuQRFiL91C7KVb5X7ghRCY8v0hjPl8N37Yf0lqkje0NPIsVu+7iPHLo/H5trPQlTn98n30JczbXL6DZZFeIPGG6eHE6bcL8OzqQ0Y7kgJAdp68LgcS0/DdnkTM+CXO6JHNn8euoeOccMy/09nTsNn4RlZecb8Jg2OxVF0uVkZdgF4AH2w9havptzHs453o817pD6IutwA3rbRDP3lNV6XyA8Miq33EVpkhi3Yg47bp7eDkoJIFkBtZeYg6c73cKZoFW06We64ut9Bka8uxy+my++3f+sus+m44lFzuh/jveC2GfiRvIVsTfQnHL2fgpwPJ0Avgo78T8GvsZeTkl4bxp+/U7aO/E2TPjTyVUm7EVEUmrTqAnw4k4V+Ld1UYtBeFJ+BK+m18sPV0peu8npmH97aclIJYTRy7nI6xS/YgcGHtfIYA4ML1LGw9fq3a/T2K9KJaV2o/cUWHf30ahcCwSOw5e8Pofq6mHI0MLLh4IxtPfhODUZ/trnSSTsMWmAKDvw0/i2bXxZ79XqqDnXit6If9lzB30wms+HdfjOrmLT0+YeV+HE1OBwAseaI3HujpKy27dDMHESeLT7/M3XQCO06nYlRXbzzatyVUKjsUFunxlcEFEz/ddgZxybew6pkB2Bx3Bdl5RXjXyI8SALz+f8fw6+HL+HB8d0zo36rcjqeyq0mvLHOhxud+LD2yd7I3npXzi/RYve8iNGoXrNh1Hr/8LxBuLg64Z+F2tPJsBAeDL7bhj/3Bi7fw8T/FP1BCFAe7axm5eKPMCJ+G7NnVh0wui7+qQ/zV0sA1+vPduJ6ZB1/3yjvUnkvJRLaJ03hf7qxeK8TM/zuGgW09sX5qoPTY/wxahgyN+2KP9PdfJ7T464QWr244itdHdcT93X2NPudMSiYmryneHolhY/Dj/ktwclBhQv9WJutU0vJSqBf4Yvs5vDiig7RMrxeY+X/H0L2Fukr9cl7dcBRRZ67jx5hLOP3uaJPlMnIKkJ1fCLWrI5o4G99NR93pc1R2SL+l3M4vwvBPdgEAVv2nP+7r5GX2c3W5BXB2UGHUZ7vh6+GCtf8dWOlzDFs5tt05xZyTX4R/fxuDkIBWUodwSzF20VfDgx/Dljdj+74CExd/y84vgkejqtXFoQ5HadoSBphaVqQXUNkZv9hXyamFaesO49wHY6THS8ILAGw+cgUju2ik0RvDPt4pW8f206nYfjoVr/96DPfefReGdGhe7nV2JFxHRk4BXl4fZ7Ken/yTgF8PF/dFWBJ5DhP6t5IdVZjDWJNsifxKdvIfhhcfvQZ/FoUPxxfvqJJMdLAt8dvh0usZ3S4owis/xyEm0fhpM6rY9czi/jJXMyo/pWUqvABAWg1av/ZfMN5Z2FyLwhPw5Q55gGoz60+0bd4YzZqUzrh8+dZtzL3TAvlheAL6t2mKr57qJ71uek4BrpfpP/RJxBlo3F3Qtnlj9PTzwPoDSfj18GX8ehgY1dUbppxNycT/Hb6Mjho3tPdqgsN3JiDMLdDj96NX8UBPX5y6psPoz3fj48d64tG+LZGcliPrm/XJYz3h59kIA/w9Zeuu7dEqS7eXng4+knQLvfw8kFeoh3cFIfdocjre2nQcJ66UhuPEG9ko0otK54Kq6D++NiYJd7k548mAVvByq9qotSK9gF4ION45iCoZUVSVj1hlLTCGckycmj+QmIYPtp7Cgge7okdLD9kyR4fSbVNQpJfqShVjgKlF+YV6jP48Ci2aNsI3T/fDB1uLr0MTOqydbGdUqBd4+rsDCOrshacD28jWEXk6FX3ejUD0rBFwb1TxedJdZ66bHAnSc8E/FT7XsIPulfTbyC/U47aVOkJm5la9Cfb+JXtwoZK+ClT/fRh+Gkk3c6TO0FVlrF/XhRvZss+GYThIy87H3/EpSLyRjXOpWXh5/RGj/ZuA4hbKypT8+BQU6bHgj5NGO3CXeOmnI3igpy9Gf74bQHFfIDcXh3ItT6/e6SOUGDbGZGi5cD0Lbe9qUmn9jDmanI55m0/gzTGdEdC2mfT4mTIHJH3fi4BeAA/3boFPJ/TC2phLuJ6Zh+lBd0tlHiwz2q1Euze34uLCsRUPFa4kUHy27Sz2nruBDc/dY/Z7E0JgzOe7kZVXiF0zh2HR3wn489g1bHlxcJU6Chub48pUy5upgP/4V9EAiud2Oj4/WLbsctpt6e/cgiIGGDNxK9Wi8Hgtzl/PRtSZ61gXcwmr911E1JnrmLByf7nWkKgz1432SwGKm1HNHbVjKXfP+Qs936k49NSW9/48VeXnMLzYhuU7z1c7vNTEfR/vxJTvD5kMLxUJNxjF9cAXe3H8cgY6vPVXheGlRNmWJlOnzQDg/T9P4XpmHq6k38anEWdkfYaGf7ILW49fu3OauBBCCLy8/ghm/Fz5RUv//W0Mjl7OwISV+6UAWKQXsstV5BcJadh8Scf6tzaewGfbzkpBp7L+RbvPXkfPBf/gw/AEo8uNndIp6+DFW5WWMZRfpEdCSiaupN9G8q3bWBl1AVfSb+PhL/dWadI6Yy0wpjrrGutYbsjYAZrhZ75kvdl5hfhg6ynE3WmR/+VQssmBGQ0VW2CqqXgOgSQMbNus3KRsvxxMxrzfT8jOTc//Q97v5PejxudeMRxxYei1DUdxT7tmRpcRUf1w6ppO1kenMlU5TfvNnkR8sycRno2djJ6qe2HtYenvx/u1xOa44n3Mb3cCx+bQQfD1cJUmHDx2OR3v/HFS9oM6cWU03hzdGU9+EyNb9+0yHVMNRyKm5xRfFb1sh+uyPr7TqXrFrvP49fBl5BYUIebNEWjk5ICTV3VYXs3+U8aUnCYyDBn3GZx+v3gzBzezzD/d+a/FUVj6RG8kpeXgLjdnjOnuY/K0+IxfjqKzjxqdvN0qPM13NDkdF29m48FeLWSPl0w/sTjiDL7dk4iVURew4blAqQXw4sKxFdb1h+iLaOnZCPd1rLjPUm5BEf46cQ333u1VpYva1id2wlrTH9YynU4Hd3d3ZGRkQK1WW2y9QggcSU7HhkOX8dOdOSn2zx6B7adT4eygkpp7yfraNGuEi1UYdWLMzOCO5UazGFr0aA+zTi1YW08/D1nfKmq4vnm6H1ZGXZBNUFhT1f0eONmr8K+uGtmEmJW5uHAsdLkFeP7HWPznHn/8q4tGtjw7rxADwyJRpBfY8dowBNTCSL+LC8fi8RXRFW7D/w72x5z7u0hhyvDg9OLCsdL95k2ccMMgTO18bRjaNG+MJ1buR/SFOzNxj+0stUybCjCFRXqcuKqTJq08uSAYjZwcUFCkxyNf7kNHbzd8/FhPqfz83+Oxet9F9Gnlgd9eGFTNLVE7zP39ZgtMFUWeSsV/v5eP7rh/6Z4KJw0j61j1zADZUVcJF0cVAts2w44E4/2FDJW93EBZj/fzg9rFUTbiyphO3m4mh5//MHkAftx/CX/Hp6B3Kw+j86uYq1ljp3LDyMOnD0GCNrPCTtzUcJTdf1lCdUN8/p3rgJnLQWWH2/lF6DG/+PT23nM3seLfffF3vBaxl27h3Ye64cV1h6VWpSUG81JZkqmWckPf7EnEw31aYOySilvkbpRpCUrLyYdTukp2Sq2y0+o5+YX41+IoXEkv7Uvz0k9H8M2k/og+fxPHr2Tg+JUMWYD5+c5FZQ/XYH9jbewDU0Ul538NNYTw8trIuysvVEeaVtKZuYR/88YY2La0s/S8+7vAy80ZP08NrHAkhaGJ/f1MLis5pRfU2Qv/uacNFj/eE6ffHYUfJg8oV7aiERh3uTnjq6f6IeKVofhpykCsnzoQTwwofd1tM4ZKf88M7oigzhoM8PfEH9MGl1vXgge7SX8/M6gNLi4ci07eagS2a4ZGTvbo08pDWj6ikxdeGt7eaJ26tSh/1PNkgPEhx+8+1A3P3dvO5Psr8e+BrfDmmE54c0wnhN5XeXmisgr1Ap3nhcsee+7HWGw8cgVJaTmY9N0B6AxOia218sy2psJLRZOIPrPqIO5ZuF3WB8nQ3XP+wi8Hk/HVrvNYG3MJX0ddwJ6zN2ThBQC2nSqegNOwr4/hqUdzBmnkFRbh1DUdvo66UC8v48EWmCqy1sic2nJyQTDu/WinNIzWlOfubYeP/zljdFmzxk6YMfJuNHZywPSf40yuo7GTPaLfHIH/O3QZg9o3R7CJGX0NfTahF+zsIGs9aNHUFbPHdDZ61Fe2BWPpE33Q//1tAIBnB/vjmUFtYGdnh58PJZd77rOD/PFInxa4f2npTsfXw1VWZsdrw/DYin3IyivEqmf6AwAc7FWY/0BXqUwvP49y63Z3dUS3Fmro9eUnvHNzKQ5kHTTFfakGtm2GgW2b4e1xXVGoF2ji7IBvnu6HPeduYMqQtnByMH3c0cSl9Cv99rjSOnm5ueD4/GDYq0qbsjv5uOHB3i2wpMwlIqJnD4ePe/H7NjzS/ODh7uWmOl/73wAEtm0GlcoO9/fwwft/nsL0oA6YsHJ/ubq995B8Ho9CvZAu/BjYtpnUXG7Kd//phz+OXjN6EEGkJI8uN32ds5LZh031j8ov1Je7on2LMvupEn8eu4bGzqUXlX3j12P4+ul+5a41VVikh0OZkU/XMm5j+Me7pN88Ozvgv0Pamqy3NbAFpops5RorKjtg18xhaOTkgBX/7is93sXH+PlGB3sVNr5wj6yz195Zw7E8pA8OzQlCSEBrPNS7tDNaB6/yQzqLhIDaxRHPDvZHR283fPVUX9nyHi3dsWh8D8TOCcKi8T2w+/X78FDvFniwVwv8Pq30HO1jff0QbGTujf8NbYtvJ/XHgDae+OjRHgCKWzf2zRqOo2+PBFA6d8b4Pi3LPX/euC7o1sJdul+2FcLV0R7+zRtjzxvDcXx+MJwdjF9t2s3FEWv/G4Cfpsgn7/o9dDD+eHEwerQsfY0nBrQyufNxcbSXJjEL6qLB/Ae6lgsvi+68zxIl4cnDSCtVSSvQqmf647G+LfHCsPZod1cTHJoTBD/P0jqUhBdjPp1Q3AQd4O+Jo/NGYlD75lDdWW+3Fu74aepAdDd4fzODO+KR3i2k7W/o5REdcJebMwLbNsMHj5SGm/njusjKPdTLF3veuA/DO2nwyWM98dfLQ8qt625N+c/bQ72MT2pXHXPv71J5ISIzmTqdXF1lW19KhK47jFRd6cFpxMmU4rmR3twqK3ftzvxPL/50BG1m/YmIkykIDNsuO2A/djkDAKQO17UxO3JVsQWmiix1Ab7KuDiqzJ5hc1D7Zgjwb4bFEcZbSEq8Pa4LcvKLEH5Ci3VTAqQj/z6tPPDi8PZwd3XEkwGt0GXe3wCKJ9D6dk8iPpvYCwDQu1VTxLw5Ao98uQ9+nq5o4eFq8sd3ZnBHjOzqjYycAmkOmrITUAV39UZi2Bj4zy7+MqldHPH4nVM2j5c5ddOjpQeOzx+JE1d0CPD3hJ1d8Y90ek7pZRJmj+kMAPjluUDZc8u2ogBA39ZNsfv1+3DhRna5afG3vDgYX+++gNdGdgQALA/pg7mb47Hsyd4AIE0qWJFB7eUTCvby85B+6L8M6YNvdifi2UH+aNWsilN2lvF4Pz880rsFnll9EO3uagJ3V0ccmfsvODuaPja5r6OXbIRC8ybOJmdKLlFyBuzh3i0xpruPyfAGFAe97i3ccbugCM/d287k6bNGTg7Y+8ZwONrbwc7ODj9MHoDmTZzR2UctjdrTqJ3x2cTepfVQ2aGzjxpBnb2kJnI/T1f8Pm0w9l+4if8YXEX8/h6+2BRX8ZXWS0wKbI3MvEKoXRxlF+r0Vrtg0j1tMHmwv2wG6/VTB2LinVamtf8NwN/xWnwfXfmw6X8PbIUf91v3tEZdG97JS7p6PdU9w2H+piTfysH1rDz8cWd07BQj/aR+P3oVF29mS0Hmw/DT+PrpfuU6UdcltsBU0ZX0imcqnXaf8T4F3zzdT3a/ZPbOoM5eWPBgV1mLxdgePjj5ziicWjAKc8Z2htrFAUufKN2JfzupH2LnBEn39Xpg6lB5016bMj+Mbe9qjGcG+SP0vvb448XBUngBilslXh3ZEf8d0hauBj/OA/w9sfXlIbhbUzpM3NFehd+nDcKXIfLWk7JK+pi4N3LEr88HIrBts3ItLiWvXaJkeKcpbi6OCGxXfLrCzs4O+2YNh8udH+rqTErq59kI97Rrho4aN9mXsFsLd3w+sTf8PIu34ejuPjj41gjZJF/minhlKF4f1REvGUxD37JpI8x/oGuNw0sJB3sVfpgcIJ3GatrYCY2cqnZs8s4DxX1nyn5+1zw7AK2bNcI6g9akisILUPw/3Rw6CH9PH1rp7KtODirpMzCkw13ofKcF8JlBbQAAs0d3Nvq8sEd6YGQXDR7v1xJbXxoCF0d7DCszbPRujRtmje6EsT18kPDeKASUmcn2iQGt8P7D3dDBqwn+O6QtFj/eq/j/cuf//uH47tj/5gg8P6y4v86vzwdiYn8/HJ77Lwxs2wwLH+mOefd3waD2zfHSiA4Y0MZT1lfskT4tkBg2Rjb9gakxnz3LnHZs79UELxt8ZirS3aDV0JJOLRiF0++Okj02rmfVW7W+DOljVjk3FweM6+mLg28F4e/pQ+FcwanSuuLmovxjfHPC45Nfx+CRL02f1ipREl5KTPn+EDJza++Cm5XhMOoqWvDHSXy3t/gKvZGv3osfoi/JjtgSw8Zg2Y5zWLHrAkLva48nB7SCnar42iYlM4C+Pa4LnhnkD11uAZo4OUhH5q9tOIr/i72MLS8Olp3KKPHWxuNwNOhv0Xb2n9ALYHpQB0wPuhuh6w5LPfpPLRiFcV/sgaO9CnYA5tzfGfe0K3+ZAWO2n07Bzax8PNbPdAdWU6LOXMeltBw8NbC12c/ZdjIFPx1IwsLxPSoNMWWdvKrDO3/EY2ZwR/Rr41n5E4wo+QrU9tTs9V12XiEam7juTl0TQiA1Mw8addWmjQ8/ocVzP8ZiztjO5c7X/3nsGkLXFc+VsubZARjY1tNoGLuZlYcjSem4r5NXpQHMmPPXs7A57iomD/aHu6sjhBD48/g19GzpgeW7zpfrR3R03ki4N3LEV7vOI+yv07j37ruw5tnijuDDP9mJC9flkzSWzMj7+baz+GbPBfzyv0BpNt+KbHguEFO/P4RbOcZ/cDwbO+GVoA7SZRZKhusa9oOKfPVefLcn0ezOscFdNfjqqX5IvJEtGxG4PKQP9AL456QWm+Ou4tfnA9HV1x3OBoF2zb6LePvOhV4XPNjV5ESfxpQdmmxoU+ggbDx8GWuMtJh18nbDg71aIP12Pr7adQGfT+xldPTeAH9P6crrbi4O0qgnZwcVJvb3QyNnh3Lz2swa3QkdvJpI1+SyFSXXzrMkc3+/GWCqIeN2AVwcVdLO7/KtHAz+cAd83V2wb/YIk89bvTcRTRs7lZu4qIQQAll5hbLWkYpculk8y+/j/f3g7GCP349exUs/HUG7uxoj8tVh0vwDRFR8Acal28+hT2sPDOlwl1XqEHEypVzzfEkgKdILHEm6hW4t3KXTlFfTb2NT3BUsujN7bdgj3fHEgNIfi5JrDGXkFODp74pn1AWKW0reHtcFR5PT8dqGo/jk8Z4Y3kmDzXFX8H+xl3FPu+Z4ZlAbxF/V4cSVDEzo74eCIj32nL2B5+9MiFcSYE5e1WHMkt24r+Nd+HZSf6hUdoi9lIaZG45h9pjORk83BPh7YtboTujso5beS8k1nwzfc0X0eoFDl26hq2/xOn45lIyM2wVY+FflV/1e8+yAcqeG54ztDF1uIV4J6oBzqVn416dReKCnLz54pDue/zEWLw7vIF3iRQiB61l58HJzQdjWU/gq6gKeH9YOIzp54XpmHpq4OOCpb4vX/2jflnh2kD8aOdmjTfPGAIovZlky1BsADs/9l9R/8Pz1LIy4c5FMW/DJYz0xvm/5PoU1wQBTiwHGmPxCPRxUdlJrijXo9QIxiWno4qOu9LpJRFT3hBCIvnATd2vcsP5AEnzcXc3a+b+75SSOX8nA2v8GmLxOTn6hHklp2ci4XYAeLT3KXbzQHHmFRXj8q/3o7echG1lXkbMpmfjXp8UjCre8OBgatQuaNnIsN6oFKB7Z4uxgX6OZXzfHXUF7rybo4qPGugNJaNm0ESJOauHr4YqfDyYXdwp/uDtm/XYMvxy6LD2v7ARwOfmFcHW0N2vb5BUWyVrrivQC7e50hP1j2mBZx/USiTeyobIDPBo5wd21/P44t6AIL6w9jDMpmbh86zYWP94TM34pnQi1s48aLw1vj3Z3uhcs23EOBUV6bD1eeZ+WurThuUD0r2brtykMMHUcYIiIGqolkWdx5dZtLBzf3aqtvmXDWnJaDiau3I9nB/tj8mB/i76WXi+QfrvAotPwl5yumxncEaEm+lMCxVePv/ejHejl54FXR3ZEbkERXvrpCG5m56Nv66a4W+OG65l5iL+agRfua4/952+io7cb3F0d8efxa3i0b0u4OTvAXmWHqUauvxXUWYNtZl5/r7JLG1SHTQSYZcuW4aOPPoJWq0XPnj2xdOlSDBhQfpIwYxhgiIhISd7ceBx7zt7AHy8ONtpqYygrrxAuDiqppSsnvxDXM/PQulnjKr3mtYzbCAzbLt3fHDoIPf08cCs7H6v2XYQdgM/vzGj8yWM9y10uhwHGiJ9//hlPP/00VqxYgYCAAHz22WfYsGEDEhIS4OVV8UWqAAYYIiJSHmv1XazodXecTkVjZwcM8PfEvnM34Oxoj/irGejbuim6+lp+FJziA0xAQAD69++PL774AgCg1+vh5+eHF198EbNmzar0+QwwREREymPu77f1B9obkZ+fj9jYWAQFlc51olKpEBQUhOjoaKPPycvLg06nk92IiIjINtXLAHPjxg0UFRVBo5HP8KfRaKDVGu+BHRYWBnd3d+nm51f1OUyIiIhIGeplgKmO2bNnIyMjQ7olJ5e/WB8RERHZhvox7WYZzZs3h729PVJS5MO4UlJS4O1d/iJ+AODs7Axn56rN4kpERETKVC9bYJycnNC3b19ERkZKj+n1ekRGRiIwMLCCZxIREVFDUC9bYABgxowZmDRpEvr164cBAwbgs88+Q3Z2Np555hlrV42IiIisrN4GmAkTJuD69euYN28etFotevXqhfDw8HIde4mIiKjhqbfzwNQU54EhIiJSHkXPA0NERERUEQYYIiIiUhwGGCIiIlIcBhgiIiJSHAYYIiIiUpx6O4y6pkoGV/GijkRERMpR8rtd2SBpmw0wmZmZAMCLOhIRESlQZmYm3N3dTS632Xlg9Ho9rl69Cjc3N9jZ2VlsvTqdDn5+fkhOTub8MhbA7WlZ3J6Ww21pWdyelmPr21IIgczMTPj6+kKlMt3TxWZbYFQqFVq2bFlr61er1Tb5wbEWbk/L4va0HG5Ly+L2tBxb3pYVtbyUYCdeIiIiUhwGGCIiIlIcBpgqcnZ2xttvvw1nZ2drV8UmcHtaFren5XBbWha3p+VwWxaz2U68REREZLvYAkNERESKwwBDREREisMAQ0RERIrDAENERESKwwBTRcuWLUObNm3g4uKCgIAAHDhwwNpVqnfmz58POzs72a1Tp07S8tzcXISGhqJZs2Zo0qQJxo8fj5SUFNk6kpKSMHbsWDRq1AheXl6YOXMmCgsL6/qtWEVUVBTGjRsHX19f2NnZYdOmTbLlQgjMmzcPPj4+cHV1RVBQEM6ePSsrk5aWhpCQEKjVanh4eGDy5MnIysqSlTl27BiGDBkCFxcX+Pn5YdGiRbX91upcZdvyP//5T7nP6qhRo2RluC2LhYWFoX///nBzc4OXlxceeughJCQkyMpY6ru9c+dO9OnTB87Ozmjfvj1Wr15d22+vzpmzPYcNG1bu8/ncc8/JyjTo7SnIbOvXrxdOTk7iu+++E/Hx8WLKlCnCw8NDpKSkWLtq9crbb78tunbtKq5duybdrl+/Li1/7rnnhJ+fn4iMjBSHDh0SAwcOFPfcc4+0vLCwUHTr1k0EBQWJI0eOiK1bt4rmzZuL2bNnW+Pt1LmtW7eKt956S/z2228CgNi4caNs+cKFC4W7u7vYtGmTOHr0qHjggQeEv7+/uH37tlRm1KhRomfPnmL//v1i9+7don379uKJJ56QlmdkZAiNRiNCQkLEiRMnxE8//SRcXV3FV199VVdvs05Uti0nTZokRo0aJfuspqWlycpwWxYLDg4Wq1atEidOnBBxcXFizJgxolWrViIrK0sqY4nv9oULF0SjRo3EjBkzxMmTJ8XSpUuFvb29CA8Pr9P3W9vM2Z733nuvmDJliuzzmZGRIS1v6NuTAaYKBgwYIEJDQ6X7RUVFwtfXV4SFhVmxVvXP22+/LXr27Gl0WXp6unB0dBQbNmyQHjt16pQAIKKjo4UQxT86KpVKaLVaqczy5cuFWq0WeXl5tVr3+qbsj65erxfe3t7io48+kh5LT08Xzs7O4qeffhJCCHHy5EkBQBw8eFAq89dffwk7Oztx5coVIYQQX375pWjatKlse77xxhuiY8eOtfyOrMdUgHnwwQdNPofb0rTU1FQBQOzatUsIYbnv9uuvvy66du0qe60JEyaI4ODg2n5LVlV2ewpRHGBefvllk89p6NuTp5DMlJ+fj9jYWAQFBUmPqVQqBAUFITo62oo1q5/Onj0LX19ftG3bFiEhIUhKSgIAxMbGoqCgQLYdO3XqhFatWknbMTo6Gt27d4dGo5HKBAcHQ6fTIT4+vm7fSD2TmJgIrVYr237u7u4ICAiQbT8PDw/069dPKhMUFASVSoWYmBipzNChQ+Hk5CSVCQ4ORkJCAm7dulVH76Z+2LlzJ7y8vNCxY0c8//zzuHnzprSM29K0jIwMAICnpycAy323o6OjZesoKWPr+9my27PE2rVr0bx5c3Tr1g2zZ89GTk6OtKyhb0+bvZijpd24cQNFRUWyDwoAaDQanD592kq1qp8CAgKwevVqdOzYEdeuXcM777yDIUOG4MSJE9BqtXBycoKHh4fsORqNBlqtFgCg1WqNbueSZQ1Zyfs3tn0Mt5+Xl5dsuYODAzw9PWVl/P39y62jZFnTpk1rpf71zahRo/DII4/A398f58+fx5tvvonRo0cjOjoa9vb23JYm6PV6TJ8+HYMGDUK3bt0AwGLfbVNldDodbt++DVdX19p4S1ZlbHsCwJNPPonWrVvD19cXx44dwxtvvIGEhAT89ttvALg9GWDI4kaPHi393aNHDwQEBKB169b45ZdfFP1lIdszceJE6e/u3bujR48eaNeuHXbu3IkRI0ZYsWb1W2hoKE6cOIE9e/ZYuyo2wdT2nDp1qvR39+7d4ePjgxEjRuD8+fNo165dXVez3uEpJDM1b94c9vb25XrUp6SkwNvb20q1UgYPDw/cfffdOHfuHLy9vZGfn4/09HRZGcPt6O3tbXQ7lyxryEref0WfQ29vb6SmpsqWFxYWIi0tjdu4Em3btkXz5s1x7tw5ANyWxkybNg1btmzBjh070LJlS+lxS323TZVRq9U2eQBkansaExAQAACyz2dD3p4MMGZycnJC3759ERkZKT2m1+sRGRmJwMBAK9as/svKysL58+fh4+ODvn37wtHRUbYdExISkJSUJG3HwMBAHD9+XPbDERERAbVajS5dutR5/esTf39/eHt7y7afTqdDTEyMbPulp6cjNjZWKrN9+3bo9XppBxgYGIioqCgUFBRIZSIiItCxY0ebPOVhrsuXL+PmzZvw8fEBwG1pSAiBadOmYePGjdi+fXu502aW+m4HBgbK1lFSxtb2s5VtT2Pi4uIAQPb5bNDb09q9iJVk/fr1wtnZWaxevVqcPHlSTJ06VXh4eMh6gJMQr776qti5c6dITEwUe/fuFUFBQaJ58+YiNTVVCFE81LJVq1Zi+/bt4tChQyIwMFAEBgZKzy8ZGjhy5EgRFxcnwsPDxV133dVghlFnZmaKI0eOiCNHjggAYvHixeLIkSPi0qVLQojiYdQeHh5i8+bN4tixY+LBBx80Ooy6d+/eIiYmRuzZs0d06NBBNvQ3PT1daDQa8dRTT4kTJ06I9evXi0aNGtnc0N+KtmVmZqZ47bXXRHR0tEhMTBTbtm0Tffr0ER06dBC5ubnSOrgtiz3//PPC3d1d7Ny5UzasNycnRypjie92ybDfmTNnilOnTolly5bZzLBfQ5Vtz3PnzokFCxaIQ4cOicTERLF582bRtm1bMXToUGkdDX17MsBU0dKlS0WrVq2Ek5OTGDBggNi/f7+1q1TvTJgwQfj4+AgnJyfRokULMWHCBHHu3Dlp+e3bt8ULL7wgmjZtKho1aiQefvhhce3aNdk6Ll68KEaPHi1cXV1F8+bNxauvvioKCgrq+q1YxY4dOwSAcrdJkyYJIYqHUs+dO1doNBrh7OwsRowYIRISEmTruHnzpnjiiSdEkyZNhFqtFs8884zIzMyUlTl69KgYPHiwcHZ2Fi1atBALFy6sq7dYZyraljk5OWLkyJHirrvuEo6OjqJ169ZiypQp5Q5IuC2LGduOAMSqVaukMpb6bu/YsUP06tVLODk5ibZt28pew1ZUtj2TkpLE0KFDhaenp3B2dhbt27cXM2fOlM0DI0TD3p52QghRd+09RERERDXHPjBERESkOAwwREREpDgMMERERKQ4DDBERESkOAwwREREpDgMMERERKQ4DDBERESkOAwwREREpDgMMERERKQ4DDBERESkOAwwREREpDgMMERERKQ4/w+NIIsX8flwQAAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "plt.plot(temp_list)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 423
+        },
+        "id": "-05arcmGGaD8",
+        "outputId": "612e958b-e4e9-484c-efaf-36fb630b0e8c"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "\n",
+              "  <div id=\"df-33e6ae40-a383-414b-9ad5-1c13177fecdf\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>mass</th>\n",
+              "      <th>intensity</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>799.899773</td>\n",
+              "      <td>45</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>799.909621</td>\n",
+              "      <td>31</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>799.919469</td>\n",
+              "      <td>16</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>799.929317</td>\n",
+              "      <td>14</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>799.939165</td>\n",
+              "      <td>27</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>176976</th>\n",
+              "      <td>3497.087414</td>\n",
+              "      <td>19</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>176977</th>\n",
+              "      <td>3497.108061</td>\n",
+              "      <td>15</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>176978</th>\n",
+              "      <td>3497.128709</td>\n",
+              "      <td>23</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>176979</th>\n",
+              "      <td>3497.149357</td>\n",
+              "      <td>23</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>176980</th>\n",
+              "      <td>3497.170005</td>\n",
+              "      <td>26</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>176981 rows × 2 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-33e6ae40-a383-414b-9ad5-1c13177fecdf')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-33e6ae40-a383-414b-9ad5-1c13177fecdf button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-33e6ae40-a383-414b-9ad5-1c13177fecdf');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-9edb80e6-40ce-4dbb-bd36-fc54879109aa\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9edb80e6-40ce-4dbb-bd36-fc54879109aa')\"\n",
+              "            title=\"Suggest charts.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: #E8F0FE;\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: #1967D2;\n",
+              "    height: 32px;\n",
+              "    padding: 0 0 0 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: #E2EBFA;\n",
+              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: #174EA6;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "    background-color: #3B4455;\n",
+              "    fill: #D2E3FC;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart:hover {\n",
+              "    background-color: #434B5C;\n",
+              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "    fill: #FFFFFF;\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const charts = await google.colab.kernel.invokeFunction(\n",
+              "          'suggestCharts', [key], {});\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-9edb80e6-40ce-4dbb-bd36-fc54879109aa button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "               mass  intensity\n",
+              "0        799.899773         45\n",
+              "1        799.909621         31\n",
+              "2        799.919469         16\n",
+              "3        799.929317         14\n",
+              "4        799.939165         27\n",
+              "...             ...        ...\n",
+              "176976  3497.087414         19\n",
+              "176977  3497.108061         15\n",
+              "176978  3497.128709         23\n",
+              "176979  3497.149357         23\n",
+              "176980  3497.170005         26\n",
+              "\n",
+              "[176981 rows x 2 columns]"
+            ]
+          },
+          "execution_count": 18,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "temp_df"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "kErdPTgfGhPh",
+        "outputId": "8967a0ad-f9b0-4641-8721-ccab8efa7300"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "CPU times: user 3.22 s, sys: 22.9 ms, total: 3.24 s\n",
+            "Wall time: 3.52 s\n"
+          ]
+        }
+      ],
+      "source": [
+        "%%time\n",
+        "\n",
+        "temp_df = pd.read_csv(file_paths[0])\n",
+        "temp_list = []\n",
+        "for i_value in np.arange(800, 3500, 1): # 0.5 is the step size\n",
+        "  lower_bound = i_value - 0.1\n",
+        "  upper_bound = i_value + 0.1\n",
+        "  int_mean_value = temp_df[(lower_bound < temp_df.mass) & (temp_df.mass < upper_bound)]['intensity'].mean()\n",
+        "  temp_list.append(int_mean_value)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 448
+        },
+        "id": "CE0uTpidKMHJ",
+        "outputId": "8b7ad1e4-53d8-49d5-c820-3aca25344c60"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "[<matplotlib.lines.Line2D at 0x7f41733a2aa0>]"
+            ]
+          },
+          "execution_count": 38,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "plt.plot(temp_list)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "jHWPYmTRKQNS",
+        "outputId": "3af5343f-69ab-4d4f-ad8f-587bf3f20f65"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "CPU times: user 6.78 s, sys: 26.2 ms, total: 6.81 s\n",
+            "Wall time: 7.93 s\n"
+          ]
+        }
+      ],
+      "source": [
+        "%%time\n",
+        "\n",
+        "temp_df = pd.read_csv(file_paths[0])\n",
+        "i_values = np.arange(1000, 3500, 0.5)\n",
+        "lower_bounds = i_values - 0.1\n",
+        "upper_bounds = i_values + 0.1\n",
+        "\n",
+        "def mean_intensity(lower, upper):\n",
+        "    return temp_df[(lower < temp_df.mass) & (temp_df.mass < upper)]['intensity'].mean()\n",
+        "\n",
+        "temp_list = [mean_intensity(lower, upper) for lower, upper in zip(lower_bounds, upper_bounds)]\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 448
+        },
+        "id": "iuCbWgpRKpPr",
+        "outputId": "92d217a3-d1c2-48b6-afd5-df876e895520"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "[<matplotlib.lines.Line2D at 0x7f4173407dc0>]"
+            ]
+          },
+          "execution_count": 40,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "plt.plot(temp_list)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "FSTzw9cNLrOt"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "wZY_DSlDMYkq"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "xd-3YWJlMYnr",
+        "outputId": "0bd6f770-8488-47bd-e26a-59f86d9dc3c0"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "CPU times: user 28.5 s, sys: 108 ms, total: 28.6 s\n",
+            "Wall time: 29.1 s\n"
+          ]
+        }
+      ],
+      "source": [
+        "%%time\n",
+        "\n",
+        "temp_df = pd.read_csv(file_paths[0])\n",
+        "i_values = np.arange(1000, 3500, 0.1)\n",
+        "lower_bounds = i_values - 0.1\n",
+        "upper_bounds = i_values + 0.1\n",
+        "\n",
+        "def mean_intensity(lower, upper):\n",
+        "    return temp_df[(lower < temp_df.mass) & (temp_df.mass < upper)]['intensity'].mean()\n",
+        "\n",
+        "temp_list = [mean_intensity(lower, upper) for lower, upper in zip(lower_bounds, upper_bounds)]\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 448
+        },
+        "id": "oWirvvChMYql",
+        "outputId": "161c0d1b-4b56-4f32-bfbb-79d4506a2a5a"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "[<matplotlib.lines.Line2D at 0x7f417327de40>]"
+            ]
+          },
+          "execution_count": 42,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "plt.plot(temp_list)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 470
+        },
+        "id": "q94kuH0-NDYK",
+        "outputId": "951c39b8-95c8-413e-95fa-d04ac4a2ca82"
+      },
+      "outputs": [
+        {
+          "ename": "KeyboardInterrupt",
+          "evalue": "ignored",
+          "output_type": "error",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-45-ff2f871989fe>\u001b[0m in \u001b[0;36m<cell line: 33>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     31\u001b[0m \u001b[0;31m# Processing all files in parallel and printing progress\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\u001b[0m \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 33\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mParallel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdelayed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprocess_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile_path\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfile_path\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfile_paths\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     34\u001b[0m     \u001b[0mresults\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m     \u001b[0mprint_progress_bar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile_paths\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1950\u001b[0m         \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1951\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1952\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0moutput\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreturn_generator\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1953\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1954\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__repr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m_get_outputs\u001b[0;34m(self, iterator, pre_dispatch)\u001b[0m\n\u001b[1;32m   1593\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1594\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieval_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1595\u001b[0;31m                 \u001b[0;32myield\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_retrieve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1596\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1597\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mGeneratorExit\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m_retrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1705\u001b[0m                 (self._jobs[0].get_status(\n\u001b[1;32m   1706\u001b[0m                     timeout=self.timeout) == TASK_PENDING)):\n\u001b[0;32m-> 1707\u001b[0;31m                 \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.01\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1708\u001b[0m                 \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1709\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+          ]
+        }
+      ],
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 470
+        },
+        "id": "T9SRPTGrOr0J",
+        "outputId": "638df829-c680-4e27-d2b5-d56a49eb3f28"
+      },
+      "outputs": [
+        {
+          "ename": "KeyboardInterrupt",
+          "evalue": "ignored",
+          "output_type": "error",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-46-b44c769447e3>\u001b[0m in \u001b[0;36m<cell line: 34>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     32\u001b[0m \u001b[0;31m# Processing all files in parallel and printing progress\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     33\u001b[0m \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 34\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mParallel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdelayed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprocess_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile_path\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfile_path\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfile_paths\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     35\u001b[0m     \u001b[0mresults\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m     \u001b[0mprint_progress_bar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile_paths\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1950\u001b[0m         \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1951\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1952\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0moutput\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreturn_generator\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1953\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1954\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__repr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m_get_outputs\u001b[0;34m(self, iterator, pre_dispatch)\u001b[0m\n\u001b[1;32m   1593\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1594\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieval_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1595\u001b[0;31m                 \u001b[0;32myield\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_retrieve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1596\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1597\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mGeneratorExit\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m_retrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1705\u001b[0m                 (self._jobs[0].get_status(\n\u001b[1;32m   1706\u001b[0m                     timeout=self.timeout) == TASK_PENDING)):\n\u001b[0;32m-> 1707\u001b[0;31m                 \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.01\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1708\u001b[0m                 \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1709\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+          ]
+        }
+      ],
+      "source": [
+        "from joblib import Parallel, delayed\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import os\n",
+        "import sys\n",
+        "\n",
+        "def extract_species(file_path):\n",
+        "    parts = file_path.split('/')\n",
+        "    idx = parts.index('1_Main') + 1\n",
+        "    return parts[idx]\n",
+        "\n",
+        "def process_file(file_path):\n",
+        "    print(f\"Loading file: {file_path}\")  # Printing the file name before loading\n",
+        "    temp_df = pd.read_csv(file_path)\n",
+        "    species_name = extract_species(file_path)\n",
+        "    i_values = np.arange(1000, 3500, 0.1)\n",
+        "    intensities = [mean_intensity(temp_df, i_value) for i_value in i_values]\n",
+        "    return species_name, intensities\n",
+        "\n",
+        "def mean_intensity(temp_df, i_value):\n",
+        "    lower_bound = i_value - 0.1\n",
+        "    upper_bound = i_value + 0.1\n",
+        "    return temp_df[(lower_bound < temp_df.mass) & (temp_df.mass < upper_bound)]['intensity'].mean()\n",
+        "\n",
+        "def print_progress_bar(iteration, total, bar_length=50):\n",
+        "    percent = int(100 * (iteration / float(total)))\n",
+        "    bar_filled = int(round(bar_length * iteration / float(total)))\n",
+        "    bar = '#' * bar_filled + '-' * (bar_length - bar_filled)\n",
+        "    sys.stdout.write(f'\\r[{bar}] {percent}%')\n",
+        "    sys.stdout.flush()\n",
+        "\n",
+        "# Processing all files in parallel and printing progress\n",
+        "results = []\n",
+        "for index, result in enumerate(Parallel(n_jobs=-1)(delayed(process_file)(file_path) for file_path in file_paths)):\n",
+        "    results.append(result)\n",
+        "    print_progress_bar(index + 1, len(file_paths))\n",
+        "\n",
+        "print(\"\\nProcessing complete!\")\n",
+        "\n",
+        "# Creating a DataFrame from the results\n",
+        "i_values_columns = np.arange(1000, 3500, 0.1)\n",
+        "columns = ['species'] + list(i_values_columns)\n",
+        "data = [tuple([species_name] + intensities) for species_name, intensities in results]\n",
+        "\n",
+        "final_df = pd.DataFrame(data, columns=columns)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 470
+        },
+        "id": "Z07tWoEqQXIP",
+        "outputId": "e36bd6e5-a9ff-4c22-be76-2275c381371d"
+      },
+      "outputs": [
+        {
+          "ename": "KeyboardInterrupt",
+          "evalue": "ignored",
+          "output_type": "error",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-5-2cf313ccd195>\u001b[0m in \u001b[0;36m<cell line: 32>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[0;31m# Processing all files in parallel and printing progress\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     31\u001b[0m \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 32\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mParallel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdelayed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprocess_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile_path\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfile_path\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfile_paths\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     33\u001b[0m     \u001b[0mresults\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m     \u001b[0mprint_progress_bar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile_paths\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1950\u001b[0m         \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1951\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1952\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0moutput\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreturn_generator\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1953\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1954\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__repr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m_get_outputs\u001b[0;34m(self, iterator, pre_dispatch)\u001b[0m\n\u001b[1;32m   1593\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1594\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieval_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1595\u001b[0;31m                 \u001b[0;32myield\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_retrieve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1596\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1597\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mGeneratorExit\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m_retrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1705\u001b[0m                 (self._jobs[0].get_status(\n\u001b[1;32m   1706\u001b[0m                     timeout=self.timeout) == TASK_PENDING)):\n\u001b[0;32m-> 1707\u001b[0;31m                 \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.01\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1708\u001b[0m                 \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1709\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+          ]
+        }
+      ],
+      "source": [
+        "from joblib import Parallel, delayed\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import os\n",
+        "import sys\n",
+        "\n",
+        "def extract_species(file_path):\n",
+        "    parts = file_path.split('/')\n",
+        "    idx = parts.index('1_Main') + 1\n",
+        "    return parts[idx]\n",
+        "\n",
+        "def process_file(file_path):\n",
+        "    print(f\"Loading file: {file_path}\")  # Printing the file name before loading\n",
+        "    temp_df = pd.read_csv(file_path, usecols=['mass', 'intensity'])\n",
+        "    species_name = extract_species(file_path)\n",
+        "\n",
+        "    bins = np.arange(999.95, 3500.05, 0.1)\n",
+        "    temp_df['bin'] = pd.cut(temp_df['mass'], bins=bins, labels=False)\n",
+        "    intensities = temp_df.groupby('bin')['intensity'].mean().values\n",
+        "\n",
+        "    return species_name, intensities\n",
+        "\n",
+        "def print_progress_bar(iteration, total, bar_length=50):\n",
+        "    percent = int(100 * (iteration / float(total)))\n",
+        "    bar_filled = int(round(bar_length * iteration / float(total)))\n",
+        "    bar = '#' * bar_filled + '-' * (bar_length - bar_filled)\n",
+        "    sys.stdout.write(f'\\r[{bar}] {percent}%')\n",
+        "    sys.stdout.flush()\n",
+        "\n",
+        "# Processing all files in parallel and printing progress\n",
+        "results = []\n",
+        "for index, result in enumerate(Parallel(n_jobs=-1)(delayed(process_file)(file_path) for file_path in file_paths)):\n",
+        "    results.append(result)\n",
+        "    print_progress_bar(index + 1, len(file_paths))\n",
+        "\n",
+        "print(\"\\nProcessing complete!\")\n",
+        "\n",
+        "# Creating a DataFrame from the results\n",
+        "i_values_columns = np.arange(1000, 3500, 0.1)\n",
+        "columns = ['species'] + list(i_values_columns)\n",
+        "data = [tuple([species_name] + list(intensities)) for species_name, intensities in results]\n",
+        "\n",
+        "final_df = pd.DataFrame(data, columns=columns)\n",
+        "\n",
+        "# Save the DataFrame to a CSV file\n",
+        "output_path = \"/content/drive/MyDrive/output_file.csv\"\n",
+        "final_df.to_csv(output_path, index=False)\n",
+        "print(f\"Saved final DataFrame to {output_path}\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "4Rptx_-u4UJS",
+        "outputId": "fe2f62a9-3d7e-4b87-fdca-3a8426d0c2dc"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[##################################################] 100%\n",
+            "Processing complete!\n",
+            "Saved final DataFrame to /content/drive/MyDrive/output_file.csv\n"
+          ]
+        }
+      ],
+      "source": [
+        "from joblib import Parallel, delayed\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import os\n",
+        "import sys\n",
+        "\n",
+        "def extract_species(file_path):\n",
+        "    parts = file_path.split('/')\n",
+        "    idx = parts.index('1_Main') + 1\n",
+        "    return parts[idx]\n",
+        "\n",
+        "def process_file(file_path):\n",
+        "    print(f\"Loading file: {file_path}\")  # Printing the file name before loading\n",
+        "    temp_df = pd.read_csv(file_path, usecols=['mass', 'intensity'])\n",
+        "    species_name = extract_species(file_path)\n",
+        "\n",
+        "    i_values = np.arange(1000, 3500, 0.1)\n",
+        "    bins = (i_values[:-1] + i_values[1:]) / 2  # Calculating bins as mid-points\n",
+        "    bins = np.insert(bins, [0, len(bins)], [i_values[0] - 0.1, i_values[-1] + 0.1])  # Adding first and last bins\n",
+        "\n",
+        "    temp_df['bin'] = pd.cut(temp_df['mass'], bins=bins, labels=False)\n",
+        "    intensities = temp_df.groupby('bin')['intensity'].mean().fillna(0).values\n",
+        "\n",
+        "    return species_name, intensities\n",
+        "\n",
+        "def print_progress_bar(iteration, total, bar_length=50):\n",
+        "    percent = int(100 * (iteration / float(total)))\n",
+        "    bar_filled = int(round(bar_length * iteration / float(total)))\n",
+        "    bar = '#' * bar_filled + '-' * (bar_length - bar_filled)\n",
+        "    sys.stdout.write(f'\\r[{bar}] {percent}%')\n",
+        "    sys.stdout.flush()\n",
+        "\n",
+        "# Processing all files in parallel and printing progress\n",
+        "results = []\n",
+        "for index, result in enumerate(Parallel(n_jobs=-1)(delayed(process_file)(file_path) for file_path in file_paths)):\n",
+        "    results.append(result)\n",
+        "    print_progress_bar(index + 1, len(file_paths))\n",
+        "\n",
+        "print(\"\\nProcessing complete!\")\n",
+        "\n",
+        "# Creating a DataFrame from the results\n",
+        "i_values_columns = np.arange(1000, 3500, 0.1)\n",
+        "columns = ['species'] + list(i_values_columns)\n",
+        "data = [tuple([species_name] + list(intensities)) for species_name, intensities in results]\n",
+        "\n",
+        "final_df = pd.DataFrame(data, columns=columns)\n",
+        "\n",
+        "# Save the DataFrame to a CSV file\n",
+        "output_path = \"/content/drive/MyDrive/output_file.csv\"\n",
+        "final_df.to_csv(output_path, index=False)\n",
+        "print(f\"Saved final DataFrame to {output_path}\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "jFyoUYUa-kan",
+        "outputId": "d8905bec-7e63-4c4c-819a-57c27ab627a7"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Saved final DataFrame to /content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/All/all_01.csv\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Save the DataFrame to a CSV file\n",
+        "output_path = \"/content/drive/MyDrive/TU21.06_Computational_ZooMS/Data/1_Main/All/all_01.csv\"\n",
+        "final_df.to_csv(output_path, index=False)\n",
+        "print(f\"Saved final DataFrame to {output_path}\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 255
+        },
+        "id": "X3RfzZmm6u1X",
+        "outputId": "55ba2eb6-00b8-4ade-e362-e63472b1c98e"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "\n",
+              "  <div id=\"df-172d99e4-2117-45ba-af02-38f7fa9ceb38\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>species</th>\n",
+              "      <th>1000.0</th>\n",
+              "      <th>1000.1</th>\n",
+              "      <th>1000.2</th>\n",
+              "      <th>1000.3000000000001</th>\n",
+              "      <th>1000.4000000000001</th>\n",
+              "      <th>1000.5000000000001</th>\n",
+              "      <th>1000.6000000000001</th>\n",
+              "      <th>1000.7000000000002</th>\n",
+              "      <th>1000.8000000000002</th>\n",
+              "      <th>...</th>\n",
+              "      <th>3499.0000000005684</th>\n",
+              "      <th>3499.1000000005683</th>\n",
+              "      <th>3499.2000000005683</th>\n",
+              "      <th>3499.300000000568</th>\n",
+              "      <th>3499.400000000568</th>\n",
+              "      <th>3499.5000000005684</th>\n",
+              "      <th>3499.6000000005683</th>\n",
+              "      <th>3499.7000000005683</th>\n",
+              "      <th>3499.8000000005686</th>\n",
+              "      <th>3499.9000000005685</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>BisonYak</td>\n",
+              "      <td>47.384615</td>\n",
+              "      <td>23.666667</td>\n",
+              "      <td>50.777778</td>\n",
+              "      <td>51.444444</td>\n",
+              "      <td>43.444444</td>\n",
+              "      <td>32.000000</td>\n",
+              "      <td>48.666667</td>\n",
+              "      <td>25.666667</td>\n",
+              "      <td>38.111111</td>\n",
+              "      <td>...</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>BisonYak</td>\n",
+              "      <td>78.384615</td>\n",
+              "      <td>94.444444</td>\n",
+              "      <td>77.888889</td>\n",
+              "      <td>84.555556</td>\n",
+              "      <td>93.555556</td>\n",
+              "      <td>97.500000</td>\n",
+              "      <td>103.222222</td>\n",
+              "      <td>108.777778</td>\n",
+              "      <td>90.222222</td>\n",
+              "      <td>...</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>BisonYak</td>\n",
+              "      <td>110.571429</td>\n",
+              "      <td>88.444444</td>\n",
+              "      <td>107.333333</td>\n",
+              "      <td>81.888889</td>\n",
+              "      <td>111.666667</td>\n",
+              "      <td>104.000000</td>\n",
+              "      <td>109.500000</td>\n",
+              "      <td>92.444444</td>\n",
+              "      <td>137.777778</td>\n",
+              "      <td>...</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>BisonYak</td>\n",
+              "      <td>91.846154</td>\n",
+              "      <td>79.222222</td>\n",
+              "      <td>81.777778</td>\n",
+              "      <td>86.555556</td>\n",
+              "      <td>90.777778</td>\n",
+              "      <td>78.400000</td>\n",
+              "      <td>77.888889</td>\n",
+              "      <td>116.222222</td>\n",
+              "      <td>90.333333</td>\n",
+              "      <td>...</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>BisonYak</td>\n",
+              "      <td>75.000000</td>\n",
+              "      <td>86.700000</td>\n",
+              "      <td>73.888889</td>\n",
+              "      <td>77.666667</td>\n",
+              "      <td>107.666667</td>\n",
+              "      <td>87.555556</td>\n",
+              "      <td>112.333333</td>\n",
+              "      <td>85.777778</td>\n",
+              "      <td>74.222222</td>\n",
+              "      <td>...</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>5 rows × 25001 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-172d99e4-2117-45ba-af02-38f7fa9ceb38')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-172d99e4-2117-45ba-af02-38f7fa9ceb38 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-172d99e4-2117-45ba-af02-38f7fa9ceb38');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-353fcef5-54b2-469d-ac6e-fc6077ce5759\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-353fcef5-54b2-469d-ac6e-fc6077ce5759')\"\n",
+              "            title=\"Suggest charts.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: #E8F0FE;\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: #1967D2;\n",
+              "    height: 32px;\n",
+              "    padding: 0 0 0 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: #E2EBFA;\n",
+              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: #174EA6;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "    background-color: #3B4455;\n",
+              "    fill: #D2E3FC;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart:hover {\n",
+              "    background-color: #434B5C;\n",
+              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "    fill: #FFFFFF;\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const charts = await google.colab.kernel.invokeFunction(\n",
+              "          'suggestCharts', [key], {});\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-353fcef5-54b2-469d-ac6e-fc6077ce5759 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "    species      1000.0     1000.1      1000.2  1000.3000000000001  \\\n",
+              "0  BisonYak   47.384615  23.666667   50.777778           51.444444   \n",
+              "1  BisonYak   78.384615  94.444444   77.888889           84.555556   \n",
+              "2  BisonYak  110.571429  88.444444  107.333333           81.888889   \n",
+              "3  BisonYak   91.846154  79.222222   81.777778           86.555556   \n",
+              "4  BisonYak   75.000000  86.700000   73.888889           77.666667   \n",
+              "\n",
+              "   1000.4000000000001  1000.5000000000001  1000.6000000000001  \\\n",
+              "0           43.444444           32.000000           48.666667   \n",
+              "1           93.555556           97.500000          103.222222   \n",
+              "2          111.666667          104.000000          109.500000   \n",
+              "3           90.777778           78.400000           77.888889   \n",
+              "4          107.666667           87.555556          112.333333   \n",
+              "\n",
+              "   1000.7000000000002  1000.8000000000002  ...  3499.0000000005684  \\\n",
+              "0           25.666667           38.111111  ...                 NaN   \n",
+              "1          108.777778           90.222222  ...                 NaN   \n",
+              "2           92.444444          137.777778  ...                 NaN   \n",
+              "3          116.222222           90.333333  ...                 NaN   \n",
+              "4           85.777778           74.222222  ...                 NaN   \n",
+              "\n",
+              "   3499.1000000005683  3499.2000000005683  3499.300000000568  \\\n",
+              "0                 NaN                 NaN                NaN   \n",
+              "1                 NaN                 NaN                NaN   \n",
+              "2                 NaN                 NaN                NaN   \n",
+              "3                 NaN                 NaN                NaN   \n",
+              "4                 NaN                 NaN                NaN   \n",
+              "\n",
+              "   3499.400000000568  3499.5000000005684  3499.6000000005683  \\\n",
+              "0                NaN                 NaN                 NaN   \n",
+              "1                NaN                 NaN                 NaN   \n",
+              "2                NaN                 NaN                 NaN   \n",
+              "3                NaN                 NaN                 NaN   \n",
+              "4                NaN                 NaN                 NaN   \n",
+              "\n",
+              "   3499.7000000005683  3499.8000000005686  3499.9000000005685  \n",
+              "0                 NaN                 NaN                 NaN  \n",
+              "1                 NaN                 NaN                 NaN  \n",
+              "2                 NaN                 NaN                 NaN  \n",
+              "3                 NaN                 NaN                 NaN  \n",
+              "4                 NaN                 NaN                 NaN  \n",
+              "\n",
+              "[5 rows x 25001 columns]"
+            ]
+          },
+          "execution_count": 11,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "final_df.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 503
+        },
+        "id": "K09k98e98sdA",
+        "outputId": "60357a1b-abe0-44d1-a252-ef19a6bebd5a"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "<ipython-input-14-f4fc217109b6>:1: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only.\n",
+            "  final_df.drop('species',1).iloc[0].plot()\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "<Axes: >"
+            ]
+          },
+          "execution_count": 14,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "final_df.drop('species',1).iloc[0].plot()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 503
+        },
+        "id": "-Ru08Ur49fEM",
+        "outputId": "e966d766-979c-4992-8bd1-8a69696f0511"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "<ipython-input-16-feba2ee74437>:1: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only.\n",
+            "  final_df.drop('species',1).iloc[100].plot()\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "<Axes: >"
+            ]
+          },
+          "execution_count": 16,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "final_df.drop('species',1).iloc[100].plot()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "Ho4gdt3s9nM9",
+        "outputId": "51f88c85-f94e-4c9b-ab84-8857d2d90501"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "<Axes: xlabel='species'>"
+            ]
+          },
+          "execution_count": 19,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Error in callback <function _draw_all_if_interactive at 0x797a63f65120> (for post_execute):\n"
+          ]
+        },
+        {
+          "ename": "KeyboardInterrupt",
+          "evalue": "ignored",
+          "output_type": "error",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36m_draw_all_if_interactive\u001b[0;34m()\u001b[0m\n\u001b[1;32m    118\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_draw_all_if_interactive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    119\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_interactive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 120\u001b[0;31m         \u001b[0mdraw_all\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    121\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/_pylab_helpers.py\u001b[0m in \u001b[0;36mdraw_all\u001b[0;34m(cls, force)\u001b[0m\n\u001b[1;32m    130\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmanager\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_all_fig_managers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    131\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mforce\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mmanager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstale\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 132\u001b[0;31m                 \u001b[0mmanager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_idle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    133\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    134\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mdraw_idle\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2080\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_idle_drawing\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2081\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_idle_draw_cntx\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2082\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2083\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2084\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    398\u001b[0m              (self.toolbar._wait_cursor_for_draw_cm() if self.toolbar\n\u001b[1;32m    399\u001b[0m               else nullcontext()):\n\u001b[0;32m--> 400\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    401\u001b[0m             \u001b[0;31m# A GUI class may be need to update a window using this draw, so\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    402\u001b[0m             \u001b[0;31m# don't forget to call the superclass.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     93\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mwraps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 95\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     96\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_rasterizing\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     97\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     70\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     73\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   3138\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3139\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3140\u001b[0;31m             mimage._draw_list_compositing_images(\n\u001b[0m\u001b[1;32m   3141\u001b[0m                 renderer, self, artists, self.suppressComposite)\n\u001b[1;32m   3142\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    129\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    132\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     70\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     73\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   3062\u001b[0m             \u001b[0m_draw_rasterized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0martists_rasterized\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3063\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3064\u001b[0;31m         mimage._draw_list_compositing_images(\n\u001b[0m\u001b[1;32m   3065\u001b[0m             renderer, self, artists, self.figure.suppressComposite)\n\u001b[1;32m   3066\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    129\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    132\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     70\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     73\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/legend.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    732\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    733\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegendPatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 734\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_legend_box\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    735\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    736\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'legend'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     37\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_rasterizing\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m     \u001b[0mdraw_wrapper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_supports_rasterization\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/offsetbox.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    410\u001b[0m         \u001b[0mto\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mgiven\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    411\u001b[0m         \"\"\"\n\u001b[0;32m--> 412\u001b[0;31m         \u001b[0mbbox\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moffsets\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_bbox_and_child_offsets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    413\u001b[0m         \u001b[0mpx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_offset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbbox\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    414\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mox\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moy\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_visible_children\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moffsets\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/offsetbox.py\u001b[0m in \u001b[0;36m_get_bbox_and_child_offsets\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    483\u001b[0m                     \u001b[0mc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_width\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwidth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    484\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 485\u001b[0;31m         \u001b[0mbboxes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_bbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_visible_children\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    486\u001b[0m         (x0, x1), xoffsets = _get_aligned_offsets(\n\u001b[1;32m    487\u001b[0m             [bbox.intervalx for bbox in bboxes], self.width, self.align)\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/offsetbox.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    483\u001b[0m                     \u001b[0mc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_width\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwidth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    484\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 485\u001b[0;31m         \u001b[0mbboxes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_bbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_visible_children\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    486\u001b[0m         (x0, x1), xoffsets = _get_aligned_offsets(\n\u001b[1;32m    487\u001b[0m             [bbox.intervalx for bbox in bboxes], self.width, self.align)\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/offsetbox.py\u001b[0m in \u001b[0;36mget_bbox\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    365\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_bbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    366\u001b[0m         \u001b[0;34m\"\"\"Return the bbox of the offsetbox, ignoring parent offsets.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 367\u001b[0;31m         \u001b[0mbbox\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moffsets\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_bbox_and_child_offsets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    368\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mbbox\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    369\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/offsetbox.py\u001b[0m in \u001b[0;36m_get_bbox_and_child_offsets\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    510\u001b[0m         \u001b[0msep\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msep\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mdpicor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    511\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 512\u001b[0;31m         \u001b[0mbboxes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_bbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_visible_children\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    513\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mbboxes\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    514\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mBbox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_bounds\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpadded\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpad\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/offsetbox.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    510\u001b[0m         \u001b[0msep\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msep\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mdpicor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    511\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 512\u001b[0;31m         \u001b[0mbboxes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_bbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_visible_children\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    513\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mbboxes\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    514\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mBbox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_bounds\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpadded\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpad\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/offsetbox.py\u001b[0m in \u001b[0;36mget_bbox\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    365\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_bbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    366\u001b[0m         \u001b[0;34m\"\"\"Return the bbox of the offsetbox, ignoring parent offsets.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 367\u001b[0;31m         \u001b[0mbbox\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moffsets\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_bbox_and_child_offsets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    368\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mbbox\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    369\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/offsetbox.py\u001b[0m in \u001b[0;36m_get_bbox_and_child_offsets\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    483\u001b[0m                     \u001b[0mc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_width\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwidth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    484\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 485\u001b[0;31m         \u001b[0mbboxes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_bbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_visible_children\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    486\u001b[0m         (x0, x1), xoffsets = _get_aligned_offsets(\n\u001b[1;32m    487\u001b[0m             [bbox.intervalx for bbox in bboxes], self.width, self.align)\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/offsetbox.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    483\u001b[0m                     \u001b[0mc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_width\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwidth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    484\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 485\u001b[0;31m         \u001b[0mbboxes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_bbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_visible_children\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    486\u001b[0m         (x0, x1), xoffsets = _get_aligned_offsets(\n\u001b[1;32m    487\u001b[0m             [bbox.intervalx for bbox in bboxes], self.width, self.align)\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/offsetbox.py\u001b[0m in \u001b[0;36mget_bbox\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    365\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_bbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    366\u001b[0m         \u001b[0;34m\"\"\"Return the bbox of the offsetbox, ignoring parent offsets.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 367\u001b[0;31m         \u001b[0mbbox\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moffsets\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_bbox_and_child_offsets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    368\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mbbox\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    369\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/offsetbox.py\u001b[0m in \u001b[0;36m_get_bbox_and_child_offsets\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    510\u001b[0m         \u001b[0msep\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msep\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mdpicor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    511\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 512\u001b[0;31m         \u001b[0mbboxes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_bbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_visible_children\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    513\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mbboxes\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    514\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mBbox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_bounds\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpadded\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpad\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/offsetbox.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    510\u001b[0m         \u001b[0msep\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msep\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mdpicor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    511\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 512\u001b[0;31m         \u001b[0mbboxes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_bbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_visible_children\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    513\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mbboxes\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    514\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mBbox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_bounds\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpadded\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpad\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/offsetbox.py\u001b[0m in \u001b[0;36mget_bbox\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    799\u001b[0m             ismath=\"TeX\" if self._text.get_usetex() else False)\n\u001b[1;32m    800\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 801\u001b[0;31m         \u001b[0mbbox\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_text\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_layout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    802\u001b[0m         \u001b[0mw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbbox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    803\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/text.py\u001b[0m in \u001b[0;36m_get_layout\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    384\u001b[0m             \u001b[0mclean_line\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mismath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_preprocess_math\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    385\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mclean_line\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 386\u001b[0;31m                 w, h, d = _get_text_metrics_with_cache(\n\u001b[0m\u001b[1;32m    387\u001b[0m                     \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclean_line\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fontproperties\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    388\u001b[0m                     ismath=ismath, dpi=self.figure.dpi)\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/text.py\u001b[0m in \u001b[0;36m_get_text_metrics_with_cache\u001b[0;34m(renderer, text, fontprop, ismath, dpi)\u001b[0m\n\u001b[1;32m     95\u001b[0m     \u001b[0;31m# Cached based on a copy of fontprop so that later in-place mutations of\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     96\u001b[0m     \u001b[0;31m# the passed-in argument do not mess up the cache.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 97\u001b[0;31m     return _get_text_metrics_with_cache_impl(\n\u001b[0m\u001b[1;32m     98\u001b[0m         weakref.ref(renderer), text, fontprop.copy(), ismath, dpi)\n\u001b[1;32m     99\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/text.py\u001b[0m in \u001b[0;36m_get_text_metrics_with_cache_impl\u001b[0;34m(renderer_ref, text, fontprop, ismath, dpi)\u001b[0m\n\u001b[1;32m    103\u001b[0m         renderer_ref, text, fontprop, ismath, dpi):\n\u001b[1;32m    104\u001b[0m     \u001b[0;31m# dpi is unused, but participates in cache invalidation (via the renderer).\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 105\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mrenderer_ref\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_text_width_height_descent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfontprop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mismath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    106\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    107\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mget_text_width_height_descent\u001b[0;34m(self, s, prop, ismath)\u001b[0m\n\u001b[1;32m    232\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    233\u001b[0m         \u001b[0mfont\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_prepare_font\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 234\u001b[0;31m         \u001b[0mfont\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mget_hinting_flag\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    235\u001b[0m         \u001b[0mw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfont\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_width_height\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# width and height of unrotated string\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    236\u001b[0m         \u001b[0md\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfont\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_descent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+          ]
+        },
+        {
+          "ename": "ValueError",
+          "evalue": "ignored",
+          "output_type": "error",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    339\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    340\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 341\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    342\u001b[0m             \u001b[0;31m# Finally look for special method names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    343\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, base64, **kwargs)\u001b[0m\n\u001b[1;32m    149\u001b[0m         \u001b[0mFigureCanvasBase\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    150\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 151\u001b[0;31m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    152\u001b[0m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    153\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'svg'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[1;32m   2364\u001b[0m                 \u001b[0;31m# force the figure dpi to 72), so we need to set it again here.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2365\u001b[0m                 \u001b[0;32mwith\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setattr_cm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2366\u001b[0;31m                     result = print_method(\n\u001b[0m\u001b[1;32m   2367\u001b[0m                         \u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2368\u001b[0m                         \u001b[0mfacecolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfacecolor\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   2230\u001b[0m                 \"bbox_inches_restore\"}\n\u001b[1;32m   2231\u001b[0m             \u001b[0mskip\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moptional_kws\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minspect\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msignature\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmeth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2232\u001b[0;31m             print_method = functools.wraps(meth)(lambda *args, **kwargs: meth(\n\u001b[0m\u001b[1;32m   2233\u001b[0m                 *args, **{k: v for k, v in kwargs.items() if k not in skip}))\n\u001b[1;32m   2234\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# Let third-parties do as they see fit.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[0;34m(self, filename_or_obj, metadata, pil_kwargs)\u001b[0m\n\u001b[1;32m    507\u001b[0m             \u001b[0;34m*\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mincluding\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mdefault\u001b[0m \u001b[0;34m'Software'\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    508\u001b[0m         \"\"\"\n\u001b[0;32m--> 509\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_print_pil\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename_or_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"png\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpil_kwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetadata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    510\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    511\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mprint_to_buffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36m_print_pil\u001b[0;34m(self, filename_or_obj, fmt, pil_kwargs, metadata)\u001b[0m\n\u001b[1;32m    455\u001b[0m         *pil_kwargs* and *metadata* are forwarded).\n\u001b[1;32m    456\u001b[0m         \"\"\"\n\u001b[0;32m--> 457\u001b[0;31m         \u001b[0mFigureCanvasAgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    458\u001b[0m         mpl.image.imsave(\n\u001b[1;32m    459\u001b[0m             \u001b[0mfilename_or_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuffer_rgba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfmt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morigin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"upper\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    392\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    393\u001b[0m         \u001b[0;31m# docstring inherited\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 394\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    395\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    396\u001b[0m         \u001b[0;31m# Acquire a lock on the shared font cache.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/_api/deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*inner_args, **inner_kwargs)\u001b[0m\n\u001b[1;32m    382\u001b[0m             \u001b[0;31m# Early return in the simple, non-deprecated case (much faster than\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    383\u001b[0m             \u001b[0;31m# calling bind()).\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 384\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minner_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0minner_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    385\u001b[0m         \u001b[0marguments\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msignature\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minner_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0minner_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marguments\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    386\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_varargs\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0marguments\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mget_renderer\u001b[0;34m(self, cleared)\u001b[0m\n\u001b[1;32m    409\u001b[0m         \u001b[0mreuse_renderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lastKey\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    410\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mreuse_renderer\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 411\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRendererAgg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    412\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lastKey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    413\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mcleared\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, width, height, dpi)\u001b[0m\n\u001b[1;32m     82\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwidth\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     83\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheight\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mheight\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 84\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_renderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_RendererAgg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwidth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mheight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     85\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_filter_renderers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     86\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mValueError\u001b[0m: Image size of 581x523642 pixels is too large. It must be less than 2^16 in each direction."
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
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+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "dd1ad2dfb15d48e0bcbbc4d11e0e4b3f": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "DescriptionStyleModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "DescriptionStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "description_width": ""
+          }
+        }
+      }
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/windows_app/README.md b/windows_app/README.md
new file mode 100644
index 0000000..9bc7d08
--- /dev/null
+++ b/windows_app/README.md
@@ -0,0 +1,16 @@
+# ZooMS Windows Classifer App 
+
+
+Please use pyinstaller to compile the app on a Windows machine. 
+
+```
+MyApp/
+|-- model/
+|   |-- weights.pth # 1DCNN weights (PyTorch)
+|-- src/
+|   |-- main.py
+|   |-- model.py
+|   |-- file_ops.py
+|   |-- gui.py
+|-- requirements.txt
+```
diff --git a/windows_app/model/model.pth b/windows_app/model/model.pth
new file mode 100644
index 0000000..5200a0b
Binary files /dev/null and b/windows_app/model/model.pth differ
diff --git a/windows_app/requirements.txt b/windows_app/requirements.txt
new file mode 100644
index 0000000..abc8f12
--- /dev/null
+++ b/windows_app/requirements.txt
@@ -0,0 +1,3 @@
+pandas >= 1.4.4
+torch >= 1.10.2
+pyinstaller >= 5.13.2
\ No newline at end of file
diff --git a/windows_app/setup.py b/windows_app/setup.py
new file mode 100644
index 0000000..a52b4d7
--- /dev/null
+++ b/windows_app/setup.py
@@ -0,0 +1,8 @@
+from cx_Freeze import setup, Executable
+
+setup(
+    name="AI_ZooMS",
+    version="0.0.1",
+    description="Find homininis with AI",
+    executables=[Executable("src/main.py")]
+)
diff --git a/windows_app/src/file_ops.py b/windows_app/src/file_ops.py
new file mode 100644
index 0000000..febe0c4
--- /dev/null
+++ b/windows_app/src/file_ops.py
@@ -0,0 +1,45 @@
+import pandas as pd
+import os
+from datetime import datetime
+
+def read_csv_files(directory):
+    file_paths = []
+    file_names = []
+    for filename in os.listdir(directory):
+        if filename.endswith('.csv'):
+            filepath = os.path.join(directory, filename)
+            file_paths.append(filepath)
+            file_names.append(filename)
+    return file_paths, file_names
+
+
+
+def save_results(results, output_directory, file_names, targets):
+    # Column names
+    column_names = [
+        'Canidae', 'Cervidae', 'CervidaeGazellaSaiga', 'Ovis', 'Equidae',
+        'CrocutaPanthera', 'BisonYak', 'Capra', 'Ursidae', 'Vulpes vulpes',
+        'Elephantidae', 'Others', 'Rhinocerotidae', 'Rangifer tarandus', 'Hominins'
+    ]
+    
+    # Updated the dataframe creation line to handle numpy arrays
+    concatenated_df = pd.concat([pd.DataFrame(result) for result in results], ignore_index=True)
+    
+    concatenated_df.columns = column_names
+    concatenated_df['Most Probable Class'] = [column_names[i] for i in targets]
+    
+    # Reorder columns to place 'Most Probable Class' as the second column
+    cols = ['Most Probable Class'] + [col for col in concatenated_df.columns if col != 'Most Probable Class']
+    concatenated_df = concatenated_df[cols]
+    
+    # Insert file names as the first column
+    concatenated_df.insert(0, 'File Name', file_names)
+
+    # Get current date and time
+    current_datetime = datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
+    
+    # Create a unique filename with current date and time
+    output_path = os.path.join(output_directory, f'results_{current_datetime}.csv')
+    
+    # Save the concatenated dataframe to the unique output path
+    concatenated_df.to_csv(output_path, index=False)
diff --git a/windows_app/src/gui.py b/windows_app/src/gui.py
new file mode 100644
index 0000000..58edc8b
--- /dev/null
+++ b/windows_app/src/gui.py
@@ -0,0 +1,92 @@
+from PyQt5.QtWidgets import QApplication, QWidget, QVBoxLayout, QHBoxLayout, QLabel, QLineEdit, QPushButton, QFileDialog
+from PyQt5.QtGui import QIcon, QFont
+
+def create_gui(main_func):
+    app = QApplication([])
+
+    window = QWidget()
+    window.setWindowTitle("ML-Based Mass Spectra Species Identifier")
+    window.setWindowIcon(QIcon('icon.png'))
+    window.setFixedSize(700, 350)
+
+    layoutV = QVBoxLayout()
+    font_label = QFont("Arial", 16, QFont.Bold)
+    font_button = QFont("Arial", 14)
+
+    layoutV.setContentsMargins(40, 40, 40, 40)
+
+    layoutH3 = QHBoxLayout()
+    model_file_path = QLineEdit(placeholderText="Select your ML model file...")
+    model_file_path.setFont(font_button)
+    browse_model_btn = QPushButton("Select Model")
+    browse_model_btn.setFont(font_button)
+    label3 = QLabel("Model File:")
+    label3.setFont(font_label)
+    layoutH3.addWidget(label3)
+    layoutH3.addWidget(model_file_path)
+    layoutH3.addWidget(browse_model_btn)
+    layoutV.addLayout(layoutH3)
+
+    layoutH1 = QHBoxLayout()
+    input_directory = QLineEdit(placeholderText="Select a directory with CSV files...")
+    input_directory.setFont(font_button)
+    browse_input_btn = QPushButton("Browse")
+    browse_input_btn.setFont(font_button)
+    label1 = QLabel("Input Directory:")
+    label1.setFont(font_label)
+    layoutH1.addWidget(label1)
+    layoutH1.addWidget(input_directory)
+    layoutH1.addWidget(browse_input_btn)
+    layoutV.addLayout(layoutH1)
+
+    layoutH2 = QHBoxLayout()
+    output_directory = QLineEdit(placeholderText="Select a directory for results...")
+    output_directory.setFont(font_button)
+    browse_output_btn = QPushButton("Browse")
+    browse_output_btn.setFont(font_button)
+    label2 = QLabel("Output Directory:")
+    label2.setFont(font_label)
+    layoutH2.addWidget(label2)
+    layoutH2.addWidget(output_directory)
+    layoutH2.addWidget(browse_output_btn)
+    layoutV.addLayout(layoutH2)
+
+    classify_btn = QPushButton("Classify")
+    classify_btn.setFont(font_button)
+    layoutV.addWidget(classify_btn)
+
+    window.setStyleSheet("""
+        QWidget {
+            background-color: #fafafa;
+            font-size: 18px;
+            color: #333;
+        }
+        QPushButton {
+            background-color: #11a611; /* Green */
+            color: white;
+            border: none;
+            border-radius: 10px;
+            padding: 14px 28px;
+        }
+        QPushButton:pressed {
+            background-color: #005900; /* Darker green on click */
+        }
+        QLineEdit {
+            background-color: #fff;
+            border: 1px solid #ccc;
+            border-radius: 10px;
+            padding: 14px;
+        }
+    """)
+
+    browse_model_btn.clicked.connect(lambda: model_file_path.setText(QFileDialog.getOpenFileName()[0]))
+    browse_input_btn.clicked.connect(lambda: input_directory.setText(QFileDialog.getExistingDirectory()))
+    browse_output_btn.clicked.connect(lambda: output_directory.setText(QFileDialog.getExistingDirectory()))
+    classify_btn.clicked.connect(lambda: main_func(model_file_path.text(), input_directory.text(), output_directory.text()))
+
+    window.setLayout(layoutV)
+    window.show()
+    app.exec_()
+
+if __name__ == "__main__":
+    create_gui(main)
diff --git a/windows_app/src/main.py b/windows_app/src/main.py
new file mode 100644
index 0000000..9d5e533
--- /dev/null
+++ b/windows_app/src/main.py
@@ -0,0 +1,74 @@
+from file_ops import read_csv_files, save_results
+from gui import create_gui
+import torch
+from PyQt5.QtWidgets import QMessageBox
+import numpy as np
+import pandas as pd
+import os 
+from model import CNN1D
+
+def mean_intensity(temp_df, bin_resolution=0.5):
+    bins = np.arange(899.9, 3500, bin_resolution)
+    temp_df['bin'] = pd.cut(temp_df['mass'], bins=bins)
+    return temp_df.groupby('bin')['intensity'].mean().values
+
+def normalize(tensor):
+    tensor[torch.isnan(tensor)] = 0
+    mean = tensor.mean()
+    std = tensor.std()
+    return (tensor - mean) / (std + torch.finfo(torch.float32).eps)
+
+def load_model(weight_path):
+    model = torch.load(weight_path, map_location=torch.device('cpu'))
+    model.eval()
+    return model
+
+def main(model_file_path, input_directory, output_directory):
+    if not model_file_path or not input_directory or not output_directory:
+        show_missing_paths_message()
+        return
+    
+    model = load_model(model_file_path)
+    file_paths, file_names = read_csv_files(input_directory)
+    results, file_names, targets = make_predictions(file_paths, model)
+    save_results(results, output_directory, file_names, targets)
+    show_done_message()
+
+def show_missing_paths_message():
+    msg = QMessageBox()
+    msg.setIcon(QMessageBox.Critical)
+    msg.setWindowTitle("Missing Path")
+    msg.setText("Please provide all required paths (Model, Input Directory, Output Directory).")
+    msg.exec_()
+
+def show_done_message():
+    msg = QMessageBox()
+    msg.setIcon(QMessageBox.Information)
+    msg.setWindowTitle("Process Completed")
+    msg.setText("The classification process is complete.")
+    msg.exec_()
+
+def make_predictions(file_paths, model):
+    results = []
+    file_names = []
+    targets = []
+    for i, file_path in enumerate(file_paths):
+        temp_df = pd.read_csv(file_path)
+        file_name = os.path.basename(file_path)
+        file_names.append(file_name)
+        
+        intensities = mean_intensity(temp_df)
+        tensor_data = torch.tensor(intensities, dtype=torch.float32)
+        tensor_data = normalize(tensor_data)
+        
+        output = model(tensor_data.unsqueeze(0).unsqueeze(0))
+        probabilities = torch.softmax(output, dim=1).detach().numpy().round(3)
+        results.append(probabilities)
+        
+        target = np.argmax(probabilities)
+        targets.append(target)
+        
+    return results, file_names, targets
+
+if __name__ == "__main__":
+    create_gui(main)
diff --git a/windows_app/src/model.py b/windows_app/src/model.py
new file mode 100644
index 0000000..d2f2c06
--- /dev/null
+++ b/windows_app/src/model.py
@@ -0,0 +1,43 @@
+import torch
+import torch.nn as nn
+
+class CNN1D(nn.Module):
+    def __init__(self, input_size, num_classes):
+        super(CNN1D, self).__init__()
+
+        self.conv1 = nn.Conv1d(1, 32, kernel_size=5, stride=1)
+
+        self.conv2 = nn.Conv1d(32, 64, kernel_size=5, stride=1)
+        #self.bn2 = nn.BatchNorm1d(32)
+
+        self.pool = nn.AvgPool1d(kernel_size=3)
+
+        output_size = (input_size - 5 + 1) // 3  # After conv1 and pool
+        output_size = (output_size - 5 + 1) // 3  # After conv2 and pool
+
+        self.fc1 = nn.Linear(64 * output_size, 128)
+        self.dropout1 = nn.Dropout(0.25)
+
+        self.fc2 = nn.Linear(128, num_classes)
+
+        self.relu = nn.ReLU()
+
+    def forward(self, x):
+        x = self.pool(self.relu(self.conv1(x)))
+        x = self.pool(self.relu(self.conv2(x)))
+
+        x = x.view(x.size(0), -1)
+
+        x = self.relu(self.fc1(x))
+        x = self.dropout1(x)
+
+        x = self.fc2(x)
+        return x
+
+
+def load_model(weight_path):
+    model = torch.load(weight_path, map_location=torch.device('cpu'))
+    model.eval()
+    return model
+
+
diff --git a/windows_app/test.ipynb b/windows_app/test.ipynb
new file mode 100644
index 0000000..8dfb0d4
--- /dev/null
+++ b/windows_app/test.ipynb
@@ -0,0 +1,84 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "55bd4cb8-4bf1-48f8-bcb3-11375f947e31",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tkinter as tk\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "ab248bcb-4c77-442b-a12d-9bb89965d029",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from cx_Freeze import setup, Executable\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "bc3e8b53-753d-4d63-9f31-ae485850acbd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch, pandas "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "74fb7e63-4b89-43ce-a98d-06871d43f416",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "('1.12.1+cu116', '1.5.0')"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "torch.__version__, pandas.__version__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "20a31617-7550-424c-8119-fd76d66c1cfa",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}