data_plot.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7kpcNzT_XR_M",
        "outputId": "216109e4-9ebc-4afb-c3e5-e4a8d2eb6a5f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive/\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive/')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "data_dir = '/content/drive/My Drive/Fungal-Fungal-interaction/data/'\n",
        "code_dir = '/content/drive/My Drive/Fungal-Fungal-interaction/'\n",
        "well_dir = '/content/drive/My Drive/Fungal-Fungal-interaction/data/wells/'\n",
        "model_dir = '/content/drive/My Drive/Fungal-Fungal-interaction/models/'\n",
        "plot_dir = '/content/drive/My Drive/Fungal-Fungal-interaction/plots/'\n",
        "# data = pd.read_csv(data_dir + '01_well_data.csv')\n",
        "# raw_paths, lbls = data['img_paths'], data['labels']"
      ],
      "metadata": {
        "id": "1EEgWQe9XmDV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv(data_dir + '01_well_data.csv')\n",
        "raw_paths, lbls = data['img_paths'], data['labels']\n",
        "name_lbls = {}\n",
        "x, y = [], []\n",
        "for i in range(len(raw_paths)):\n",
        "  img_name = raw_paths[i].split('/')[-2] + '/' + raw_paths[i].split('/')[-1]\n",
        "  lbl = int(lbls[i])\n",
        "  # img = cv2.imread(well_dir+img_name)\n",
        "  # img = cv2.resize(img, dsize=(224, 224))\n",
        "  #img = np.array(load_img(well_dir+img_name, target_size=(224, 224)))\n",
        "  #x.append(img)\n",
        "  y.append(lbl)\n",
        "\n",
        "\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Sample data: distribution of three classes\n",
        "# Replace these values with your actual data\n",
        "class_distribution = [y.count(0), y.count(1), y.count(2)]\n",
        "class_labels = ['Class 1', 'Class 2', 'Class 3']\n",
        "\n",
        "# Creating the pie chart\n",
        "plt.figure(figsize=(8, 8))\n",
        "plt.pie(class_distribution, labels=class_labels, autopct='%1.1f%%', startangle=140)\n",
        "plt.title('Data Distribution')\n",
        "\n",
        "# Showing the plot\n",
        "plt.savefig(plot_dir+'Pie char.svg', dpi=1200, transparent=True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 675
        },
        "id": "elRU3B4tF09n",
        "outputId": "deb3fd38-5d3c-404d-f1b9-968107b20b1d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#import glob\n",
        "#pathes = glob.glob('/content/drive/My Drive/Fungal-Fungal-interaction/data/wells/*/')\n",
        "#print(pathes)\n",
        "# ! pip ininstall matplotlib\n",
        "# ! pip install matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import os\n",
        "\n",
        "\n",
        "data = pd.read_csv(data_dir + '01_well_data.csv')\n",
        "plate_names = [str(well) for well in os.listdir(well_dir)]\n",
        "print(plate_names)\n",
        "print(type(data['img_paths'][0].split('/')[-2]))\n",
        "\n",
        "paths, lbls = data['img_paths'], data['labels']\n",
        "\n",
        "cls_numbers = []\n",
        "for name in plate_names:\n",
        "  counter1, counter2, counter3 = 0, 0, 0\n",
        "  for i, path in enumerate(paths):\n",
        "    lbl = int(lbls[i])\n",
        "    plate = path.split('/')[-2]\n",
        "    if name == plate:\n",
        "      if lbl==0:\n",
        "        counter1+=1\n",
        "      elif lbl==1:\n",
        "        counter2+=1\n",
        "      else:\n",
        "        counter3+=1\n",
        "  cls_numbers.append([counter1, counter2, counter3])\n",
        "del data\n",
        "cls_numbers = np.array(cls_numbers)\n",
        "# Mode names\n",
        "print(cls_numbers)\n",
        "classes = ['class1', 'class2', 'class3']\n",
        "\n",
        "# Number of models\n",
        "n_plates = len(plate_names)\n",
        "\n",
        "# Width of the bars\n",
        "bar_width = 0.3\n",
        "\n",
        "# Set up the figure and axes\n",
        "fig, ax = plt.subplots(figsize=(20, 10))\n",
        "\n",
        "# Create bars for each mode\n",
        "colors = ['cornflowerblue', 'lightsteelblue', 'lavender']\n",
        "for i in range(len(classes)):\n",
        "    ax.bar(np.arange(n_plates) + i * bar_width, cls_numbers[:, i], width=bar_width, label=classes[i], color=colors[i])\n",
        "\n",
        "# Set the position of the x ticks\n",
        "ax.set_xticks(np.arange(n_plates) + bar_width * 1.5)\n",
        "ax.set_xticklabels(plate_names)\n",
        "# plt.xticks(np.arange(n_plates) + bar_width * 1.5)\n",
        "# plt.set_xticklabels(plate_names)\n",
        "ax.set_ylim(0, 90)\n",
        "plt.grid()\n",
        "\n",
        "# Adding labels and title\n",
        "plt.xlabel('plates')\n",
        "plt.ylabel('number of classes')\n",
        "\n",
        "\n",
        "plt.title('Histogram of classes in each plate')\n",
        "\n",
        "# Adding legend\n",
        "plt.legend(loc='best', fontsize=20)\n",
        "\n",
        "# Show plot\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.savefig(plot_dir+'plates_distribution.svg', transparent=True, dpi=1200)\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 993
        },
        "id": "AbfjPbnCXiQu",
        "outputId": "221eac63-39e3-4bdb-cb98-2c60b5e91cb0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['1154', '1155', '1161', '1162', '1163', '1164', '1165', '1166', '1167', '1169', '1172', '1173', '1174', '1175', '1178', '1176', '1177', '1180', '1179', '1181']\n",
            "<class 'str'>\n",
            "[[33 62  1]\n",
            " [77 16  3]\n",
            " [75 21  0]\n",
            " [46 49  0]\n",
            " [78 18  0]\n",
            " [83 13  0]\n",
            " [57 39  0]\n",
            " [ 0  0  0]\n",
            " [ 0  0  0]\n",
            " [ 0  0  0]\n",
            " [46 24 17]\n",
            " [50 20 19]\n",
            " [41 26 17]\n",
            " [48 23 14]\n",
            " [28 66  0]\n",
            " [63 29  0]\n",
            " [70 13  0]\n",
            " [49 46  0]\n",
            " [28 66  0]\n",
            " [65 25  0]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1000 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(plate_names)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6lWOjABufmWZ",
        "outputId": "edb02d66-a383-4dcb-9d17-4668dcdf6959"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['1154', '1155', '1161', '1162', '1163', '1164', '1165', '1166', '1167', '1169', '1172', '1173', '1174', '1175', '1178', '1176', '1177', '1180', '1179', '1181']\n"
          ]
        }
      ]
    }
  ]
}