Plot_Data.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Plot Data.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "fI9O72gxc_bS"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import plotly as pl\n",
        "import plotly.express as px"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/gdrive')\n",
        "%cd /gdrive"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xu1ZJ83qeMKw",
        "outputId": "2303fd84-6f85-4de2-badd-6906352a0a25"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /gdrive\n",
            "/gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sessions = [9,12,14,17,21,30,32]\n",
        "data = []\n",
        "for session in sessions:\n",
        "  dat = pd.read_csv('/gdrive/Shareddrives/NoNameGroup - NMA/Results/Topological measures/all_measures_session_' + str(session)+'.csv')\n",
        "  data.append(dat)"
      ],
      "metadata": {
        "id": "BtCvNbt2eikR"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataframe = data[1]\n",
        "dataframe"
      ],
      "metadata": {
        "id": "cL09zTA6fCRh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ***`Data for plotly`***"
      ],
      "metadata": {
        "id": "ZQ8OjBC_udoG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataframe = pd.concat(data)\n",
        "datareward = dataframe[dataframe['reward'] >0]\n",
        "datanoreward = dataframe[dataframe['reward'] <0]\n",
        "data2 = pd.concat([datareward, datanoreward], axis=0)\n",
        "data2['reward'] = data2['reward'].astype(str)"
      ],
      "metadata": {
        "id": "bOGkjWqsuhER"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(data2.keys())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aXDsyzoMNBtI",
        "outputId": "2e78d622-7395-43dc-8a91-42b7932cb512"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['Unnamed: 0', 'reward', 'session', 'trial', 'mean_degree', 'std_degree',\n",
            "       'entropy_degree', 'mean_strenght', 'std_strenght', 'entropy_strenght',\n",
            "       'mean_closeness', 'std_closeness', 'entropy_closeness',\n",
            "       'mean_clustering', 'std_clustering', 'entropy_clustering', 'geodd',\n",
            "       'smallworld', 'mean_clustering_midbrain',\n",
            "       'mean_clustering_basal_ganglia', 'std_clustering_midbrain',\n",
            "       'std_clustering_basal_ganglia', 'mean_degree_midbrain',\n",
            "       'mean_degree_basal_ganglia', 'std_degree_midbrain',\n",
            "       'std_degree_basal_ganglia', 'mean_strenght_midbrain',\n",
            "       'mean_strenght_basal_ganglia', 'std_strenght_midbrain',\n",
            "       'std_strenght_basal_ganglia', 'mean_closeness_midbrain',\n",
            "       'mean_closeness_basal_ganglia', 'std_closeness_midbrain',\n",
            "       'std_closeness_basal_ganglia'],\n",
            "      dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#, trendline=\"ols\"\n",
        "fig = px.scatter(data2, x='mean_closeness_basal_ganglia', y='mean_closeness_midbrain', color=\"reward\", marginal_y=\"violin\",\n",
        "           marginal_x=\"histogram\", template=\"simple_white\", title = 'Some Topological properties')\n",
        "\n",
        "fig.update_layout(\n",
        "    font_family=\"Arial\",\n",
        "    font_color=\"black\",\n",
        "    font_size = 16,\n",
        "    title_font_family=\"Arial\",\n",
        "    title_font_color=\"black\",\n",
        "    legend_title_font_color=\"black\"\n",
        ")\n",
        "fig.update_xaxes(title_font_family=\"Arial\")\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "nvTvY-EvuhOb",
        "outputId": "8e36b3f7-ac76-4bf3-ad1e-8465f28d48f4"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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