HDDM_for_RT.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "HDDM for RT.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPp9nkgs+Yj0eqEmzf2iQZv",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/nmningmei/BESA_readers/blob/master/HDDM_for_RT.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KFoYKHWn2J1v",
        "outputId": "a5f469d8-c6d5-4d39-990d-c50ece00d516"
      },
      "source": [
        "!pip install -U pip"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: pip in /usr/local/lib/python3.7/dist-packages (21.2.4)\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "euay6FP02Kpa",
        "outputId": "2632dcae-eb73-4a7f-b320-cf2e610a0408"
      },
      "source": [
        "!pip cache purge"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Files removed: 4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D2IOxO242L_m",
        "outputId": "302a507e-3951-4138-f4ea-1b618d9ddf6e"
      },
      "source": [
        "!pip install numpy==1.19.3\n",
        "!pip install -U mkl\n",
        "!pip install pymc\n",
        "!pip install kabuki\n",
        "!pip install hddm==0.8.0"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: numpy==1.19.3 in /usr/local/lib/python3.7/dist-packages (1.19.3)\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n",
            "Requirement already satisfied: mkl in /usr/local/lib/python3.7/dist-packages (2019.0)\n",
            "Collecting mkl\n",
            "  Downloading mkl-2021.3.0-py2.py3-none-manylinux1_x86_64.whl (278.6 MB)\n",
            "\u001b[K     |████████████████████████████████| 278.6 MB 32 kB/s \n",
            "\u001b[?25hCollecting tbb==2021.*\n",
            "  Downloading tbb-2021.3.0-py2.py3-none-manylinux1_x86_64.whl (4.1 MB)\n",
            "\u001b[K     |████████████████████████████████| 4.1 MB 34.8 MB/s \n",
            "\u001b[?25hRequirement already satisfied: intel-openmp==2021.* in /usr/local/lib/python3.7/dist-packages (from mkl) (2021.3.0)\n",
            "Installing collected packages: tbb, mkl\n",
            "  Attempting uninstall: mkl\n",
            "    Found existing installation: mkl 2019.0\n",
            "    Uninstalling mkl-2019.0:\n",
            "      Successfully uninstalled mkl-2019.0\n",
            "Successfully installed mkl-2021.3.0 tbb-2021.3.0\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n",
            "Collecting pymc\n",
            "  Downloading pymc-2.3.8.tar.gz (385 kB)\n",
            "\u001b[K     |████████████████████████████████| 385 kB 5.3 MB/s \n",
            "\u001b[?25hBuilding wheels for collected packages: pymc\n",
            "  Building wheel for pymc (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pymc: filename=pymc-2.3.8-cp37-cp37m-linux_x86_64.whl size=1353046 sha256=058b41c9816de93d5d0c97b6d171c0864ebe853a20129f760dd18fc06c66e8a6\n",
            "  Stored in directory: /root/.cache/pip/wheels/3b/9b/1d/2a2ddbe80519db2698745f0988ce50c36ad38aac886a4ee0ca\n",
            "Successfully built pymc\n",
            "Installing collected packages: pymc\n",
            "Successfully installed pymc-2.3.8\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n",
            "Collecting kabuki\n",
            "  Downloading kabuki-0.6.3.tar.gz (44 kB)\n",
            "\u001b[K     |████████████████████████████████| 44 kB 1.6 MB/s \n",
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            "Requirement already satisfied: pymc>=2.3.6 in /usr/local/lib/python3.7/dist-packages (from kabuki) (2.3.8)\n",
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            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.12.0->kabuki) (2018.9)\n",
            "Building wheels for collected packages: kabuki\n",
            "  Building wheel for kabuki (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for kabuki: filename=kabuki-0.6.3-py3-none-any.whl size=36545 sha256=02f4091905964c9bf86ee12abf16a1ea9b7fec1c6d802133977c215ad62077d4\n",
            "  Stored in directory: /root/.cache/pip/wheels/29/cb/6a/02f80c7cde0e973c5ec0f4202aee3ab1b10d84682052d99af5\n",
            "Successfully built kabuki\n",
            "Installing collected packages: kabuki\n",
            "Successfully installed kabuki-0.6.3\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n",
            "Collecting hddm==0.8.0\n",
            "  Downloading HDDM-0.8.0.tar.gz (601 kB)\n",
            "\u001b[K     |████████████████████████████████| 601 kB 5.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: NumPy>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from hddm==0.8.0) (1.19.3)\n",
            "Requirement already satisfied: SciPy>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from hddm==0.8.0) (1.4.1)\n",
            "Requirement already satisfied: kabuki>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from hddm==0.8.0) (0.6.3)\n",
            "Requirement already satisfied: PyMC>=2.3.3 in /usr/local/lib/python3.7/dist-packages (from hddm==0.8.0) (2.3.8)\n",
            "Requirement already satisfied: pandas>=0.12.0 in /usr/local/lib/python3.7/dist-packages (from hddm==0.8.0) (1.1.5)\n",
            "Requirement already satisfied: patsy in /usr/local/lib/python3.7/dist-packages (from hddm==0.8.0) (0.5.1)\n",
            "Requirement already satisfied: matplotlib>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from kabuki>=0.6.0->hddm==0.8.0) (3.2.2)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=1.0.0->kabuki>=0.6.0->hddm==0.8.0) (1.3.1)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=1.0.0->kabuki>=0.6.0->hddm==0.8.0) (2.8.2)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=1.0.0->kabuki>=0.6.0->hddm==0.8.0) (2.4.7)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=1.0.0->kabuki>=0.6.0->hddm==0.8.0) (0.10.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from cycler>=0.10->matplotlib>=1.0.0->kabuki>=0.6.0->hddm==0.8.0) (1.15.0)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.12.0->hddm==0.8.0) (2018.9)\n",
            "Building wheels for collected packages: hddm\n",
            "  Building wheel for hddm (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for hddm: filename=HDDM-0.8.0-cp37-cp37m-linux_x86_64.whl size=556582 sha256=fc55913fae65c0c05fb10860755b49df7218c7cc455e75c6837d995cde699478\n",
            "  Stored in directory: /root/.cache/pip/wheels/13/13/8b/4fb929469ddb2844cc33d7e3331aaae7668152b496f4fab402\n",
            "Successfully built hddm\n",
            "Installing collected packages: hddm\n",
            "Successfully installed hddm-0.8.0\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S3SV03qe2QE3"
      },
      "source": [
        "import hddm"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iSDqYOdD2XUk"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xBS6Z4Rw3VkW"
      },
      "source": [
        "df = pd.read_csv('behavioral stacked dataframe.csv')"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R9l5N3QK3uUA",
        "outputId": "6983cfbc-4d2d-4211-e019-1499597ff48b"
      },
      "source": [
        "df['response.rt_raw'].values"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.46018523, 0.63030195, 0.44028515, ..., 0.7003628 , 0.61026728,\n",
              "       0.61031032])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3lFhwG1Z3gX5",
        "outputId": "c9412457-47ae-44d9-9aa6-d46cd6c2f9e3"
      },
      "source": [
        "df_for_fit = df[['response.rt_raw','response.keys_raw','sub','visible.keys_raw']]\n",
        "df_for_fit.columns = ['rt','response','subject','visibility']\n",
        "df_for_fit['subj_idx'] = df_for_fit['subject'].apply(lambda x: int(x[-1]))"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8g9ALdcT4Qjo"
      },
      "source": [
        "df_for_fit = df_for_fit.dropna(axis = 0)\n",
        "df_for_fit = df_for_fit[df_for_fit['visibility'] != 99]"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fu4_xeUE4Rrk",
        "outputId": "8645c728-ba3e-475e-aa37-f98527af38a8"
      },
      "source": [
        "res = []\n",
        "for visibility, df_sub in df_for_fit.groupby(['visibility']):\n",
        "    # Instantiate model object passing it our data (no need to call flip_errors() before passing it).\n",
        "    # This will tailor an individual hierarchical DDM around your dataset.\n",
        "    m = hddm.HDDM(df_sub,\n",
        "                is_group_model = True,\n",
        "                bias = True, # include the bias term, so we will have {a,v,t,b} parameters\n",
        "                )\n",
        "    # find a good starting point which helps with the convergence.\n",
        "    m.find_starting_values()\n",
        "    # start drawing 1000 samples and discarding 5000 as burn-in\n",
        "    m.sample(2000, burn=100)\n",
        "    stats = m.gen_stats()\n",
        "    stats['visibility'] = visibility\n",
        "    res.append(stats)\n",
        "res = pd.concat(res)"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/scipy/optimize/optimize.py:1985: RuntimeWarning: invalid value encountered in double_scalars\n",
            "  tmp2 = (x - v) * (fx - fw)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            " [-----------------100%-----------------] 2001 of 2000 complete in 184.2 sec"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/scipy/optimize/optimize.py:1985: RuntimeWarning: invalid value encountered in double_scalars\n",
            "  tmp2 = (x - v) * (fx - fw)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            " [-----------------100%-----------------] 2001 of 2000 complete in 160.2 sec"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/scipy/optimize/optimize.py:1985: RuntimeWarning: invalid value encountered in double_scalars\n",
            "  tmp2 = (x - v) * (fx - fw)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            " [-----------------100%-----------------] 2000 of 2000 complete in 144.5 sec"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "rcsJ4NwHJjv6",
        "outputId": "0da00472-d434-4f42-a8d0-70fc995d03c3"
      },
      "source": [
        "res['attribute'] = list(res.index)\n",
        "res['awareness'] = res['visibility'].map({1:'Unconscious',2:'Glimpse',3:'Conscious'})\n",
        "res.head()"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>2.5q</th>\n",
              "      <th>25q</th>\n",
              "      <th>50q</th>\n",
              "      <th>75q</th>\n",
              "      <th>97.5q</th>\n",
              "      <th>mc err</th>\n",
              "      <th>visibility</th>\n",
              "      <th>attribute</th>\n",
              "      <th>awareness</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>a</th>\n",
              "      <td>4.03368</td>\n",
              "      <td>0.721612</td>\n",
              "      <td>2.64706</td>\n",
              "      <td>3.55426</td>\n",
              "      <td>4.03677</td>\n",
              "      <td>4.5068</td>\n",
              "      <td>5.47371</td>\n",
              "      <td>0.0313272</td>\n",
              "      <td>1</td>\n",
              "      <td>a</td>\n",
              "      <td>Unconscious</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>a_std</th>\n",
              "      <td>1.95129</td>\n",
              "      <td>0.574739</td>\n",
              "      <td>1.0612</td>\n",
              "      <td>1.52646</td>\n",
              "      <td>1.86694</td>\n",
              "      <td>2.28723</td>\n",
              "      <td>3.30492</td>\n",
              "      <td>0.0276709</td>\n",
              "      <td>1</td>\n",
              "      <td>a_std</td>\n",
              "      <td>Unconscious</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>a_subj.1</th>\n",
              "      <td>1.91821</td>\n",
              "      <td>0.142522</td>\n",
              "      <td>1.67912</td>\n",
              "      <td>1.81179</td>\n",
              "      <td>1.90692</td>\n",
              "      <td>2.00744</td>\n",
              "      <td>2.22495</td>\n",
              "      <td>0.0122143</td>\n",
              "      <td>1</td>\n",
              "      <td>a_subj.1</td>\n",
              "      <td>Unconscious</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>a_subj.2</th>\n",
              "      <td>7.31426</td>\n",
              "      <td>0.930973</td>\n",
              "      <td>5.29906</td>\n",
              "      <td>6.76533</td>\n",
              "      <td>7.38991</td>\n",
              "      <td>7.9214</td>\n",
              "      <td>8.9189</td>\n",
              "      <td>0.0910839</td>\n",
              "      <td>1</td>\n",
              "      <td>a_subj.2</td>\n",
              "      <td>Unconscious</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>a_subj.3</th>\n",
              "      <td>4.48463</td>\n",
              "      <td>0.557318</td>\n",
              "      <td>3.56636</td>\n",
              "      <td>4.04747</td>\n",
              "      <td>4.43183</td>\n",
              "      <td>4.86301</td>\n",
              "      <td>5.56904</td>\n",
              "      <td>0.0534446</td>\n",
              "      <td>1</td>\n",
              "      <td>a_subj.3</td>\n",
              "      <td>Unconscious</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             mean       std     2.5q  ... visibility attribute    awareness\n",
              "a         4.03368  0.721612  2.64706  ...          1         a  Unconscious\n",
              "a_std     1.95129  0.574739   1.0612  ...          1     a_std  Unconscious\n",
              "a_subj.1  1.91821  0.142522  1.67912  ...          1  a_subj.1  Unconscious\n",
              "a_subj.2  7.31426  0.930973  5.29906  ...          1  a_subj.2  Unconscious\n",
              "a_subj.3  4.48463  0.557318  3.56636  ...          1  a_subj.3  Unconscious\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nI25UXkr63uY"
      },
      "source": [
        "from matplotlib import pyplot as plt\n",
        "import seaborn as sns\n",
        "sns.set_context('poster')\n",
        "sns.set_style('whitegrid')"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "CDvW-J8xKzGn",
        "outputId": "e6c580fb-00da-4c46-b069-6e0c5f19551b"
      },
      "source": [
        "def subj_map():\n",
        "    temp = {'sub-01':'sub-01',\n",
        "            'sub-03':'sub-02',\n",
        "            'sub-04':'sub-03',\n",
        "            'sub-05':'sub-04',\n",
        "            'sub-02':'sub-05',\n",
        "            'sub-06':'sub-06',\n",
        "            'sub-07':'sub-07',}\n",
        "    return temp\n",
        "def chance_map():\n",
        "    temp = {'sub-01':True,\n",
        "            'sub-02':True,\n",
        "            'sub-03':True,\n",
        "            'sub-04':True,\n",
        "            'sub-05':False,\n",
        "            'sub-06':False,\n",
        "            'sub-07':False,}\n",
        "    return temp\n",
        "def row_map():\n",
        "    return {'v':0,\n",
        "            'a':0,\n",
        "            't':1,\n",
        "            'z':1}\n",
        "def col_map():\n",
        "    return {'v':0,\n",
        "            'a':1,\n",
        "            't':0,\n",
        "            'z':1}\n",
        "res['sub'] = res['attribute'].apply(lambda x: 'sub-0' + x.split('.')[-1])\n",
        "res['subject'] = res['sub'].map(subj_map())\n",
        "res['chance'] = res['subject'].map(chance_map())\n",
        "res['Type'] = res['attribute'].apply(lambda x: x[0])\n",
        "res['row'] = res['Type'].map(row_map())\n",
        "res['col'] = res['Type'].map(col_map())\n",
        "res.head()"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>2.5q</th>\n",
              "      <th>25q</th>\n",
              "      <th>50q</th>\n",
              "      <th>75q</th>\n",
              "      <th>97.5q</th>\n",
              "      <th>mc err</th>\n",
              "      <th>visibility</th>\n",
              "      <th>attribute</th>\n",
              "      <th>awareness</th>\n",
              "      <th>sub</th>\n",
              "      <th>subject</th>\n",
              "      <th>chance</th>\n",
              "      <th>Type</th>\n",
              "      <th>row</th>\n",
              "      <th>col</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>a</th>\n",
              "      <td>4.03368</td>\n",
              "      <td>0.721612</td>\n",
              "      <td>2.64706</td>\n",
              "      <td>3.55426</td>\n",
              "      <td>4.03677</td>\n",
              "      <td>4.5068</td>\n",
              "      <td>5.47371</td>\n",
              "      <td>0.0313272</td>\n",
              "      <td>1</td>\n",
              "      <td>a</td>\n",
              "      <td>Unconscious</td>\n",
              "      <td>sub-0a</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>a</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>a_std</th>\n",
              "      <td>1.95129</td>\n",
              "      <td>0.574739</td>\n",
              "      <td>1.0612</td>\n",
              "      <td>1.52646</td>\n",
              "      <td>1.86694</td>\n",
              "      <td>2.28723</td>\n",
              "      <td>3.30492</td>\n",
              "      <td>0.0276709</td>\n",
              "      <td>1</td>\n",
              "      <td>a_std</td>\n",
              "      <td>Unconscious</td>\n",
              "      <td>sub-0a_std</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>a</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>a_subj.1</th>\n",
              "      <td>1.91821</td>\n",
              "      <td>0.142522</td>\n",
              "      <td>1.67912</td>\n",
              "      <td>1.81179</td>\n",
              "      <td>1.90692</td>\n",
              "      <td>2.00744</td>\n",
              "      <td>2.22495</td>\n",
              "      <td>0.0122143</td>\n",
              "      <td>1</td>\n",
              "      <td>a_subj.1</td>\n",
              "      <td>Unconscious</td>\n",
              "      <td>sub-01</td>\n",
              "      <td>sub-01</td>\n",
              "      <td>True</td>\n",
              "      <td>a</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>a_subj.2</th>\n",
              "      <td>7.31426</td>\n",
              "      <td>0.930973</td>\n",
              "      <td>5.29906</td>\n",
              "      <td>6.76533</td>\n",
              "      <td>7.38991</td>\n",
              "      <td>7.9214</td>\n",
              "      <td>8.9189</td>\n",
              "      <td>0.0910839</td>\n",
              "      <td>1</td>\n",
              "      <td>a_subj.2</td>\n",
              "      <td>Unconscious</td>\n",
              "      <td>sub-02</td>\n",
              "      <td>sub-05</td>\n",
              "      <td>False</td>\n",
              "      <td>a</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>a_subj.3</th>\n",
              "      <td>4.48463</td>\n",
              "      <td>0.557318</td>\n",
              "      <td>3.56636</td>\n",
              "      <td>4.04747</td>\n",
              "      <td>4.43183</td>\n",
              "      <td>4.86301</td>\n",
              "      <td>5.56904</td>\n",
              "      <td>0.0534446</td>\n",
              "      <td>1</td>\n",
              "      <td>a_subj.3</td>\n",
              "      <td>Unconscious</td>\n",
              "      <td>sub-03</td>\n",
              "      <td>sub-02</td>\n",
              "      <td>True</td>\n",
              "      <td>a</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             mean       std     2.5q      25q  ... chance Type row col\n",
              "a         4.03368  0.721612  2.64706  3.55426  ...    NaN    a   0   1\n",
              "a_std     1.95129  0.574739   1.0612  1.52646  ...    NaN    a   0   1\n",
              "a_subj.1  1.91821  0.142522  1.67912  1.81179  ...   True    a   0   1\n",
              "a_subj.2  7.31426  0.930973  5.29906  6.76533  ...  False    a   0   1\n",
              "a_subj.3  4.48463  0.557318  3.56636  4.04747  ...   True    a   0   1\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 558
        },
        "id": "oX9R05ByLVBx",
        "outputId": "c2aaed73-b697-4df5-80f0-449dceaafab3"
      },
      "source": [
        "df_plot = res[res['attribute'].apply(lambda x : '_subj' in x)]\n",
        "\n",
        "g = sns.catplot(x = 'awareness',\n",
        "                y = 'mean',\n",
        "                hue = 'chance',\n",
        "                hue_order = [True,False],\n",
        "                row = 'row',\n",
        "                col = 'col',\n",
        "                data = df_plot,\n",
        "                kind = 'swarm',\n",
        "                sharey = False,\n",
        "                aspect = 1.25,\n",
        "                )\n",
        "g.set_axis_labels('','')\n",
        "for ax,ylabel in zip(g.axes.flatten(),\n",
        "                    ['Drift rate (A.U.)',\n",
        "                        'Decision boundary (A.U.)',\n",
        "                        'Unrespond time (sec)',\n",
        "                        'Bias (A.U.)']):\n",
        "    ax.set(ylabel = ylabel,title = '')"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1034.58x720 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 916
        },
        "id": "tIwNwHlt9clY",
        "outputId": "d652059d-cc13-4f25-9b27-06d4b2af3e09"
      },
      "source": [
        "fig,axes = plt.subplots(figsize = (22,12),\n",
        "                        nrows = 2,\n",
        "                        ncols = 2,\n",
        "                        sharex = True,\n",
        "                        sharey = False,)\n",
        "for ax,parameter_name,ylabel in zip(axes.flatten(),['v','a','t','z'],\n",
        "                                    ['Drift rate (A.U.)',\n",
        "                                     'Decision boundary (A.U.)',\n",
        "                                     'Unrespond time (sec)',\n",
        "                                     'Bias (A.U.)']):\n",
        "    df_sub = res[res['attribute'].apply(lambda x: f'{parameter_name}_subj' in x)]\n",
        "    df_sub['sub'] = \n",
        "    ax = sns.swarmplot(x = 'awareness',y = 'mean',data = df_sub,ax = ax,)\n",
        "    ax.set(xlabel = '',ylabel = ylabel)\n"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:12: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  if sys.path[0] == '':\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:12: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  if sys.path[0] == '':\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:12: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  if sys.path[0] == '':\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:12: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  if sys.path[0] == '':\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1584x864 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "etumX6G9-aGa"
      },
      "source": [
        "stats.to_csv('HDDM RT results.csv',index = False)"
      ],
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ntf5EiLZDfdl"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}