tacsBehaviouralAnalysis.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import shapiro\n",
    "from scipy.stats import levene\n",
    "from scipy import stats\n",
    "import statistics\n",
    "import pingouin as pg\n",
    "from scipy.stats import friedmanchisquare\n",
    "import plotly.graph_objects as go\n",
    "import plotly.express as px\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the path to the logfile directory\n",
    "filepath = \"/\"\n",
    "# Get a list of filenames in the specified directory\n",
    "filepaths = os.listdir(filepath)\n",
    "\n",
    "# Define the range of trials\n",
    "trials = range(0, 200)\n",
    "\n",
    "# Define the desired order of file processing\n",
    "order = [1, 2, 0]\n",
    "filepaths = [filepaths[i] for i in order]\n",
    "\n",
    "# Specify any outliers to be excluded from analysis\n",
    "# Commented out for now\n",
    "# outlier = []\n",
    "outlier = ['']\n",
    "\n",
    "# Initialize empty lists and dictionaries\n",
    "rt_all = []\n",
    "accuracy_all = []\n",
    "hit_length = {k:[] for k in filepaths}\n",
    "acc_dict = {k:[] for k in filepaths}\n",
    "dprime_dict = dict()\n",
    "hit_rate_dict = dict()\n",
    "fa_rate_dict = dict()\n",
    "timeDurationDf = pd.DataFrame()\n",
    "\n",
    "# Iterate through each file in the filepaths list\n",
    "for file in filepaths:\n",
    "    # Get the list of files within the specific file's directory\n",
    "    files = os.listdir(filepath + file + \"/\")\n",
    "\n",
    "    # Initialize a DataFrame for the subject's data\n",
    "    sub_df = pd.DataFrame(index=trials)\n",
    "\n",
    "    # Initialize lists to store specific condition data\n",
    "    rt_cond = []\n",
    "    subject_name = []\n",
    "    accuracy_cond = []\n",
    "    timeDuration = []\n",
    "\n",
    "    # Exclude outliers from the files list\n",
    "    files_except_outliers = [i for i in files if i[:10] not in outlier]\n",
    "\n",
    "    # Initialize DataFrames to store dprime, false alarm rate, and hit rate for each subject\n",
    "    dprime_df = pd.DataFrame(index=['dprime'])\n",
    "    fa_rate_df = pd.DataFrame(index=['falserate'])\n",
    "    hit_rate_df = pd.DataFrame(index=['hitrate'])\n",
    "\n",
    "    # Iterate through each file (excluding outliers) in the current file's directory\n",
    "    for i in files_except_outliers:\n",
    "        # Generate the filename for the current subject\n",
    "        filename = \"sub_\" + i[8:10]\n",
    "\n",
    "        # Read the data from the file into a DataFrame\n",
    "        df = pd.read_csv(filepath + file + '/' + i, skiprows=3, delimiter=\"\\t\")\n",
    "        \n",
    "        # Data cleaning and manipulation steps\n",
    "        df1 = df.drop(df.dropna(subset=['Time']).index).reset_index()\n",
    "        df1.dropna(axis=1, how='all', inplace=True)\n",
    "        df1.columns = df1.iloc[0]\n",
    "        df1.drop(index=0, inplace=True)\n",
    "        df2 = df1.drop(df1.iloc[:, [0, 1]], axis=1)\n",
    "        df2[\"RT\"] = df2[\"RT\"].astype(float) / 10\n",
    "        df2 = df2[df2[\"Code\"].str.len() > 5]\n",
    "        df2[[\"TRIAL TYPE\", \"BLOCK\", \"STIM\", \"BACK\", \"TRIAL NUMBER\", \"STIM NAME\", \"STIM IDX\", \"tCOND\", \"tRESP\"]] = df2[\"Code\"].str.split(';', expand=True)\n",
    "        df2 = df2.drop(df2.iloc[:, [0]], axis=1)\n",
    "        df2[\"BLOCK\"] = df2[\"BLOCK\"].astype(float)\n",
    "        df2 = df2[df2['BLOCK'] == 1]\n",
    "\n",
    "        \n",
    "        cols = [\"BLOCK\", \"TRIAL NUMBER\", \"tCOND\", \"tRESP\"]\n",
    "        for col in cols:\n",
    "            temp = df2[col]\n",
    "            df2.drop(labels=[col], axis=1, inplace=True)\n",
    "            df2.insert(1, col, temp)\n",
    "\n",
    "        time = df2[\"Time\"].tolist()\n",
    "        timeDuration.append(int(time[-1]) / 60 / 10000)\n",
    "\n",
    "        # Reaction time (RT) calculations\n",
    "        df_rt = df2[df2[\"tRESP\"] == \"Hit\"]\n",
    "        df_miss = df2[df2['tRESP'] == \"Miss\"]\n",
    "        df_CR = df2[df2['tRESP'] == \"Correct Rejection\"]\n",
    "        df_fa = df2[df2['tRESP'] == \"False Alarm\"]\n",
    "        rt_cond.append(df_rt[\"RT\"].mean())\n",
    "\n",
    "        if np.isnan(df_rt[\"RT\"].mean()):\n",
    "            print(filename)\n",
    "\n",
    "        subject_name.append(filename)\n",
    "\n",
    "        # Accuracy calculations\n",
    "        accuracy_cond.append((len(df_rt) + len(df_CR)) / 200 * 100)\n",
    "\n",
    "        # Store hit length for the current file\n",
    "        hit_length[file].append(len(df_rt))\n",
    "\n",
    "        # Store accuracy values for the current file\n",
    "        acc_dict[file] = accuracy_cond\n",
    "\n",
    "        # Calculate hit rate and false alarm rate\n",
    "        hit_rate = len(df_rt) / (len(df_rt) + len(df_miss))\n",
    "        fa_rate = round(len(df_fa) / (len(df_fa) + len(df_CR)), 6)\n",
    "\n",
    "        # Adjust extreme hit rate and false alarm rate values\n",
    "        if hit_rate == 1:\n",
    "            hit_rate = 0.99\n",
    "        if hit_rate == 0:\n",
    "            hit_rate = 0.01\n",
    "        if fa_rate == 1:\n",
    "            fa_rate = 0.9967\n",
    "        if fa_rate == 0:\n",
    "            fa_rate = 0.0033\n",
    "\n",
    "        # Calculate d' (d prime) value\n",
    "        dprime_df[filename] = stats.norm.ppf(hit_rate) - stats.norm.ppf(fa_rate)\n",
    "        fa_rate_df[filename] = fa_rate\n",
    "        hit_rate_df[filename] = hit_rate\n",
    "\n",
    "        # Normality tests (Shapiro-Wilk test)\n",
    "        # Commented out for now\n",
    "        # n,p = shapiro(df_rt[\"RT\"])\n",
    "        # if p > 0.05:\n",
    "        #     print(filename, n, p)\n",
    "\n",
    "    # Store hit rate, false alarm rate, and d' (d prime) values for the current file\n",
    "    hit_rate_dict[file] = hit_rate_df\n",
    "    fa_rate_dict[file] = fa_rate_df\n",
    "    rt_all.append(rt_cond)\n",
    "    accuracy_all.append(accuracy_cond)\n",
    "    timeDurationDf[file] = timeDuration\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "creating data frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a DataFrame for reaction time (RT) data\n",
    "dataRt = pd.DataFrame(index=subject_name)\n",
    "for ind, i in enumerate(filepaths):\n",
    "    dataRt[i] = rt_all[ind]\n",
    "dataRt.sort_index(inplace=True)\n",
    "\n",
    "# Create a dictionary to store d' (d prime) values\n",
    "dprime_dictt = dict()\n",
    "for key in fa_rate_dict.keys():\n",
    "    dprime_dictt[key] = pd.DataFrame({'d_prime': (stats.norm.ppf(hit_rate_dict[key]) - stats.norm.ppf(fa_rate_dict[key]))[0]},\n",
    "                                     index=fa_rate_df.T.index)\n",
    "\n",
    "# Total hits across conditions\n",
    "hit_conds = pd.DataFrame()\n",
    "hit_conds['Sham'] = hit_length['Sham']\n",
    "hit_conds['Fixed dose'] = hit_length['Fixed dose']\n",
    "hit_conds['Individualised dose'] = hit_length['Individualised dose']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sham MEAN: 7.920449761904761\n",
      "Sham SD: 0.9901866654484326\n",
      "Sham MIN: 6.834925\n",
      "Sham MAX: 12.611841666666667\n",
      "\n",
      "Fixed dose MEAN: 7.817544000000002\n",
      "Fixed dose SD: 0.8356580457918187\n",
      "Fixed dose MIN: 6.648283333333333\n",
      "Fixed dose MAX: 10.307738333333333\n",
      "\n",
      "Individualised dose MEAN: 7.894285142857143\n",
      "Individualised dose SD: 0.8142409110284136\n",
      "Individualised dose MIN: 6.42042\n",
      "Individualised dose MAX: 10.649965\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Total stimulation duration (minutes)\n",
    "for i, row in timeDurationDf.T.iterrows():\n",
    "    print(i + \" MEAN: \" + str(row.mean()))\n",
    "    print(i + \" SD: \" + str(row.std()))\n",
    "    print(i + \" MIN: \" + str(row.min()))\n",
    "    print(i + \" MAX: \" + str(row.max()))\n",
    "    print(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sham\n",
      "Mean: 38.714\n",
      "Standard Deviation: 7.54\n",
      "Variance: 56.86\n",
      "\n",
      "Fixed dose\n",
      "Mean: 39.543\n",
      "Standard Deviation: 8.4\n",
      "Variance: 70.49\n",
      "\n",
      "Individualised dose\n",
      "Mean: 38.8\n",
      "Standard Deviation: 7.53\n",
      "Variance: 56.64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print mean, standard deviation, and variance of hit counts\n",
    "for column in hit_conds.columns:\n",
    "    print(column)\n",
    "    print('Mean:', round(hit_conds[column].mean(), 3))\n",
    "    print('Standard Deviation:', round(hit_conds[column].std(), 2))\n",
    "    print('Variance:', round(hit_conds[column].var(), 2))\n",
    "    print('')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sham\n",
      "Mean: 90.243\n",
      "SD 5.407\n",
      "Variance: 29.24\n",
      "\n",
      "Fixed dose\n",
      "Mean: 90.757\n",
      "SD 6.445\n",
      "Variance: 41.53\n",
      "\n",
      "Individualised dose\n",
      "Mean: 90.129\n",
      "SD 5.625\n",
      "Variance: 31.64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print mean accuracy across conditions\n",
    "for key in acc_dict:\n",
    "    print(f\"{key}\\nMean: {round(statistics.mean(acc_dict[key]), 3)}\")\n",
    "    print(f\"SD {round(statistics.stdev(acc_dict[key]), 3)}\")\n",
    "    print(f\"Variance: {round(statistics.variance(acc_dict[key]),2)}\")\n",
    "    print(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sham\n",
      "Mean: d_prime    2.569\n",
      "dtype: float64\n",
      "SD: d_prime    0.73\n",
      "dtype: float64\n",
      "Variance: d_prime    0.53\n",
      "dtype: float64\n",
      "\n",
      "Fixed dose\n",
      "Mean: d_prime    2.716\n",
      "dtype: float64\n",
      "SD: d_prime    0.94\n",
      "dtype: float64\n",
      "Variance: d_prime    0.88\n",
      "dtype: float64\n",
      "\n",
      "Individualised dose\n",
      "Mean: d_prime    2.599\n",
      "dtype: float64\n",
      "SD: d_prime    0.82\n",
      "dtype: float64\n",
      "Variance: d_prime    0.67\n",
      "dtype: float64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print mean, standard deviation, and variance of d' (d prime) values\n",
    "for key in dprime_dictt:\n",
    "    print(key)\n",
    "    print(f\"Mean: {round(dprime_dictt[key].mean(),3)}\")\n",
    "    print(f\"SD: {round(dprime_dictt[key].std(),2)}\")\n",
    "    print(f\"Variance: {round(dprime_dictt[key].var(),2)}\")\n",
    "    print(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sham\n",
      "Mean: 716.264\n",
      "SD: 166.59\n",
      "Variance: 27753.3\n",
      "\n",
      "Fixed dose\n",
      "Mean: 698.118\n",
      "SD: 155.58\n",
      "Variance: 24205.43\n",
      "\n",
      "Individualised dose\n",
      "Mean: 718.811\n",
      "SD: 144.89\n",
      "Variance: 20991.92\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print mean, standard deviation, and variance of RT(ms) values\n",
    "for key in dataRt.columns:\n",
    "    print(key)\n",
    "    print(f\"Mean: {round(dataRt[key].mean(),3)}\")\n",
    "    print(f\"SD: {round(dataRt[key].std(),2)}\")\n",
    "    print(f\"Variance: {round(dataRt[key].var(),2)}\")\n",
    "    print(\"\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ploting distribution of hits across conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot histograms of hit counts\n",
    "plt.hist(hit_length[\"Fixed dose\"], bins=5, range=(15, 55), alpha=0.5, label=\"Fixed dose\")\n",
    "plt.hist(hit_length[\"Individualised dose\"], bins=5, range=(15, 55), alpha=0.5, label=\"Individualised dose\")\n",
    "plt.hist(hit_length[\"Sham\"], bins=5, range=(15, 55), alpha=0.5, label=\"Sham\")\n",
    "plt.xlabel(\"HITS\")\n",
    "plt.ylabel(\"No of Subjects\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "normality- condition wise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--- Accuracy ---\n",
      "Shapiro-Wilk normality test:\n",
      "Sham: Test statistic=0.913947343826294, p-value=0.00957991648465395\n",
      "Fixed dose: Test statistic=0.9143509268760681, p-value=0.009834753349423409\n",
      "Individualised dose: Test statistic=0.9510295391082764, p-value=0.12188240140676498\n",
      "\n",
      "--- Reaction Time (RT) ---\n",
      "Shapiro-Wilk normality test:\n",
      "Sham: Test statistic=0.9280670881271362, p-value=0.02451764978468418\n",
      "Fixed dose: Test statistic=0.9664640426635742, p-value=0.3537101745605469\n",
      "Individualised dose: Test statistic=0.9304971694946289, p-value=0.028943926095962524\n",
      "\n",
      "--- d' (d prime) ---\n",
      "Shapiro-Wilk normality test:\n",
      "Sham: Test statistic=0.981633722782135, p-value=0.8109832406044006\n",
      "Fixed dose: Test statistic=0.9593436121940613, p-value=0.21850988268852234\n",
      "Individualised dose: Test statistic=0.9811519384384155, p-value=0.7960168719291687\n"
     ]
    }
   ],
   "source": [
    "# Shapiro-Wilk normality test and Levene's test for accuracy, RT, and d' data\n",
    "for data_type, data_dict in [(\"Accuracy\", acc_dict), (\"Reaction Time (RT)\", dataRt), (\"d' (d prime)\", dprime_dictt)]:\n",
    "    print(f\"\\n--- {data_type} ---\")\n",
    "    \n",
    "    # Shapiro-Wilk normality test\n",
    "    print(\"Shapiro-Wilk normality test:\")\n",
    "    for key, data in data_dict.items():\n",
    "        test, p = shapiro(data)\n",
    "        print(f\"{key}: Test statistic={test}, p-value={p}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Levene's test for accuracy:\n",
      "Test statistic=0.36215996751928603, p-value=0.6970625419914223\n",
      "Levene's test for reaction time (RT):\n",
      "Test statistic=0.31584228513832774, p-value=0.7298849931401192\n",
      "Levene's test for d'prime\n",
      "0.9475318308715849 0.3910823082461661\n"
     ]
    }
   ],
   "source": [
    "# Perform Levene's test for equality of variances on accuracy data\n",
    "print(\"Levene's test for accuracy:\")\n",
    "l_acc, p_acc = levene(*accuracy_all)\n",
    "print(f\"Test statistic={l_acc}, p-value={p_acc}\")\n",
    "\n",
    "# Perform Levene's test for equality of variances on RT data\n",
    "print(\"Levene's test for reaction time (RT):\")\n",
    "l_rt, p_rt = levene(*rt_all)\n",
    "print(f\"Test statistic={l_rt}, p-value={p_rt}\")\n",
    "# Perform Levene's test for equality of variances on d' (d prime) data\n",
    "print(\"Levene's test for d'prime\")\n",
    "l, pp = levene(dprime_dictt[\"Fixed dose\"][\"d_prime\"].to_numpy(), dprime_dictt[\"Individualised dose\"][\"d_prime\"].to_numpy(),\n",
    "               dprime_dictt[\"Sham\"][\"d_prime\"].to_numpy())\n",
    "print(l, pp)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "one way anova   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RT - Friedman test (ANOVA):\n",
      "          Source         W  ddof1    Q     p-unc\n",
      "Friedman  Within  0.005714      2  0.4  0.818731\n",
      "Accuracy - Friedman test (ANOVA):\n",
      "          Source         W  ddof1         Q     p-unc\n",
      "Friedman  Within  0.013251      2  0.927536  0.628909\n",
      "d' (d prime) - Friedman test (ANOVA):\n",
      "          Source         W  ddof1    Q     p-unc\n",
      "Friedman  Within  0.017143      2  1.2  0.548812\n",
      " \n"
     ]
    }
   ],
   "source": [
    "# Friedman test (ANOVA) on RT data\n",
    "friedman_rt = pg.friedman(dataRt, method='chisq')\n",
    "print(\"RT - Friedman test (ANOVA):\")\n",
    "print(friedman_rt)\n",
    "\n",
    "# Friedman test (ANOVA) on accuracy data\n",
    "acc_df = pd.DataFrame({\n",
    "    \"Sham\": acc_dict[\"Sham\"],\n",
    "    \"Fixed dose\": acc_dict[\"Fixed dose\"],\n",
    "    \"Individualised dose\": acc_dict[\"Individualised dose\"]\n",
    "})\n",
    "friedman_acc = pg.friedman(acc_df, method='chisq')\n",
    "print(\"Accuracy - Friedman test (ANOVA):\")\n",
    "print(friedman_acc)\n",
    "\n",
    "# Friedman test (ANOVA) on d' (d prime) data\n",
    "dprime_df_cond = pd.DataFrame({\n",
    "    \"Sham\": dprime_dictt['Sham']['d_prime'],\n",
    "    \"Fixed dose\": dprime_dictt['Fixed dose']['d_prime'],\n",
    "    \"Individualised dose\": dprime_dictt['Individualised dose']['d_prime']\n",
    "})\n",
    "friedman_dprime = pg.friedman(dprime_df_cond, method=\"chisq\")\n",
    "print(\"d' (d prime) - Friedman test (ANOVA):\")\n",
    "print(friedman_dprime)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Violin plots"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "defining functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define function to calculate lower and upper adjacent values based on quartiles\n",
    "def adjacent_values(vals, q1, q3):\n",
    "    upper_adjacent_value = q3 + (q3 - q1) * 1.5\n",
    "    upper_adjacent_value = np.clip(upper_adjacent_value, q3, vals[-1])\n",
    "\n",
    "    lower_adjacent_value = q1 - (q3 - q1) * 1.5\n",
    "    lower_adjacent_value = np.clip(lower_adjacent_value, vals[0], q1)\n",
    "    return lower_adjacent_value, upper_adjacent_value\n",
    "\n",
    "# Define function to set axis style\n",
    "def set_axis_style(ax, labels):\n",
    "    ax.get_xaxis().set_tick_params(direction='out')\n",
    "    ax.xaxis.set_ticks_position('bottom')\n",
    "    ax.set_xticks(np.arange(1, len(labels) + 1))\n",
    "    ax.set_xticklabels(labels, fontsize=18, fontname=\"sans\")\n",
    "    ax.set_xlim(0.25, len(labels) + 0.75)\n",
    "    ax.set_xlabel('Sample name')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "rt across cond"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare the data to plot\n",
    "data_to_plot = [dataRt['Sham'], dataRt['Fixed dose'], dataRt['Individualised dose']]\n",
    "\n",
    "# Create the figure and axes\n",
    "fig = plt.figure(figsize=(12, 7))\n",
    "ax = fig.add_axes([0.09, 0.1, 0.85, 0.84])\n",
    "\n",
    "# Configure axis and plot style\n",
    "ax.tick_params(axis='y', which='major', labelsize=12)\n",
    "ax.spines.right.set_visible(False)\n",
    "ax.spines.top.set_visible(False)\n",
    "ax.spines.left.set_visible(False)\n",
    "ax.spines.bottom.set_visible(False)\n",
    "ax.grid(True, axis=\"y\", color='w', linestyle='-', linewidth=1.2, which=\"major\", zorder=-1.0)\n",
    "ax.patch.set_facecolor('#f0f9ff')\n",
    "plt.rc(\"axes\", axisbelow=True)\n",
    "\n",
    "# Create the violin plot\n",
    "bp = ax.violinplot(data_to_plot, showmeans=False, showmedians=False, showextrema=True)\n",
    "label = ['Sham', 'Fixed Dose', 'Individualised dose']\n",
    "\n",
    "# Set axis style\n",
    "for ax in [ax]:\n",
    "    set_axis_style(ax, label)\n",
    "\n",
    "# Set plot labels and title\n",
    "ax.set_xlabel('Stimulation condition', fontsize=22, fontname=\"sans-serif\")\n",
    "ax.set_ylabel('RT (ms)', fontsize=18, fontname=\"sans-serif\")\n",
    "ax.set_title('Reaction time', fontsize=26, fontname=\"sans-serif\")\n",
    "\n",
    "# Customize violin plot appearance\n",
    "for pc in bp['bodies']:\n",
    "    pc.set_facecolor('#cfe5f3')\n",
    "    pc.set_edgecolor('#1d78b6')\n",
    "    pc.set_alpha(0.7)\n",
    "\n",
    "# Calculate quartiles, whiskers, and medians\n",
    "quartile1, medians, quartile3 = np.percentile(data_to_plot, [25, 50, 75], axis=1)\n",
    "whiskers = np.array([\n",
    "    adjacent_values(sorted_array, q1, q3)\n",
    "    for sorted_array, q1, q3 in zip(data_to_plot, quartile1, quartile3)])\n",
    "whiskers_min, whiskers_max = whiskers[:, 0], whiskers[:, 0]\n",
    "\n",
    "# Plot medians, quartiles, and whiskers\n",
    "inds = np.arange(1, len(medians) + 1)\n",
    "ax.scatter(inds, medians, marker='o', color='black', s=30, zorder=3)\n",
    "ax.vlines(inds, quartile1, quartile3, color='black', linestyle='-', lw=5)\n",
    "ax.vlines(inds, whiskers_min, whiskers_max, color='black', linestyle='-', lw=4)\n",
    "\n",
    "# Uncomment the following line if you want to save the plot as an image file\n",
    "# plt.savefig(\".jpg\", format=\"jpg\", dpi=500)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_to_plot = [acc_dict['Sham'], acc_dict['Fixed dose'], acc_dict['Individualised dose']]\n",
    "fig = plt.figure(figsize=(12, 7))\n",
    "\n",
    "# Create an axes instance\n",
    "ax = fig.add_axes([0.09, 0.1, 0.85, 0.84])\n",
    "ax.tick_params(axis='y', which='major', labelsize=12)\n",
    "ax.spines.right.set_visible(False)\n",
    "ax.spines.top.set_visible(False)\n",
    "ax.spines.left.set_visible(False)\n",
    "ax.spines.bottom.set_visible(False)\n",
    "ax.grid(True, axis=\"y\", color='w', linestyle='-', linewidth=1.2, which=\"major\", zorder=-1.0)\n",
    "ax.patch.set_facecolor('#f0f9ff')\n",
    "plt.rc(\"axes\", axisbelow=True)\n",
    "\n",
    "# Create the violin plot\n",
    "bp = ax.violinplot(data_to_plot, showmeans=False, showmedians=False, showextrema=True)\n",
    "labels = ['Sham', 'Fixed Dose', 'Individualised dose']\n",
    "\n",
    "# Set axis style\n",
    "for ax in [ax]:\n",
    "    set_axis_style(ax, labels)\n",
    "\n",
    "# Set plot labels and title\n",
    "ax.set_xlabel('Stimulation condition', fontsize=22, fontname=\"sans-serif\")\n",
    "ax.set_ylabel('Accuracy (%)', fontsize=18, fontname=\"sans-serif\")\n",
    "ax.set_title('Accuracy', fontsize=26, fontname=\"sans-serif\")\n",
    "\n",
    "# Customize violin plot appearance\n",
    "for pc in bp['bodies']:\n",
    "    pc.set_facecolor('#cfe5f3')\n",
    "    pc.set_edgecolor('#1d78b6')\n",
    "    pc.set_alpha(1)\n",
    "\n",
    "# Calculate quartiles, whiskers, and medians\n",
    "quartile1, medians, quartile3 = np.percentile(data_to_plot, [25, 50, 75], axis=1)\n",
    "whiskers = np.array([\n",
    "    adjacent_values(sorted_array, q1, q3)\n",
    "    for sorted_array, q1, q3 in zip(data_to_plot, quartile1, quartile3)])\n",
    "whiskers_min, whiskers_max = whiskers[:, 0], whiskers[:, 1]\n",
    "\n",
    "# Plot medians, quartiles, and whiskers\n",
    "inds = np.arange(1, len(medians) + 1)\n",
    "ax.scatter(inds, medians, marker='o', color='white', s=30, zorder=3)\n",
    "ax.vlines(inds, quartile1, quartile3, color='k', linestyle='-', lw=5)\n",
    "ax.vlines(inds, whiskers_min, whiskers_max, color='k', linestyle='-', lw=1)\n",
    "\n",
    "# Uncomment the following line if you want to save the plot as an image file\n",
    "# plt.savefig(\".jpg\", format=\"jpg\", dpi=500)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "dprime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fix_dprime = dprime_dictt['Sham']['d_prime'].to_numpy()\n",
    "sham_dprime = dprime_dictt['Fixed dose']['d_prime'].to_numpy()\n",
    "ind_dprime = dprime_dictt['Individualised dose']['d_prime'].to_numpy()\n",
    "\n",
    "data_to_plot = [sham_dprime, fix_dprime, ind_dprime]\n",
    "fig = plt.figure(figsize=(12, 7))\n",
    "\n",
    "# Create an axes instance\n",
    "ax = fig.add_axes([0.09, 0.1, 0.85, 0.84])\n",
    "ax.tick_params(axis='y', which='major', labelsize=12)\n",
    "ax.spines.right.set_visible(False)\n",
    "ax.spines.top.set_visible(False)\n",
    "ax.spines.left.set_visible(False)\n",
    "ax.spines.bottom.set_visible(False)\n",
    "ax.grid(True, axis=\"y\", color='w', linestyle='-', linewidth=1.2, which=\"major\", zorder=-1.0)\n",
    "ax.patch.set_facecolor('#f0f9ff')\n",
    "plt.rc(\"axes\", axisbelow=True)\n",
    "\n",
    "# Create the violin plot\n",
    "bp = ax.violinplot(data_to_plot, showmeans=False, showmedians=False, showextrema=True)\n",
    "labels = ['Sham', 'Fixed Dose', 'Individualised dose']\n",
    "\n",
    "# Set axis style\n",
    "for ax in [ax]:\n",
    "    set_axis_style(ax, labels)\n",
    "\n",
    "ax.set_xlabel('Stimulation condition', fontsize=22, fontname=\"sans-serif\")\n",
    "ax.set_ylabel(\"d'\", fontsize=18, fontname=\"sans-serif\")\n",
    "ax.set_title(\"d'\", fontsize=26, fontname=\"sans-serif\")\n",
    "\n",
    "# Customize violin plot appearance\n",
    "for pc in bp['bodies']:\n",
    "    pc.set_facecolor('#cfe5f3')\n",
    "    pc.set_edgecolor('#1d78b6')\n",
    "    pc.set_alpha(1)\n",
    "\n",
    "# Calculate quartiles, whiskers, and medians\n",
    "quartile1, medians, quartile3 = np.percentile(data_to_plot, [25, 50, 75], axis=1)\n",
    "whiskers = np.array([\n",
    "    adjacent_values(sorted_array, q1, q3)\n",
    "    for sorted_array, q1, q3 in zip(data_to_plot, quartile1, quartile3)])\n",
    "whiskers_min, whiskers_max = whiskers[:, 0], whiskers[:, 1]\n",
    "\n",
    "inds = np.arange(1, len(medians) + 1)\n",
    "ax.scatter(inds, medians, marker='o', color='white', s=30, zorder=3)\n",
    "ax.vlines(inds, quartile1, quartile3, color='k', linestyle='-', lw=5)\n",
    "ax.vlines(inds, whiskers_min, whiskers_max, color='k', linestyle='-', lw=1)\n",
    "\n",
    "# Uncomment the following line if you want to save the plot as an image file\n",
    "# plt.savefig(\"/dPrime_sham.jpg\", format=\"jpg\", dpi=500)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "acc_fix = acc_dict[\"Fixed dose\"]\n",
    "acc_ind = acc_dict[\"Individualised dose\"]\n",
    "\n",
    "rt_fix = dataRt[\"Fixed dose\"].to_list()\n",
    "rt_ind = dataRt[\"Individualised dose\"].to_list()\n",
    "\n",
    "cond1 = [\"Fixed\"]*(len(acc_fix) + len(rt_fix))\n",
    "cond2 = [\"Individualised\"]*(len(acc_ind) + len(rt_ind))\n",
    "\n",
    "\n",
    "\n",
    "roi1 = [\"Accuracy\"]*len(acc_fix)\n",
    "roi2 = [\"RT\"]*len(rt_fix)\n",
    "roi3 = [\"Accuracy\"]*len(acc_ind)\n",
    "roi4 = [\"RT\"]*len(rt_ind)\n",
    "\n",
    "df_plot = pd.DataFrame({\"data\": acc_fix + rt_fix + acc_ind +  rt_ind, \"cond\" : cond1 + cond2, \"ROI\": roi1 + roi2 + roi3 + roi4})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
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        "yaxis": {
         "title": {
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   "source": [
    "show_legend = [True,True,False,False]\n",
    "\n",
    "for ii in range(2):\n",
    "    fig = go.Figure()\n",
    "    i = ii # For accuracy::if ii = 0 ; RT:: ii = 1 for accuracy\n",
    "    fig.add_trace(go.Violin(x=df_plot['ROI'][(df_plot['cond'] == 'Fixed') &\n",
    "                                    (df_plot['ROI'] == pd.unique(df_plot['ROI'])[i])],\n",
    "                        y=df_plot['data'][(df_plot['cond'] == 'Fixed')&\n",
    "                                            (df_plot['ROI'] == pd.unique(df_plot['ROI'])[i])],\n",
    "                        legendgroup='Fixed', scalegroup='Fixed', name='Fixed',\n",
    "                        side='negative',\n",
    "                        pointpos=-1.07, # where to position points\n",
    "                        line_color='lightseagreen',\n",
    "                        showlegend=show_legend[i],\n",
    "                        xaxis = \"x2\"))\n",
    "            \n",
    "\n",
    "    fig.add_trace(go.Violin(x=df_plot['ROI'][(df_plot['cond'] == 'Individualised') &\n",
    "                                    (df_plot['ROI'] == pd.unique(df_plot['ROI'])[i])],\n",
    "                        y=df_plot['data'][(df_plot['cond'] == 'Individualised')&\n",
    "                                            (df_plot['ROI'] == pd.unique(df_plot['ROI'])[i])],\n",
    "                        legendgroup='Individualised', scalegroup='Individualised', name='Individualised',\n",
    "                        side='positive',\n",
    "                        pointpos=1.1, # where to position points\n",
    "                        line_color='mediumpurple',\n",
    "                        showlegend=show_legend[i],\n",
    "                        xaxis=\"x2\"\n",
    "                        ))\n",
    "                        \n",
    "\n",
    "\n",
    "    # update characteristics shared by all traces\n",
    "    fig.update_traces(meanline_visible=True,\n",
    "                    points='all', # show all points\n",
    "                    jitter=0.05,  # add some jitter on points for better visibility\n",
    "                    scalemode='count') #scale violin plot area with total count\n",
    "    if ii == 1:\n",
    "        fig.update_layout(\n",
    "            title_text=\"Behavioural Performance\",\n",
    "            # xaxis_title = \"ROI\",\n",
    "            yaxis_title = 'RT (ms)', #\"Rt (ms)\" if i =0 ## change for Accuracy and RT\n",
    "            violingap=0.3, violingroupgap=0, violinmode='overlay',\n",
    "            autosize = False,\n",
    "            width = 700, \n",
    "            height = 500,\n",
    "            font_size = 20,\n",
    "            plot_bgcolor = '#f0f9ff')\n",
    "    else:\n",
    "        fig.update_layout(\n",
    "            title_text=\"Behavioural Performance\",\n",
    "            # xaxis_title = \"ROI\",\n",
    "            yaxis_title = 'Accuracy(%)',\n",
    "            violingap=0.3, violingroupgap=0, violinmode='overlay',\n",
    "            autosize = False,\n",
    "            width = 700, \n",
    "            height = 500,\n",
    "            font_size = 20,\n",
    "            plot_bgcolor = '#f0f9ff')\n",
    "    # fig.write_image(\"/RT.jpg\", scale = 12)\n",
    "    fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dPrime \n",
    "data_fix = pd.DataFrame()\n",
    "data_ind = pd.DataFrame()\n",
    "data_fix[\"dprime\"] = dprime_dictt['Fixed dose']\n",
    "data_fix[\"cond\"] = [\"Fixed\"]*len(data_fix) \n",
    "data_ind[\"dprime\"] = dprime_dictt['Individualised dose']\n",
    "data_ind[\"cond\"] = [\"Individualised\"]*len(data_ind)\n",
    "\n",
    "df_dprime_violin = pd.concat([data_fix, data_ind])\n",
    "df_dprime_violin[\"label\"] = [\"_\"]*len(df_dprime_violin)\n"
   ]
  },
  {
   "cell_type": "code",
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    "show_legend = [True,True,False,False]\n",
    "\n",
    "fig = go.Figure()\n",
    "\n",
    "# for i in range(0,len(pd.unique(df_plot['ROI']))):\n",
    "i = 0\n",
    "fig.add_trace(go.Violin(x= df_dprime_violin['label'][(df_dprime_violin['cond'] == 'Fixed') &\n",
    "                                (df_dprime_violin['label'] == pd.unique(df_dprime_violin['label'])[i])],\n",
    "                    y=df_dprime_violin['dprime'][(df_dprime_violin['cond'] == 'Fixed')&\n",
    "                                        (df_dprime_violin['label'] == pd.unique(df_dprime_violin['label'])[i])],\n",
    "                                        \n",
    "                                        legendgroup='Fixed', scalegroup='Fixed', name='Fixed',\n",
    "                    side='negative',\n",
    "                    pointpos=-1.07, # where to position points\n",
    "                    line_color='lightseagreen',\n",
    "                    showlegend=show_legend[i],\n",
    "                    ))\n",
    "        \n",
    "\n",
    "fig.add_trace(go.Violin(x=df_dprime_violin['label'][(df_dprime_violin['cond'] == 'Individualised') &\n",
    "                                (df_dprime_violin['label'] == pd.unique(df_dprime_violin['label'])[i])],\n",
    "                    y=df_dprime_violin['dprime'][(df_dprime_violin['cond'] == 'Individualised')&\n",
    "                                        (df_dprime_violin['label'] == pd.unique(df_dprime_violin['label'])[i])],\n",
    "                    legendgroup='Individualised', scalegroup='Individualised', name='Individualised',\n",
    "                    side='positive',\n",
    "                    pointpos=1.1, # where to position points\n",
    "                    line_color='mediumpurple',\n",
    "                    showlegend=show_legend[i],\n",
    "                    \n",
    "                    ))                    \n",
    "\n",
    "\n",
    "# update characteristics shared by all traces\n",
    "fig.update_traces(meanline_visible=True,\n",
    "                  points='all', # show all points\n",
    "                  jitter=0.05,  # add some jitter on points for better visibility\n",
    "                  scalemode='count') #scale violin plot area with total count\n",
    "fig.update_layout(\n",
    "    title_text=\"Behavioural Performance\",\n",
    "    # xaxis_title = \"ROI\",\n",
    "    yaxis_title = \"d'\",\n",
    "    violingap=0.3, violingroupgap=0, violinmode='overlay',\n",
    "    autosize = False,\n",
    "    width = 700, \n",
    "    height = 500,\n",
    "    font_size = 20,\n",
    "    plot_bgcolor = '#f0f9ff' )\n",
    "# fig.write_image(\"/dPrime.jpg\", scale = 12)\n",
    "fig.show()"
   ]
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