{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Defining Output variable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os \n",
    "import zipfile\n",
    "import mne\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import plot_roc_curve\n",
    "from sklearn.model_selection import cross_val_score\n",
    "import warnings\n",
    "import imblearn \n",
    "from imblearn.over_sampling import RandomOverSampler "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ignore warning \n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Meta Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "ndar = pd.read_csv(\"E:/B_SNIP_DATA/ODDBALLP_Meta/ndar_subject01.txt\", delimiter = \"\\t\" )\n",
    "\n",
    "# make first row the columns names \n",
    "ndar.columns = ndar.iloc[0]\n",
    "\n",
    "# remove first row \n",
    "ndar = ndar.drop(ndar.index[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of subjects (proband, control, relatives) = 4403\n"
     ]
    }
   ],
   "source": [
    "print('number of subjects (proband, control, relatives) = ' + str(len(ndar)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "percent_missing = ndar.isnull().sum() * 100 / len(ndar)\n",
    "missing_value_df = pd.DataFrame({'column_name': ndar.columns,\n",
    "                                 'percent_missing': percent_missing})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collection_id\n",
      "0.0\n",
      "ndar_subject01_id\n",
      "0.0\n",
      "dataset_id\n",
      "0.0\n",
      "The NDAR Global Unique Identifier (GUID) for research subject\n",
      "0.0\n",
      "Subject ID how it's defined in lab/project\n",
      "0.0\n",
      "Date on which the interview/genetic test/sampling/imaging/biospecimen was completed. MM/DD/YYYY\n",
      "0.0\n",
      "Age in months at the time of the interview/test/sampling/imaging.\n",
      "0.295253236429707\n",
      "Sex of subject at birth\n",
      "0.20440608675902794\n",
      "Race of study subject\n",
      "0.0\n",
      "Ethnic group\n",
      "100.0\n",
      "Phenotype/diagnosis for the subject\n",
      "0.0\n",
      "Description of the phenotype for the subject\n",
      "0.0\n",
      "Is this study of twins?\n",
      "0.0\n",
      "Was it sibling study? Study of sibling(s) of autistic child.\n",
      "0.0\n",
      "Was it family study? Study of biological mother, biological father and/or sibling of proband.\n",
      "0.0\n",
      "Family Pedigree User-Defined ID\n",
      "0.0\n",
      "The NDAR Global Unique Identifier (GUID) for subject's biological mother\n",
      "100.0\n",
      "site specific mother ID\n",
      "74.58550987962752\n",
      "The NDAR Global Unique Identifier (GUID) for subject's biological father\n",
      "100.0\n",
      "site specific father ID\n",
      "91.61935044287985\n",
      "The NDAR Global Unique Identifier (GUID) for subject's sibling\n",
      "100.0\n",
      "site specific sibling1 ID\n",
      "69.77061094708154\n",
      "Type of Sibling\n",
      "69.77061094708154\n",
      "The NDAR Global Unique Identifier (GUID) for subject's sibling\n",
      "100.0\n",
      "site specific sibling2 ID\n",
      "92.52782193958664\n",
      "sibling type\n",
      "92.52782193958664\n",
      "The NDAR Global Unique Identifier (GUID) for subject's sibling\n",
      "100.0\n",
      "site specific sibling3 ID\n",
      "97.86509198273905\n",
      "sibling type\n",
      "97.86509198273905\n",
      "The NDAR Global Unique Identifier (GUID) for subject's sibling\n",
      "100.0\n",
      "site specific sibling4 ID\n",
      "99.7728821258233\n",
      "sibling type\n",
      "99.7728821258233\n",
      "Zygosity\n",
      "100.0\n",
      "Was a sample taken at this interview/during this project time\n",
      "0.0\n",
      "Original, user-defined Sample ID\n",
      "100.0\n",
      "Sample description: tissue type, i.e. blood, saliva, brain etc.\n",
      "100.0\n",
      "Biorepository where sample is stored\n",
      "100.0\n",
      "Biorepository Sample ID\n",
      "100.0\n",
      "Biorepository Patient ID\n",
      "100.0\n",
      "Original, user-defined cell line ID\n",
      "100.0\n",
      "Biorepository cell line ID\n",
      "100.0\n",
      "AGRE subject ID\n",
      "100.0\n",
      "SFARI subject ID\n",
      "100.0\n",
      "CPEA/STAART site name\n",
      "100.0\n",
      "CPEA/STAART subject ID\n",
      "100.0\n",
      "blood ID\n",
      "100.0\n",
      "ADI: Diagnosis\n",
      "100.0\n",
      "ADOS Diagnosis\n",
      "100.0\n",
      "AGP family ID\n",
      "100.0\n",
      "AGP subject ID\n",
      "100.0\n",
      "Site\n",
      "100.0\n",
      "Miscellaneous comments on study, interview, methodology relevant to this form data\n",
      "100.0\n",
      "Week in level/study\n",
      "100.0\n",
      "Study; The code for each individual study\n",
      "100.0\n",
      "Education\n",
      "100.0\n",
      "Mother Education\n",
      "100.0\n",
      "What handedness do you consider yourself?\n",
      "100.0\n",
      "Does the subject speak English as a primary language?\n",
      "100.0\n",
      "Subject's Primary Language\n",
      "100.0\n",
      "Description of monitored physiological responses\n",
      "100.0\n",
      "Father Education\n",
      "100.0\n",
      "Current employment status\n",
      "100.0\n",
      "Medication information\n",
      "100.0\n",
      "log of family income from wave one of the study\n",
      "100.0\n",
      "sampling weights used to account for sample design and non response rate\n",
      "100.0\n",
      "stratum\n",
      "100.0\n",
      "Current Grade\n",
      "100.0\n",
      "Special Education\n",
      "100.0\n",
      "Specify special education services\n",
      "100.0\n",
      "Brain Injury?\n",
      "100.0\n",
      "Treatment Group Assignment\n",
      "100.0\n",
      "Form used/assessment name\n",
      "100.0\n",
      "Visit name\n",
      "100.0\n",
      "Reason for not conducting interview\n",
      "100.0\n",
      "Reason Other - Please specify\n",
      "100.0\n",
      "Living Situation\n",
      "100.0\n",
      "Height - Metric Unit\n",
      "100.0\n",
      "Weight - Metric Unit\n",
      "100.0\n",
      "body mass index of subject\n",
      "100.0\n",
      "Adverse Events: assessment completed?\n",
      "100.0\n",
      "Changes made to meds\n",
      "100.0\n",
      "Ongoing capacity to consent\n",
      "100.0\n",
      "Consented to blood collection\n",
      "100.0\n",
      "Taken for blood collection\n",
      "100.0\n",
      "Personal details. Country of origin\n",
      "100.0\n",
      "Country of Origin\n",
      "100.0\n",
      "Are you currently employed or in school?\n",
      "100.0\n",
      "Year in study\n",
      "100.0\n",
      "DSM-IV-tr Chronic motor tic disorder\n",
      "100.0\n",
      "DSM-IV-tr Chronic vocal tic disorder\n",
      "100.0\n",
      "Chronic tic disorder- combined type (one motor and one vocal tic)\n",
      "100.0\n",
      "Provisional Tic Disorder (DSM-V)\n",
      "100.0\n",
      "DSM-IV-tr Transient Tic Disorder\n",
      "100.0\n",
      "DSM-IV-tr Tic Disorder NOS\n",
      "100.0\n",
      "Transient Tic Disorder - Age at onset of first episode (months)\n",
      "100.0\n",
      "Current tic symptoms (past week)\n",
      "100.0\n",
      "Tic disorder override- clinician override of algorithm\n",
      "100.0\n",
      "DSM-IV-tr OC disorders\n",
      "100.0\n",
      "OC disorder descriptions\n",
      "100.0\n",
      "Onset of OCD\n",
      "100.0\n",
      "Current OC symptoms (past week)\n",
      "100.0\n",
      "OC disorder override- clinician override of algorithm\n",
      "100.0\n",
      "DSM-IV-tr Trichotillomania\n",
      "100.0\n",
      "Trichotillomania (hairpulling) Onset Age\n",
      "100.0\n",
      "Current Trich symptoms (past week)\n",
      "100.0\n",
      "ADHD: Combined type present in past 6 months\n",
      "100.0\n",
      "DSM-IV-tr ADHD predominantly inattentive subtype\n",
      "100.0\n",
      "DSM-IV-tr ADHD predominantly hyperactive-Impulusive subtype\n",
      "100.0\n",
      "Grant number (1st or 2nd)\n",
      "100.0\n",
      "Subclinical ADHD\n",
      "100.0\n",
      "ADHD Age of Onset, months\n",
      "100.0\n",
      "Current ADHD symptoms (past week)\n",
      "100.0\n",
      "ADHD disorder override- clinician override of algorithm\n",
      "100.0\n",
      "Flagged for Atypical presentation\n",
      "100.0\n",
      "Flagged for Psychosis\n",
      "100.0\n",
      "Flagged for PDD or Autism Spectrum\n",
      "100.0\n",
      "Flagged for Other Neurological condition\n",
      "100.0\n",
      "Flagged for Congenital anomalies\n",
      "100.0\n",
      "Flagged for Genetic Syndrome/ Chromosomal abnormality\n",
      "100.0\n",
      "Data submitted to coordinating center\n",
      "100.0\n",
      "Flagged for Other significant PSYCHIATRIC history\n",
      "100.0\n",
      "Flagged for  Other significant MEDICAL history\n",
      "100.0\n",
      "Flagged for Other\n",
      "100.0\n",
      "If participated in another genetic study\n",
      "100.0\n",
      "Name of other genetic study\n",
      "100.0\n",
      "Subsite ID\n",
      "100.0\n",
      "Subject evaluated (Y/N)\n",
      "100.0\n",
      "Evaluation date\n",
      "100.0\n",
      "Code for multiple birth\n",
      "100.0\n",
      "DSM-IV-tr Tourette's disorder\n",
      "100.0\n",
      "Training or randomized case\n",
      "100.0\n",
      "Race of Respondent Specify\n",
      "100.0\n",
      "Respondent\n",
      "100.0\n",
      "Date when subject was diagnosed\n",
      "100.0\n",
      "Number of years at position\n",
      "100.0\n",
      "Title of Employment Position\n",
      "100.0\n",
      "Timepoint/visit label\n",
      "100.0\n",
      "Treatment Group Assignment - Description\n",
      "100.0\n",
      "Long form of Subject ID\n",
      "100.0\n",
      "site specific sibling6 ID\n",
      "100.0\n",
      "sibling type\n",
      "100.0\n",
      "The NDAR Global Unique Identifier (GUID) for subject's sibling\n",
      "100.0\n",
      "site specific sibling7 ID\n",
      "100.0\n",
      "sibling type\n",
      "100.0\n",
      "The NDAR Global Unique Identifier (GUID) for subject's sibling\n",
      "100.0\n",
      "site specific sibling8 ID\n",
      "100.0\n",
      "sibling type\n",
      "100.0\n",
      "The NDAR Global Unique Identifier (GUID) for subject's sibling\n",
      "100.0\n",
      "site specific sibling9 ID\n",
      "100.0\n",
      "sibling type\n",
      "100.0\n",
      "The NDAR Global Unique Identifier (GUID) for subject's sibling\n",
      "100.0\n",
      "site specific sibling10 ID\n",
      "100.0\n",
      "sibling type\n",
      "100.0\n",
      "The NDAR Global Unique Identifier (GUID) for subject's sibling\n",
      "100.0\n",
      "site specific sibling5 ID\n",
      "100.0\n",
      "sibling type\n",
      "100.0\n",
      "The NDAR Global Unique Identifier (GUID) for subject's sibling\n",
      "100.0\n",
      "The NDAR Global Unique Identifier (GUID) for non-biological mother or female caregiver\n",
      "100.0\n",
      "The NDAR Global Unique Identifier (GUID) for non-biological father or male caregiver\n",
      "100.0\n",
      "Subject ID for non-biological mother or female caregiver how it is defined in lab/project\n",
      "100.0\n",
      "Subject ID for non-biological father or male caregiver how it is defined in lab/project\n",
      "100.0\n",
      "collection_title\n",
      "0.0\n"
     ]
    }
   ],
   "source": [
    "#get column name and missing percent \n",
    "for i in range(len(missing_value_df)):\n",
    "    print(missing_value_df['column_name'][i])\n",
    "    print(missing_value_df['percent_missing'][i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop columns with 90% missing \n",
    "ndar = ndar.dropna(axis='columns', thresh = 0.9*len(ndar))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>collection_id</th>\n",
       "      <th>ndar_subject01_id</th>\n",
       "      <th>dataset_id</th>\n",
       "      <th>The NDAR Global Unique Identifier (GUID) for research subject</th>\n",
       "      <th>Subject ID how it's defined in lab/project</th>\n",
       "      <th>Date on which the interview/genetic test/sampling/imaging/biospecimen was completed. MM/DD/YYYY</th>\n",
       "      <th>Age in months at the time of the interview/test/sampling/imaging.</th>\n",
       "      <th>Sex of subject at birth</th>\n",
       "      <th>Race of study subject</th>\n",
       "      <th>Phenotype/diagnosis for the subject</th>\n",
       "      <th>Description of the phenotype for the subject</th>\n",
       "      <th>Is this study of twins?</th>\n",
       "      <th>Was it sibling study? Study of sibling(s) of autistic child.</th>\n",
       "      <th>Was it family study? Study of biological mother, biological father and/or sibling of proband.</th>\n",
       "      <th>Family Pedigree User-Defined ID</th>\n",
       "      <th>Was a sample taken at this interview/during this project time</th>\n",
       "      <th>collection_title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2274</td>\n",
       "      <td>323862</td>\n",
       "      <td>11294</td>\n",
       "      <td>NDAR_INVRCC8D3DU</td>\n",
       "      <td>615</td>\n",
       "      <td>09/16/2008</td>\n",
       "      <td>276</td>\n",
       "      <td>F</td>\n",
       "      <td>More than one race</td>\n",
       "      <td>Control</td>\n",
       "      <td>Healthy Control</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>F00615</td>\n",
       "      <td>No</td>\n",
       "      <td>Bipolar &amp; Schizophrenia Consortium for Parsing...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2274</td>\n",
       "      <td>323863</td>\n",
       "      <td>11294</td>\n",
       "      <td>NDAR_INV9L4Z5GM3</td>\n",
       "      <td>619</td>\n",
       "      <td>10/05/2009</td>\n",
       "      <td>288</td>\n",
       "      <td>M</td>\n",
       "      <td>White</td>\n",
       "      <td>Case</td>\n",
       "      <td>Proband</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>F00619</td>\n",
       "      <td>No</td>\n",
       "      <td>Bipolar &amp; Schizophrenia Consortium for Parsing...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2274</td>\n",
       "      <td>323864</td>\n",
       "      <td>11294</td>\n",
       "      <td>NDAR_INVPVMWRZFZ</td>\n",
       "      <td>624</td>\n",
       "      <td>10/17/2011</td>\n",
       "      <td>432</td>\n",
       "      <td>F</td>\n",
       "      <td>White</td>\n",
       "      <td>Relative</td>\n",
       "      <td>Child Daughter</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>F00732</td>\n",
       "      <td>No</td>\n",
       "      <td>Bipolar &amp; Schizophrenia Consortium for Parsing...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2274</td>\n",
       "      <td>323865</td>\n",
       "      <td>11294</td>\n",
       "      <td>NDAR_INVBKWEEAP9</td>\n",
       "      <td>625</td>\n",
       "      <td>04/19/2009</td>\n",
       "      <td>324</td>\n",
       "      <td>M</td>\n",
       "      <td>Asian</td>\n",
       "      <td>Relative</td>\n",
       "      <td>Sibling Brother</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>F01344</td>\n",
       "      <td>No</td>\n",
       "      <td>Bipolar &amp; Schizophrenia Consortium for Parsing...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2274</td>\n",
       "      <td>323866</td>\n",
       "      <td>11294</td>\n",
       "      <td>NDAR_INVP4JGYX3T</td>\n",
       "      <td>630</td>\n",
       "      <td>06/07/2010</td>\n",
       "      <td>528</td>\n",
       "      <td>F</td>\n",
       "      <td>Black or African American</td>\n",
       "      <td>Relative</td>\n",
       "      <td>Parent Mother</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>F00930</td>\n",
       "      <td>No</td>\n",
       "      <td>Bipolar &amp; Schizophrenia Consortium for Parsing...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4399</th>\n",
       "      <td>2274</td>\n",
       "      <td>323500</td>\n",
       "      <td>11101</td>\n",
       "      <td>NDAR_INVL85D03YE</td>\n",
       "      <td>9923</td>\n",
       "      <td>07/31/2008</td>\n",
       "      <td>552</td>\n",
       "      <td>F</td>\n",
       "      <td>White</td>\n",
       "      <td>Case</td>\n",
       "      <td>Proband</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>F09900</td>\n",
       "      <td>No</td>\n",
       "      <td>Bipolar &amp; Schizophrenia Consortium for Parsing...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4400</th>\n",
       "      <td>2274</td>\n",
       "      <td>323501</td>\n",
       "      <td>11101</td>\n",
       "      <td>NDAR_INVLLLTJ32N</td>\n",
       "      <td>9952</td>\n",
       "      <td>05/13/2011</td>\n",
       "      <td>372</td>\n",
       "      <td>M</td>\n",
       "      <td>Black or African American</td>\n",
       "      <td>Control</td>\n",
       "      <td>Healthy Control</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>F08183</td>\n",
       "      <td>No</td>\n",
       "      <td>Bipolar &amp; Schizophrenia Consortium for Parsing...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4401</th>\n",
       "      <td>2274</td>\n",
       "      <td>323502</td>\n",
       "      <td>11101</td>\n",
       "      <td>NDAR_INVVG9HNY35</td>\n",
       "      <td>9959</td>\n",
       "      <td>10/11/2011</td>\n",
       "      <td>216</td>\n",
       "      <td>F</td>\n",
       "      <td>White</td>\n",
       "      <td>Control</td>\n",
       "      <td>Healthy Control</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>F09357</td>\n",
       "      <td>No</td>\n",
       "      <td>Bipolar &amp; Schizophrenia Consortium for Parsing...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4402</th>\n",
       "      <td>2274</td>\n",
       "      <td>323503</td>\n",
       "      <td>11101</td>\n",
       "      <td>NDAR_INVAFB72FXJ</td>\n",
       "      <td>9983</td>\n",
       "      <td>10/06/2010</td>\n",
       "      <td>216</td>\n",
       "      <td>F</td>\n",
       "      <td>Black or African American</td>\n",
       "      <td>Relative</td>\n",
       "      <td>Child Daughter</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>F09375</td>\n",
       "      <td>No</td>\n",
       "      <td>Bipolar &amp; Schizophrenia Consortium for Parsing...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4403</th>\n",
       "      <td>2274</td>\n",
       "      <td>323504</td>\n",
       "      <td>11101</td>\n",
       "      <td>NDAR_INVA0MR96EA</td>\n",
       "      <td>9986</td>\n",
       "      <td>02/10/2009</td>\n",
       "      <td>744</td>\n",
       "      <td>F</td>\n",
       "      <td>White</td>\n",
       "      <td>Relative</td>\n",
       "      <td>Parent Mother</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>F09820</td>\n",
       "      <td>No</td>\n",
       "      <td>Bipolar &amp; Schizophrenia Consortium for Parsing...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4403 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "0    collection_id ndar_subject01_id dataset_id  \\\n",
       "1             2274            323862      11294   \n",
       "2             2274            323863      11294   \n",
       "3             2274            323864      11294   \n",
       "4             2274            323865      11294   \n",
       "5             2274            323866      11294   \n",
       "...            ...               ...        ...   \n",
       "4399          2274            323500      11101   \n",
       "4400          2274            323501      11101   \n",
       "4401          2274            323502      11101   \n",
       "4402          2274            323503      11101   \n",
       "4403          2274            323504      11101   \n",
       "\n",
       "0    The NDAR Global Unique Identifier (GUID) for research subject  \\\n",
       "1                                      NDAR_INVRCC8D3DU              \n",
       "2                                      NDAR_INV9L4Z5GM3              \n",
       "3                                      NDAR_INVPVMWRZFZ              \n",
       "4                                      NDAR_INVBKWEEAP9              \n",
       "5                                      NDAR_INVP4JGYX3T              \n",
       "...                                                 ...              \n",
       "4399                                   NDAR_INVL85D03YE              \n",
       "4400                                   NDAR_INVLLLTJ32N              \n",
       "4401                                   NDAR_INVVG9HNY35              \n",
       "4402                                   NDAR_INVAFB72FXJ              \n",
       "4403                                   NDAR_INVA0MR96EA              \n",
       "\n",
       "0    Subject ID how it's defined in lab/project  \\\n",
       "1                                           615   \n",
       "2                                           619   \n",
       "3                                           624   \n",
       "4                                           625   \n",
       "5                                           630   \n",
       "...                                         ...   \n",
       "4399                                       9923   \n",
       "4400                                       9952   \n",
       "4401                                       9959   \n",
       "4402                                       9983   \n",
       "4403                                       9986   \n",
       "\n",
       "0    Date on which the interview/genetic test/sampling/imaging/biospecimen was completed. MM/DD/YYYY  \\\n",
       "1                                            09/16/2008                                                \n",
       "2                                            10/05/2009                                                \n",
       "3                                            10/17/2011                                                \n",
       "4                                            04/19/2009                                                \n",
       "5                                            06/07/2010                                                \n",
       "...                                                 ...                                                \n",
       "4399                                         07/31/2008                                                \n",
       "4400                                         05/13/2011                                                \n",
       "4401                                         10/11/2011                                                \n",
       "4402                                         10/06/2010                                                \n",
       "4403                                         02/10/2009                                                \n",
       "\n",
       "0    Age in months at the time of the interview/test/sampling/imaging.  \\\n",
       "1                                                   276                  \n",
       "2                                                   288                  \n",
       "3                                                   432                  \n",
       "4                                                   324                  \n",
       "5                                                   528                  \n",
       "...                                                 ...                  \n",
       "4399                                                552                  \n",
       "4400                                                372                  \n",
       "4401                                                216                  \n",
       "4402                                                216                  \n",
       "4403                                                744                  \n",
       "\n",
       "0    Sex of subject at birth      Race of study subject  \\\n",
       "1                          F         More than one race   \n",
       "2                          M                      White   \n",
       "3                          F                      White   \n",
       "4                          M                      Asian   \n",
       "5                          F  Black or African American   \n",
       "...                      ...                        ...   \n",
       "4399                       F                      White   \n",
       "4400                       M  Black or African American   \n",
       "4401                       F                      White   \n",
       "4402                       F  Black or African American   \n",
       "4403                       F                      White   \n",
       "\n",
       "0    Phenotype/diagnosis for the subject  \\\n",
       "1                                Control   \n",
       "2                                   Case   \n",
       "3                               Relative   \n",
       "4                               Relative   \n",
       "5                               Relative   \n",
       "...                                  ...   \n",
       "4399                                Case   \n",
       "4400                             Control   \n",
       "4401                             Control   \n",
       "4402                            Relative   \n",
       "4403                            Relative   \n",
       "\n",
       "0    Description of the phenotype for the subject Is this study of twins?  \\\n",
       "1                                 Healthy Control                      No   \n",
       "2                                         Proband                      No   \n",
       "3                                  Child Daughter                      No   \n",
       "4                                 Sibling Brother                      No   \n",
       "5                                   Parent Mother                      No   \n",
       "...                                           ...                     ...   \n",
       "4399                                      Proband                      No   \n",
       "4400                              Healthy Control                      No   \n",
       "4401                              Healthy Control                      No   \n",
       "4402                               Child Daughter                      No   \n",
       "4403                                Parent Mother                      No   \n",
       "\n",
       "0    Was it sibling study? Study of sibling(s) of autistic child.  \\\n",
       "1                                                    No             \n",
       "2                                                    No             \n",
       "3                                                    No             \n",
       "4                                                    No             \n",
       "5                                                    No             \n",
       "...                                                 ...             \n",
       "4399                                                 No             \n",
       "4400                                                 No             \n",
       "4401                                                 No             \n",
       "4402                                                 No             \n",
       "4403                                                 No             \n",
       "\n",
       "0    Was it family study? Study of biological mother, biological father and/or sibling of proband.  \\\n",
       "1                                                   Yes                                              \n",
       "2                                                   Yes                                              \n",
       "3                                                   Yes                                              \n",
       "4                                                   Yes                                              \n",
       "5                                                   Yes                                              \n",
       "...                                                 ...                                              \n",
       "4399                                                Yes                                              \n",
       "4400                                                Yes                                              \n",
       "4401                                                Yes                                              \n",
       "4402                                                Yes                                              \n",
       "4403                                                Yes                                              \n",
       "\n",
       "0    Family Pedigree User-Defined ID  \\\n",
       "1                             F00615   \n",
       "2                             F00619   \n",
       "3                             F00732   \n",
       "4                             F01344   \n",
       "5                             F00930   \n",
       "...                              ...   \n",
       "4399                          F09900   \n",
       "4400                          F08183   \n",
       "4401                          F09357   \n",
       "4402                          F09375   \n",
       "4403                          F09820   \n",
       "\n",
       "0    Was a sample taken at this interview/during this project time  \\\n",
       "1                                                    No              \n",
       "2                                                    No              \n",
       "3                                                    No              \n",
       "4                                                    No              \n",
       "5                                                    No              \n",
       "...                                                 ...              \n",
       "4399                                                 No              \n",
       "4400                                                 No              \n",
       "4401                                                 No              \n",
       "4402                                                 No              \n",
       "4403                                                 No              \n",
       "\n",
       "0                                      collection_title  \n",
       "1     Bipolar & Schizophrenia Consortium for Parsing...  \n",
       "2     Bipolar & Schizophrenia Consortium for Parsing...  \n",
       "3     Bipolar & Schizophrenia Consortium for Parsing...  \n",
       "4     Bipolar & Schizophrenia Consortium for Parsing...  \n",
       "5     Bipolar & Schizophrenia Consortium for Parsing...  \n",
       "...                                                 ...  \n",
       "4399  Bipolar & Schizophrenia Consortium for Parsing...  \n",
       "4400  Bipolar & Schizophrenia Consortium for Parsing...  \n",
       "4401  Bipolar & Schizophrenia Consortium for Parsing...  \n",
       "4402  Bipolar & Schizophrenia Consortium for Parsing...  \n",
       "4403  Bipolar & Schizophrenia Consortium for Parsing...  \n",
       "\n",
       "[4403 rows x 17 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ndar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for duplicates in ndar subject id \n",
    "ndar['The NDAR Global Unique Identifier (GUID) for research subject'].is_unique\n",
    "#there are duplicates "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "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>collection_id</th>\n",
       "      <th>ndar_subject01_id</th>\n",
       "      <th>dataset_id</th>\n",
       "      <th>The NDAR Global Unique Identifier (GUID) for research subject</th>\n",
       "      <th>Subject ID how it's defined in lab/project</th>\n",
       "      <th>Date on which the interview/genetic test/sampling/imaging/biospecimen was completed. MM/DD/YYYY</th>\n",
       "      <th>Age in months at the time of the interview/test/sampling/imaging.</th>\n",
       "      <th>Sex of subject at birth</th>\n",
       "      <th>Race of study subject</th>\n",
       "      <th>Phenotype/diagnosis for the subject</th>\n",
       "      <th>Description of the phenotype for the subject</th>\n",
       "      <th>Is this study of twins?</th>\n",
       "      <th>Was it sibling study? Study of sibling(s) of autistic child.</th>\n",
       "      <th>Was it family study? Study of biological mother, biological father and/or sibling of proband.</th>\n",
       "      <th>Family Pedigree User-Defined ID</th>\n",
       "      <th>Was a sample taken at this interview/during this project time</th>\n",
       "      <th>collection_title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1969</th>\n",
       "      <td>2274</td>\n",
       "      <td>326360</td>\n",
       "      <td>11323</td>\n",
       "      <td>NDAR_INV26JW9T73</td>\n",
       "      <td>2721</td>\n",
       "      <td>03/11/2009</td>\n",
       "      <td>528</td>\n",
       "      <td>F</td>\n",
       "      <td>Black or African American</td>\n",
       "      <td>Relative</td>\n",
       "      <td>Parent Mother</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>F02667</td>\n",
       "      <td>No</td>\n",
       "      <td>Bipolar &amp; Schizophrenia Consortium for Parsing...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970</th>\n",
       "      <td>2274</td>\n",
       "      <td>326361</td>\n",
       "      <td>11323</td>\n",
       "      <td>NDAR_INVRJ2P6Y7F</td>\n",
       "      <td>2728</td>\n",
       "      <td>10/27/2010</td>\n",
       "      <td>348</td>\n",
       "      <td>M</td>\n",
       "      <td>Black or African American</td>\n",
       "      <td>Control</td>\n",
       "      <td>Healthy Control</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>F03717</td>\n",
       "      <td>No</td>\n",
       "      <td>Bipolar &amp; Schizophrenia Consortium for Parsing...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971</th>\n",
       "      <td>2274</td>\n",
       "      <td>326362</td>\n",
       "      <td>11323</td>\n",
       "      <td>NDAR_INVUY4LRUDV</td>\n",
       "      <td>2729</td>\n",
       "      <td>05/21/2009</td>\n",
       "      <td>528</td>\n",
       "      <td>F</td>\n",
       "      <td>White</td>\n",
       "      <td>Relative</td>\n",
       "      <td>Sibling Sister</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>F02541</td>\n",
       "      <td>No</td>\n",
       "      <td>Bipolar &amp; Schizophrenia Consortium for Parsing...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972</th>\n",
       "      <td>2274</td>\n",
       "      <td>326363</td>\n",
       "      <td>11323</td>\n",
       "      <td>NDAR_INVYH3WHUY2</td>\n",
       "      <td>2730</td>\n",
       "      <td>02/05/2011</td>\n",
       "      <td>228</td>\n",
       "      <td>M</td>\n",
       "      <td>White</td>\n",
       "      <td>Case</td>\n",
       "      <td>Proband</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>F03429</td>\n",
       "      <td>No</td>\n",
       "      <td>Bipolar &amp; Schizophrenia Consortium for Parsing...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973</th>\n",
       "      <td>2274</td>\n",
       "      <td>326364</td>\n",
       "      <td>11323</td>\n",
       "      <td>NDAR_INVBLVPJFM8</td>\n",
       "      <td>2732</td>\n",
       "      <td>01/30/2009</td>\n",
       "      <td>540</td>\n",
       "      <td>F</td>\n",
       "      <td>White</td>\n",
       "      <td>Relative</td>\n",
       "      <td>Parent Mother</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>F02774</td>\n",
       "      <td>No</td>\n",
       "      <td>Bipolar &amp; Schizophrenia Consortium for Parsing...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4399</th>\n",
       "      <td>2274</td>\n",
       "      <td>323500</td>\n",
       "      <td>11101</td>\n",
       "      <td>NDAR_INVL85D03YE</td>\n",
       "      <td>9923</td>\n",
       "      <td>07/31/2008</td>\n",
       "      <td>552</td>\n",
       "      <td>F</td>\n",
       "      <td>White</td>\n",
       "      <td>Case</td>\n",
       "      <td>Proband</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>F09900</td>\n",
       "      <td>No</td>\n",
       "      <td>Bipolar &amp; Schizophrenia Consortium for Parsing...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4400</th>\n",
       "      <td>2274</td>\n",
       "      <td>323501</td>\n",
       "      <td>11101</td>\n",
       "      <td>NDAR_INVLLLTJ32N</td>\n",
       "      <td>9952</td>\n",
       "      <td>05/13/2011</td>\n",
       "      <td>372</td>\n",
       "      <td>M</td>\n",
       "      <td>Black or African American</td>\n",
       "      <td>Control</td>\n",
       "      <td>Healthy Control</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>F08183</td>\n",
       "      <td>No</td>\n",
       "      <td>Bipolar &amp; Schizophrenia Consortium for Parsing...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4401</th>\n",
       "      <td>2274</td>\n",
       "      <td>323502</td>\n",
       "      <td>11101</td>\n",
       "      <td>NDAR_INVVG9HNY35</td>\n",
       "      <td>9959</td>\n",
       "      <td>10/11/2011</td>\n",
       "      <td>216</td>\n",
       "      <td>F</td>\n",
       "      <td>White</td>\n",
       "      <td>Control</td>\n",
       "      <td>Healthy Control</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>F09357</td>\n",
       "      <td>No</td>\n",
       "      <td>Bipolar &amp; Schizophrenia Consortium for Parsing...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4402</th>\n",
       "      <td>2274</td>\n",
       "      <td>323503</td>\n",
       "      <td>11101</td>\n",
       "      <td>NDAR_INVAFB72FXJ</td>\n",
       "      <td>9983</td>\n",
       "      <td>10/06/2010</td>\n",
       "      <td>216</td>\n",
       "      <td>F</td>\n",
       "      <td>Black or African American</td>\n",
       "      <td>Relative</td>\n",
       "      <td>Child Daughter</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>F09375</td>\n",
       "      <td>No</td>\n",
       "      <td>Bipolar &amp; Schizophrenia Consortium for Parsing...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4403</th>\n",
       "      <td>2274</td>\n",
       "      <td>323504</td>\n",
       "      <td>11101</td>\n",
       "      <td>NDAR_INVA0MR96EA</td>\n",
       "      <td>9986</td>\n",
       "      <td>02/10/2009</td>\n",
       "      <td>744</td>\n",
       "      <td>F</td>\n",
       "      <td>White</td>\n",
       "      <td>Relative</td>\n",
       "      <td>Parent Mother</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>F09820</td>\n",
       "      <td>No</td>\n",
       "      <td>Bipolar &amp; Schizophrenia Consortium for Parsing...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1988 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "0    collection_id ndar_subject01_id dataset_id  \\\n",
       "1969          2274            326360      11323   \n",
       "1970          2274            326361      11323   \n",
       "1971          2274            326362      11323   \n",
       "1972          2274            326363      11323   \n",
       "1973          2274            326364      11323   \n",
       "...            ...               ...        ...   \n",
       "4399          2274            323500      11101   \n",
       "4400          2274            323501      11101   \n",
       "4401          2274            323502      11101   \n",
       "4402          2274            323503      11101   \n",
       "4403          2274            323504      11101   \n",
       "\n",
       "0    The NDAR Global Unique Identifier (GUID) for research subject  \\\n",
       "1969                                   NDAR_INV26JW9T73              \n",
       "1970                                   NDAR_INVRJ2P6Y7F              \n",
       "1971                                   NDAR_INVUY4LRUDV              \n",
       "1972                                   NDAR_INVYH3WHUY2              \n",
       "1973                                   NDAR_INVBLVPJFM8              \n",
       "...                                                 ...              \n",
       "4399                                   NDAR_INVL85D03YE              \n",
       "4400                                   NDAR_INVLLLTJ32N              \n",
       "4401                                   NDAR_INVVG9HNY35              \n",
       "4402                                   NDAR_INVAFB72FXJ              \n",
       "4403                                   NDAR_INVA0MR96EA              \n",
       "\n",
       "0    Subject ID how it's defined in lab/project  \\\n",
       "1969                                       2721   \n",
       "1970                                       2728   \n",
       "1971                                       2729   \n",
       "1972                                       2730   \n",
       "1973                                       2732   \n",
       "...                                         ...   \n",
       "4399                                       9923   \n",
       "4400                                       9952   \n",
       "4401                                       9959   \n",
       "4402                                       9983   \n",
       "4403                                       9986   \n",
       "\n",
       "0    Date on which the interview/genetic test/sampling/imaging/biospecimen was completed. MM/DD/YYYY  \\\n",
       "1969                                         03/11/2009                                                \n",
       "1970                                         10/27/2010                                                \n",
       "1971                                         05/21/2009                                                \n",
       "1972                                         02/05/2011                                                \n",
       "1973                                         01/30/2009                                                \n",
       "...                                                 ...                                                \n",
       "4399                                         07/31/2008                                                \n",
       "4400                                         05/13/2011                                                \n",
       "4401                                         10/11/2011                                                \n",
       "4402                                         10/06/2010                                                \n",
       "4403                                         02/10/2009                                                \n",
       "\n",
       "0    Age in months at the time of the interview/test/sampling/imaging.  \\\n",
       "1969                                                528                  \n",
       "1970                                                348                  \n",
       "1971                                                528                  \n",
       "1972                                                228                  \n",
       "1973                                                540                  \n",
       "...                                                 ...                  \n",
       "4399                                                552                  \n",
       "4400                                                372                  \n",
       "4401                                                216                  \n",
       "4402                                                216                  \n",
       "4403                                                744                  \n",
       "\n",
       "0    Sex of subject at birth      Race of study subject  \\\n",
       "1969                       F  Black or African American   \n",
       "1970                       M  Black or African American   \n",
       "1971                       F                      White   \n",
       "1972                       M                      White   \n",
       "1973                       F                      White   \n",
       "...                      ...                        ...   \n",
       "4399                       F                      White   \n",
       "4400                       M  Black or African American   \n",
       "4401                       F                      White   \n",
       "4402                       F  Black or African American   \n",
       "4403                       F                      White   \n",
       "\n",
       "0    Phenotype/diagnosis for the subject  \\\n",
       "1969                            Relative   \n",
       "1970                             Control   \n",
       "1971                            Relative   \n",
       "1972                                Case   \n",
       "1973                            Relative   \n",
       "...                                  ...   \n",
       "4399                                Case   \n",
       "4400                             Control   \n",
       "4401                             Control   \n",
       "4402                            Relative   \n",
       "4403                            Relative   \n",
       "\n",
       "0    Description of the phenotype for the subject Is this study of twins?  \\\n",
       "1969                                Parent Mother                      No   \n",
       "1970                              Healthy Control                      No   \n",
       "1971                               Sibling Sister                      No   \n",
       "1972                                      Proband                      No   \n",
       "1973                                Parent Mother                      No   \n",
       "...                                           ...                     ...   \n",
       "4399                                      Proband                      No   \n",
       "4400                              Healthy Control                      No   \n",
       "4401                              Healthy Control                      No   \n",
       "4402                               Child Daughter                      No   \n",
       "4403                                Parent Mother                      No   \n",
       "\n",
       "0    Was it sibling study? Study of sibling(s) of autistic child.  \\\n",
       "1969                                                 No             \n",
       "1970                                                 No             \n",
       "1971                                                 No             \n",
       "1972                                                 No             \n",
       "1973                                                 No             \n",
       "...                                                 ...             \n",
       "4399                                                 No             \n",
       "4400                                                 No             \n",
       "4401                                                 No             \n",
       "4402                                                 No             \n",
       "4403                                                 No             \n",
       "\n",
       "0    Was it family study? Study of biological mother, biological father and/or sibling of proband.  \\\n",
       "1969                                                Yes                                              \n",
       "1970                                                Yes                                              \n",
       "1971                                                Yes                                              \n",
       "1972                                                Yes                                              \n",
       "1973                                                Yes                                              \n",
       "...                                                 ...                                              \n",
       "4399                                                Yes                                              \n",
       "4400                                                Yes                                              \n",
       "4401                                                Yes                                              \n",
       "4402                                                Yes                                              \n",
       "4403                                                Yes                                              \n",
       "\n",
       "0    Family Pedigree User-Defined ID  \\\n",
       "1969                          F02667   \n",
       "1970                          F03717   \n",
       "1971                          F02541   \n",
       "1972                          F03429   \n",
       "1973                          F02774   \n",
       "...                              ...   \n",
       "4399                          F09900   \n",
       "4400                          F08183   \n",
       "4401                          F09357   \n",
       "4402                          F09375   \n",
       "4403                          F09820   \n",
       "\n",
       "0    Was a sample taken at this interview/during this project time  \\\n",
       "1969                                                 No              \n",
       "1970                                                 No              \n",
       "1971                                                 No              \n",
       "1972                                                 No              \n",
       "1973                                                 No              \n",
       "...                                                 ...              \n",
       "4399                                                 No              \n",
       "4400                                                 No              \n",
       "4401                                                 No              \n",
       "4402                                                 No              \n",
       "4403                                                 No              \n",
       "\n",
       "0                                      collection_title  \n",
       "1969  Bipolar & Schizophrenia Consortium for Parsing...  \n",
       "1970  Bipolar & Schizophrenia Consortium for Parsing...  \n",
       "1971  Bipolar & Schizophrenia Consortium for Parsing...  \n",
       "1972  Bipolar & Schizophrenia Consortium for Parsing...  \n",
       "1973  Bipolar & Schizophrenia Consortium for Parsing...  \n",
       "...                                                 ...  \n",
       "4399  Bipolar & Schizophrenia Consortium for Parsing...  \n",
       "4400  Bipolar & Schizophrenia Consortium for Parsing...  \n",
       "4401  Bipolar & Schizophrenia Consortium for Parsing...  \n",
       "4402  Bipolar & Schizophrenia Consortium for Parsing...  \n",
       "4403  Bipolar & Schizophrenia Consortium for Parsing...  \n",
       "\n",
       "[1988 rows x 17 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# inspect some duplicates \n",
    "# these are all the duplicated ndar subject ids\n",
    "ndar[ndar['The NDAR Global Unique Identifier (GUID) for research subject'].duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>collection_id</th>\n",
       "      <th>ndar_subject01_id</th>\n",
       "      <th>dataset_id</th>\n",
       "      <th>The NDAR Global Unique Identifier (GUID) for research subject</th>\n",
       "      <th>Subject ID how it's defined in lab/project</th>\n",
       "      <th>Date on which the interview/genetic test/sampling/imaging/biospecimen was completed. MM/DD/YYYY</th>\n",
       "      <th>Age in months at the time of the interview/test/sampling/imaging.</th>\n",
       "      <th>Sex of subject at birth</th>\n",
       "      <th>Race of study subject</th>\n",
       "      <th>Phenotype/diagnosis for the subject</th>\n",
       "      <th>Description of the phenotype for the subject</th>\n",
       "      <th>Is this study of twins?</th>\n",
       "      <th>Was it sibling study? Study of sibling(s) of autistic child.</th>\n",
       "      <th>Was it family study? Study of biological mother, biological father and/or sibling of proband.</th>\n",
       "      <th>Family Pedigree User-Defined ID</th>\n",
       "      <th>Was a sample taken at this interview/during this project time</th>\n",
       "      <th>collection_title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>853</th>\n",
       "      <td>2274</td>\n",
       "      <td>324392</td>\n",
       "      <td>11294</td>\n",
       "      <td>NDAR_INVYH3WHUY2</td>\n",
       "      <td>2730</td>\n",
       "      <td>02/05/2011</td>\n",
       "      <td>228</td>\n",
       "      <td>M</td>\n",
       "      <td>White</td>\n",
       "      <td>Case</td>\n",
       "      <td>Proband</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>F03429</td>\n",
       "      <td>No</td>\n",
       "      <td>Bipolar &amp; Schizophrenia Consortium for Parsing...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972</th>\n",
       "      <td>2274</td>\n",
       "      <td>326363</td>\n",
       "      <td>11323</td>\n",
       "      <td>NDAR_INVYH3WHUY2</td>\n",
       "      <td>2730</td>\n",
       "      <td>02/05/2011</td>\n",
       "      <td>228</td>\n",
       "      <td>M</td>\n",
       "      <td>White</td>\n",
       "      <td>Case</td>\n",
       "      <td>Proband</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>F03429</td>\n",
       "      <td>No</td>\n",
       "      <td>Bipolar &amp; Schizophrenia Consortium for Parsing...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "0    collection_id ndar_subject01_id dataset_id  \\\n",
       "853           2274            324392      11294   \n",
       "1972          2274            326363      11323   \n",
       "\n",
       "0    The NDAR Global Unique Identifier (GUID) for research subject  \\\n",
       "853                                    NDAR_INVYH3WHUY2              \n",
       "1972                                   NDAR_INVYH3WHUY2              \n",
       "\n",
       "0    Subject ID how it's defined in lab/project  \\\n",
       "853                                        2730   \n",
       "1972                                       2730   \n",
       "\n",
       "0    Date on which the interview/genetic test/sampling/imaging/biospecimen was completed. MM/DD/YYYY  \\\n",
       "853                                          02/05/2011                                                \n",
       "1972                                         02/05/2011                                                \n",
       "\n",
       "0    Age in months at the time of the interview/test/sampling/imaging.  \\\n",
       "853                                                 228                  \n",
       "1972                                                228                  \n",
       "\n",
       "0    Sex of subject at birth Race of study subject  \\\n",
       "853                        M                 White   \n",
       "1972                       M                 White   \n",
       "\n",
       "0    Phenotype/diagnosis for the subject  \\\n",
       "853                                 Case   \n",
       "1972                                Case   \n",
       "\n",
       "0    Description of the phenotype for the subject Is this study of twins?  \\\n",
       "853                                       Proband                      No   \n",
       "1972                                      Proband                      No   \n",
       "\n",
       "0    Was it sibling study? Study of sibling(s) of autistic child.  \\\n",
       "853                                                  No             \n",
       "1972                                                 No             \n",
       "\n",
       "0    Was it family study? Study of biological mother, biological father and/or sibling of proband.  \\\n",
       "853                                                 Yes                                              \n",
       "1972                                                Yes                                              \n",
       "\n",
       "0    Family Pedigree User-Defined ID  \\\n",
       "853                           F03429   \n",
       "1972                          F03429   \n",
       "\n",
       "0    Was a sample taken at this interview/during this project time  \\\n",
       "853                                                  No              \n",
       "1972                                                 No              \n",
       "\n",
       "0                                      collection_title  \n",
       "853   Bipolar & Schizophrenia Consortium for Parsing...  \n",
       "1972  Bipolar & Schizophrenia Consortium for Parsing...  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# i picked a random ndar id which was a proband \n",
    "ndar[ndar['The NDAR Global Unique Identifier (GUID) for research subject'] == 'NDAR_INVYH3WHUY2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# doesn't matter which one is dropped as the only difference is ndar_subject01_id and dataset_id \n",
    "ndar = ndar.drop_duplicates(subset = 'The NDAR Global Unique Identifier (GUID) for research subject')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>collection_id</th>\n",
       "      <th>ndar_subject01_id</th>\n",
       "      <th>dataset_id</th>\n",
       "      <th>The NDAR Global Unique Identifier (GUID) for research subject</th>\n",
       "      <th>Subject ID how it's defined in lab/project</th>\n",
       "      <th>Date on which the interview/genetic test/sampling/imaging/biospecimen was completed. MM/DD/YYYY</th>\n",
       "      <th>Age in months at the time of the interview/test/sampling/imaging.</th>\n",
       "      <th>Sex of subject at birth</th>\n",
       "      <th>Race of study subject</th>\n",
       "      <th>Phenotype/diagnosis for the subject</th>\n",
       "      <th>Description of the phenotype for the subject</th>\n",
       "      <th>Is this study of twins?</th>\n",
       "      <th>Was it sibling study? Study of sibling(s) of autistic child.</th>\n",
       "      <th>Was it family study? Study of biological mother, biological father and/or sibling of proband.</th>\n",
       "      <th>Family Pedigree User-Defined ID</th>\n",
       "      <th>Was a sample taken at this interview/during this project time</th>\n",
       "      <th>collection_title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2274</td>\n",
       "      <td>323862</td>\n",
       "      <td>11294</td>\n",
       "      <td>NDAR_INVRCC8D3DU</td>\n",
       "      <td>615</td>\n",
       "      <td>09/16/2008</td>\n",
       "      <td>276</td>\n",
       "      <td>F</td>\n",
       "      <td>More than one race</td>\n",
       "      <td>Control</td>\n",
       "      <td>Healthy Control</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>F00615</td>\n",
       "      <td>No</td>\n",
       "      <td>Bipolar &amp; Schizophrenia Consortium for Parsing...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2274</td>\n",
       "      <td>323863</td>\n",
       "      <td>11294</td>\n",
       "      <td>NDAR_INV9L4Z5GM3</td>\n",
       "      <td>619</td>\n",
       "      <td>10/05/2009</td>\n",
       "      <td>288</td>\n",
       "      <td>M</td>\n",
       "      <td>White</td>\n",
       "      <td>Case</td>\n",
       "      <td>Proband</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>F00619</td>\n",
       "      <td>No</td>\n",
       "      <td>Bipolar &amp; Schizophrenia Consortium for Parsing...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2274</td>\n",
       "      <td>323864</td>\n",
       "      <td>11294</td>\n",
       "      <td>NDAR_INVPVMWRZFZ</td>\n",
       "      <td>624</td>\n",
       "      <td>10/17/2011</td>\n",
       "      <td>432</td>\n",
       "      <td>F</td>\n",
       "      <td>White</td>\n",
       "      <td>Relative</td>\n",
       "      <td>Child Daughter</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>F00732</td>\n",
       "      <td>No</td>\n",
       "      <td>Bipolar &amp; Schizophrenia Consortium for Parsing...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2274</td>\n",
       "      <td>323865</td>\n",
       "      <td>11294</td>\n",
       "      <td>NDAR_INVBKWEEAP9</td>\n",
       "      <td>625</td>\n",
       "      <td>04/19/2009</td>\n",
       "      <td>324</td>\n",
       "      <td>M</td>\n",
       "      <td>Asian</td>\n",
       "      <td>Relative</td>\n",
       "      <td>Sibling Brother</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>F01344</td>\n",
       "      <td>No</td>\n",
       "      <td>Bipolar &amp; Schizophrenia Consortium for Parsing...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2274</td>\n",
       "      <td>323866</td>\n",
       "      <td>11294</td>\n",
       "      <td>NDAR_INVP4JGYX3T</td>\n",
       "      <td>630</td>\n",
       "      <td>06/07/2010</td>\n",
       "      <td>528</td>\n",
       "      <td>F</td>\n",
       "      <td>Black or African American</td>\n",
       "      <td>Relative</td>\n",
       "      <td>Parent Mother</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>F00930</td>\n",
       "      <td>No</td>\n",
       "      <td>Bipolar &amp; Schizophrenia Consortium for Parsing...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>4127</th>\n",
       "      <td>2274</td>\n",
       "      <td>327352</td>\n",
       "      <td>11323</td>\n",
       "      <td>NDAR_INVCKKWW1BH</td>\n",
       "      <td>6609</td>\n",
       "      <td>10/29/2009</td>\n",
       "      <td>264</td>\n",
       "      <td>M</td>\n",
       "      <td>White</td>\n",
       "      <td>Control</td>\n",
       "      <td>Healthy Control</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>F07803</td>\n",
       "      <td>No</td>\n",
       "      <td>Bipolar &amp; Schizophrenia Consortium for Parsing...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4128</th>\n",
       "      <td>2274</td>\n",
       "      <td>327353</td>\n",
       "      <td>11323</td>\n",
       "      <td>NDAR_INVAC8TENBY</td>\n",
       "      <td>6611</td>\n",
       "      <td>08/04/2009</td>\n",
       "      <td>360</td>\n",
       "      <td>M</td>\n",
       "      <td>White</td>\n",
       "      <td>Case</td>\n",
       "      <td>Proband</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>F07035</td>\n",
       "      <td>No</td>\n",
       "      <td>Bipolar &amp; Schizophrenia Consortium for Parsing...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4129</th>\n",
       "      <td>2274</td>\n",
       "      <td>327354</td>\n",
       "      <td>11323</td>\n",
       "      <td>NDAR_INVPYE0C01W</td>\n",
       "      <td>6618</td>\n",
       "      <td>06/19/2009</td>\n",
       "      <td>228</td>\n",
       "      <td>F</td>\n",
       "      <td>White</td>\n",
       "      <td>Relative</td>\n",
       "      <td>Child Daughter</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>F06518</td>\n",
       "      <td>No</td>\n",
       "      <td>Bipolar &amp; Schizophrenia Consortium for Parsing...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4130</th>\n",
       "      <td>2274</td>\n",
       "      <td>327355</td>\n",
       "      <td>11323</td>\n",
       "      <td>NDAR_INVDKV84ECB</td>\n",
       "      <td>6625</td>\n",
       "      <td>01/09/2012</td>\n",
       "      <td>300</td>\n",
       "      <td>M</td>\n",
       "      <td>Black or African American</td>\n",
       "      <td>Control</td>\n",
       "      <td>Healthy Control</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>F06687</td>\n",
       "      <td>No</td>\n",
       "      <td>Bipolar &amp; Schizophrenia Consortium for Parsing...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4131</th>\n",
       "      <td>2274</td>\n",
       "      <td>327356</td>\n",
       "      <td>11323</td>\n",
       "      <td>NDAR_INVPPGN6A8A</td>\n",
       "      <td>6627</td>\n",
       "      <td>06/16/2009</td>\n",
       "      <td>744</td>\n",
       "      <td>F</td>\n",
       "      <td>White</td>\n",
       "      <td>Relative</td>\n",
       "      <td>Parent Mother</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>F07871</td>\n",
       "      <td>No</td>\n",
       "      <td>Bipolar &amp; Schizophrenia Consortium for Parsing...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2415 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "0    collection_id ndar_subject01_id dataset_id  \\\n",
       "1             2274            323862      11294   \n",
       "2             2274            323863      11294   \n",
       "3             2274            323864      11294   \n",
       "4             2274            323865      11294   \n",
       "5             2274            323866      11294   \n",
       "...            ...               ...        ...   \n",
       "4127          2274            327352      11323   \n",
       "4128          2274            327353      11323   \n",
       "4129          2274            327354      11323   \n",
       "4130          2274            327355      11323   \n",
       "4131          2274            327356      11323   \n",
       "\n",
       "0    The NDAR Global Unique Identifier (GUID) for research subject  \\\n",
       "1                                      NDAR_INVRCC8D3DU              \n",
       "2                                      NDAR_INV9L4Z5GM3              \n",
       "3                                      NDAR_INVPVMWRZFZ              \n",
       "4                                      NDAR_INVBKWEEAP9              \n",
       "5                                      NDAR_INVP4JGYX3T              \n",
       "...                                                 ...              \n",
       "4127                                   NDAR_INVCKKWW1BH              \n",
       "4128                                   NDAR_INVAC8TENBY              \n",
       "4129                                   NDAR_INVPYE0C01W              \n",
       "4130                                   NDAR_INVDKV84ECB              \n",
       "4131                                   NDAR_INVPPGN6A8A              \n",
       "\n",
       "0    Subject ID how it's defined in lab/project  \\\n",
       "1                                           615   \n",
       "2                                           619   \n",
       "3                                           624   \n",
       "4                                           625   \n",
       "5                                           630   \n",
       "...                                         ...   \n",
       "4127                                       6609   \n",
       "4128                                       6611   \n",
       "4129                                       6618   \n",
       "4130                                       6625   \n",
       "4131                                       6627   \n",
       "\n",
       "0    Date on which the interview/genetic test/sampling/imaging/biospecimen was completed. MM/DD/YYYY  \\\n",
       "1                                            09/16/2008                                                \n",
       "2                                            10/05/2009                                                \n",
       "3                                            10/17/2011                                                \n",
       "4                                            04/19/2009                                                \n",
       "5                                            06/07/2010                                                \n",
       "...                                                 ...                                                \n",
       "4127                                         10/29/2009                                                \n",
       "4128                                         08/04/2009                                                \n",
       "4129                                         06/19/2009                                                \n",
       "4130                                         01/09/2012                                                \n",
       "4131                                         06/16/2009                                                \n",
       "\n",
       "0    Age in months at the time of the interview/test/sampling/imaging.  \\\n",
       "1                                                   276                  \n",
       "2                                                   288                  \n",
       "3                                                   432                  \n",
       "4                                                   324                  \n",
       "5                                                   528                  \n",
       "...                                                 ...                  \n",
       "4127                                                264                  \n",
       "4128                                                360                  \n",
       "4129                                                228                  \n",
       "4130                                                300                  \n",
       "4131                                                744                  \n",
       "\n",
       "0    Sex of subject at birth      Race of study subject  \\\n",
       "1                          F         More than one race   \n",
       "2                          M                      White   \n",
       "3                          F                      White   \n",
       "4                          M                      Asian   \n",
       "5                          F  Black or African American   \n",
       "...                      ...                        ...   \n",
       "4127                       M                      White   \n",
       "4128                       M                      White   \n",
       "4129                       F                      White   \n",
       "4130                       M  Black or African American   \n",
       "4131                       F                      White   \n",
       "\n",
       "0    Phenotype/diagnosis for the subject  \\\n",
       "1                                Control   \n",
       "2                                   Case   \n",
       "3                               Relative   \n",
       "4                               Relative   \n",
       "5                               Relative   \n",
       "...                                  ...   \n",
       "4127                             Control   \n",
       "4128                                Case   \n",
       "4129                            Relative   \n",
       "4130                             Control   \n",
       "4131                            Relative   \n",
       "\n",
       "0    Description of the phenotype for the subject Is this study of twins?  \\\n",
       "1                                 Healthy Control                      No   \n",
       "2                                         Proband                      No   \n",
       "3                                  Child Daughter                      No   \n",
       "4                                 Sibling Brother                      No   \n",
       "5                                   Parent Mother                      No   \n",
       "...                                           ...                     ...   \n",
       "4127                              Healthy Control                      No   \n",
       "4128                                      Proband                      No   \n",
       "4129                               Child Daughter                      No   \n",
       "4130                              Healthy Control                      No   \n",
       "4131                                Parent Mother                      No   \n",
       "\n",
       "0    Was it sibling study? Study of sibling(s) of autistic child.  \\\n",
       "1                                                    No             \n",
       "2                                                    No             \n",
       "3                                                    No             \n",
       "4                                                    No             \n",
       "5                                                    No             \n",
       "...                                                 ...             \n",
       "4127                                                 No             \n",
       "4128                                                 No             \n",
       "4129                                                 No             \n",
       "4130                                                 No             \n",
       "4131                                                 No             \n",
       "\n",
       "0    Was it family study? Study of biological mother, biological father and/or sibling of proband.  \\\n",
       "1                                                   Yes                                              \n",
       "2                                                   Yes                                              \n",
       "3                                                   Yes                                              \n",
       "4                                                   Yes                                              \n",
       "5                                                   Yes                                              \n",
       "...                                                 ...                                              \n",
       "4127                                                Yes                                              \n",
       "4128                                                Yes                                              \n",
       "4129                                                Yes                                              \n",
       "4130                                                Yes                                              \n",
       "4131                                                Yes                                              \n",
       "\n",
       "0    Family Pedigree User-Defined ID  \\\n",
       "1                             F00615   \n",
       "2                             F00619   \n",
       "3                             F00732   \n",
       "4                             F01344   \n",
       "5                             F00930   \n",
       "...                              ...   \n",
       "4127                          F07803   \n",
       "4128                          F07035   \n",
       "4129                          F06518   \n",
       "4130                          F06687   \n",
       "4131                          F07871   \n",
       "\n",
       "0    Was a sample taken at this interview/during this project time  \\\n",
       "1                                                    No              \n",
       "2                                                    No              \n",
       "3                                                    No              \n",
       "4                                                    No              \n",
       "5                                                    No              \n",
       "...                                                 ...              \n",
       "4127                                                 No              \n",
       "4128                                                 No              \n",
       "4129                                                 No              \n",
       "4130                                                 No              \n",
       "4131                                                 No              \n",
       "\n",
       "0                                      collection_title  \n",
       "1     Bipolar & Schizophrenia Consortium for Parsing...  \n",
       "2     Bipolar & Schizophrenia Consortium for Parsing...  \n",
       "3     Bipolar & Schizophrenia Consortium for Parsing...  \n",
       "4     Bipolar & Schizophrenia Consortium for Parsing...  \n",
       "5     Bipolar & Schizophrenia Consortium for Parsing...  \n",
       "...                                                 ...  \n",
       "4127  Bipolar & Schizophrenia Consortium for Parsing...  \n",
       "4128  Bipolar & Schizophrenia Consortium for Parsing...  \n",
       "4129  Bipolar & Schizophrenia Consortium for Parsing...  \n",
       "4130  Bipolar & Schizophrenia Consortium for Parsing...  \n",
       "4131  Bipolar & Schizophrenia Consortium for Parsing...  \n",
       "\n",
       "[2415 rows x 17 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ndar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EEG Data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# open eeg_sub_files01.txt \n",
    "eeg_sub = pd.read_csv(\"E:/B_SNIP_DATA/Package_1201513/eeg_sub_files01.txt\", delimiter = \"\\t\" )\n",
    "# make first row the columns names \n",
    "eeg_sub.columns = eeg_sub.iloc[0]\n",
    "# remove first row \n",
    "eeg_sub = eeg_sub.drop(eeg_sub.index[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>collection_id</th>\n",
       "      <th>eeg_sub_files01_id</th>\n",
       "      <th>dataset_id</th>\n",
       "      <th>The NDAR Global Unique Identifier (GUID) for research subject</th>\n",
       "      <th>Subject ID how it's defined in lab/project</th>\n",
       "      <th>Date on which the interview/genetic test/sampling/imaging/biospecimen was completed. MM/DD/YYYY</th>\n",
       "      <th>Age in months at the time of the interview/test/sampling/imaging.</th>\n",
       "      <th>Sex of subject at birth</th>\n",
       "      <th>Miscellaneous comments on study, interview, methodology relevant to this form data</th>\n",
       "      <th>EEG cap used for this experimental condition</th>\n",
       "      <th>...</th>\n",
       "      <th>Checks if completed</th>\n",
       "      <th>Date of PI/clinician signature</th>\n",
       "      <th>Name of task being rated</th>\n",
       "      <th>Image description, i.e. DTI, fMRI, Fast SPGR, phantom, EEG, dynamic PET</th>\n",
       "      <th>NaN</th>\n",
       "      <th>Visit number</th>\n",
       "      <th>Drug Name</th>\n",
       "      <th>Drug Dosage</th>\n",
       "      <th>Subject ID how it's defined in lab/project (used for source IDs that change depending on timepoint/visit/etc.)</th>\n",
       "      <th>collection_title</th>\n",
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       "  <tbody>\n",
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       "      <th>1</th>\n",
       "      <td>2126</td>\n",
       "      <td>17415</td>\n",
       "      <td>11775</td>\n",
       "      <td>NDAR_INVABGU27LY</td>\n",
       "      <td>10527</td>\n",
       "      <td>08/12/2015</td>\n",
       "      <td>288</td>\n",
       "      <td>F</td>\n",
       "      <td>Oddball</td>\n",
       "      <td>NaN</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>Psychosis and Affective Research Domains and I...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2126</td>\n",
       "      <td>17358</td>\n",
       "      <td>11775</td>\n",
       "      <td>NDAR_INVBKU3GXW1</td>\n",
       "      <td>10510</td>\n",
       "      <td>08/10/2015</td>\n",
       "      <td>336</td>\n",
       "      <td>F</td>\n",
       "      <td>Oddball</td>\n",
       "      <td>NaN</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>Psychosis and Affective Research Domains and I...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2126</td>\n",
       "      <td>17383</td>\n",
       "      <td>11775</td>\n",
       "      <td>NDAR_INVM3K9WGEK</td>\n",
       "      <td>10518</td>\n",
       "      <td>08/10/2015</td>\n",
       "      <td>624</td>\n",
       "      <td>M</td>\n",
       "      <td>Oddball</td>\n",
       "      <td>NaN</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>Psychosis and Affective Research Domains and I...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2126</td>\n",
       "      <td>17364</td>\n",
       "      <td>11775</td>\n",
       "      <td>NDAR_INV01CPDEUH</td>\n",
       "      <td>10511</td>\n",
       "      <td>08/10/2015</td>\n",
       "      <td>243</td>\n",
       "      <td>F</td>\n",
       "      <td>Oddball</td>\n",
       "      <td>NaN</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>Psychosis and Affective Research Domains and I...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2126</td>\n",
       "      <td>17273</td>\n",
       "      <td>11775</td>\n",
       "      <td>NDAR_INVWZEMC4RV</td>\n",
       "      <td>10129</td>\n",
       "      <td>02/05/2015</td>\n",
       "      <td>481</td>\n",
       "      <td>M</td>\n",
       "      <td>Oddball</td>\n",
       "      <td>NaN</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>Psychosis and Affective Research Domains and I...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1544</th>\n",
       "      <td>2274</td>\n",
       "      <td>14008</td>\n",
       "      <td>11449</td>\n",
       "      <td>NDAR_INV19YHXY5M</td>\n",
       "      <td>7618</td>\n",
       "      <td>05/12/2011</td>\n",
       "      <td>264</td>\n",
       "      <td>F</td>\n",
       "      <td>Oddball</td>\n",
       "      <td>NaN</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>Bipolar &amp; Schizophrenia Consortium for Parsing...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1545</th>\n",
       "      <td>2274</td>\n",
       "      <td>14524</td>\n",
       "      <td>11449</td>\n",
       "      <td>NDAR_INVBKEUWJR7</td>\n",
       "      <td>6052</td>\n",
       "      <td>06/09/2010</td>\n",
       "      <td>348</td>\n",
       "      <td>F</td>\n",
       "      <td>Oddball</td>\n",
       "      <td>NaN</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>Bipolar &amp; Schizophrenia Consortium for Parsing...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1546</th>\n",
       "      <td>2274</td>\n",
       "      <td>13642</td>\n",
       "      <td>11449</td>\n",
       "      <td>NDAR_INVLD61G3KT</td>\n",
       "      <td>7760</td>\n",
       "      <td>04/16/2009</td>\n",
       "      <td>204</td>\n",
       "      <td>M</td>\n",
       "      <td>Oddball</td>\n",
       "      <td>NaN</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>Bipolar &amp; Schizophrenia Consortium for Parsing...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1547</th>\n",
       "      <td>2274</td>\n",
       "      <td>13710</td>\n",
       "      <td>11449</td>\n",
       "      <td>NDAR_INVUA7FRWF6</td>\n",
       "      <td>6239</td>\n",
       "      <td>04/03/2009</td>\n",
       "      <td>444</td>\n",
       "      <td>F</td>\n",
       "      <td>Oddball</td>\n",
       "      <td>NaN</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>Bipolar &amp; Schizophrenia Consortium for Parsing...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1548</th>\n",
       "      <td>2274</td>\n",
       "      <td>13946</td>\n",
       "      <td>11449</td>\n",
       "      <td>NDAR_INV6W32793C</td>\n",
       "      <td>6543</td>\n",
       "      <td>05/19/2011</td>\n",
       "      <td>312</td>\n",
       "      <td>F</td>\n",
       "      <td>Oddball</td>\n",
       "      <td>NaN</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>Bipolar &amp; Schizophrenia Consortium for Parsing...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1548 rows × 44 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "0    collection_id eeg_sub_files01_id dataset_id  \\\n",
       "1             2126              17415      11775   \n",
       "2             2126              17358      11775   \n",
       "3             2126              17383      11775   \n",
       "4             2126              17364      11775   \n",
       "5             2126              17273      11775   \n",
       "...            ...                ...        ...   \n",
       "1544          2274              14008      11449   \n",
       "1545          2274              14524      11449   \n",
       "1546          2274              13642      11449   \n",
       "1547          2274              13710      11449   \n",
       "1548          2274              13946      11449   \n",
       "\n",
       "0    The NDAR Global Unique Identifier (GUID) for research subject  \\\n",
       "1                                      NDAR_INVABGU27LY              \n",
       "2                                      NDAR_INVBKU3GXW1              \n",
       "3                                      NDAR_INVM3K9WGEK              \n",
       "4                                      NDAR_INV01CPDEUH              \n",
       "5                                      NDAR_INVWZEMC4RV              \n",
       "...                                                 ...              \n",
       "1544                                   NDAR_INV19YHXY5M              \n",
       "1545                                   NDAR_INVBKEUWJR7              \n",
       "1546                                   NDAR_INVLD61G3KT              \n",
       "1547                                   NDAR_INVUA7FRWF6              \n",
       "1548                                   NDAR_INV6W32793C              \n",
       "\n",
       "0    Subject ID how it's defined in lab/project  \\\n",
       "1                                         10527   \n",
       "2                                         10510   \n",
       "3                                         10518   \n",
       "4                                         10511   \n",
       "5                                         10129   \n",
       "...                                         ...   \n",
       "1544                                       7618   \n",
       "1545                                       6052   \n",
       "1546                                       7760   \n",
       "1547                                       6239   \n",
       "1548                                       6543   \n",
       "\n",
       "0    Date on which the interview/genetic test/sampling/imaging/biospecimen was completed. MM/DD/YYYY  \\\n",
       "1                                            08/12/2015                                                \n",
       "2                                            08/10/2015                                                \n",
       "3                                            08/10/2015                                                \n",
       "4                                            08/10/2015                                                \n",
       "5                                            02/05/2015                                                \n",
       "...                                                 ...                                                \n",
       "1544                                         05/12/2011                                                \n",
       "1545                                         06/09/2010                                                \n",
       "1546                                         04/16/2009                                                \n",
       "1547                                         04/03/2009                                                \n",
       "1548                                         05/19/2011                                                \n",
       "\n",
       "0    Age in months at the time of the interview/test/sampling/imaging.  \\\n",
       "1                                                   288                  \n",
       "2                                                   336                  \n",
       "3                                                   624                  \n",
       "4                                                   243                  \n",
       "5                                                   481                  \n",
       "...                                                 ...                  \n",
       "1544                                                264                  \n",
       "1545                                                348                  \n",
       "1546                                                204                  \n",
       "1547                                                444                  \n",
       "1548                                                312                  \n",
       "\n",
       "0    Sex of subject at birth  \\\n",
       "1                          F   \n",
       "2                          F   \n",
       "3                          M   \n",
       "4                          F   \n",
       "5                          M   \n",
       "...                      ...   \n",
       "1544                       F   \n",
       "1545                       F   \n",
       "1546                       M   \n",
       "1547                       F   \n",
       "1548                       F   \n",
       "\n",
       "0    Miscellaneous comments on study, interview, methodology relevant to this form data  \\\n",
       "1                                               Oddball                                   \n",
       "2                                               Oddball                                   \n",
       "3                                               Oddball                                   \n",
       "4                                               Oddball                                   \n",
       "5                                               Oddball                                   \n",
       "...                                                 ...                                   \n",
       "1544                                            Oddball                                   \n",
       "1545                                            Oddball                                   \n",
       "1546                                            Oddball                                   \n",
       "1547                                            Oddball                                   \n",
       "1548                                            Oddball                                   \n",
       "\n",
       "0    EEG cap used for this experimental condition  ... Checks if completed  \\\n",
       "1                                             NaN  ...                 NaN   \n",
       "2                                             NaN  ...                 NaN   \n",
       "3                                             NaN  ...                 NaN   \n",
       "4                                             NaN  ...                 NaN   \n",
       "5                                             NaN  ...                 NaN   \n",
       "...                                           ...  ...                 ...   \n",
       "1544                                          NaN  ...                 NaN   \n",
       "1545                                          NaN  ...                 NaN   \n",
       "1546                                          NaN  ...                 NaN   \n",
       "1547                                          NaN  ...                 NaN   \n",
       "1548                                          NaN  ...                 NaN   \n",
       "\n",
       "0    Date of PI/clinician signature Name of task being rated  \\\n",
       "1                               NaN                      NaN   \n",
       "2                               NaN                      NaN   \n",
       "3                               NaN                      NaN   \n",
       "4                               NaN                      NaN   \n",
       "5                               NaN                      NaN   \n",
       "...                             ...                      ...   \n",
       "1544                            NaN                      NaN   \n",
       "1545                            NaN                      NaN   \n",
       "1546                            NaN                      NaN   \n",
       "1547                            NaN                      NaN   \n",
       "1548                            NaN                      NaN   \n",
       "\n",
       "0    Image description, i.e. DTI, fMRI, Fast SPGR, phantom, EEG, dynamic PET  \\\n",
       "1                                                   NaN                        \n",
       "2                                                   NaN                        \n",
       "3                                                   NaN                        \n",
       "4                                                   NaN                        \n",
       "5                                                   NaN                        \n",
       "...                                                 ...                        \n",
       "1544                                                NaN                        \n",
       "1545                                                NaN                        \n",
       "1546                                                NaN                        \n",
       "1547                                                NaN                        \n",
       "1548                                                NaN                        \n",
       "\n",
       "0    NaN Visit number Drug Name Drug Dosage  \\\n",
       "1    NaN          NaN       NaN         NaN   \n",
       "2    NaN          NaN       NaN         NaN   \n",
       "3    NaN          NaN       NaN         NaN   \n",
       "4    NaN          NaN       NaN         NaN   \n",
       "5    NaN          NaN       NaN         NaN   \n",
       "...   ..          ...       ...         ...   \n",
       "1544 NaN          NaN       NaN         NaN   \n",
       "1545 NaN          NaN       NaN         NaN   \n",
       "1546 NaN          NaN       NaN         NaN   \n",
       "1547 NaN          NaN       NaN         NaN   \n",
       "1548 NaN          NaN       NaN         NaN   \n",
       "\n",
       "0    Subject ID how it's defined in lab/project (used for source IDs that change depending on timepoint/visit/etc.)  \\\n",
       "1                                                   NaN                                                               \n",
       "2                                                   NaN                                                               \n",
       "3                                                   NaN                                                               \n",
       "4                                                   NaN                                                               \n",
       "5                                                   NaN                                                               \n",
       "...                                                 ...                                                               \n",
       "1544                                                NaN                                                               \n",
       "1545                                                NaN                                                               \n",
       "1546                                                NaN                                                               \n",
       "1547                                                NaN                                                               \n",
       "1548                                                NaN                                                               \n",
       "\n",
       "0                                      collection_title  \n",
       "1     Psychosis and Affective Research Domains and I...  \n",
       "2     Psychosis and Affective Research Domains and I...  \n",
       "3     Psychosis and Affective Research Domains and I...  \n",
       "4     Psychosis and Affective Research Domains and I...  \n",
       "5     Psychosis and Affective Research Domains and I...  \n",
       "...                                                 ...  \n",
       "1544  Bipolar & Schizophrenia Consortium for Parsing...  \n",
       "1545  Bipolar & Schizophrenia Consortium for Parsing...  \n",
       "1546  Bipolar & Schizophrenia Consortium for Parsing...  \n",
       "1547  Bipolar & Schizophrenia Consortium for Parsing...  \n",
       "1548  Bipolar & Schizophrenia Consortium for Parsing...  \n",
       "\n",
       "[1548 rows x 44 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eeg_sub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of subjects = 1548\n"
     ]
    }
   ],
   "source": [
    "print('number of subjects = ' + str(len(eeg_sub)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "percent_missing = eeg_sub.isnull().sum() * 100 / len(eeg_sub)\n",
    "missing_value_df = pd.DataFrame({'column_name': eeg_sub.columns,\n",
    "                                 'percent_missing': percent_missing})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "collection_id\n",
      "0.0\n",
      "eeg_sub_files01_id\n",
      "0.0\n",
      "dataset_id\n",
      "0.0\n",
      "The NDAR Global Unique Identifier (GUID) for research subject\n",
      "0.0\n",
      "Subject ID how it's defined in lab/project\n",
      "0.0\n",
      "Date on which the interview/genetic test/sampling/imaging/biospecimen was completed. MM/DD/YYYY\n",
      "0.0\n",
      "Age in months at the time of the interview/test/sampling/imaging.\n",
      "0.0\n",
      "Sex of subject at birth\n",
      "0.0\n",
      "Miscellaneous comments on study, interview, methodology relevant to this form data\n",
      "0.0\n",
      "EEG cap used for this experimental condition\n",
      "100.0\n",
      "Occipitofrontal Circumference/Head Circumference (cm)\n",
      "100.0\n",
      "ID for the Experiment/settings/run\n",
      "0.0\n",
      "Misc notes regarding the experiment for this subject\n",
      "100.0\n",
      "Information regarding termination of the experiment\n",
      "100.0\n",
      "\"Information regarding validity (e.g., inclusion criteria, QA assessment, etc.) of the experiment for this subject\"\n",
      "100.0\n",
      "Behavioral Performance - accuracy\n",
      "100.0\n",
      "Behavioral Performance - reaction time\n",
      "100.0\n",
      "Data file 1\n",
      "0.0\n",
      "type of data file 1\n",
      "100.0\n",
      "data file 2\n",
      "100.0\n",
      "data file 2 type/description\n",
      "100.0\n",
      "Data file 3\n",
      "100.0\n",
      "Data file 3 type\n",
      "100.0\n",
      "Data file 4\n",
      "100.0\n",
      "Data file 4 type\n",
      "100.0\n",
      "Included trials in the final data for this experimental condition\n",
      "100.0\n",
      "Validity of the reduced data for this experimental condition\n",
      "100.0\n",
      "Head Circumference (in cm)\n",
      "100.0\n",
      "Study; The code for each individual study\n",
      "100.0\n",
      "Week in level/study\n",
      "100.0\n",
      "Site\n",
      "100.0\n",
      "QEEG Results: microvolt2\n",
      "100.0\n",
      "Post QEEG Results: microvolt2\n",
      "100.0\n",
      "Visit name\n",
      "100.0\n",
      "Checks if completed\n",
      "100.0\n",
      "Date of PI/clinician signature\n",
      "100.0\n",
      "Name of task being rated\n",
      "100.0\n",
      "Image description, i.e. DTI, fMRI, Fast SPGR, phantom, EEG, dynamic PET\n",
      "100.0\n",
      "nan\n",
      "100.0\n",
      "Visit number\n",
      "100.0\n",
      "Drug Name\n",
      "100.0\n",
      "Drug Dosage\n",
      "100.0\n",
      "Subject ID how it's defined in lab/project (used for source IDs that change depending on timepoint/visit/etc.)\n",
      "100.0\n",
      "collection_title\n",
      "0.0\n"
     ]
    }
   ],
   "source": [
    "#get column name and missing percent \n",
    "for i in range(len(missing_value_df)):\n",
    "    print(missing_value_df['column_name'][i])\n",
    "    print(missing_value_df['percent_missing'][i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "eeg_sub = eeg_sub.dropna(axis='columns', thresh = 0.9*len(eeg_sub))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "eeg_sub = eeg_sub.drop(['collection_id', \n",
    "              'dataset_id',\n",
    "              \"Date on which the interview/genetic test/sampling/imaging/biospecimen was completed. MM/DD/YYYY\", \n",
    "             \"Age in months at the time of the interview/test/sampling/imaging.\", \n",
    "             \"Sex of subject at birth\", \n",
    "             \"Miscellaneous comments on study, interview, methodology relevant to this form data\", \n",
    "             \"ID for the Experiment/settings/run\", \n",
    "             \"collection_title\"], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "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>eeg_sub_files01_id</th>\n",
       "      <th>The NDAR Global Unique Identifier (GUID) for research subject</th>\n",
       "      <th>Subject ID how it's defined in lab/project</th>\n",
       "      <th>Data file 1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17415</td>\n",
       "      <td>NDAR_INVABGU27LY</td>\n",
       "      <td>10527</td>\n",
       "      <td>s3://NDAR_Central_1/submission_12476/S8940GSR-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17358</td>\n",
       "      <td>NDAR_INVBKU3GXW1</td>\n",
       "      <td>10510</td>\n",
       "      <td>s3://NDAR_Central_1/submission_12476/S7602VAY-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17383</td>\n",
       "      <td>NDAR_INVM3K9WGEK</td>\n",
       "      <td>10518</td>\n",
       "      <td>s3://NDAR_Central_1/submission_12476/S8155SWP-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17364</td>\n",
       "      <td>NDAR_INV01CPDEUH</td>\n",
       "      <td>10511</td>\n",
       "      <td>s3://NDAR_Central_1/submission_12476/S3398PRN-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>17273</td>\n",
       "      <td>NDAR_INVWZEMC4RV</td>\n",
       "      <td>10129</td>\n",
       "      <td>s3://NDAR_Central_1/submission_12476/S5791DTR-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1544</th>\n",
       "      <td>14008</td>\n",
       "      <td>NDAR_INV19YHXY5M</td>\n",
       "      <td>7618</td>\n",
       "      <td>s3://NDAR_Central_3/submission_12178/S3414TYX-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1545</th>\n",
       "      <td>14524</td>\n",
       "      <td>NDAR_INVBKEUWJR7</td>\n",
       "      <td>6052</td>\n",
       "      <td>s3://NDAR_Central_3/submission_12178/S4493PFB-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1546</th>\n",
       "      <td>13642</td>\n",
       "      <td>NDAR_INVLD61G3KT</td>\n",
       "      <td>7760</td>\n",
       "      <td>s3://NDAR_Central_3/submission_12178/S9308RMP-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1547</th>\n",
       "      <td>13710</td>\n",
       "      <td>NDAR_INVUA7FRWF6</td>\n",
       "      <td>6239</td>\n",
       "      <td>s3://NDAR_Central_3/submission_12178/S5003QJU-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1548</th>\n",
       "      <td>13946</td>\n",
       "      <td>NDAR_INV6W32793C</td>\n",
       "      <td>6543</td>\n",
       "      <td>s3://NDAR_Central_3/submission_12178/S8870GOQ-...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1548 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "0    eeg_sub_files01_id  \\\n",
       "1                 17415   \n",
       "2                 17358   \n",
       "3                 17383   \n",
       "4                 17364   \n",
       "5                 17273   \n",
       "...                 ...   \n",
       "1544              14008   \n",
       "1545              14524   \n",
       "1546              13642   \n",
       "1547              13710   \n",
       "1548              13946   \n",
       "\n",
       "0    The NDAR Global Unique Identifier (GUID) for research subject  \\\n",
       "1                                      NDAR_INVABGU27LY              \n",
       "2                                      NDAR_INVBKU3GXW1              \n",
       "3                                      NDAR_INVM3K9WGEK              \n",
       "4                                      NDAR_INV01CPDEUH              \n",
       "5                                      NDAR_INVWZEMC4RV              \n",
       "...                                                 ...              \n",
       "1544                                   NDAR_INV19YHXY5M              \n",
       "1545                                   NDAR_INVBKEUWJR7              \n",
       "1546                                   NDAR_INVLD61G3KT              \n",
       "1547                                   NDAR_INVUA7FRWF6              \n",
       "1548                                   NDAR_INV6W32793C              \n",
       "\n",
       "0    Subject ID how it's defined in lab/project  \\\n",
       "1                                         10527   \n",
       "2                                         10510   \n",
       "3                                         10518   \n",
       "4                                         10511   \n",
       "5                                         10129   \n",
       "...                                         ...   \n",
       "1544                                       7618   \n",
       "1545                                       6052   \n",
       "1546                                       7760   \n",
       "1547                                       6239   \n",
       "1548                                       6543   \n",
       "\n",
       "0                                           Data file 1  \n",
       "1     s3://NDAR_Central_1/submission_12476/S8940GSR-...  \n",
       "2     s3://NDAR_Central_1/submission_12476/S7602VAY-...  \n",
       "3     s3://NDAR_Central_1/submission_12476/S8155SWP-...  \n",
       "4     s3://NDAR_Central_1/submission_12476/S3398PRN-...  \n",
       "5     s3://NDAR_Central_1/submission_12476/S5791DTR-...  \n",
       "...                                                 ...  \n",
       "1544  s3://NDAR_Central_3/submission_12178/S3414TYX-...  \n",
       "1545  s3://NDAR_Central_3/submission_12178/S4493PFB-...  \n",
       "1546  s3://NDAR_Central_3/submission_12178/S9308RMP-...  \n",
       "1547  s3://NDAR_Central_3/submission_12178/S5003QJU-...  \n",
       "1548  s3://NDAR_Central_3/submission_12178/S8870GOQ-...  \n",
       "\n",
       "[1548 rows x 4 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eeg_sub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for duplicates in eeg_sub df['Data file 1']\n",
    "eeg_sub['Data file 1'].is_unique\n",
    "\n",
    "# no duplicates in eeg_sub"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merge Meta Data and EEG File Path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "eeg_ndar = pd.merge(ndar,eeg_sub,on='The NDAR Global Unique Identifier (GUID) for research subject')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select rows where 'Phenotype/diagnosis for the subject' == 'Control' or 'Case'\n",
    "eeg_ndar = eeg_ndar.loc[(eeg_ndar['Phenotype/diagnosis for the subject'] == 'Control') | (eeg_ndar['Phenotype/diagnosis for the subject'] == 'Case')]\n",
    "\n",
    "# drop unnecessary columns \n",
    "eeg_ndar = eeg_ndar.drop(['collection_id', 'ndar_subject01_id', 'dataset_id', \"Subject ID how it's defined in lab/project_x\", \n",
    "                         'Date on which the interview/genetic test/sampling/imaging/biospecimen was completed. MM/DD/YYYY', \n",
    "                         'Age in months at the time of the interview/test/sampling/imaging.', 'Sex of subject at birth', \n",
    "                         'Race of study subject', 'Description of the phenotype for the subject', 'Is this study of twins?',\n",
    "                         'Was it sibling study? Study of sibling(s) of autistic child.', \n",
    "                         'Was it family study? Study of biological mother, biological father and/or sibling of proband.',\n",
    "                         'Family Pedigree User-Defined ID', 'Was a sample taken at this interview/during this project time', \n",
    "                         'collection_title', 'eeg_sub_files01_id', \"Subject ID how it's defined in lab/project_y\"], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_name = []\n",
    "for i in eeg_ndar['Data file 1'].str.split(\"/\"):\n",
    "    file_name.append(i[-1])\n",
    "\n",
    "#make filename a column \n",
    "eeg_ndar['file_name'] = file_name\n",
    "    \n",
    "#reset index\n",
    "eeg_ndar = eeg_ndar.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# only select rows or subject we have EEG data on \n",
    "new_eeg_ndar = eeg_ndar[eeg_ndar['file_name'].isin(os.listdir(\"E:/B_SNIP_DATA/Package_1201513/eeg_sub_files01/\"))]\n",
    "new_eeg_ndar = new_eeg_ndar.reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "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>The NDAR Global Unique Identifier (GUID) for research subject</th>\n",
       "      <th>Phenotype/diagnosis for the subject</th>\n",
       "      <th>Data file 1</th>\n",
       "      <th>file_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NDAR_INVRCC8D3DU</td>\n",
       "      <td>Control</td>\n",
       "      <td>s3://NDAR_Central_2/submission_12181/S3383SOA-...</td>\n",
       "      <td>S3383SOA-3-3.zip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NDAR_INVDVZXE87V</td>\n",
       "      <td>Control</td>\n",
       "      <td>s3://NDAR_Central_2/submission_12181/S8285BFS-...</td>\n",
       "      <td>S8285BFS-3-3.zip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NDAR_INV9XVG94Y6</td>\n",
       "      <td>Case</td>\n",
       "      <td>s3://NDAR_Central_2/submission_12181/S8429UBR-...</td>\n",
       "      <td>S8429UBR-3-3.zip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NDAR_INVCVMMHNMV</td>\n",
       "      <td>Control</td>\n",
       "      <td>s3://NDAR_Central_2/submission_12181/S1759AAD-...</td>\n",
       "      <td>S1759AAD-3-3.zip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NDAR_INV99VZ6EVT</td>\n",
       "      <td>Case</td>\n",
       "      <td>s3://NDAR_Central_2/submission_12181/S8202AET-...</td>\n",
       "      <td>S8202AET-3-4.zip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>608</th>\n",
       "      <td>NDAR_INV51PJN164</td>\n",
       "      <td>Case</td>\n",
       "      <td>s3://NDAR_Central_2/submission_12149/S0877XHS-...</td>\n",
       "      <td>S0877XHS-1-3.zip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>NDAR_INVXBEY0FWW</td>\n",
       "      <td>Case</td>\n",
       "      <td>s3://NDAR_Central_3/submission_12178/S5649CKG-...</td>\n",
       "      <td>S5649CKG-1-4.zip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>NDAR_INVKYYATZRE</td>\n",
       "      <td>Case</td>\n",
       "      <td>s3://NDAR_Central_3/submission_12178/S6755OBR-...</td>\n",
       "      <td>S6755OBR-2-4.zip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>NDAR_INVDKV84ECB</td>\n",
       "      <td>Control</td>\n",
       "      <td>s3://NDAR_Central_2/submission_12149/S7536HAV-...</td>\n",
       "      <td>S7536HAV-1-2.zip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>NDAR_INVDKV84ECB</td>\n",
       "      <td>Control</td>\n",
       "      <td>s3://NDAR_Central_2/submission_12149/S7536HAV-...</td>\n",
       "      <td>S7536HAV-1-3.zip</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>613 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "0   The NDAR Global Unique Identifier (GUID) for research subject  \\\n",
       "0                                     NDAR_INVRCC8D3DU              \n",
       "1                                     NDAR_INVDVZXE87V              \n",
       "2                                     NDAR_INV9XVG94Y6              \n",
       "3                                     NDAR_INVCVMMHNMV              \n",
       "4                                     NDAR_INV99VZ6EVT              \n",
       "..                                                 ...              \n",
       "608                                   NDAR_INV51PJN164              \n",
       "609                                   NDAR_INVXBEY0FWW              \n",
       "610                                   NDAR_INVKYYATZRE              \n",
       "611                                   NDAR_INVDKV84ECB              \n",
       "612                                   NDAR_INVDKV84ECB              \n",
       "\n",
       "0   Phenotype/diagnosis for the subject  \\\n",
       "0                               Control   \n",
       "1                               Control   \n",
       "2                                  Case   \n",
       "3                               Control   \n",
       "4                                  Case   \n",
       "..                                  ...   \n",
       "608                                Case   \n",
       "609                                Case   \n",
       "610                                Case   \n",
       "611                             Control   \n",
       "612                             Control   \n",
       "\n",
       "0                                          Data file 1         file_name  \n",
       "0    s3://NDAR_Central_2/submission_12181/S3383SOA-...  S3383SOA-3-3.zip  \n",
       "1    s3://NDAR_Central_2/submission_12181/S8285BFS-...  S8285BFS-3-3.zip  \n",
       "2    s3://NDAR_Central_2/submission_12181/S8429UBR-...  S8429UBR-3-3.zip  \n",
       "3    s3://NDAR_Central_2/submission_12181/S1759AAD-...  S1759AAD-3-3.zip  \n",
       "4    s3://NDAR_Central_2/submission_12181/S8202AET-...  S8202AET-3-4.zip  \n",
       "..                                                 ...               ...  \n",
       "608  s3://NDAR_Central_2/submission_12149/S0877XHS-...  S0877XHS-1-3.zip  \n",
       "609  s3://NDAR_Central_3/submission_12178/S5649CKG-...  S5649CKG-1-4.zip  \n",
       "610  s3://NDAR_Central_3/submission_12178/S6755OBR-...  S6755OBR-2-4.zip  \n",
       "611  s3://NDAR_Central_2/submission_12149/S7536HAV-...  S7536HAV-1-2.zip  \n",
       "612  s3://NDAR_Central_2/submission_12149/S7536HAV-...  S7536HAV-1-3.zip  \n",
       "\n",
       "[613 rows x 4 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find which subject id's we have eeg data on \n",
    "new_eeg_ndar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unzip EEG files "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# unzip files (only needs to be run once)\n",
    "#for i in range(len(new_eeg_ndar['file_name'])):\n",
    "#    directory = \"E:/B_SNIP_DATA/Package_1201513/eeg_sub_files01/\" + new_eeg_ndar['file_name'][i]\n",
    "#    with zipfile.ZipFile(directory, 'r') as zip_ref:\n",
    "#            zip_ref.extractall(\"E:/B_SNIP_DATA/Package_1201513/eeg_sub_files01/\" + new_eeg_ndar['file_name'][i][0:-4])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extract data and Calculate ERP and features "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#define N200 and P300 channels \n",
    "P300_chann = ['CP1', 'CP2', 'CP3', 'CP4', 'CP5', 'CP6']\n",
    "N200_chann = ['FC1', 'FC2', 'FC3', 'FC4', 'FC5', 'FC6']\n",
    "\n",
    "#define empty lists for collection \n",
    "p300_amp_coll = {}\n",
    "p300_lat_coll = {}\n",
    "p300_amp_avg = []\n",
    "p300_amp_var = []\n",
    "p300_lat_avg = []\n",
    "p300_lat_var = []\n",
    "\n",
    "n200_amp_coll = {}\n",
    "n200_lat_coll = {}\n",
    "n200_amp_avg = []\n",
    "n200_amp_var = []\n",
    "n200_lat_avg = []\n",
    "n200_lat_var = []\n",
    "\n",
    "\n",
    "# for mean amplitude \n",
    "p300_m_amp_avg = []\n",
    "p300_m_amp_var = []\n",
    "\n",
    "n200_m_amp_avg = []\n",
    "n200_m_amp_var = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading 0 ... 884279  =      0.000 ...   884.279 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 882919  =      0.000 ...   882.919 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 883039  =      0.000 ...   883.039 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 878119  =      0.000 ...   878.119 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 879479  =      0.000 ...   879.479 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 877759  =      0.000 ...   877.759 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
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      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 880159  =      0.000 ...   880.159 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
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      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 879319  =      0.000 ...   879.319 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 890479  =      0.000 ...   890.479 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 842639  =      0.000 ...   842.639 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "97 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 879839  =      0.000 ...   879.839 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
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      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 878799  =      0.000 ...   878.799 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
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      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 881879  =      0.000 ...   881.879 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 879359  =      0.000 ...   879.359 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 882559  =      0.000 ...   882.559 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 879999  =      0.000 ...   879.999 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 877759  =      0.000 ...   877.759 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 871359  =      0.000 ...   871.359 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 874159  =      0.000 ...   874.159 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "201 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 879959  =      0.000 ...   879.959 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 871639  =      0.000 ...   871.639 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 871239  =      0.000 ...   871.239 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 870119  =      0.000 ...   870.119 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1831039  =      0.000 ...  1831.039 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1794639  =      0.000 ...  1794.639 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 888199  =      0.000 ...   888.199 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1781999  =      0.000 ...  1781.999 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1773759  =      0.000 ...  1773.759 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 890559  =      0.000 ...   890.559 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1798879  =      0.000 ...  1798.879 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1840399  =      0.000 ...  1840.399 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1818319  =      0.000 ...  1818.319 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1768639  =      0.000 ...  1768.639 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 882519  =      0.000 ...   882.519 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1845919  =      0.000 ...  1845.919 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1772639  =      0.000 ...  1772.639 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 934639  =      0.000 ...   934.639 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 878799  =      0.000 ...   878.799 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1775439  =      0.000 ...  1775.439 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 901879  =      0.000 ...   901.879 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 884839  =      0.000 ...   884.839 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 880399  =      0.000 ...   880.399 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 883119  =      0.000 ...   883.119 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 879079  =      0.000 ...   879.079 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 877479  =      0.000 ...   877.479 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 875719  =      0.000 ...   875.719 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 112959  =      0.000 ...   112.959 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "14 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 878879  =      0.000 ...   878.879 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 890079  =      0.000 ...   890.079 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 877879  =      0.000 ...   877.879 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 873079  =      0.000 ...   873.079 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 873159  =      0.000 ...   873.159 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 870959  =      0.000 ...   870.959 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 882159  =      0.000 ...   882.159 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 879639  =      0.000 ...   879.639 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 882799  =      0.000 ...   882.799 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 882479  =      0.000 ...   882.479 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 869879  =      0.000 ...   869.879 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 876679  =      0.000 ...   876.679 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading 0 ... 875879  =      0.000 ...   875.879 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 877439  =      0.000 ...   877.439 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 881639  =      0.000 ...   881.639 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 871599  =      0.000 ...   871.599 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 870519  =      0.000 ...   870.519 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 868879  =      0.000 ...   868.879 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 879639  =      0.000 ...   879.639 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 874719  =      0.000 ...   874.719 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 871639  =      0.000 ...   871.639 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1780879  =      0.000 ...  1780.879 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1800079  =      0.000 ...  1800.079 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1809359  =      0.000 ...  1809.359 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1770159  =      0.000 ...  1770.159 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 885479  =      0.000 ...   885.479 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1794639  =      0.000 ...  1794.639 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1817839  =      0.000 ...  1817.839 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1821519  =      0.000 ...  1821.519 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1791839  =      0.000 ...  1791.839 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1818239  =      0.000 ...  1818.239 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 892879  =      0.000 ...   892.879 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 878999  =      0.000 ...   878.999 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 876119  =      0.000 ...   876.119 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 897559  =      0.000 ...   897.559 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 879359  =      0.000 ...   879.359 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 879439  =      0.000 ...   879.439 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 872879  =      0.000 ...   872.879 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 879399  =      0.000 ...   879.399 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 875919  =      0.000 ...   875.919 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 889359  =      0.000 ...   889.359 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 868199  =      0.000 ...   868.199 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 879079  =      0.000 ...   879.079 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 869679  =      0.000 ...   869.679 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 874359  =      0.000 ...   874.359 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 868119  =      0.000 ...   868.119 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 868319  =      0.000 ...   868.319 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "199 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 870319  =      0.000 ...   870.319 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 875239  =      0.000 ...   875.239 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 868079  =      0.000 ...   868.079 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 868959  =      0.000 ...   868.959 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 868079  =      0.000 ...   868.079 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 880799  =      0.000 ...   880.799 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1800239  =      0.000 ...  1800.239 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1812159  =      0.000 ...  1812.159 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 890039  =      0.000 ...   890.039 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1773679  =      0.000 ...  1773.679 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1797199  =      0.000 ...  1797.199 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 883199  =      0.000 ...   883.199 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1769919  =      0.000 ...  1769.919 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1765279  =      0.000 ...  1765.279 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1769839  =      0.000 ...  1769.839 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 879479  =      0.000 ...   879.479 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 886919  =      0.000 ...   886.919 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 880319  =      0.000 ...   880.319 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 616559  =      0.000 ...   616.559 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "71 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1836399  =      0.000 ...  1836.399 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 889119  =      0.000 ...   889.119 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1770399  =      0.000 ...  1770.399 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1779039  =      0.000 ...  1779.039 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1766239  =      0.000 ...  1766.239 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading 0 ... 1788319  =      0.000 ...  1788.319 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1832799  =      0.000 ...  1832.799 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1795199  =      0.000 ...  1795.199 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1771359  =      0.000 ...  1771.359 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1768239  =      0.000 ...  1768.239 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1768239  =      0.000 ...  1768.239 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 898839  =      0.000 ...   898.839 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1788239  =      0.000 ...  1788.239 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1772799  =      0.000 ...  1772.799 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1785999  =      0.000 ...  1785.999 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 890479  =      0.000 ...   890.479 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 871839  =      0.000 ...   871.839 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 884039  =      0.000 ...   884.039 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 876359  =      0.000 ...   876.359 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 868599  =      0.000 ...   868.599 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 869599  =      0.000 ...   869.599 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 872079  =      0.000 ...   872.079 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 875159  =      0.000 ...   875.159 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 871279  =      0.000 ...   871.279 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 871719  =      0.000 ...   871.719 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 869599  =      0.000 ...   869.599 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 870039  =      0.000 ...   870.039 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 871439  =      0.000 ...   871.439 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 874359  =      0.000 ...   874.359 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 873519  =      0.000 ...   873.519 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 868279  =      0.000 ...   868.279 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 873959  =      0.000 ...   873.959 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 878399  =      0.000 ...   878.399 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 876879  =      0.000 ...   876.879 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 874039  =      0.000 ...   874.039 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 868519  =      0.000 ...   868.519 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 877359  =      0.000 ...   877.359 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1797279  =      0.000 ...  1797.279 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1758079  =      0.000 ...  1758.079 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1776479  =      0.000 ...  1776.479 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 885039  =      0.000 ...   885.039 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1780159  =      0.000 ...  1780.159 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1790559  =      0.000 ...  1790.559 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 883079  =      0.000 ...   883.079 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1821119  =      0.000 ...  1821.119 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1784399  =      0.000 ...  1784.399 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1787999  =      0.000 ...  1787.999 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1759919  =      0.000 ...  1759.919 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1748879  =      0.000 ...  1748.879 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1788319  =      0.000 ...  1788.319 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1768399  =      0.000 ...  1768.399 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 894559  =      0.000 ...   894.559 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1768879  =      0.000 ...  1768.879 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1768159  =      0.000 ...  1768.159 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 870639  =      0.000 ...   870.639 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 872279  =      0.000 ...   872.279 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 870279  =      0.000 ...   870.279 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 868599  =      0.000 ...   868.599 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 878119  =      0.000 ...   878.119 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 870519  =      0.000 ...   870.519 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 868959  =      0.000 ...   868.959 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 882159  =      0.000 ...   882.159 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 870799  =      0.000 ...   870.799 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 870279  =      0.000 ...   870.279 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading 0 ... 875959  =      0.000 ...   875.959 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 877159  =      0.000 ...   877.159 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1785439  =      0.000 ...  1785.439 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1791759  =      0.000 ...  1791.759 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 892519  =      0.000 ...   892.519 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 882639  =      0.000 ...   882.639 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1801199  =      0.000 ...  1801.199 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1797519  =      0.000 ...  1797.519 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1805679  =      0.000 ...  1805.679 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1775039  =      0.000 ...  1775.039 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 882999  =      0.000 ...   882.999 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1785199  =      0.000 ...  1785.199 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 884359  =      0.000 ...   884.359 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1789359  =      0.000 ...  1789.359 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1789279  =      0.000 ...  1789.279 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 870599  =      0.000 ...   870.599 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 873599  =      0.000 ...   873.599 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 871999  =      0.000 ...   871.999 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 878879  =      0.000 ...   878.879 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 873159  =      0.000 ...   873.159 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 868559  =      0.000 ...   868.559 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 876639  =      0.000 ...   876.639 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 870599  =      0.000 ...   870.599 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 872479  =      0.000 ...   872.479 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 868559  =      0.000 ...   868.559 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 871199  =      0.000 ...   871.199 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 876959  =      0.000 ...   876.959 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 869839  =      0.000 ...   869.839 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 870879  =      0.000 ...   870.879 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 880759  =      0.000 ...   880.759 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1770159  =      0.000 ...  1770.159 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1799359  =      0.000 ...  1799.359 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 885959  =      0.000 ...   885.959 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 876319  =      0.000 ...   876.319 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 881959  =      0.000 ...   881.959 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1803759  =      0.000 ...  1803.759 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 881919  =      0.000 ...   881.919 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 910439  =      0.000 ...   910.439 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1779599  =      0.000 ...  1779.599 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1797599  =      0.000 ...  1797.599 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1810639  =      0.000 ...  1810.639 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1772559  =      0.000 ...  1772.559 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1785439  =      0.000 ...  1785.439 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1806959  =      0.000 ...  1806.959 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1767119  =      0.000 ...  1767.119 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1801759  =      0.000 ...  1801.759 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 871999  =      0.000 ...   871.999 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 871559  =      0.000 ...   871.559 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 868479  =      0.000 ...   868.479 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 868319  =      0.000 ...   868.319 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 875839  =      0.000 ...   875.839 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 868479  =      0.000 ...   868.479 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 874239  =      0.000 ...   874.239 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 872759  =      0.000 ...   872.759 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 869799  =      0.000 ...   869.799 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 868479  =      0.000 ...   868.479 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 877879  =      0.000 ...   877.879 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 872039  =      0.000 ...   872.039 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 871239  =      0.000 ...   871.239 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading 0 ... 872879  =      0.000 ...   872.879 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 867839  =      0.000 ...   867.839 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1791919  =      0.000 ...  1791.919 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1791999  =      0.000 ...  1791.999 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1780319  =      0.000 ...  1780.319 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 883079  =      0.000 ...   883.079 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1793439  =      0.000 ...  1793.439 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1769679  =      0.000 ...  1769.679 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 887999  =      0.000 ...   887.999 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1780879  =      0.000 ...  1780.879 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 883199  =      0.000 ...   883.199 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1805759  =      0.000 ...  1805.759 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1800799  =      0.000 ...  1800.799 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 913839  =      0.000 ...   913.839 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 883399  =      0.000 ...   883.399 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1772239  =      0.000 ...  1772.239 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1770399  =      0.000 ...  1770.399 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1813599  =      0.000 ...  1813.599 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1818639  =      0.000 ...  1818.639 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 882639  =      0.000 ...   882.639 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 872679  =      0.000 ...   872.679 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 874919  =      0.000 ...   874.919 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 869839  =      0.000 ...   869.839 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 875679  =      0.000 ...   875.679 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 868479  =      0.000 ...   868.479 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 872119  =      0.000 ...   872.119 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 872199  =      0.000 ...   872.199 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 871919  =      0.000 ...   871.919 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 869199  =      0.000 ...   869.199 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 868879  =      0.000 ...   868.879 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 879399  =      0.000 ...   879.399 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 884319  =      0.000 ...   884.319 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 877039  =      0.000 ...   877.039 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 600959  =      0.000 ...   600.959 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "71 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 868399  =      0.000 ...   868.399 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 868599  =      0.000 ...   868.599 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 879919  =      0.000 ...   879.919 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1792799  =      0.000 ...  1792.799 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 891239  =      0.000 ...   891.239 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1780639  =      0.000 ...  1780.639 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1798559  =      0.000 ...  1798.559 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 917199  =      0.000 ...   917.199 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 883399  =      0.000 ...   883.399 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1088599  =      0.000 ...  1088.599 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 884519  =      0.000 ...   884.519 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 884879  =      0.000 ...   884.879 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 916239  =      0.000 ...   916.239 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 897479  =      0.000 ...   897.479 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1801759  =      0.000 ...  1801.759 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1807679  =      0.000 ...  1807.679 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1779839  =      0.000 ...  1779.839 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1797599  =      0.000 ...  1797.599 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1806239  =      0.000 ...  1806.239 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 888599  =      0.000 ...   888.599 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 898479  =      0.000 ...   898.479 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 886999  =      0.000 ...   886.999 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1785439  =      0.000 ...  1785.439 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1786159  =      0.000 ...  1786.159 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1801839  =      0.000 ...  1801.839 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading 0 ... 1806879  =      0.000 ...  1806.879 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1786879  =      0.000 ...  1786.879 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 904679  =      0.000 ...   904.679 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1808319  =      0.000 ...  1808.319 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1789199  =      0.000 ...  1789.199 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1777199  =      0.000 ...  1777.199 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 889799  =      0.000 ...   889.799 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1809119  =      0.000 ...  1809.119 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1786879  =      0.000 ...  1786.879 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1788159  =      0.000 ...  1788.159 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1784079  =      0.000 ...  1784.079 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 883479  =      0.000 ...   883.479 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1784079  =      0.000 ...  1784.079 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1791759  =      0.000 ...  1791.759 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1811039  =      0.000 ...  1811.039 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1766239  =      0.000 ...  1766.239 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 884119  =      0.000 ...   884.119 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1812399  =      0.000 ...  1812.399 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1777919  =      0.000 ...  1777.919 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1809759  =      0.000 ...  1809.759 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1767839  =      0.000 ...  1767.839 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1843759  =      0.000 ...  1843.759 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1788879  =      0.000 ...  1788.879 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1837759  =      0.000 ...  1837.759 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1771919  =      0.000 ...  1771.919 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1767999  =      0.000 ...  1767.999 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1837359  =      0.000 ...  1837.359 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1807039  =      0.000 ...  1807.039 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1782799  =      0.000 ...  1782.799 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1780799  =      0.000 ...  1780.799 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1801839  =      0.000 ...  1801.839 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 884199  =      0.000 ...   884.199 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 883399  =      0.000 ...   883.399 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "99 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1807759  =      0.000 ...  1807.759 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1770159  =      0.000 ...  1770.159 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1778399  =      0.000 ...  1778.399 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1826799  =      0.000 ...  1826.799 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1790799  =      0.000 ...  1790.799 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1777759  =      0.000 ...  1777.759 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1782399  =      0.000 ...  1782.399 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1783919  =      0.000 ...  1783.919 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1814959  =      0.000 ...  1814.959 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1792959  =      0.000 ...  1792.959 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 902159  =      0.000 ...   902.159 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1761839  =      0.000 ...  1761.839 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1817759  =      0.000 ...  1817.759 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 886479  =      0.000 ...   886.479 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1769519  =      0.000 ...  1769.519 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1777599  =      0.000 ...  1777.599 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1806879  =      0.000 ...  1806.879 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1772479  =      0.000 ...  1772.479 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 897519  =      0.000 ...   897.519 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1768959  =      0.000 ...  1768.959 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 886399  =      0.000 ...   886.399 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1766719  =      0.000 ...  1766.719 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1774399  =      0.000 ...  1774.399 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1774479  =      0.000 ...  1774.479 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1765279  =      0.000 ...  1765.279 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1769679  =      0.000 ...  1769.679 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 projection items activated\n",
      "Reading 0 ... 1784079  =      0.000 ...  1784.079 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1786559  =      0.000 ...  1786.559 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1795359  =      0.000 ...  1795.359 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1775279  =      0.000 ...  1775.279 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1778239  =      0.000 ...  1778.239 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1773679  =      0.000 ...  1773.679 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1761759  =      0.000 ...  1761.759 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1799519  =      0.000 ...  1799.519 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1817279  =      0.000 ...  1817.279 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 884879  =      0.000 ...   884.879 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1836959  =      0.000 ...  1836.959 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1814399  =      0.000 ...  1814.399 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1789279  =      0.000 ...  1789.279 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1804559  =      0.000 ...  1804.559 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 885759  =      0.000 ...   885.759 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 899879  =      0.000 ...   899.879 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1773039  =      0.000 ...  1773.039 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1782639  =      0.000 ...  1782.639 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1769039  =      0.000 ...  1769.039 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1792799  =      0.000 ...  1792.799 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1781999  =      0.000 ...  1781.999 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1811839  =      0.000 ...  1811.839 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1772879  =      0.000 ...  1772.879 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1799359  =      0.000 ...  1799.359 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1830319  =      0.000 ...  1830.319 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1786719  =      0.000 ...  1786.719 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1795199  =      0.000 ...  1795.199 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 883119  =      0.000 ...   883.119 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1762479  =      0.000 ...  1762.479 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1792559  =      0.000 ...  1792.559 secs...\n",
      "Used Annotations descriptions: ['2']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 888479  =      0.000 ...   888.479 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 887039  =      0.000 ...   887.039 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1769839  =      0.000 ...  1769.839 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 886479  =      0.000 ...   886.479 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 885559  =      0.000 ...   885.559 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1777759  =      0.000 ...  1777.759 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1799759  =      0.000 ...  1799.759 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 886439  =      0.000 ...   886.439 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1801359  =      0.000 ...  1801.359 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1768719  =      0.000 ...  1768.719 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 880799  =      0.000 ...   880.799 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 918359  =      0.000 ...   918.359 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1768719  =      0.000 ...  1768.719 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 838319  =      0.000 ...   838.319 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "95 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 870479  =      0.000 ...   870.479 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 869159  =      0.000 ...   869.159 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 870799  =      0.000 ...   870.799 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 875279  =      0.000 ...   875.279 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 871279  =      0.000 ...   871.279 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 871559  =      0.000 ...   871.559 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 889039  =      0.000 ...   889.039 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1772479  =      0.000 ...  1772.479 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1765119  =      0.000 ...  1765.119 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 877039  =      0.000 ...   877.039 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 877719  =      0.000 ...   877.719 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 913119  =      0.000 ...   913.119 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 877839  =      0.000 ...   877.839 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 889039  =      0.000 ...   889.039 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 878399  =      0.000 ...   878.399 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 projection items activated\n",
      "Reading 0 ... 893039  =      0.000 ...   893.039 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 879759  =      0.000 ...   879.759 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 879239  =      0.000 ...   879.239 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1781839  =      0.000 ...  1781.839 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 879439  =      0.000 ...   879.439 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 881279  =      0.000 ...   881.279 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 876159  =      0.000 ...   876.159 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 878639  =      0.000 ...   878.639 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 877239  =      0.000 ...   877.239 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 876479  =      0.000 ...   876.479 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 880519  =      0.000 ...   880.519 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 879199  =      0.000 ...   879.199 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 882119  =      0.000 ...   882.119 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 882399  =      0.000 ...   882.399 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 879399  =      0.000 ...   879.399 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1765599  =      0.000 ...  1765.599 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 896519  =      0.000 ...   896.519 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 880959  =      0.000 ...   880.959 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 879519  =      0.000 ...   879.519 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 868719  =      0.000 ...   868.719 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 867639  =      0.000 ...   867.639 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 886239  =      0.000 ...   886.239 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 870559  =      0.000 ...   870.559 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 868399  =      0.000 ...   868.399 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 870879  =      0.000 ...   870.879 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 887159  =      0.000 ...   887.159 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 874879  =      0.000 ...   874.879 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 874599  =      0.000 ...   874.599 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 871759  =      0.000 ...   871.759 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 870399  =      0.000 ...   870.399 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 876079  =      0.000 ...   876.079 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 878439  =      0.000 ...   878.439 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 876999  =      0.000 ...   876.999 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 877919  =      0.000 ...   877.919 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1031119  =      0.000 ...  1031.119 secs...\n",
      "Used Annotations descriptions: ['207']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "1 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 884359  =      0.000 ...   884.359 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 879439  =      0.000 ...   879.439 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 881759  =      0.000 ...   881.759 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 892279  =      0.000 ...   892.279 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 885399  =      0.000 ...   885.399 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 883559  =      0.000 ...   883.559 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 885759  =      0.000 ...   885.759 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1811919  =      0.000 ...  1811.919 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 885719  =      0.000 ...   885.719 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 899039  =      0.000 ...   899.039 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 878279  =      0.000 ...   878.279 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 875759  =      0.000 ...   875.759 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 879679  =      0.000 ...   879.679 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 880279  =      0.000 ...   880.279 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 881039  =      0.000 ...   881.039 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 879439  =      0.000 ...   879.439 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 882359  =      0.000 ...   882.359 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 879199  =      0.000 ...   879.199 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 872279  =      0.000 ...   872.279 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 873759  =      0.000 ...   873.759 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 869479  =      0.000 ...   869.479 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 877919  =      0.000 ...   877.919 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 870999  =      0.000 ...   870.999 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 868919  =      0.000 ...   868.919 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 projection items activated\n",
      "Reading 0 ... 871239  =      0.000 ...   871.239 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 868399  =      0.000 ...   868.399 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 868439  =      0.000 ...   868.439 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 876239  =      0.000 ...   876.239 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 871319  =      0.000 ...   871.319 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 868319  =      0.000 ...   868.319 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 869599  =      0.000 ...   869.599 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 876079  =      0.000 ...   876.079 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 892919  =      0.000 ...   892.919 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 870159  =      0.000 ...   870.159 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 870479  =      0.000 ...   870.479 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 879279  =      0.000 ...   879.279 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 877239  =      0.000 ...   877.239 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 878439  =      0.000 ...   878.439 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 886159  =      0.000 ...   886.159 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 881599  =      0.000 ...   881.599 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 884239  =      0.000 ...   884.239 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 879839  =      0.000 ...   879.839 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 879839  =      0.000 ...   879.839 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 880319  =      0.000 ...   880.319 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1778879  =      0.000 ...  1778.879 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 1768799  =      0.000 ...  1768.799 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 885039  =      0.000 ...   885.039 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 877359  =      0.000 ...   877.359 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 879439  =      0.000 ...   879.439 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 876519  =      0.000 ...   876.519 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 889439  =      0.000 ...   889.439 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 875919  =      0.000 ...   875.919 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 892839  =      0.000 ...   892.839 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 890599  =      0.000 ...   890.599 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 887399  =      0.000 ...   887.399 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 86839  =      0.000 ...    86.839 secs...\n",
      "Reading 0 ... 928559  =      0.000 ...   928.559 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 893079  =      0.000 ...   893.079 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 921879  =      0.000 ...   921.879 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 920199  =      0.000 ...   920.199 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 896639  =      0.000 ...   896.639 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 912559  =      0.000 ...   912.559 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 871519  =      0.000 ...   871.519 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 905279  =      0.000 ...   905.279 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 891359  =      0.000 ...   891.359 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 895359  =      0.000 ...   895.359 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 883039  =      0.000 ...   883.039 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 871919  =      0.000 ...   871.919 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 888199  =      0.000 ...   888.199 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 509999  =      0.000 ...   509.999 secs...\n",
      "Reading 0 ... 896439  =      0.000 ...   896.439 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 891759  =      0.000 ...   891.759 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 893919  =      0.000 ...   893.919 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 878119  =      0.000 ...   878.119 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 885599  =      0.000 ...   885.599 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 916919  =      0.000 ...   916.919 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 874839  =      0.000 ...   874.839 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 872759  =      0.000 ...   872.759 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 907479  =      0.000 ...   907.479 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 900119  =      0.000 ...   900.119 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 886839  =      0.000 ...   886.839 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 876559  =      0.000 ...   876.559 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 880919  =      0.000 ...   880.919 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 914599  =      0.000 ...   914.599 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 899359  =      0.000 ...   899.359 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 879079  =      0.000 ...   879.079 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 875559  =      0.000 ...   875.559 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 893039  =      0.000 ...   893.039 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 879999  =      0.000 ...   879.999 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 873719  =      0.000 ...   873.719 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 485719  =      0.000 ...   485.719 secs...\n",
      "Reading 0 ... 876519  =      0.000 ...   876.519 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 894119  =      0.000 ...   894.119 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 511719  =      0.000 ...   511.719 secs...\n",
      "Reading 0 ... 886719  =      0.000 ...   886.719 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 890159  =      0.000 ...   890.159 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 891439  =      0.000 ...   891.439 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 882119  =      0.000 ...   882.119 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 891479  =      0.000 ...   891.479 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 879399  =      0.000 ...   879.399 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 509919  =      0.000 ...   509.919 secs...\n",
      "Reading 0 ... 641159  =      0.000 ...   641.159 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "66 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 890679  =      0.000 ...   890.679 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 874999  =      0.000 ...   874.999 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 893799  =      0.000 ...   893.799 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 867759  =      0.000 ...   867.759 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "98 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 899239  =      0.000 ...   899.239 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 890559  =      0.000 ...   890.559 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 873279  =      0.000 ...   873.279 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 891359  =      0.000 ...   891.359 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 875599  =      0.000 ...   875.599 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 510919  =      0.000 ...   510.919 secs...\n",
      "Reading 0 ... 508679  =      0.000 ...   508.679 secs...\n",
      "Reading 0 ... 875519  =      0.000 ...   875.519 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 888119  =      0.000 ...   888.119 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 75759  =      0.000 ...    75.759 secs...\n",
      "Reading 0 ... 892679  =      0.000 ...   892.679 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 901439  =      0.000 ...   901.439 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 899799  =      0.000 ...   899.799 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 872519  =      0.000 ...   872.519 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 758839  =      0.000 ...   758.839 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "87 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 879479  =      0.000 ...   879.479 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 873279  =      0.000 ...   873.279 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 872359  =      0.000 ...   872.359 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 756119  =      0.000 ...   756.119 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "50 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 52639  =      0.000 ...    52.639 secs...\n",
      "Reading 0 ... 510479  =      0.000 ...   510.479 secs...\n",
      "Reading 0 ... 899959  =      0.000 ...   899.959 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 877759  =      0.000 ...   877.759 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 923439  =      0.000 ...   923.439 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 830919  =      0.000 ...   830.919 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "95 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 910759  =      0.000 ...   910.759 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 73599  =      0.000 ...    73.599 secs...\n",
      "Reading 0 ... 870559  =      0.000 ...   870.559 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 881679  =      0.000 ...   881.679 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 881679  =      0.000 ...   881.679 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 868039  =      0.000 ...   868.039 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "97 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 537719  =      0.000 ...   537.719 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "60 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 883679  =      0.000 ...   883.679 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 896239  =      0.000 ...   896.239 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 883159  =      0.000 ...   883.159 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 889999  =      0.000 ...   889.999 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 936759  =      0.000 ...   936.759 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 511359  =      0.000 ...   511.359 secs...\n",
      "Reading 0 ... 881759  =      0.000 ...   881.759 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 157519  =      0.000 ...   157.519 secs...\n",
      "Reading 0 ... 885359  =      0.000 ...   885.359 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 872399  =      0.000 ...   872.399 secs...\n",
      "Used Annotations descriptions: ['2']\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "100 matching events found\n",
      "Setting baseline interval to [-0.5, 0.0] sec\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Reading 0 ... 21079  =      0.000 ...    21.079 secs...\n"
     ]
    }
   ],
   "source": [
    "#instead of deleting subjects who do not have all channels one by one do it using a loop\n",
    "index_drop = []\n",
    "for i in range(len(new_eeg_ndar['file_name'])):\n",
    "    directory = \"E:/B_SNIP_DATA/Package_1201513/eeg_sub_files01/\" + new_eeg_ndar['file_name'][i][0:-4] \n",
    "    directory = \"E:/B_SNIP_DATA/Package_1201513/eeg_sub_files01/\" + new_eeg_ndar['file_name'][i][0:-4] + \"/\" + os.listdir(directory)[0]\n",
    "     \n",
    "    # Load EEG data from .cnt file using mne\n",
    "    try:\n",
    "        raw = mne.io.read_raw_cnt(input_fname = directory, eog = (), preload  = False, verbose = None)\n",
    "        raw.load_data()\n",
    "        \n",
    "        #calculate ERP \n",
    "        events =  mne.events_from_annotations(raw, regexp = '2')\n",
    "        epochs = mne.Epochs(raw, events=events[0], tmin=-0.5, tmax=0.5)\n",
    "        erp = epochs.average()\n",
    "        \n",
    "        #calculate P300 and N200 features \n",
    "        p300_amp = [None] * 6\n",
    "        p300_lat = [None] * 6\n",
    "        n200_amp = [None] * 6\n",
    "        n200_lat = [None] * 6\n",
    "        \n",
    "        p300_m_amp = [None] * 6\n",
    "        n200_m_amp = [None] * 6\n",
    "        \n",
    "        for j in range(len(P300_chann)):     #Get P300 and P200 for respective channels \n",
    "            erp_P300_chann = erp.copy().pick([P300_chann[j]]) #select j-th P300 channel \n",
    "            ch_p300, p300_lat[j] , p300_amp[j] = erp_P300_chann.get_peak(ch_type='eeg', tmin=0.25, tmax=0.4, mode='pos', return_amplitude=True) #calculate P300 for channel \n",
    "            \n",
    "            erp_P300_chann = erp.copy().pick([P300_chann[j]]).crop(tmin=0.25, tmax=0.4)\n",
    "            p300_m_amp[j] = erp_P300_chann.data.mean(axis=1)[0] * 1e6 #calculate P300 for channel  for mean amp\n",
    "\n",
    "            erp_N200_chann = erp.copy().pick_channels([N200_chann[j]]) #select j-th N200 channel \n",
    "            ch_n200, n200_lat[j], n200_amp[j] = erp_N200_chann.get_peak(ch_type='eeg', tmin=0.2, tmax=0.35, mode='neg', return_amplitude=True) #calculate N200 for channel \n",
    "        \n",
    "            erp_N200_chann = erp.copy().pick([N200_chann[j]]).crop(tmin = 0.2, tmax = 0.35) #select j-th N200 channel \n",
    "            n200_m_amp[j] = erp_N200_chann.data.mean(axis=1)[0] * 1e6#calculate N200 for channel \n",
    "        \n",
    "        p300_amp_coll[i] = p300_amp\n",
    "        p300_amp_avg.append(np.mean(p300_amp))\n",
    "        p300_amp_var.append(np.var(p300_amp))\n",
    "        p300_lat_coll[i] = p300_lat\n",
    "        p300_lat_avg.append(np.mean(p300_lat))\n",
    "        p300_lat_var.append(np.var(p300_lat))\n",
    "    \n",
    "        n200_amp_coll[i] = n200_amp\n",
    "        n200_amp_avg.append(np.mean(n200_amp))\n",
    "        n200_amp_var.append(np.var(n200_amp))\n",
    "        n200_lat_coll[i] = n200_lat\n",
    "        n200_lat_avg.append(np.mean(n200_lat))\n",
    "        n200_lat_var.append(np.var(n200_lat))\n",
    "        \n",
    "        #mean amplitude \n",
    "        p300_m_amp_avg.append(np.mean(p300_amp))\n",
    "        p300_m_amp_var.append(np.var(p300_amp))\n",
    "\n",
    "        n200_m_amp_avg.append(np.mean(n200_amp))\n",
    "        n200_m_amp_var.append(np.var(n200_amp))\n",
    "\n",
    "    except:\n",
    "        new_eeg_ndar = new_eeg_ndar.drop([i], axis = 0)\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Results Section Plots "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_peak_measures(ch, tmin, tmax, lat, amp):\n",
    "    print(f'Channel: {ch}')\n",
    "    print(f'Time Window: {tmin * 1e3:.3f} - {tmax * 1e3:.3f} ms')\n",
    "    print(f'Peak Latency: {lat * 1e3:.3f} ms')\n",
    "    print(f'Peak Amplitude: {amp * 1e6:.3f} µV')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def graph_300_200(sub):\n",
    "    #define N200 and P300 channels \n",
    "    directory = \"E:/B_SNIP_DATA/Package_1201513/eeg_sub_files01/\" + new_eeg_ndar['file_name'][sub][0:-4] \n",
    "    directory = \"E:/B_SNIP_DATA/Package_1201513/eeg_sub_files01/\" + new_eeg_ndar['file_name'][sub][0:-4] + \"/\" + os.listdir(directory)[0]\n",
    "     \n",
    "    # Load EEG data from .cnt file using mne\n",
    "    raw = mne.io.read_raw_cnt(input_fname = directory, eog = (), preload  = False, verbose = None)\n",
    "    raw.load_data()\n",
    "        \n",
    "    #calculate ERP \n",
    "    events =  mne.events_from_annotations(raw, regexp = '2')\n",
    "    epochs = mne.Epochs(raw, events=events[0], tmin=-0.5, tmax=0.5)\n",
    "    erp = epochs.average()\n",
    "    \n",
    "    P300_chann = ['CP1', 'CP2', 'CP3', 'CP4', 'CP5', 'CP6']\n",
    "    N200_chann = ['FC1', 'FC2', 'FC3', 'FC4', 'FC5', 'FC6']\n",
    "    \n",
    "    erp_P300_chann = erp.copy().pick(P300_chann)\n",
    "\n",
    "    #calculate P300\n",
    "    ch, lat, amp = erp_P300_chann.get_peak(ch_type='eeg', tmin=0.25, tmax=0.4, mode='pos', return_amplitude=True)\n",
    "    # Print output\n",
    "    print('** PEAK MEASURES FOR P300**')\n",
    "    print_peak_measures(ch = ch , tmin = 0.25, tmax = 0.4,lat = lat, amp = amp)\n",
    "\n",
    "    #plot P300 Peak \n",
    "    fig, ax = plt.subplots(nrows=1, ncols=1)\n",
    "    times = np.array([0.25, 0.4])\n",
    "\n",
    "    title = \"Centro-Parietal (P300) ERP\"\n",
    "    erp_P300_chann.plot(axes=ax, time_unit='ms', show=False, titles=title)\n",
    "    ax.plot(lat * 1e3, amp * 1e6, marker='*', color='C6')\n",
    "    ax.axvspan(*(times * 1e3), facecolor='C1', alpha=0.3)\n",
    "    ax.set_xlim(-0, 500)  # Show zoomed in around peak\n",
    "\n",
    "    erp_N200_chann = erp.copy().pick_channels(N200_chann)\n",
    "\n",
    "    #calculate N200\n",
    "    ch, lat, amp = erp_N200_chann.get_peak(ch_type='eeg', tmin=0.2, tmax=0.35, mode='neg', return_amplitude=True)\n",
    "    # Print output\n",
    "    print('** PEAK MEASURES FOR N200**')\n",
    "    print_peak_measures(ch = ch , tmin = 0.2, tmax = 0.35,lat = lat, amp = amp)\n",
    "\n",
    "    #plot N200 Peak \n",
    "    fig, ax = plt.subplots(nrows=1, ncols=1)\n",
    "    times = np.array([0.2, 0.35])\n",
    "\n",
    "    title = \"Fronto-Central (N200) ERP\"\n",
    "    erp_N200_chann.plot(axes=ax, time_unit='ms', show=False, titles=title)\n",
    "    ax.plot(lat * 1e3, amp * 1e6, marker='*', color='C6')\n",
    "    ax.axvspan(*(times * 1e3), facecolor='C1', alpha=0.3)\n",
    "    ax.set_xlim(0, 500)  # Show zoomed in around peak"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Machine Learning "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total number of subjects = 515\n",
      "number of controls  = 188\n",
      "number of cases = 327\n"
     ]
    }
   ],
   "source": [
    "print (\"total number of subjects = \" + str(len(new_eeg_ndar)))\n",
    "print(\"number of controls  = \" + str(len(new_eeg_ndar[new_eeg_ndar[\"Phenotype/diagnosis for the subject\"] == 'Control'])))\n",
    "print(\"number of cases = \" + str(len(new_eeg_ndar[new_eeg_ndar[\"Phenotype/diagnosis for the subject\"] == 'Case'])))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "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>The NDAR Global Unique Identifier (GUID) for research subject</th>\n",
       "      <th>Phenotype/diagnosis for the subject</th>\n",
       "      <th>Data file 1</th>\n",
       "      <th>file_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NDAR_INVRCC8D3DU</td>\n",
       "      <td>Control</td>\n",
       "      <td>s3://NDAR_Central_2/submission_12181/S3383SOA-...</td>\n",
       "      <td>S3383SOA-3-3.zip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NDAR_INVDVZXE87V</td>\n",
       "      <td>Control</td>\n",
       "      <td>s3://NDAR_Central_2/submission_12181/S8285BFS-...</td>\n",
       "      <td>S8285BFS-3-3.zip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NDAR_INV9XVG94Y6</td>\n",
       "      <td>Case</td>\n",
       "      <td>s3://NDAR_Central_2/submission_12181/S8429UBR-...</td>\n",
       "      <td>S8429UBR-3-3.zip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NDAR_INVCVMMHNMV</td>\n",
       "      <td>Control</td>\n",
       "      <td>s3://NDAR_Central_2/submission_12181/S1759AAD-...</td>\n",
       "      <td>S1759AAD-3-3.zip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NDAR_INV99VZ6EVT</td>\n",
       "      <td>Case</td>\n",
       "      <td>s3://NDAR_Central_2/submission_12181/S8202AET-...</td>\n",
       "      <td>S8202AET-3-4.zip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>NDAR_INV6DNN435E</td>\n",
       "      <td>Case</td>\n",
       "      <td>s3://NDAR_Central_3/submission_12178/S7497CNL-...</td>\n",
       "      <td>S7497CNL-1-3.zip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>605</th>\n",
       "      <td>NDAR_INVRGWPJH0B</td>\n",
       "      <td>Case</td>\n",
       "      <td>s3://NDAR_Central_3/submission_12178/S8946DLD-...</td>\n",
       "      <td>S8946DLD-1-3.zip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>607</th>\n",
       "      <td>NDAR_INVN1V9A4HG</td>\n",
       "      <td>Case</td>\n",
       "      <td>s3://NDAR_Central_3/submission_12178/S0239NYN-...</td>\n",
       "      <td>S0239NYN-1-3.zip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>NDAR_INVXBEY0FWW</td>\n",
       "      <td>Case</td>\n",
       "      <td>s3://NDAR_Central_3/submission_12178/S5649CKG-...</td>\n",
       "      <td>S5649CKG-1-4.zip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>NDAR_INVKYYATZRE</td>\n",
       "      <td>Case</td>\n",
       "      <td>s3://NDAR_Central_3/submission_12178/S6755OBR-...</td>\n",
       "      <td>S6755OBR-2-4.zip</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>515 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "0   The NDAR Global Unique Identifier (GUID) for research subject  \\\n",
       "0                                     NDAR_INVRCC8D3DU              \n",
       "1                                     NDAR_INVDVZXE87V              \n",
       "2                                     NDAR_INV9XVG94Y6              \n",
       "3                                     NDAR_INVCVMMHNMV              \n",
       "4                                     NDAR_INV99VZ6EVT              \n",
       "..                                                 ...              \n",
       "604                                   NDAR_INV6DNN435E              \n",
       "605                                   NDAR_INVRGWPJH0B              \n",
       "607                                   NDAR_INVN1V9A4HG              \n",
       "609                                   NDAR_INVXBEY0FWW              \n",
       "610                                   NDAR_INVKYYATZRE              \n",
       "\n",
       "0   Phenotype/diagnosis for the subject  \\\n",
       "0                               Control   \n",
       "1                               Control   \n",
       "2                                  Case   \n",
       "3                               Control   \n",
       "4                                  Case   \n",
       "..                                  ...   \n",
       "604                                Case   \n",
       "605                                Case   \n",
       "607                                Case   \n",
       "609                                Case   \n",
       "610                                Case   \n",
       "\n",
       "0                                          Data file 1         file_name  \n",
       "0    s3://NDAR_Central_2/submission_12181/S3383SOA-...  S3383SOA-3-3.zip  \n",
       "1    s3://NDAR_Central_2/submission_12181/S8285BFS-...  S8285BFS-3-3.zip  \n",
       "2    s3://NDAR_Central_2/submission_12181/S8429UBR-...  S8429UBR-3-3.zip  \n",
       "3    s3://NDAR_Central_2/submission_12181/S1759AAD-...  S1759AAD-3-3.zip  \n",
       "4    s3://NDAR_Central_2/submission_12181/S8202AET-...  S8202AET-3-4.zip  \n",
       "..                                                 ...               ...  \n",
       "604  s3://NDAR_Central_3/submission_12178/S7497CNL-...  S7497CNL-1-3.zip  \n",
       "605  s3://NDAR_Central_3/submission_12178/S8946DLD-...  S8946DLD-1-3.zip  \n",
       "607  s3://NDAR_Central_3/submission_12178/S0239NYN-...  S0239NYN-1-3.zip  \n",
       "609  s3://NDAR_Central_3/submission_12178/S5649CKG-...  S5649CKG-1-4.zip  \n",
       "610  s3://NDAR_Central_3/submission_12178/S6755OBR-...  S6755OBR-2-4.zip  \n",
       "\n",
       "[515 rows x 4 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_eeg_ndar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Define Data\n",
    "new_eeg_ndar = new_eeg_ndar.reset_index(drop = True)\n",
    "\n",
    "X1 = pd.DataFrame(list(zip(p300_amp_avg, p300_amp_var, p300_lat_avg, p300_lat_var, n200_amp_avg, n200_amp_var, n200_lat_avg, n200_lat_var)),\n",
    "               columns =['p300_amp_avg', 'p300_amp_var', 'p300_lat_avg', 'p300_lat_var', 'n200_amp_avg', 'n200_amp_var', 'n200_lat_avg', 'n200_lat_var'])\n",
    "\n",
    "X2 = pd.DataFrame(list(zip(p300_m_amp_avg, p300_m_amp_var, n200_m_amp_avg, n200_m_amp_var)),\n",
    "               columns =['p300_m_amp_avg', 'p300_m_amp_var', 'n200_m_amp_avg', 'n200_m_amp_var'])\n",
    "\n",
    "Y = pd.get_dummies(new_eeg_ndar[\"Phenotype/diagnosis for the subject\"])\n",
    "Y = Y.drop(['Control'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train Test Split \n",
    "x1_train, x1_test, y1_train, y1_test = train_test_split(X1, Y, test_size=0.33)\n",
    "x2_train, x2_test, y2_train, y2_test = train_test_split(X2, Y, test_size=0.33)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training and Evaluation "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy Score 0.6705882352941176\n",
      "[[  0  56]\n",
      " [  0 114]]\n",
      "Cross-Validation Accuracy Scores [0.63106796 0.63106796 0.63106796 0.6407767  0.6407767 ]\n",
      "Test Accuracy Score 0.6705882352941176\n",
      "precision =  0.6705882352941176\n",
      "Sensitivity :  0.0\n",
      "Specificity :  1.0\n",
      "f1 score =  0.8028169014084507\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Logistic Regression \n",
    "from sklearn.linear_model import LogisticRegression\n",
    "lr = LogisticRegression().fit(x1_train, y1_train)\n",
    "y1_pred = lr.predict(x1_test)\n",
    "\n",
    "#accuracy\n",
    "print('Test Accuracy Score', lr.score(x1_test, y1_test))\n",
    "\n",
    "#confusion matrix\n",
    "print(confusion_matrix(y1_test, y1_pred))\n",
    "\n",
    "#ROC curve \n",
    "plot_roc_curve(lr, x1_test, y1_test)\n",
    "\n",
    "#cross valiation \n",
    "print('Cross-Validation Accuracy Scores', cross_val_score(lr, X1, Y, cv=5))\n",
    "\n",
    "cm1 = confusion_matrix(y1_test, y1_pred)\n",
    "#accuracy\n",
    "print('Test Accuracy Score', lr.score(x1_test, y1_test))\n",
    "#print precision \n",
    "from sklearn.metrics import precision_score\n",
    "print('precision = ', precision_score(y1_test,y1_pred))\n",
    "#recall/sensetivity\n",
    "sensitivity1 = cm1[0,0]/(cm1[0,0]+cm1[0,1])\n",
    "print('Sensitivity : ', sensitivity1 )\n",
    "#specificity\n",
    "specificity1 = cm1[1,1]/(cm1[1,0]+cm1[1,1])\n",
    "print('Specificity : ', specificity1)\n",
    "#print f1 score \n",
    "from sklearn.metrics import f1_score\n",
    "print('f1 score = ', f1_score(y1_test,y1_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy Score 0.5823529411764706\n",
      "[[ 0 71]\n",
      " [ 0 99]]\n",
      "Cross-Validation Accuracy Scores [0.63106796 0.63106796 0.63106796 0.6407767  0.6407767 ]\n",
      "Test Accuracy Score 0.5823529411764706\n",
      "precision =  0.5823529411764706\n",
      "Sensitivity :  0.0\n",
      "Specificity :  1.0\n",
      "f1 score =  0.7360594795539033\n"
     ]
    },
    {
     "data": {
      "image/png": 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7ty95b2b3A88qEcgtyTkAPf2L5J50CcH3qntidy82sysJWgMVALPc/W0zGx1un17dc0vmqMWPSN2XbtC5vRpozt3nA/OT1qVMANx95N5cS+KhMX9E6r6oPYslD6jFj0h+itqzWPKAeviK5KfIOQIzawi0dfd3Y4xHskxP/yL5J1KOwMx+CKwEFoTLXcxsXoxxiYhIhkQtGrqRYMiIrwDcfSXQLo6AREQks6ImBMXuviXWSEREJCui1hGsMrMRQIGZdQSuBl6JLywREcmUqDmCqwjmK94JPEowHPXPYopJREQyKGqO4Ch3/wXwiziDkepL1wM4Cs0KJpKfouYIbjOzd8zsV2Z2dKwRSbWkG/M/CvUZEMlPUWcoO93MvgMMBWaYWVPgMXe/JdbopErUB0BEqiNyz2J3/8zdpwKjCfoUTIgrKBERyZxIOQIz+x4wDDgX2Eww7eR/xBiXpFHRPAAiIlUVtbL4PmA20NfdkyeXkSzQPAAiUlOi1hF0izsQqTrVCYhITag0ITCzx919qJm9Rdn5hqPMUCYiIjkgXY7gmvDvmXEHIiIi2VFpqyF33xi+/am7f5T4An4af3giIhK3qM1Hf5Bi3YCaDERERLIjXR3BFQRP/oeb2ZsJm5oAf40zMBERyYx0dQSPAn8EfgOMT1i/1d2/iC0qKUf9BkQkLumKhtzd1wFjgK0JL8zswHhDk0TJYwmp34CI1JQoOYIzgRUEzUctYZsDh8cUl6SgfgMiEodKEwJ3PzP82z4z4YiISKZFnbz+NDNrFL6/wMxuM7O28YYmIiKZELX56N3AdjPrDPwn8BHwUGxRiYhIxlRl8noHBgN3uPsdBE1IRUQkx0UdfXSrmV0PXAj0MLMCoH58YYmISKZEzREMI5i4/v+5+2dAa+DW2KISEZGMiZQQhD/+jwDNzOxMYIe7PxhrZCIikhFRWw0NBZYCPyaYt/hvZnZuhOP6m9m7ZrbWzMan2H6+mb0Zvl4JK6NFRCSDotYR/AI4yd03AZhZS2AR8ERFB4T1CNMIBqzbACwzs3nuvjphtw+BXu7+pZkNAGYAXat+GyIiUl1R6wj2KUkEQpsjHHsysNbdP3D3bwnmOR6cuIO7v+LuX4aLrwFtIsYjIiI1JGqOYIGZPU8wbzEElcfz0xzTGvg4YXkDlT/tX0owwF05ZnYZcBlA27bqxyYiUpOizll8rZn9COhOMN7QDHefm+YwS7HOU6zDzE4nSAi6V3D9GQTFRhQWFqY8h4iIVE+6+Qg6ApOBDsBbwDh3/6SyYxJsAA5NWG4DfJriGscBM4EB7r454rlFRKSGpCvnnwU8CwwhGIH0d1U49zKgo5m1N7N9gfOAeYk7hOMVPQlc6O7vVeHcIiJSQ9IVDTVx93vC9++a2etRT+zuxWZ2JfA8UADMcve3zWx0uH06MAFoAdxlZhAMZVFY1ZsQEZHqS5cQNDCz4/lXeX/DxGV3rzRhcPf5JFUqhwlAyftRwKiqBl3XJc9GBpqRTETiky4h2AjclrD8WcKyA33iCCrflcxGlvjDrxnJRCQu6SamOT1TgUhZmo1MRDIlaocyERGpo6J2KJMYJdcJqD5ARDJJOYJaoKROoITqA0QkkyLlCCxo23k+cLi73xy2//+Ouy+NNbo8ojoBEcmWqDmCu4BTgOHh8laCkUVFRCTHRa0j6OruJ5jZGwDhsNH7xhhXnaY6ARGpTaLmCHaF8ws4lM5HsCe2qOo41QmISG0SNUcwFZgLtDKz/wLOBW6ILao8oDoBEaktog5D/YiZrQC+TzC8xNnuvibWyEREJCOithpqC2wHnklc5+7r4wpMREQyI2rR0HME9QMGNADaA+8CR8cUl4iIZEjUoqFjE5fN7ATg8lgiqmM0kqiI1HbV6lkcDj99Ug3HUicltxACtRISkdolah3B2ITFfYATgKJYIspxFfURUAshEamtouYImiS89iOoMxgcV1C5TH0ERCTXpM0RhB3JGrv7tRmIp05QDkBEckmlOQIzq+fuuwmKgkREpA5KlyNYSpAIrDSzecD/At+UbHT3J2OMTUREMiBqP4IDgc0EcxSX9CdwQAmBiEiOS5cQtApbDK3iXwlACY8tKpGIdu3axYYNG9ixY0e2QxGpFRo0aECbNm2oX79+5GPSJQQFQGPKJgAllBBI1m3YsIEmTZrQrl07gvmTRPKXu7N582Y2bNhA+/btIx+XLiHY6O43711oIvHZsWOHEgGRkJnRokULioqq1s0rXT8C/e+SWk+JgMi/VOf/Q7ocwferF0rdlWrsoEQaR0hEck2lOQJ3/yJTgeSKVGMHJVJP4vzTuHHjvT7H8uXLufrqqyvcvm7dOh599NHI+wO0a9eOY489luOOO45evXrx0Ucf7XWcNWX69Ok8+OCDNXKujRs3cuaZZ5ZZd80119C6dWv27PnXRIo33ngjkydPLrNfu3bt+PzzzwH47LPPOO+88+jQoQOdOnVi4MCBvPfee3sV286dOxk2bBhHHHEEXbt2Zd26dSn36927N0cddRRdunShS5cubNq0CQg+p2OPPZYuXbrQvXt3Vq9eDUBRURH9+/ffq9jKcPecep144omeTUOnv+JDp7+S1RjkX1avXp3tELxRo0axX2Px4sU+aNCgKh1z2GGHeVFRkbu7T5gwwUeNGrXXcezZs8d379691+epSePGjfOnnnqqdHn37t1+6KGHeteuXX3x4sWl6ydOnOi33nprmWNLPqM9e/Z4t27d/O677y7d9sYbb/iSJUv2KrZp06b55Zdf7u7us2fP9qFDh6bcr1evXr5s2bJy67ds2VL6/umnn/Z+/fqVLo8cOdJffvnllOdL9f8CWO4V/K5G7UcgUuvd9MzbrP604txadXQ6pCkTf1j1aTdWrlzJ6NGj2b59Ox06dGDWrFk0b96cZcuWcemll9KoUSO6d+/OH//4R1atWsULL7zA5MmTefbZZ3nxxRe55pprgKC8d8mSJYwfP541a9bQpUsXLr74Yo4//vjS/bdt28ZVV13F8uXLMTMmTpzIkCFDysRzyimnMHXqVCB4mhw9ejTr1wfzSk2ZMoXTTjuNoqIiRowYwebNmznppJNYsGABK1asYNu2bQwYMIDTTz+dV199laeeeorHH3+cxx9/nJ07d3LOOedw00038c033zB06FA2bNjA7t27+eUvf8mwYcMYP3488+bNo169evTt25fJkydz44030rhxY8aNG1fhZ9W7d2+6du3K4sWL+eqrr7j33nvp0aNHuc/6D3/4A7fcckvp8uLFiznmmGMYNmwYs2fPpnfv3mm/r8WLF1O/fn1Gjx5duq5Lly5V/drLefrpp7nxxhsBOPfcc7nyyitx98jl+E2b/quY+Ztvvilz3Nlnn80jjzzCaaedttdx5nVCkK68PxXVAUgUF110Eb/73e/o1asXEyZM4KabbmLKlClccsklzJgxg1NPPZXx48enPHby5MlMmzaN0047jW3bttGgQQMmTZpU+sMP8MILL5Tu/6tf/YpmzZrx1ltvAfDll1+WO+eCBQs4++yzgaDY5N///d/p3r0769evp1+/fqxZs4abbrqJPn36cP3117NgwQJmzJhRevy7777Lfffdx1133cXChQt5//33Wbp0Ke7OWWedxZIlSygqKuKQQw7hueeeA2DLli188cUXzJ07l3feeQcz46uvvor8WQEUFxezdOlS5s+fz0033cSiRYvKHPvhhx/SvHlz9ttvv9J1s2fPZvjw4QwePJif//zn7Nq1K22b+lWrVnHiiSdWuk+JHj16sHXr1nLrJ0+ezBlnnFFm3SeffMKhhx4KQL169WjWrBmbN2/moIMOKnf8JZdcQkFBAUOGDOGGG24o/dGfNm0at912G99++y1/+ctfSvcvLCzkhhtqZur4vE4ISsr7q/LDrjqA2qs6T+5x2LJlC1999RW9evUC4OKLL+bHP/4xX331FVu3buXUU08FYMSIEaU/7IlOO+00xo4dy/nnn8+PfvQj2rRpU+n1Fi1axJw5c0qXmzdvXvr+9NNP5//+7/9o1apV6VPzokWLSsuaAb7++mu2bt3Kyy+/zNy5cwHo379/mfMcdthhdOvWDYCFCxeycOFCjj/+eAC2bdvG+++/T48ePRg3bhzXXXcdZ555Jj169KC4uJgGDRowatQoBg0aVK4sv6LPqsSPfvQjAE488cSU5esbN26kZcuWpcvffvst8+fP5/bbb6dJkyZ07dqVhQsXMmjQoAqfwqvayuall16KvG9QIpP+eo888gitW7dm69atDBkyhIceeoiLLroIgDFjxjBmzBgeffRRbrnlFh544AEAWrVqxaefflql2CsSa0JgZv2BOwg6ps1090lJ2y3cPpBgTuSRHkx6kzEaKVQyJdWPQirjx49n0KBBzJ8/n27dupV7Ck513op+zBYvXkyjRo0YOXIkEyZM4LbbbmPPnj28+uqrNGzYMHJ8jRo1KrPf9ddfz+WXl5+kcMWKFcyfP5/rr7+evn37MmHCBJYuXcqf//xn5syZw5133lnmqTadkif9goICiouLy21v2LBhmV7lCxYsYMuWLRx7bDCp4vbt29l///0ZNGgQLVq0YOPGjWWO37p1KwcccABHH300TzzxRKSYqpIjaNOmDR9//DFt2rShuLiYLVu2cOCBB5Y7tnXr4OGySZMmjBgxgqVLl5YmBCXOO+88rrjiitLlHTt2lPsOq6taM5RFEQ5fPQ0YAHQChptZp6TdBgAdw9dlwN1xxSOSKc2aNaN58+alT44PPfQQvXr1onnz5jRp0oTXXnsNoMxTfKJ//OMfHHvssVx33XUUFhbyzjvv0KRJk5Q/PgB9+/blzjvvLF1OLhpq2LAhU6ZM4cEHH+SLL74ot//KlSsB6N69O48//jgQPPWnKmIC6NevH7NmzWLbtm1AUPyxadMmPv30U/bff38uuOACxo0bx+uvv862bdvYsmULAwcOZMqUKaXXSvdZRXXkkUeWySnMnj2bmTNnsm7dOtatW8eHH37IwoUL2b59Oz179mTevHmln+OTTz5J586dKSgooE+fPuzcuZN77rmn9FzLli3jxRdfLHfNl156iZUrV5Z7JScCAGeddVbpE/wTTzxBnz59yiXaxcXFpS2Xdu3axbPPPssxxxwDwPvvv1+633PPPUfHjh1Ll997773S/fZWnDmCk4G17v4BgJnNIZjMZnXCPoOBB8Ma7dfM7AAzO9jdN5Y/3d5JVZGo8n6pCdu3by9TfDN27FgeeOCB0grQww8/nPvuuw+Ae++9l5/85Cc0atSI3r1706xZs3LnmzJlCosXL6agoIBOnToxYMAA9tlnH+rVq0fnzp0ZOXJkabEMwA033MCYMWM45phjKCgoYOLEiaVFKiUOPvhghg8fzrRp05g6dSpjxozhuOOOo7i4mJ49ezJ9+nQmTpzI8OHDeeyxx+jVqxcHH3wwTZo0Kf3BL9G3b1/WrFnDKacEOenGjRvz8MMPs3btWq699lr22Wcf6tevz913383WrVsZPHgwO3bswN25/fbby91vRZ9VFI0aNaJDhw6sXbuWQw45hOeff57f//73ZbZ3796dZ555hmHDhnHllVfSvXt3zIxWrVoxc+ZMICiumTt3Lj/72c+YNGkSDRo0oF27dqV1FdV16aWXcuGFF3LEEUdw4IEHlkn8u3TpwsqVK9m5cyf9+vVj165d7N69mzPOOIOf/OQnANx5550sWrSI+vXr07x589JEBYLc3qBBg/YqvhIWNbta5RObnQv0d/dR4fKFQFd3vzJhn2eBSe7+crj8Z+A6d1+edK7LCHIMtG3b9sTqtIeuqEXJ4C6tGdG1bZXPJ7XDmjVr+N73vpftMCLbtm1bab+DSZMmsXHjRu64444sRxXYuXMnBQUF1KtXj1dffZUrrrii3BN8bTR37lxWrFhRpuVQPujZsydPP/10mbqcEqn+X5jZCncvTHWuOHMEUQaqizSYnbvPAGYAFBYWVivlqi0ViZLfnnvuOX7zm99QXFzMYYcdxv3335/tkEqtX7+eoUOHsmfPHvbdd98yxSS12TnnnMPmzZuzHUZGFRUVMXbs2JSJQHXEmRBsAA5NWG4DJFdxR9lHpM4YNmwYw4YNy3YYKXXs2JE33ngj22FUy6hRo7IdQka1bNmytDlwTYitshhYBnQ0s/Zmti9wHjAvaZ95wEUW6AZsiaN+QOq2uIo3RXJRdf4/xJYjcPdiM7sSeJ6g+egsd3/bzEaH26cD8wmajq4laD56SVzxSN3UoEEDNm/eTIsWLTQKqeQ9D+cjaNCgQZWOi62yOC6FhYW+fPny9DtKXtAMZSJlVTRDWbYqi0ViV79+/SrNxCQi5cVZRyAiIjlACYGISJ5TQiAikudyrrLYzIqA6k61dBDweQ2Gkwt0z/lB95wf9uaeD3P3lqk25FxCsDfMbHlFteZ1le45P+ie80Nc96yiIRGRPKeEQEQkz+VbQjAj/S51ju45P+ie80Ms95xXdQQiIlJevuUIREQkiRICEZE8VycTAjPrb2bvmtlaMxufYruZ2dRw+5tmdkI24qxJEe75/PBe3zSzV8ysczbirEnp7jlhv5PMbHc4a15Oi3LPZtbbzFaa2dtmVn7S3RwT4d92MzN7xsz+Ht5zTo9ibGazzGyTma2qYHvN/365e516EQx5/Q/gcGBf4O9Ap6R9BgJ/JJghrRvwt2zHnYF7PhVoHr4fkA/3nLDfXwiGPD8323Fn4Hs+gGBe8Lbhcqtsx52Be/458NvwfUvgC2DfbMe+F/fcEzgBWFXB9hr//aqLOYKTgbXu/oG7fwvMAQYn7TMYeNADrwEHmNnBmQ60BqW9Z3d/xd2/DBdfI5gNLpdF+Z4BrgL+AGzKZHAxiXLPI4An3X09gLvn+n1HuWcHmlgwIUVjgoSgOLNh1hx3X0JwDxWp8d+vupgQtAY+TljeEK6r6j65pKr3cynBE0UuS3vPZtYaOAeYnsG44hTlez4SaG5mL5jZCjO7KGPRxSPKPd8JfI9gmtu3gGvcfU9mwsuKGv/9qovzEaSapiq5jWyUfXJJ5Psxs9MJEoLusUYUvyj3PAW4zt1315HZy6Lccz3gROD7QEPgVTN7zd3fizu4mES5537ASqAP0AH4k5m95O5fxxxbttT471ddTAg2AIcmLLcheFKo6j65JNL9mNlxwExggLtvzlBscYlyz4XAnDAROAgYaGbF7v5URiKseVH/bX/u7t8A35jZEqAzkKsJQZR7vgSY5EEB+loz+xD4LrA0MyFmXI3/ftXFoqFlQEcza29m+wLnAfOS9pkHXBTWvncDtrj7xkwHWoPS3rOZtQWeBC7M4afDRGnv2d3bu3s7d28HPAH8NIcTAYj2b/tpoIeZ1TOz/YGuwJoMx1mTotzzeoIcEGb2b8BRwAcZjTKzavz3q87lCNy92MyuBJ4naHEwy93fNrPR4fbpBC1IBgJrge0ETxQ5K+I9TwBaAHeFT8jFnsMjN0a85zolyj27+xozWwC8CewBZrp7ymaIuSDi9/wr4H4ze4ug2OQ6d8/Z4anNbDbQGzjIzDYAE4H6EN/vl4aYEBHJc3WxaEhERKpACYGISJ5TQiAikueUEIiI5DklBCIieU4JQR4IR95cmfBqV8m+22rgeveb2YfhtV43s1OqcY6ZZtYpfP/zpG2v7G2M4XlKPpdV4eiVB6TZv4uZDazGdQ42s2fD973NbIuZvWFma8xsYjXOd1bJKJxmdnbJ5xQu32xmZ1T1nCmucb+lGa01HMYichPk8N6fjbBfytE3zWyymfWJej2JTglBfvinu3dJeK3LwDWvdfcuwHjg91U92N1HufvqcPHnSdtO3fvwgH99LscQDPI1Js3+XQjab1fVWOCehOWX3P14gp7PF5jZiVU5mbvPc/dJ4eLZQKeEbRPcfVE1YqxN7gf6p1j/O4J/T1LDlBDkITNrbGZ/Dp/W3zKzcqN2hk+xSxKemHuE6/ua2avhsf9rZo3TXG4JcER47NjwXKvM7GfhukZm9pwFY8mvMrNh4foXzKzQzCYBDcM4Hgm3bQv/Ppb4hB4+xQ4xswIzu9XMllkwXvvlET6WVwkH7jKzky2Ys+GN8O9RYa/Wm4FhYSzDwthnhdd5I9XnGBoCLEheGQ4DsQLoEOY2XgvjnWtmzcNYrjaz1eH6OeG6kWZ2p5mdCpwF3BrG1KHkSd7MBpjZ4wmfTW8zeyZ8X6Xv0MwmhPe4ysxmmJUZuOmC8DNaZWYnh/tH/VxSqmj0TXf/CGhhZt+pyvkkgkyNsa1X9l7AboJBuVYCcwl6lDcNtx1E0EOxpHPhtvDvfwC/CN8XAE3CfZcAjcL11wETUlzvfsKx/4EfA38jGAjtLaARwVDBbwPHE/xI3pNwbLPw7wtAYWJMCfuUxHgO8ED4fl+CERkbApcBN4Tr9wOWA+1TxLkt4f7+F+gfLjcF6oXvzwD+EL4fCdyZcPyvgQvC9wcQjOfTKOka7YEVCcu9gWfD9y2AdcDRBD2Be4XrbwamhO8/BfYruUZyHImfdeJy+B2vT/iu7gYuqOZ3eGDC+oeAHyZ8R/eE73sSjp9f0eeSdO+FBL2eK/o3244U4/ET5KyGZPv/VF171bkhJiSlf3pQTAOAmdUHfm1mPQmGIWgN/BvwWcIxy4BZ4b5PuftKM+tFUAzx1/ChcF+CJ+lUbjWzG4AigtFOvw/M9eApGDN7EuhB8KQ82cx+S/Aj8VIV7uuPwFQz24+gKGGJu//TzPoCxyWUcTcDOgIfJh3f0MxWEvzorAD+lLD/A2bWkWBUx/oVXL8vcJaZjQuXGwBtKTu2z8HhZ5Coh5m9QfDZTyIYROwAdy+ZTewBgoQJggTiETN7CniqgjjK8WBohgXAD83sCWAQ8J9AVb7DEqeb2X8C+wMHEiTiz4TbZofXW2JmTS2oZ6noc0mMbzkwKur9JNgEHFKN46QSSgjy0/kEMzmd6O67zGwdwX/WUuF/7J4EPyAPmdmtwJfAn9x9eIRrXOvuT5QsWAUVmO7+XlhGPhD4jZktdPebo9yEu+8wsxcIhiEeRvijRDDezFXu/nyaU/zT3buYWTPgWYI6gqkEY9csdvdzLKhYf6GC443g6fTdyq5B0mdLUEdwZulJgutXZBDB0/ZZwC/N7OhK9k32GME9fQEsc/etYbFO1O8QM2sA3EWQO/vYzG6k7P0kj1HjVPC5WDAg3N5qQPCZSg1SHUF+agZsChOB04HDkncws8PCfe4B7iWYOu814DQzKynz39/Mjox4zSXA2eExjQiKdV4ys0OA7e7+MDA5vE6yXWHOJJU5BINu9SAYmIzw7xUlx5jZkeE1U3L3LcDVwLjwmGbAJ+HmkQm7biUoIivxPHBVSZm5mR2f4vTvEeQ4KhRe/0sL62GAC4EXzWwf4FB3X0zwNH8AQbFaouSYEr1A8Hn+hCBRgKp/hyU/+p+HdQnJLYlK6nS6E4yCuYVon0t1HQnk7CB6tZUSgvz0CFBoZssJcgfvpNinN7AyLMIYAtzh7kUEP4yzzexNgh+V70a5oLu/TlDuvJSgzmCmu78BHAssDYtofgHckuLwGcCbFlYWJ1lI8MS8yIOpDCGYc2E18LoFTRB/T5rcbxjL3wmGOf5vgtzJXwnqD0osBjqVVBYT5Bzqh7GtCpeTz/sN8I+SH95KXExQnPYmQeukm8NrP2zBqJpvALe7+1dJx80Brg0rZTskXXs3QU5nQPiXqn6H4fXuIajfeYqgyDDRlxY0551OUAQIET4XCxoCzEx1TQtG33wVOMrMNpjZpeH6+gQND5ZXFK9Uj0YfFYmZmZ1DUAx3Q7ZjyWXh53iCu/8y27HUNaojEImZu881sxbZjqMOqAf8T7aDqIuUIxARyXOqIxARyXNKCERE8pwSAhGRPKeEQEQkzykhEBHJc/8fc23+9dQpxVMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Logistic Regression \n",
    "from sklearn.linear_model import LogisticRegression\n",
    "lr = LogisticRegression().fit(x2_train, y1_train)\n",
    "y2_pred = lr.predict(x2_test)\n",
    "\n",
    "#accuracy\n",
    "print('Test Accuracy Score', lr.score(x2_test, y2_test))\n",
    "\n",
    "#confusion matrix\n",
    "print(confusion_matrix(y2_test, y2_pred))\n",
    "\n",
    "#ROC curve \n",
    "plot_roc_curve(lr, x2_test, y2_test)\n",
    "\n",
    "#cross valiation \n",
    "print('Cross-Validation Accuracy Scores', cross_val_score(lr, X2, Y, cv=5))\n",
    "\n",
    "cm1 = confusion_matrix(y2_test, y2_pred)\n",
    "#accuracy\n",
    "print('Test Accuracy Score', lr.score(x2_test, y2_test))\n",
    "#print precision \n",
    "from sklearn.metrics import precision_score\n",
    "print('precision = ', precision_score(y2_test,y2_pred))\n",
    "#recall/sensetivity\n",
    "sensitivity1 = cm1[0,0]/(cm1[0,0]+cm1[0,1])\n",
    "print('Sensitivity : ', sensitivity1 )\n",
    "#specificity\n",
    "specificity1 = cm1[1,1]/(cm1[1,0]+cm1[1,1])\n",
    "print('Specificity : ', specificity1)\n",
    "#print f1 score \n",
    "from sklearn.metrics import f1_score\n",
    "print('f1 score = ', f1_score(y2_test,y2_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy Score 0.6705882352941176\n",
      "[[  0  56]\n",
      " [  0 114]]\n",
      "Cross-Validation Accuracy Scores [0.63106796 0.63106796 0.63106796 0.6407767  0.6407767 ]\n",
      "Test Accuracy Score 0.6705882352941176\n",
      "precision =  0.6705882352941176\n",
      "Sensitivity :  0.0\n",
      "Specificity :  1.0\n",
      "f1 score =  0.8028169014084507\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# SVM \n",
    "from sklearn import svm\n",
    "clf = svm.SVC(C = 2, kernel = 'poly', degree = 10) \n",
    "clf.fit(x1_train, y1_train)\n",
    "y1_pred = clf.predict(x1_test)\n",
    "\n",
    "#accuracy\n",
    "print('Test Accuracy Score', clf.score(x1_test, y1_test))\n",
    "\n",
    "#confusion matrix\n",
    "print(confusion_matrix(y1_test, y1_pred))\n",
    "\n",
    "#ROC curve \n",
    "plot_roc_curve(clf, x1_test, y1_test)\n",
    "\n",
    "#cross valiation \n",
    "print('Cross-Validation Accuracy Scores', cross_val_score(clf, X1, Y, cv=5))\n",
    "\n",
    "cm1 = confusion_matrix(y1_test, y1_pred)\n",
    "#accuracy\n",
    "print('Test Accuracy Score', clf.score(x1_test, y1_test))\n",
    "#print precision \n",
    "from sklearn.metrics import precision_score\n",
    "print('precision = ', precision_score(y1_test,y1_pred))\n",
    "#recall/sensetivity\n",
    "sensitivity1 = cm1[0,0]/(cm1[0,0]+cm1[0,1])\n",
    "print('Sensitivity : ', sensitivity1 )\n",
    "#specificity\n",
    "specificity1 = cm1[1,1]/(cm1[1,0]+cm1[1,1])\n",
    "print('Specificity : ', specificity1)\n",
    "#print f1 score \n",
    "from sklearn.metrics import f1_score\n",
    "print('f1 score = ', f1_score(y1_test,y1_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy Score 0.5823529411764706\n",
      "[[ 0 71]\n",
      " [ 0 99]]\n",
      "Cross-Validation Accuracy Scores [0.63106796 0.63106796 0.63106796 0.6407767  0.6407767 ]\n",
      "Test Accuracy Score 0.5823529411764706\n",
      "precision =  0.5823529411764706\n",
      "Sensitivity :  0.0\n",
      "Specificity :  1.0\n",
      "f1 score =  0.7360594795539033\n"
     ]
    },
    {
     "data": {
      "image/png": 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WUiIQEUlzleqP1syahOMLi0g12ZJfwMP/+Ixtuw9EXicvX919SfWJOlTl6cB4oCmQZWb9gJvc/XuJDE6ktlu/fS/X/nEWudv20qV1k0qtm3NcS7IruY5IWaLeEfwO+CZhh3PuvsDMhiYsKpE0sCIvn+vGz2JXQSETbxjMKdmtUh2SpKnIj4bcfW04cH0xjVAmUkUL1+3g2xNmAzB59Kn07tA8xRFJOouaCNaGj4fczBoAtwJLEheWSO31+Ze7uHrcTDIb1ee57wyia9umqQ5J0lzUVkM3A7cAHYFcoD+g+gGRKnjv883sKijkz0oCcpSIekdwvLsf0qeQmX0NeL/6QxKp2X715lJWbMovd/kXW/YA0KaJhveQo0PURPA4MCDCPJG0VlTk/GHaCto0bUCbpmWf6M1g+IntaJpRqdbbIglT4V+imZ0GnA60NbMfxyzKBOomMjCRmmBLfgG/eG0xew8EbSfcg/nXnZrND4f3SGFkItHFuyRpQPDuQD2gWcz8ncDliQpKpKZYkLudKQvW06VNEzLqB9dGfTpmMqiLmoJKzRGv99HpwHQze8bdv0hSTCI1wsEiZ8mGXQA8clV/+nVukdqARKoo6kPKPWb2a6A3wXgEALj72QmJSuQoVlB4kJfnrWPcjJWs2rybrm2a0LlV41SHJVJlURPBROAvwAUETUm/TTBIjUja2LnvABNnrmHC+6vI21XASR2b88Q1AxjR51jq1rH4GxA5SkVNBK3d/Y9m9sOYx0XTExmYyNFi0859THh/NRNnfsGugkKG9GjDI1f15/RurSn1tr1IjRQ1ERR3i7jBzM4H1gOdEhOSyNFh1ebdjJuxgpfmrqOwqIhzT2rPd8/sRp+O6g5CapeoieB+M2sO/D+C9wcygR/FW8nMRgCPEjQ1He/uY8opdwowE7jK3V+MGJNIQixYu52x01fw5qKN1K9bhytyOnHjkK5kt1FPn1I7RR2q8vXw4w7gLCh5s7hcZlYXeAL4BkG3FB+Z2RR3X1xGuV8Bb1UudJHqtWvfAb43cR7vfb6ZZhn1+O6Z3bj+a11o20xvAEvtFu+FsrrAlQR9DL3p7gvN7ALgp0Aj4OQKVh8ELHf3leG2JgMXA4tLlfsB8BJwSpWOQKSa/PbtZfx7+WbuGHEC156aRbOM+qkOSSQp4t0R/BHoDMwGHjOzL4DTgDvd/dU463YE1sZM5wKDYwuYWUfgUuBsKkgEZjYaGA2QlZUVZ7cilbdw3Q6e/WA13xqcxXeHdUt1OCJJFS8R5AB93b3IzDKAzUB3d98YYdtlNafwUtOPAHe4+8GKWl+4+zhgHEBOTk7pbYgckYNFzl2vLqRVkwbc/s0TUh2OSNLFSwT73b0IwN33mdmyiEkAgjuAzjHTnQhaG8XKASaHSaANcJ6ZFUa42xCpNpNmr2HB2u387qp+NG+kx0GSfuIlghPM7JPwswHdwmkD3N37VrDuR0APM+sCrANGAtfEFnD3LsWfzewZ4HUlAUmmvF0FPPTmUk7r2ppL+ndMdTgiKREvEZxY1Q27e6GZfZ+gNVBdYIK7LzKzm8PlY6u6bZHq8uDUJew9cJBfXtJHL4dJ2orX6dwRdTTn7lOBqaXmlZkA3H3UkexLpLI+XLGFlz9exy1ndaN7O40UJukr6lCVIrXK/sIifvb3hXRu1Yjvn6VxAyS9aYgkSUtPv7eS5Zvy+dOoU2jUQGMsSXqLfEdgZo3M7PhEBiOSDGu37uHxdz5nRO9jOeuEdqkORyTlIiUCM7sQmA+8GU73N7MpCYxLJCHcnXunLKKOGfdc2CvV4YgcFaLeEdxL0GXEdgB3nw9kJyIgkUT6x+Iv+dfSTdw2vCcdWjRKdTgiR4WoiaDQ3XckNBKRBNtdUMgvpizihGObMepr2akOR+SoEbWyeKGZXQPUNbMewK3AB4kLS+TI7dlfyMq83azcvJsVm/L5cOUW1u/Yx2NXn0z9umowJ1IsaiL4AXAXUAC8QPCS2P2JCkokKndn064CVmzKZ0VePivydrMiL5+VebtZt31vSTkz6NyyMbd/83hyslulMGKRo0/URHC8u99FkAxEEmrfgYPc/uInbN+zv8JyO/YeYGXebvILCkvmNWlQl27tmnJKdktGtu1Mt3ZN6da2Kce1bkxGfTUTFSlL1ETwWzNrD/wNmOzuixIYk6S53G17eG3Berq0aULLxuV3Ate8UX0uH9iJbm2b0LVtcMI/JrOhuooQqaSoI5SdZWbHEgxSM87MMoG/uLseD0nC/PgbPbmwX4dUhyFS60WuMXP3je7+GHAzwTsF9yQqKBERSZ6oL5SdaGb3mtlC4PcELYY6JTQyERFJiqh1BH8CJgHnuHvpwWVERKQGi1pHcGqiAxERkdSoMBGY2V/d/Uoz+5RDxxuOMkKZiIjUAPHuCH4Y/ntBogOR9LVw3Q5+9eZSCg8G1xp7DhxMcUQi6aXCymJ33xB+/J67fxH7A3wv8eFJOvhwxRbe+3wzBYUHOVjkNKxbhyE92tC/c4tUhyaSFqJWFn8DuKPUvHPLmCdSKbv2HWDR+qA/wz9/ZzBNG2qsJJFki1dH8F2CK/+uZvZJzKJmwPuJDExqt7xdBTzzwSr+/OEX7NpXyNkntKOxuoAQSYl4l18vAG8ADwJ3xszf5e5bExaV1GrPfrCaB6Yu4cDBIs7tcyw3De1GPz0GEkmZeInA3X21md1SeoGZtVIykKr4x+KNHJPZkGevH0TXtk1THY5I2otyR3ABMJeg+Whsb14OdE1QXFJDbdixl5+8/Cl795ff8mfJhp30PKaZkoDIUaLCRODuF4T/dklOOFLTfZq7g2mf5dGnYyZNGpT953Vi+0wuUGdyIkeNSE00zOxrwHx3321m1wIDgEfcfU1Co5Maa8x/9KVPx+apDkNEIoja++gfgD1m1g/4H+AL4LmERSUiIklTmcHrHbgYeNTdHyVoQipSYt+Bg8xdsy3VYYhIJUV9e2eXmf0EuA4YYmZ1gfKHjpK0snPfAZ6f+QUT/r2azfkF9O/cguNaN051WCISUdREcBVwDfBf7r7RzLKAXycuLEm2HXuDk/n0ZXkEN3/RuMPSjbvILyhkSI82fPfM/pzWrbWGixSpQaJ2Q73RzCYCp5jZBcBsd/9zYkOTZPhy5z7++O9VvDBrDfkFhfTr1Jwmlezm4Zzex/BfX+uiymGRGipqq6ErCe4AphG8S/C4md3u7i/GWW8E8ChQFxjv7mNKLf8WX/VXlA98190XVOoIpMrGv7eSh978jMKiIs7v24GbhnbVyVwkDUW99LsLOMXdNwGYWVvgn0C5iSCsR3iCoMO6XOAjM5vi7otjiq0CznT3bWZ2LjAOGFz5w5Cq+NeSTbRvkcFz/zWYLD3TF0lbURNBneIkENpC/BZHg4Dl7r4SwMwmE7Q6KkkE7v5BTPmZaBzkhHF3/vtvn/DFlt0l8z7buIsT22cqCYikuaiJ4E0ze4tg3GIIKo+nxlmnI7A2ZjqXiq/2v0PQwd1hzGw0MBogKysrSrxSSkFhES/Ny+W41o3p1LIRAH07N+eCvnrDVyTdRa0svt3M/gM4g6COYJy7vxJntbKajZTZHMXMziJIBGeUs/9xBI+NyMnJid6kRQ5z1Smd+d6w7qkOQ0SOIvHGI+gBPAx0Az4F/tvd10Xcdi7QOWa6E7C+jH30BcYD57r7lojbFhGRahLvOf8E4HXgMoIeSB+vxLY/AnqYWRczawCMBKbEFgjfR3gZuM7dl1Vi2yIiUk3iPRpq5u5Ph58/M7N5UTfs7oVm9n3gLYLmoxPcfZGZ3RwuHwvcA7QGngxfQCp095zKHoSIiFRdvESQYWYn89Xz/kax0+5eYWJw96mUqlQOE0Dx5xuAGyobtIiIVJ94iWAD8NuY6Y0x0w6cnYigREQkeeINTHNWsgKR6rV26x5uf3EB+w4UAVSq/yARSS9Ru6GWGmbR+p3MXLmVOgaZjerTvHEDzj6hHUN7tE11aCJylKlc72JS49x/yUn06pCZ6jBE5CimOwIRkTQXKRFY4FozuyeczjKzQYkNTUREkiHqHcGTwGnA1eH0LoKeRUVEpIaLWkcw2N0HmNnHAGG30Q0SGJdU0psLN/KH6SuCIcOA7XsPpDgiEakpoiaCA+H4Ag4l4xEUJSwqqbTpyzaxZP1OTu/eGoCWTRrQv3MLurZtkuLIRORoFzURPAa8ArQzsweAy4G7ExZVmpnw71W8Oj9qX35ly922lxaN6/PM9aq6EZHKidoN9UQzmwt8naB7iUvcfUlCI0sjby3ayBdb9jAgq0WVt9G6SQNysltVX1AikjaijlmcBewBXoud5+5rEhVYTTX10w38YdqKSq2zMi+fPh2b8yddzYtICkR9NPR/BPUDBmQAXYDPgN4JiqvGmv5ZHp99uYszureJvE7bZg05/6T2CYxKRKR8UR8NnRQ7bWYDgJsSElENc99ri5m9+qvxdHK37aVV4wZMGHVKCqMSEYmuSl1MuPs8M9OZDnj9k/XUq2Oc2D7oxuGYZhmc0kXP6kWk5ohaR/DjmMk6wAAgLyER1UBnHt+WB/+jb6rDEBGpkqh3BM1iPhcS1Bm8VP3hiIhIssVNBOGLZE3d/fYkxCMiIklWYV9DZlbP3Q8SPAoSEZFaKN4dwWyCJDDfzKYAfwN2Fy9095cTGJuIiCRB1DqCVsAWgjGKi98ncECJQESkhouXCNqFLYYW8lUCKFbrB8GdvWorv3x9MYVF5R/qlt37kxiRSOUdOHCA3Nxc9u3bl+pQJAkyMjLo1KkT9evXj7xOvERQF2jKoQmgWK1PBB+t3sqn63bw9RPaUadOWV8BdG7ZiAv7dUhyZCLR5ebm0qxZM7KzszEr++9Yagd3Z8uWLeTm5tKlS5fI68VLBBvc/b4jC63me/LaATSsVzfVYYhUyb59+5QE0oSZ0bp1a/LyKveaV7wRyvSXI1ILKAmkj6r8ruMlgq9XLRQREakpKkwE7r41WYGISO31wAMP0Lt3b/r27Uv//v2ZNWsW9957Lz/5yU8OKTd//nxOPPFEAPLz87npppvo1q0bvXv3ZujQocyaNeuwbbs7Z599Njt37iyZ98orr2BmLF26tGTetGnTuOCCCw5Zd9SoUbz44otAUKl+55130qNHD/r06cOgQYN44403jvjYH3zwQbp3787xxx/PW2+9VWHZhx9+GDNj8+bNAKxevZpGjRrRv39/+vfvz80331xSdvjw4Wzbtu2I44MqdjonIhLVhx9+yOuvv868efNo2LAhmzdvZv/+/Vx99dWce+65PPjggyVlJ0+ezDXXXAPADTfcQJcuXfj888+pU6cOK1euZMmSw8fDmjp1Kv369SMzM7Nk3qRJkzjjjDOYPHky9957b6Q4f/azn7FhwwYWLlxIw4YN+fLLL5k+ffoRHfvixYuZPHkyixYtYv369QwfPpxly5ZRt+7hdY5r167l7bffJisr65D53bp1Y/78+YeVv+6663jyySe56667jihGUCIQSSu/eG0Ri9fvjF+wEnp1yOTnF5Y/NMmGDRto06YNDRs2BKBNm6/G6mjRogWzZs1i8ODBAPz1r3/lrbfeYsWKFcyaNYuJEydSp07w4KJr16507dr1sO1PnDiR0aNHl0zn5+fz/vvv8+6773LRRRdFSgR79uzh6aefZtWqVSVxHnPMMVx55ZXxv4AK/P3vf2fkyJE0bNiQLl260L17d2bPns1pp512WNnbbruNhx56iIsvvjjSti+66CKGDBmiRFAZK/Py+dFf5rPvwMHI62zVOwIiR+ycc87hvvvuo2fPngwfPpyrrrqKM888E4Crr76ayZMnM3jwYGbOnEnr1q3p0aMHU6ZMoX///mVeOZf2/vvv89RTT5VMv/rqq4wYMYKePXvSqlUr5s2bx4ABFfeSs3z5crKysg65qyjPbbfdxrvvvnvY/JEjR3LnnXceMm/dunWceuqpJdOdOnVi3brDxyefMmUKHTt2pF+/foctW7VqFSeffDKZmZncf//9DBkyBICWLVtSUFDAli1baN26ddy4K5I2iWDJhl18kruDM7q3oVlGtMPu1hay2zRR01GpNSq6ck+Upk2bMnfuXN577z3effddrrrqKsaMGcOoUaMYOXIkp59+Or/5zW+YPHkyV199daW3v3XrVpo1+6qD5EmTJvGjH/0ICE7OkyZNYsCAAeW2pqlsK5vf/e53kcu6H/66Ven97dmzhwceeIB//OMfh5Vt3749a9asoXXr1sydO5dLLrmERYsWlSSsdu3asX79+qM7EZjZCOBRghfTxrv7mFLLLVx+HsGYyKPcfV4iY7rnwl70PKZZ/IIiUm3q1q3LsGHDGDZsGCeddBLPPvsso0aNonPnzmRnZzN9+nReeuklPvzwQwB69+7NggULKCoqKnk0VJ569eqVlNuyZQvvvPMOCxcuxMw4ePAgZsZDDz1E69atD6tc3bp1K23atKF79+6sWbOGXbt2HZJUylKZO4JOnTqxdu3akunc3Fw6dDj0BdQVK1awatWqkruB3NxcBgwYwOzZszn22GNLHlUNHDiQbt26sWzZMnJycoDgHZFGjRpVGG8k7p6QH4KT/wqgK9AAWAD0KlXmPOANgvcVTgVmxdvuwIEDvSpeX7Dej7vjdf9s484qrS9SUy1evDil+1+6dKkvW7asZPquu+7yW265pWT6iSee8H79+vmZZ555yHpXXHGF33333V5UVOTu7suWLfNXX331sO0PHjzYP//8c3d3Hzt2rI8ePfqQ5UOHDvUZM2b4vn37PDs7u+T7WL16tWdlZfn27dvd3f3222/3UaNGeUFBgbu7r1+/3p977rkjOvaFCxd63759fd++fb5y5Urv0qWLFxYWVrjOcccd53l5ee7uvmnTppLyK1as8A4dOviWLVvc3b2oqMg7dOjgBw4cOGwbZf3OgTleznk13nsER2IQsNzdV7r7fmAyULoW5GLgz2GcM4EWZqZR3EVqkfz8fL797W/Tq1cv+vbty+LFiw+pwL3iiitYtGgRI0eOPGS98ePHs3HjRrp3785JJ53EjTfeeNjVNMD555/PtGnTgOCx0KWXXnrI8ssuu4wXXniBhg0b8vzzz3P99dfTv39/Lr/8csaPH0/z5s0BuP/++2nbti29evWiT58+XHLJJbRt2/aIjr13795ceeWV9OrVixEjRvDEE0+U1HvccMMNzJkzp8L1Z8yYQd++fenXrx+XX345Y8eOpVWrYCjcuXPncuqpp1KvXjU82CkvQxzpD3A5weOg4unrgN+XKvM6cEbM9L+AnDK2NRqYA8zJysqqMJuWZ87qrf7d5+f4um17qrS+SE2V6juCRFu/fr0PHz481WEk3a233ur//Oc/y1xW2TuCRNYRROmoLlJndu4+DhgHkJOTU6XO7gYe15KBxw2syqoichRr3749N954Izt37ozU6qe26NOnD1//evV0/pDIRJALdI6Z7gSsr0IZEZEKHWl7/5roxhtvrLZtJbKO4COgh5l1MbMGwEhgSqkyU4D/tMCpwA5335DAmETSkpfRjFFqp6r8rhN2R+DuhWb2feAtghZEE9x9kZndHC4fC0wlaDm0nKD56PWJikckXWVkZJS8dKReSGs3D8cjyMjIqNR6VtOuFHJycjxeTbuIfEUjlKWX8kYoM7O57p5T1jpp82axSLqqX79+pUarkvSTyDoCERGpAZQIRETSnBKBiEiaq3GVxWaWB3xRxdXbAJurMZyaQMecHnTM6eFIjvk4dy+zz4walwiOhJnNKa/WvLbSMacHHXN6SNQx69GQiEiaUyIQEUlz6ZYIxqU6gBTQMacHHXN6SMgxp1UdgYiIHC7d7ghERKQUJQIRkTRXKxOBmY0ws8/MbLmZ3VnGcjOzx8Lln5jZgFTEWZ0iHPO3wmP9xMw+MLN+qYizOsU75phyp5jZQTO7PJnxJUKUYzazYWY238wWmdn0ZMdY3SL8bTc3s9fMbEF4zDW6F2Mzm2Bmm8xsYTnLq//8Vd7QZTX1h6DL6xVAV6ABsADoVarMecAbBCOknQrMSnXcSTjm04GW4edz0+GYY8q9Q9Dl+eWpjjsJv+cWwGIgK5xul+q4k3DMPwV+FX5uC2wFGqQ69iM45qHAAGBhOcur/fxVG+8IBgHL3X2lu+8HJgMXlypzMfBnD8wEWphZ+2QHWo3iHrO7f+Du28LJmQSjwdVkUX7PAD8AXgI2JTO4BIlyzNcAL7v7GgB3r+nHHeWYHWhmwWALTQkSQWFyw6w+7j6D4BjKU+3nr9qYCDoCa2Omc8N5lS1Tk1T2eL5DcEVRk8U9ZjPrCFwKjE1iXIkU5ffcE2hpZtPMbK6Z/WfSokuMKMf8e+BEgmFuPwV+6O5FyQkvJar9/FUbxyMoawim0m1ko5SpSSIfj5mdRZAIzkhoRIkX5ZgfAe5w94O1ZGSuKMdcDxgIfB1oBHxoZjPdfVmig0uQKMf8TWA+cDbQDXjbzN5z950Jji1Vqv38VRsTQS7QOWa6E8GVQmXL1CSRjsfM+gLjgXPdfUuSYkuUKMecA0wOk0Ab4DwzK3T3V5MSYfWL+re92d13A7vNbAbQD6ipiSDKMV8PjPHgAfpyM1sFnADMTk6ISVft56/a+GjoI6CHmXUxswbASGBKqTJTgP8Ma99PBXa4+4ZkB1qN4h6zmWUBLwPX1eCrw1hxj9ndu7h7trtnAy8C36vBSQCi/W3/HRhiZvXMrDEwGFiS5DirU5RjXkNwB4SZHQMcD6xMapTJVe3nr1p3R+DuhWb2feAtghYHE9x9kZndHC4fS9CC5DxgObCH4Iqixop4zPcArYEnwyvkQq/BPTdGPOZaJcoxu/sSM3sT+AQoAsa7e5nNEGuCiL/nXwLPmNmnBI9N7nD3Gts9tZlNAoYBbcwsF/g5UB8Sd/5SFxMiImmuNj4aEhGRSlAiEBFJc0oEIiJpTolARCTNKRGIiKQ5JYI0EPa8OT/mJ7uCsvnVsL9nzGxVuK95ZnZaFbYx3sx6hZ9/WmrZB0caY7id4u9lYdh7ZYs45fub2XlV2E97M3s9/DzMzHaY2cdmtsTMfl6F7V1U3AunmV1S/D2F0/eZ2fDKbrOMfTxjcXprDbuxiNwEOTz21yOUK7P3TTN72MzOjro/iU6JID3sdff+MT+rk7DP2929P3An8FRlV3b3G9x9cTj501LLTj/y8ICvvpc+BJ183RKnfH+C9tuV9WPg6Zjp99z9ZII3n681s4GV2Zi7T3H3MeHkJUCvmGX3uPs/qxDj0eQZYEQZ8x8n+HuSaqZEkIbMrKmZ/Su8Wv/UzA7rtTO8ip0Rc8U8JJx/jpl9GK77NzNrGmd3M4Du4bo/Dre10Mx+FM5rYmb/Z0Ff8gvN7Kpw/jQzyzGzMUCjMI6J4bL88N+/xF6hh1exl5lZXTP7tZl9ZEF/7TdF+Fo+JOy4y8wGWTBmw8fhv8eHb7XeB1wVxnJVGPuEcD8fl/U9hi4D3iw9M+wGYi7QLbzbmBnG+4qZtQxjudXMFofzJ4fzRpnZ783sdOAi4NdhTN2Kr+TN7Fwz+2vMdzPMzF4LP1fqd2hm94THuNDMxpkd0nHTteF3tNDMBoXlo34vZSqv9013/wJobWbHVmZ7EkGy+tjWT+p+gIMEnXLNB14heKM8M1zWhuANxeKXC/PDf/8fcFf4uS7QLCw7A2gSzr8DuKeM/T1D2Pc/cAUwi6AjtE+BJgRdBS8CTiY4ST4ds27z8N9pQE5sTDFlimO8FHg2/NyAoEfGRsBo4O5wfkNgDtCljDjzY47vb8CIcDoTqBd+Hg68FH4eBfw+Zv3/Ba4NP7cg6M+nSal9dAHmxkwPA14PP7cGVgO9Cd4EPjOcfx/wSPh5PdCweB+l44j9rmOnw9/xmpjf1R+Aa6v4O2wVM/854MKY39HT4eehhP3nl/e9lDr2HIK3nsv7m82mjP74Ce6sLkv1/6na9lPrupiQMu314DENAGZWH/hfMxtK0A1BR+AYYGPMOh8BE8Kyr7r7fDM7k+AxxPvhRWEDgivpsvzazO4G8gh6O/068IoHV8GY2cvAEIIr5YfN7FcEJ4n3KnFcbwCPmVlDgkcJM9x9r5mdA/SNecbdHOgBrCq1fiMzm09w0pkLvB1T/lkz60HQq2P9cvZ/DnCRmf13OJ0BZHFo3z7tw+8g1hAz+5jgux9D0IlYC3cvHk3sWYLEBEGCmGhmrwKvlhPHYTzomuFN4EIzexE4H/gfoDK/w2Jnmdn/AI2BVgRJ/LVw2aRwfzPMLNOCepbyvpfY+OYAN0Q9nhibgA5VWE8qoESQnr5FMJLTQHc/YGarCf6zlgj/Yw8lOIE8Z2a/BrYBb7v71RH2cbu7v1g8YeVUYLr7svAZ+XnAg2b2D3e/L8pBuPs+M5tG0A3xVYQnJYL+Zn7g7m/F2cRed+9vZs2B1wnqCB4j6LvmXXe/1IKK9WnlrG8EV6efVbQPSn23BHUEF5RsJNh/ec4nuNq+CPiZmfWuoGxpfyE4pq3AR+6+K3ysE/V3iJllAE8S3J2tNbN7OfR4SvdR45TzvVjQIdyRyiD4TqUaqY4gPTUHNoVJ4CzguNIFzOy4sMzTwB8Jhs6bCXzNzIqf+Tc2s54R9zkDuCRcpwnBY533zKwDsMfdnwceDvdT2oHwzqQskwk63RpC0DEZ4b/fLV7HzHqG+yyTu+8AbgX+O1ynObAuXDwqpugugkdkxd4CflD8zNzMTi5j88sI7jjKFe5/m4X1MMB1wHQzqwN0dvd3Ca7mWxA8VotVOqZY0wi+zxsJkgJU/ndYfNLfHNYllG5JVFyncwZBL5g7iPa9VFVPoMZ2one0UiJITxOBHDObQ3B3sLSMMsOA+eEjjMuAR909j+DEOMnMPiE4qZwQZYfuPo/gufNsgjqD8e7+MXASMDt8RHMXcH8Zq48DPrGwsriUfxBcMf/Tg6EMIRhzYTEwz4ImiE8R5+43jGUBQTfHDxHcnbxPUH9Q7F2gV3FlMcGdQ/0wtoXhdOnt7gZWFJ94K/BtgsdpnxC0Trov3PfzFvSq+THwO3ffXmq9ycDtYaVst1L7Pkhwp3Nu+C+V/R2G+3uaoH7nVYJHhrG2WdCcdyzBI0CI8L1Y0BBgfFn7tKD3zQ+B480s18y+E86vT9DwYE558UrVqPdRkQQzs0sJHsPdnepYarLwexzg7j9LdSy1jeoIRBLM3V8xs9apjqMWqAf8JtVB1Ea6IxARSXOqIxARSXNKBCIiaU6JQEQkzSkRiIikOSUCEZE09/8BreWxnlcHTEwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# SVM \n",
    "from sklearn import svm\n",
    "clf = svm.SVC(C = 2, kernel = 'poly', degree = 5) \n",
    "clf.fit(x2_train, y2_train)\n",
    "y2_pred = clf.predict(x2_test)\n",
    "\n",
    "#accuracy\n",
    "print('Test Accuracy Score', clf.score(x2_test, y2_test))\n",
    "\n",
    "#confusion matrix\n",
    "print(confusion_matrix(y2_test, y2_pred))\n",
    "\n",
    "#ROC curve \n",
    "plot_roc_curve(clf, x2_test, y2_test)\n",
    "\n",
    "#cross valiation \n",
    "print('Cross-Validation Accuracy Scores', cross_val_score(clf, X2, Y, cv=5))\n",
    "\n",
    "cm1 = confusion_matrix(y2_test, y2_pred)\n",
    "#accuracy\n",
    "print('Test Accuracy Score', clf.score(x2_test, y2_test))\n",
    "#print precision \n",
    "from sklearn.metrics import precision_score\n",
    "print('precision = ', precision_score(y2_test,y2_pred))\n",
    "#recall/sensetivity\n",
    "sensitivity1 = cm1[0,0]/(cm1[0,0]+cm1[0,1])\n",
    "print('Sensitivity : ', sensitivity1 )\n",
    "#specificity\n",
    "specificity1 = cm1[1,1]/(cm1[1,0]+cm1[1,1])\n",
    "print('Specificity : ', specificity1)\n",
    "#print f1 score \n",
    "from sklearn.metrics import f1_score\n",
    "print('f1 score = ', f1_score(y2_test,y2_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Random Forrest "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy Score 0.6176470588235294\n",
      "[[23 33]\n",
      " [32 82]]\n",
      "Cross-Validation Accuracy Scores [0.60194175 0.63106796 0.58252427 0.67961165 0.54368932]\n",
      "Test Accuracy Score 0.6176470588235294\n",
      "precision =  0.7130434782608696\n",
      "Sensitivity :  0.4107142857142857\n",
      "Specificity :  0.7192982456140351\n",
      "f1 score =  0.7161572052401747\n"
     ]
    },
    {
     "data": {
      "image/png": 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RE4AJAJWVlXXPYc1UU8ff1C9ztwWYtW+FMoLVEbEOICI+lbQwYyYASQlg+5zl3iS9jXJVApPSTGAb4DBJazOUNqyFuY7frHwVyggGSJqdvhawY7osICJitwaOfRHoL6kf8E/gm8DxuTtERL+a15ImAg84E2gZjanucdWOWXkrlBEMbOqJI2KtpLNJegN1AG6OiHmSTk+3j2/qua2wxlT3uGrHrLwVGnSuWQPNRcRDwEN11uXNACJiTHOuVc48AYyZNUfmyeut7fIEMGbWHFmHmLA2znf/ZtZUmUsEkjpL2qWYwZiZWellyggkjQSqgYfT5QpJU4oYl5mZlUjWEsGlJENGvA8QEdVA32IEZGZmpZU1I1gbESuLGomZmbWKrI3FcyUdD3SQ1B84B5hWvLDMzKxUspYIvksyX/G/gT+RDEf9vSLFZGZmJZS1RLBLRPwI+FExg7GGeSYwMyuGrCWCX0l6WdJlkgYXNSKrl2cCM7NiyDpD2QhJnweOBSZI6gZMjoifFTU624AfHDOzlpb5yeJ0+OlrJT0B/AC4GHBGUCQNjR9kZtaSsj5QNlDSpZLmkkxROY1kfgErEo8fZGalkrVEcAtwJ/DViKg7uYwViauBzKwUsrYR7F3sQMzMrHU0mBFI+r8RcaykOaw/33CWGcqsDs8aZmZtUaESwbnp7yOKHUg58KxhZtYWFZqh7K305ZkRcUHuNklXABdseJQ1xPX+ZtbWZH2g7KA86w5tyUDMzKx1FGojOAM4E9hB0uycTV2BZ4oZmJmZlUahNoI/AX8FfgFcmLN+VUS8W7SozMysZAplBBERiyWdVXeDpO7ODMzM2r8sJYIjgBkk3UeVsy2AHYoUl5mZlUihXkNHpL/7lSYcMzMrtaxjDe0raYv09YmSfiWpT3FDMzOzUsg61tDvgaGShpKMPHoTcDvwpWIF1t559FAzay8aM3l9AKOA30TEb0i6kFo9PHqombUXWUsEqyRdBJwE7C+pA7BZ8cJqm5oyVpCfIjazti5riWA0ycT1304nqOkFXFm0qNqo+qaKzMd3/2bWXmQdhvp/JN0BfFHSEcALEXFbcUNrm3yXb2Ybm6y9ho4FXgC+QTJv8fOSjslw3CGSXpG0SNKFebafIGl2+jMtbYw2M7MSytpG8CPgixHxNoCkHsBjwJ/rOyBtR7ieZMC6ZcCLkqZExPyc3V4HvhQR70k6FJgAVDU+GWZm1lRZ2wg2qckEUisyHLsXsCgiXouI1cAkkl5HtSJiWkS8ly4+h+dBNjMruawlgoclPUIybzEkjccPFTimF7A0Z3kZDd/t/xfJAHcbkHQacBpAnz5+js3MrCVlbSw+X9LRwH4k4w1NiIh7CxymPOsizzokjSDJCPar5/oTSKqNqKyszHsOMzNrmkLzEfQHrgJ2BOYA50VEto70SQlg+5zl3sCbea6xG3AjcGhErMh4bjMzayGFSgQ3A7cBU4GRwHXA0RnP/SLQX1I/4J/AN4Hjc3dIxyu6BzgpIhY2Iu4W4wnlzazcFcoIukbEDenrVyTNzHriiFgr6WzgEaADcHNEzJN0erp9PHAxsDXwO0mQDGVR2dhENIcnlDezclcoI+gkaXf+t76/c+5yRDSYMUTEQ9RpVE4zgJrXpwKnNjboluaHxMysnBXKCN4CfpWz/D85ywF8uRhBmZlZ6RSamGZEqQIxM7PWkfWBMjMz20g5IzAzK3POCMzMylzW0UeVzlV8cbrcR9JexQ3NzMxKIWuJ4HfAMOC4dHkVyciiZmbWzmUddK4qIvaQNAsgHTa6YxHjMjOzEslaIliTzi8QUDsfwbqiRWVmZiWTNSO4FrgX2FbSz4GngcuLFpWZmZVM1mGo75A0AziQZHiJr0XEgqJGZmZmJZEpI0hHCf0YuD93XUS8UazAzMysNLI2Fj9I0j4goBPQD3gFGFykuMzMrESyVg3tmrssaQ/gO0WJyMzMSqpJTxanw09/sYVjMTOzVpC1jeD7OYubAHsAy4sSkZmZlVTWNoKuOa/XkrQZ3N3y4RRPfVNSevpJMyt3BTOC9EGyLhFxfgniKZr6pqT09JNmVu4azAgkbZrOPbxHqQIqJk9JaWa2oUIlghdI2gOqJU0B7gI+qtkYEfcUMTYzMyuBrG0E3YEVJHMU1zxPEIAzAjOzdq5QRrBt2mNoLv+bAdSIokVlG4U1a9awbNkyPv3009YOxaxsdOrUid69e7PZZptlPqZQRtAB6ML6GUANZwTWoGXLltG1a1f69u2LlO9PyMxaUkSwYsUKli1bRr9+/TIfVygjeCsixjYvNCtXn376qTMBsxKSxNZbb83y5Y17zKvQk8X+D7ZmcSZgVlpN+Z8rlBEc2LRQzMysvWgwI4iId0sViFkxdOjQgYqKCoYMGcLIkSN5//33W+S8EydO5Oyzz26Rc/Xt25ddd92ViooKKioqmDZtWouct67q6moeeuih9db99a9/pbKykoEDBzJgwADOO+88AC699FKuuuqqFrv2PvvsU/v6/PPPZ/DgwZx//vmMHz+e2267rVnnnjVrFqeeeup660aNGsWwYes/MzRmzBj+/Oc/r7euS5cuta8XLlzIYYcdxk477cTAgQM59thj+de//tWs2N59910OOugg+vfvz0EHHcR7772Xd7/333+fY445hgEDBjBw4ECeffZZAO666y4GDx7MJptswvTp02v3nzNnDmPGjGlWbLmaNOicWXvRuXNnqqurmTt3Lt27d+f6669v7ZDyeuKJJ6iurqa6unq9L82GrF27tlHXqJsRzJ07l7PPPps//vGPLFiwgLlz57LDDjs06pxZ5WZuf/jDH5g5cyZXXnklp59+OieffHLm8+RL8+WXX853v/vd2uX333+fmTNn8v777/P6669nOu+nn37K4YcfzhlnnMGiRYtYsGABZ5xxRqPr2usaN24cBx54IP/4xz848MADGTduXN79zj33XA455BBefvllXnrpJQYOHAjAkCFDuOeeezjggAPW23/XXXdl2bJlvPFGy0wJk/U5ArNm+en985j/5gctes5BX+jGJSOzT4kxbNgwZs+eDcALL7zA9773PT755BM6d+7MLbfcwi677MLEiROZMmUKH3/8Ma+++ipHHXUUv/zlLwG45ZZb+MUvfkHPnj3Zeeed+dznPgfAkiVL+Pa3v83y5cvp0aMHt9xyC3369GHMmDF07tyZl19+mSVLlnDLLbdw66238uyzz1JVVcXEiRPrjbWhc3bv3p1Zs2axxx57cOaZZ3LWWWexfPlyNt98c2644QYGDBjAXXfdxU9/+lM6dOjAlltuyWOPPcbFF1/MJ598wtNPP81FF13Egw8+yI9+9CMGDBgAwKabbsqZZ565QSw33HADEyZMYPXq1ey0007cfvvtbL755htcY+rUqcybN49TTjmF1atXs27dOu6++2769+9Ply5d+PDDDznyyCP56KOPqKqq4qKLLmLBggV06dKF8847j1dffTVvWuqm+eqrr66NbdWqVcyePZuhQ4fWrrv77rsZOXIk2223HZMmTeKiiy4q+Lfxpz/9iWHDhjFy5MjadSNGjCh4XCH33XcfTz75JADf+ta3GD58OFdcccV6+3zwwQdMnTq19u+hY8eOdOzYEaA2Q8hn5MiRTJo0iR/84AfNjtMlAisLn332GY8//jhHHnkkAAMGDGDq1KnMmjWLsWPH8sMf/rB23+rqaiZPnsycOXOYPHkyS5cu5a233uKSSy7hmWee4dFHH2X+/Pm1+5999tmcfPLJzJ49mxNOOIFzzjmndtt7773H3/72N379618zcuRI/vu//5t58+YxZ84cqqura/cbMWIEFRUVVFVVFTznwoULeeyxx7j66qs57bTTuO6665gxYwZXXXVV7Rf52LFjeeSRR3jppZeYMmUKHTt2ZOzYsYwePZrq6mpGjx7N3Llz2XPPPQu+d0cffTQvvvhi7Z3qTTfdlPcaAOPHj+fcc8+lurqa6dOn07t37/XONWXKlNpS2ujRo9fbVl9a6qY51/Tp0xkyZMh66+68806OO+44jjvuOO68886C6QMyvxerVq2qrcKr+5P7N1HjX//6Fz179gSgZ8+evP322xvs89prr9GjRw9OOeUUdt99d0499VQ++uijDfarq7KykqeeeipD6gpzicBKojF37i3pk08+oaKigsWLF7Pnnnty0EEHAbBy5Uq+9a1v8Y9//ANJrFmzpvaYAw88kC233BKAQYMGsWTJEt555x2GDx9Ojx49ABg9ejQLFy4E4Nlnn+Wee5KH7E866aT17tBGjhyJJHbddVe22247dt01meNp8ODBLF68mIqKCiCpGtpmm21qj2vonN/4xjfo0KEDH374IdOmTeMb3/hG7bZ///vfAOy7776MGTOGY489lqOPPrpZ7+HcuXP58Y9/zPvvv8+HH37IwQcfXO81hg0bxs9//nOWLVvG0UcfTf/+/TNdo6G05Ka5rrfeeqv2M4Hki3fRokXst99+SGLTTTdl7ty5DBkyJG9vmsb2sOnatet6GXhLWLt2LTNnzuS6666jqqqKc889l3HjxnHZZZc1eNy2227Lm2++2SIxFLVEIOkQSa9IWiTpwjzbJenadPvsjWVwO2s7au4+lyxZwurVq2vbCH7yk58wYsQI5s6dy/3337/e0881VT6QNDbX1Etn/dLI3a/mXJtsssl6591kk00aVcefe84tttgCgHXr1rHVVlvVti1UV1ezYMECILkz/9nPfsbSpUupqKhgxYoVG5xz8ODBzJgxo+C1x4wZw29/+1vmzJnDJZdcUvte5bvG8ccfX3vXf/DBB/O3v/0tU/oaSktumuvq3Lnzep/d5MmTee+99+jXrx99+/Zl8eLFTJo0CYCtt956vcbad999tzbzzfpeNLZEsN122/HWW28BSaa17bbbbrBP79696d27d21p8JhjjmHmzJkFY/n000/p3Llzwf2yKFpGkA5ffT1wKDAIOE7SoDq7HQr0T39OA35frHisvG255ZZce+21XHXVVaxZs4aVK1fSq1cy/HhDdfU1qqqqePLJJ1mxYgVr1qzhrrvuqt22zz771H7Z3HHHHey3337NjjfLObt160a/fv1qY4kIXnrpJQBeffVVqqqqGDt2LNtssw1Lly6la9eurFq1qvb4888/n8svv7y2ZLNu3Tp+9atfbXCdVatW0bNnT9asWcMdd9xRuz7fNV577TV22GEHzjnnHI488sjaNplCGkpLQwYOHMiiRYtql++8804efvhhFi9ezOLFi5kxY0bt+zh8+HAmT57M6tWrgeRzr2kHOP7445k2bRoPPvhg7bkefvhh5syZs971akoE+X4GDar79QZHHnkkt956KwC33noro0aN2mCfz3/+82y//fa88sorADz++ON5z1XXwoULN6gWa6pilgj2AhZFxGsRsRqYBNR9F0YBt0XiOWArST2LGJOVsd13352hQ4fWNrBddNFF7Lvvvnz22WcFj+3ZsyeXXnopw4YN4ytf+Qp77PG/hddrr72WW265hd12243bb7+d3/zmN82ONes577jjDm666SaGDh3K4MGDue+++4DkS37XXXdlyJAhHHDAAQwdOpQRI0Ywf/58KioqmDx5MrvtthvXXHMNxx13HAMHDmTIkCG1d6+5LrvsMqqqqjjooINqG5bru8bkyZMZMmQIFRUVvPzyy43qEVRfWhoyYMAAVq5cyapVq1i8eDFvvPEGe++9d+32fv360a1bN55//nmOOOII9t9/f/bcc08qKip45plnahtuO3fuzAMPPMB1111H//79GTRoEBMnTsx7B98YF154IY8++ij9+/fn0Ucf5cILk4qRN998k8MOO6x2v+uuu44TTjiB3Xbbjerq6to2q3vvvZfevXvz7LPPcvjhh9dWy0FSnXj44Yc3K74aiijOkEGSjgEOiYhT0+WTgKqIODtnnweAcRHxdLr8OHBBREyvc67TSEoM9OnTZ88lS5Y0Op6f3j8PaL266nK0YMGCBns9mLWEX//613Tt2nWDZwk2Zv/+97/50pe+xNNPP82mm27Y1Jvvf0/SjIiozHe+YpYIsgxUl2kwu4iYEBGVEVGZ2zDUGJeMHOxMwGwjdMYZZ6zX/lIO3njjDcaNG5c3E2iKYvYaWgZsn7PcG6jbxJ1lHzOzenXq1ImTTjqptcMoqf79+2fukZVFMUsELwL9JfWT1BH4JjClzj5TgJPT3kN7AysjYsNKSmu3ilX1aGb5NeV/rmglgnSu47OBR0jmNbg5IuZJOj3dPh54CDgMWAR8DJxSrHis9Dp16sSKFSvYeuutPQqpWQnUzEfQqVOnRh1XtMbiYqmsrIzcwZes7fIMZWalV98MZQ01FvvJYiuazTbbrFGzJJlZ6/BYQ2ZmZc4ZgZlZmXNGYGZW5tpdY7Gk5UDjHy1ObAO804LhtAdOc3lwmstDc9L8nxGR94ncdpcRNIek6fW1mm+snOby4DSXh2Kl2VVDZmZlzhmBmVmZK7eMYEJrB9AKnOby4DSXh6KkuazaCMzMbEPlViIwM7M6nBGYmZW5jTIjkHSIpFckLZJ0YZ7tknRtun22pD3ynac9yZDmE9K0zpY0TdLQ1oizJRVKc85+X5T0WTprXruWJc2ShkuqljRP0t9LHWNLy/C3vaWk+yW9lKa5XY9iLOlmSW9LmlvP9pb//oqIjeqHZMjrV4EdgI7AS8CgOvscBvyVZIa0vYHnWzvuEqR5H+A/0teHlkOac/b7G8mQ58e0dtwl+Jy3AuYDfdLlbVs77hKk+YfAFenrHsC7QMfWjr0ZaT4A2AOYW8/2Fv/+2hhLBHsBiyLitYhYDUwCRtXZZxRwWySeA7aS1LPUgbaggmmOiGkR8V66+BzJbHDtWZbPGeC7wN3A26UMrkiypPl44J6IeAMgItp7urOkOYCuSia96EKSEawtbZgtJyKmkqShPi3+/bUxZgS9gKU5y8vSdY3dpz1pbHr+i+SOoj0rmGZJvYCjgPEljKuYsnzOOwP/IelJSTMknVyy6IojS5p/CwwkmeZ2DnBuRKwrTXitosW/vzbG+QjyTYVVt49sln3ak8zpkTSCJCPYr6gRFV+WNF8DXBARn20kM6RlSfOmwJ7AgUBn4FlJz0XEwmIHVyRZ0nwwUA18GdgReFTSUxHxQZFjay0t/v21MWYEy4Dtc5Z7k9wpNHaf9iRTeiTtBtwIHBoRK0oUW7FkSXMlMCnNBLYBDpO0NiL+UpIIW17Wv+13IuIj4CNJU4GhQHvNCLKk+RRgXCQV6IskvQ4MAF4oTYgl1+LfXxtj1dCLQH9J/SR1BL4JTKmzzxTg5LT1fW9gZUS8VepAW1DBNEvqA9wDnNSO7w5zFUxzRPSLiL4R0Rf4M3BmO84EINvf9n3A/pI2lbQ5UAUsKHGcLSlLmt8gKQEhaTtgF+C1kkZZWi3+/bXRlQgiYq2ks4FHSHoc3BwR8ySdnm4fT9KD5DBgEfAxyR1Fu5UxzRcDWwO/S++Q10Y7HrkxY5o3KlnSHBELJD0MzAbWATdGRN5uiO1Bxs/5MmCipDkk1SYXRES7HZ5a0p3AcGAbScuAS4DNoHjfXx5iwsyszG2MVUNmZtYIzgjMzMqcMwIzszLnjMDMrMw5IzAzK3POCMpAOvJmdc5P3wb2/bAFrjdR0uvptWZKGtaEc9woaVD6+od1tk1rbozpeWrel7np6JVbFdi/QtJhTbhOT0kPpK+HS1opaZakBZIuacL5jqwZhVPS12rep3R5rKSvNPacea4xUQVGa02HscjcBTlN+wMZ9ss7+qakqyR9Oev1LDtnBOXhk4ioyPlZXIJrnh8RFcCFwB8ae3BEnBoR89PFH9bZtk/zwwP+930ZQjLI11kF9q8g6b/dWN8HbshZfioidid58vlESXs25mQRMSUixqWLXwMG5Wy7OCIea0KMbclE4JA8668j+XuyFuaMoAxJ6iLp8fRufY6kDUbtTO9ip+bcMe+frv+qpGfTY++S1KXA5aYCO6XHfj8911xJ30vXbSHpQSVjyc+VNDpd/6SkSknjgM5pHHek2z5Mf0/OvUNP72K/LqmDpCslvahkvPbvZHhbniUduEvSXkrmbJiV/t4lfap1LDA6jWV0GvvN6XVm5XsfU18HHq67Mh0GYgawY1raeC6N915J/5HGco6k+en6Sem6MZJ+K2kf4EjgyjSmHWvu5CUdKun/5rw3wyXdn75u1Gco6eI0jXMlTZDWG7jpxPQ9mitpr3T/rO9LXvWNvhkRS4CtJX2+MeezDEo1xrZ/Wu8H+IxkUK5q4F6SJ8q7pdu2IXlCsebhwg/T3/8H+FH6ugPQNd13KrBFuv4C4OI815tIOvY/8A3geZKB0OYAW5AMFTwP2J3kS/KGnGO3TH8/CVTmxpSzT02MRwG3pq87kozI2Bk4Dfhxuv5zwHSgX544P8xJ313AIelyN2DT9PVXgLvT12OA3+YcfzlwYvp6K5LxfLaoc41+wIyc5eHAA+nrrYHFwGCSJ4G/lK4fC1yTvn4T+FzNNerGkfte5y6nn/EbOZ/V74ETm/gZds9ZfzswMuczuiF9fQDp+Pn1vS910l5J8tRzfX+zfckzHj9Jyerrrf0/tbH9bHRDTFhen0RSTQOApM2AyyUdQDIMQS9gO+B/co55Ebg53fcvEVEt6Usk1RDPpDeFHUnupPO5UtKPgeUko50eCNwbyV0wku4B9ie5U75K0hUkXxJPNSJdfwWulfQ5kqqEqRHxiaSvArvl1HFvCfQHXq9zfGdJ1SRfOjOAR3P2v1VSf5JRHTer5/pfBY6UdF663Anow/pj+/RM34Nc+0uaRfLejyMZRGyriKiZTexWkowJkgziDkl/Af5STxwbiGRohoeBkZL+DBwO/ABozGdYY4SkHwCbA91JMvH70213ptebKqmbknaW+t6X3PimA6dmTU+Ot4EvNOE4a4AzgvJ0AslMTntGxBpJi0n+WWul/9gHkHyB3C7pSuA94NGIOC7DNc6PiD/XLKieBsyIWJjWkR8G/ELS/4uIsVkSERGfSnqSZBji0aRfSiTjzXw3Ih4pcIpPIqJC0pbAAyRtBNeSjF3zREQcpaRh/cl6jhfJ3ekrDV2DOu8tSRvBEbUnSa5fn8NJ7raPBH4iaXAD+9Y1mSRN7wIvRsSqtFon62eIpE7A70hKZ0slXcr66ak7Rk1Qz/uiZEC45upE8p5aC3IbQXnaEng7zQRGAP9ZdwdJ/5nucwNwE8nUec8B+0qqqfPfXNLOGa85FfhaeswWJNU6T0n6AvBxRPwRuCq9Tl1r0pJJPpNIBt3an2RgMtLfZ9QcI2nn9Jp5RcRK4BzgvPSYLYF/ppvH5Oy6iqSKrMYjwHdr6swl7Z7n9AtJShz1Sq//ntJ2GOAk4O+SNgG2j4gnSO7mtyKpVstVN6ZcT5K8n/8fSaYAjf8Ma77030nbEur2JKpp09mPZBTMlWR7X5pqZ6DdDqLXVjkjKE93AJWSppOUDl7Os89woDqtwvg68JuIWE7yxXinpNkkXyoDslwwImaS1Du/QNJmcGNEzAJ2BV5Iq2h+BPwsz+ETgNlKG4vr+H8kd8yPRTKVISRzLswHZirpgvgHCpR+01heIhnm+JckpZNnSNoPajwBDKppLCYpOWyWxjY3Xa573o+AV2u+eBvwLZLqtNkkvZPGptf+o5JRNWcBv46I9+scNwk4P22U3bHOtT8jKekcmv6msZ9her0bSNp3/kJSZZjrPSXdeceTVAFChvdFSUeAG/NdU8nom88Cu0haJum/0vWbkXQ8mF5fvNY0Hn3UrMgkHUVSDffj1o6lPUvfxz0i4ietHcvGxm0EZkUWEfdK2rq149gIbApc3dpBbIxcIjAzK3NuIzAzK3POCMzMypwzAjOzMueMwMyszDkjMDMrc/8/36nYD+BmCUMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rfc = RandomForestClassifier(max_depth = 10)\n",
    "rfc.fit(x1_train, y1_train) \n",
    "y1_pred = rfc.predict(x1_test)\n",
    "\n",
    "# accuracy \n",
    "print('Test Accuracy Score', rfc.score(x1_test,y1_test))\n",
    "\n",
    "#confusion matrix\n",
    "print(confusion_matrix(y1_test, y1_pred))\n",
    "\n",
    "#ROC curve \n",
    "plot_roc_curve(rfc, x1_test, y1_test)\n",
    "\n",
    "#cross valiation \n",
    "print('Cross-Validation Accuracy Scores', cross_val_score(rfc, X1, Y, cv=5))\n",
    "\n",
    "cm1 = confusion_matrix(y1_test, y1_pred)\n",
    "#accuracy\n",
    "print('Test Accuracy Score', rfc.score(x1_test, y1_test))\n",
    "#print precision \n",
    "from sklearn.metrics import precision_score\n",
    "print('precision = ', precision_score(y1_test,y1_pred))\n",
    "#recall/sensetivity\n",
    "sensitivity1 = cm1[0,0]/(cm1[0,0]+cm1[0,1])\n",
    "print('Sensitivity : ', sensitivity1 )\n",
    "#specificity\n",
    "specificity1 = cm1[1,1]/(cm1[1,0]+cm1[1,1])\n",
    "print('Specificity : ', specificity1)\n",
    "#print f1 score \n",
    "from sklearn.metrics import f1_score\n",
    "print('f1 score = ', f1_score(y1_test,y1_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy Score 0.5941176470588235\n",
      "[[14 57]\n",
      " [12 87]]\n",
      "Cross-Validation Accuracy Scores [0.5631068  0.63106796 0.5631068  0.55339806 0.52427184]\n",
      "Test Accuracy Score 0.5941176470588235\n",
      "precision =  0.6041666666666666\n",
      "Sensitivity :  0.19718309859154928\n",
      "Specificity :  0.8787878787878788\n",
      "f1 score =  0.7160493827160493\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rfc = RandomForestClassifier(max_depth = 10)\n",
    "rfc.fit(x2_train, y2_train) \n",
    "y2_pred = rfc.predict(x2_test)\n",
    "\n",
    "# accuracy \n",
    "print('Test Accuracy Score', rfc.score(x2_test,y2_test))\n",
    "\n",
    "#confusion matrix\n",
    "print(confusion_matrix(y2_test, y2_pred))\n",
    "\n",
    "#ROC curve \n",
    "plot_roc_curve(rfc, x2_test, y2_test)\n",
    "\n",
    "#cross valiation \n",
    "print('Cross-Validation Accuracy Scores', cross_val_score(rfc, X2, Y, cv=5))\n",
    "\n",
    "cm1 = confusion_matrix(y2_test, y2_pred)\n",
    "#accuracy\n",
    "print('Test Accuracy Score', rfc.score(x2_test, y2_test))\n",
    "#print precision \n",
    "from sklearn.metrics import precision_score\n",
    "print('precision = ', precision_score(y2_test,y2_pred))\n",
    "#recall/sensetivity\n",
    "sensitivity1 = cm1[0,0]/(cm1[0,0]+cm1[0,1])\n",
    "print('Sensitivity : ', sensitivity1 )\n",
    "#specificity\n",
    "specificity1 = cm1[1,1]/(cm1[1,0]+cm1[1,1])\n",
    "print('Specificity : ', specificity1)\n",
    "#print f1 score \n",
    "from sklearn.metrics import f1_score\n",
    "print('f1 score = ', f1_score(y2_test,y2_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Decision Trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy Score 0.6294117647058823\n",
      "[[28 28]\n",
      " [35 79]]\n",
      "Cross-Validation Accuracy Scores [0.59223301 0.60194175 0.54368932 0.6407767  0.54368932]\n",
      "Test Accuracy Score 0.6294117647058823\n",
      "precision =  0.7383177570093458\n",
      "Sensitivity :  0.5\n",
      "Specificity :  0.6929824561403509\n",
      "f1 score =  0.7149321266968325\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dtc = DecisionTreeClassifier()\n",
    "dtc.fit(x1_train, y1_train) \n",
    "y1_pred = dtc.predict(x1_test)\n",
    "\n",
    "# accuracy \n",
    "print('Test Accuracy Score', dtc.score(x1_test,y1_test))\n",
    "\n",
    "#confusion matrix\n",
    "print(confusion_matrix(y1_test, y1_pred))\n",
    "\n",
    "#ROC curve \n",
    "plot_roc_curve(dtc, x1_test, y1_test)\n",
    "\n",
    "#cross valiation \n",
    "print('Cross-Validation Accuracy Scores', cross_val_score(dtc, X1, Y, cv=5))\n",
    "\n",
    "cm1 = confusion_matrix(y1_test, y1_pred)\n",
    "#accuracy\n",
    "print('Test Accuracy Score', dtc.score(x1_test, y1_test))\n",
    "#print precision \n",
    "from sklearn.metrics import precision_score\n",
    "print('precision = ', precision_score(y1_test,y1_pred))\n",
    "#recall/sensetivity\n",
    "sensitivity1 = cm1[0,0]/(cm1[0,0]+cm1[0,1])\n",
    "print('Sensitivity : ', sensitivity1 )\n",
    "#specificity\n",
    "specificity1 = cm1[1,1]/(cm1[1,0]+cm1[1,1])\n",
    "print('Specificity : ', specificity1)\n",
    "#print f1 score \n",
    "from sklearn.metrics import f1_score\n",
    "print('f1 score = ', f1_score(y1_test,y1_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy Score 0.5588235294117647\n",
      "[[23 48]\n",
      " [27 72]]\n",
      "Cross-Validation Accuracy Scores [0.51456311 0.52427184 0.5631068  0.57281553 0.54368932]\n",
      "Test Accuracy Score 0.5588235294117647\n",
      "precision =  0.6\n",
      "Sensitivity :  0.323943661971831\n",
      "Specificity :  0.7272727272727273\n",
      "f1 score =  0.6575342465753425\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dtc = DecisionTreeClassifier()\n",
    "dtc.fit(x2_train, y2_train) \n",
    "y2_pred = dtc.predict(x2_test)\n",
    "\n",
    "# accuracy \n",
    "print('Test Accuracy Score', dtc.score(x2_test,y2_test))\n",
    "\n",
    "#confusion matrix\n",
    "print(confusion_matrix(y2_test, y2_pred))\n",
    "\n",
    "#ROC curve \n",
    "plot_roc_curve(dtc, x2_test, y2_test)\n",
    "\n",
    "#cross valiation \n",
    "print('Cross-Validation Accuracy Scores', cross_val_score(dtc, X2, Y, cv=5))\n",
    "\n",
    "cm1 = confusion_matrix(y2_test, y2_pred)\n",
    "#accuracy\n",
    "print('Test Accuracy Score', dtc.score(x2_test, y2_test))\n",
    "#print precision \n",
    "from sklearn.metrics import precision_score\n",
    "print('precision = ', precision_score(y2_test,y2_pred))\n",
    "#recall/sensetivity\n",
    "sensitivity1 = cm1[0,0]/(cm1[0,0]+cm1[0,1])\n",
    "print('Sensitivity : ', sensitivity1 )\n",
    "#specificity\n",
    "specificity1 = cm1[1,1]/(cm1[1,0]+cm1[1,1])\n",
    "print('Specificity : ', specificity1)\n",
    "#print f1 score \n",
    "from sklearn.metrics import f1_score\n",
    "print('f1 score = ', f1_score(y2_test,y2_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy Score 0.6647058823529411\n",
      "[[20 36]\n",
      " [21 93]]\n",
      "Cross-Validation Accuracy Scores [0.61165049 0.5631068  0.62135922 0.67961165 0.59223301]\n",
      "Test Accuracy Score 0.6647058823529411\n",
      "precision =  0.7209302325581395\n",
      "Sensitivity :  0.35714285714285715\n",
      "Specificity :  0.8157894736842105\n",
      "f1 score =  0.765432098765432\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#train test split\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier(n_neighbors=15)\n",
    "knn.fit(x1_train, y1_train) \n",
    "y1_pred = knn.predict(x1_test)\n",
    "\n",
    "# accuracy \n",
    "print('Test Accuracy Score', knn.score(x1_test,y1_test))\n",
    "\n",
    "#confusion matrix\n",
    "C = confusion_matrix(y1_test, y1_pred)\n",
    "print(C)\n",
    "\n",
    "#ROC curve \n",
    "plot_roc_curve(knn, x1_test, y1_test)\n",
    "\n",
    "#cross valiation \n",
    "print('Cross-Validation Accuracy Scores', cross_val_score(knn, X1, Y, cv=5, scoring = 'accuracy'))\n",
    "\n",
    "cm1 = confusion_matrix(y1_test, y1_pred)\n",
    "#accuracy\n",
    "print('Test Accuracy Score', knn.score(x1_test, y1_test))\n",
    "#print precision \n",
    "from sklearn.metrics import precision_score\n",
    "print('precision = ', precision_score(y1_test,y1_pred))\n",
    "#recall/sensetivity\n",
    "sensitivity1 = cm1[0,0]/(cm1[0,0]+cm1[0,1])\n",
    "print('Sensitivity : ', sensitivity1 )\n",
    "#specificity\n",
    "specificity1 = cm1[1,1]/(cm1[1,0]+cm1[1,1])\n",
    "print('Specificity : ', specificity1)\n",
    "#print f1 score \n",
    "from sklearn.metrics import f1_score\n",
    "print('f1 score = ', f1_score(y1_test,y1_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy Score 0.5882352941176471\n",
      "[[11 60]\n",
      " [10 89]]\n",
      "Cross-Validation Accuracy Scores [0.58252427 0.67961165 0.59223301 0.60194175 0.62135922]\n",
      "Test Accuracy Score 0.5882352941176471\n",
      "precision =  0.5973154362416108\n",
      "Sensitivity :  0.15492957746478872\n",
      "Specificity :  0.898989898989899\n",
      "f1 score =  0.7177419354838711\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#train test split\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier(n_neighbors=15)\n",
    "knn.fit(x2_train, y2_train) \n",
    "y2_pred = knn.predict(x2_test)\n",
    "\n",
    "# accuracy \n",
    "print('Test Accuracy Score', knn.score(x2_test,y2_test))\n",
    "\n",
    "#confusion matrix\n",
    "C = confusion_matrix(y2_test, y2_pred)\n",
    "print(C)\n",
    "\n",
    "#ROC curve \n",
    "plot_roc_curve(knn, x2_test, y2_test)\n",
    "\n",
    "#cross valiation \n",
    "print('Cross-Validation Accuracy Scores', cross_val_score(knn, X2, Y, cv=5, scoring = 'accuracy'))\n",
    "\n",
    "cm1 = confusion_matrix(y2_test, y2_pred)\n",
    "#accuracy\n",
    "print('Test Accuracy Score', knn.score(x2_test, y2_test))\n",
    "#print precision \n",
    "from sklearn.metrics import precision_score\n",
    "print('precision = ', precision_score(y2_test,y2_pred))\n",
    "#recall/sensetivity\n",
    "sensitivity1 = cm1[0,0]/(cm1[0,0]+cm1[0,1])\n",
    "print('Sensitivity : ', sensitivity1 )\n",
    "#specificity\n",
    "specificity1 = cm1[1,1]/(cm1[1,0]+cm1[1,1])\n",
    "print('Specificity : ', specificity1)\n",
    "#print f1 score \n",
    "from sklearn.metrics import f1_score\n",
    "print('f1 score = ', f1_score(y2_test,y2_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Oversampling "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# do oversampling before or after train test split, stack exchange says after and only on training dataset\n",
    "# try doing undersampling instead, as this won't introduce duplicates and artificially increase performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total subjects = 654\n",
      "total subjects = 327\n",
      "total subjects = 654\n",
      "total subjects = 327\n"
     ]
    }
   ],
   "source": [
    "#define oversamples\n",
    "oversample = RandomOverSampler(sampling_strategy='minority')\n",
    "# fit and apply the transform\n",
    "x1_over, y1_over = oversample.fit_resample(X1, Y)\n",
    "x1_train, x1_test, y1_train, y1_test = train_test_split(x1_over, y1_over, test_size=0.33)\n",
    "print(\"total subjects = \" + str(len(y1_over)))\n",
    "print(\"total subjects = \" + str(len(y1_over[y1_over['Case'] == 0])))\n",
    "\n",
    "\n",
    "#define oversamples\n",
    "oversample = RandomOverSampler(sampling_strategy='minority')\n",
    "# fit and apply the transform\n",
    "x2_over, y2_over = oversample.fit_resample(X2, Y)\n",
    "x2_train, x2_test, y2_train, y2_test = train_test_split(x2_over, y2_over, test_size=0.33)\n",
    "print(\"total subjects = \" + str(len(y2_over)))\n",
    "print(\"total subjects = \" + str(len(y2_over[y2_over['Case'] == 0])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy Score 0.5601851851851852\n",
      "[[70 31]\n",
      " [64 51]]\n",
      "Cross-Validation Accuracy Scores [0.57251908 0.57251908 0.6259542  0.58015267 0.56153846]\n",
      "Test Accuracy Score 0.5601851851851852\n",
      "precision =  0.6219512195121951\n",
      "Sensitivity :  0.693069306930693\n",
      "Specificity :  0.4434782608695652\n",
      "f1 score =  0.5177664974619289\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#train test split\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier(n_neighbors=20)\n",
    "knn.fit(x1_train, y1_train) \n",
    "y1_pred = knn.predict(x1_test)\n",
    "\n",
    "# accuracy \n",
    "print('Test Accuracy Score', knn.score(x1_test,y1_test))\n",
    "\n",
    "#confusion matrix\n",
    "C = confusion_matrix(y1_test, y1_pred)\n",
    "print(C)\n",
    "\n",
    "#ROC curve \n",
    "plot_roc_curve(knn, x1_test, y1_test)\n",
    "\n",
    "#cross valiation \n",
    "print('Cross-Validation Accuracy Scores', cross_val_score(knn, x1_over, y1_over, cv=5, scoring = 'accuracy'))\n",
    "\n",
    "cm1 = confusion_matrix(y1_test, y1_pred)\n",
    "#accuracy\n",
    "print('Test Accuracy Score', knn.score(x1_test, y1_test))\n",
    "#print precision \n",
    "from sklearn.metrics import precision_score\n",
    "print('precision = ', precision_score(y1_test,y1_pred))\n",
    "#recall/sensetivity\n",
    "sensitivity1 = cm1[0,0]/(cm1[0,0]+cm1[0,1])\n",
    "print('Sensitivity : ', sensitivity1 )\n",
    "#specificity\n",
    "specificity1 = cm1[1,1]/(cm1[1,0]+cm1[1,1])\n",
    "print('Specificity : ', specificity1)\n",
    "#print f1 score \n",
    "from sklearn.metrics import f1_score\n",
    "print('f1 score = ', f1_score(y1_test,y1_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy Score 0.5925925925925926\n",
      "[[69 47]\n",
      " [41 59]]\n",
      "Cross-Validation Accuracy Scores [0.55725191 0.58015267 0.47328244 0.60305344 0.52307692]\n",
      "Test Accuracy Score 0.5925925925925926\n",
      "precision =  0.5566037735849056\n",
      "Sensitivity :  0.5948275862068966\n",
      "Specificity :  0.59\n",
      "f1 score =  0.5728155339805824\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#train test split\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier(n_neighbors=15)\n",
    "knn.fit(x2_train, y2_train) \n",
    "y2_pred = knn.predict(x2_test)\n",
    "\n",
    "# accuracy \n",
    "print('Test Accuracy Score', knn.score(x2_test,y2_test))\n",
    "\n",
    "#confusion matrix\n",
    "C = confusion_matrix(y2_test, y2_pred)\n",
    "print(C)\n",
    "\n",
    "#ROC curve \n",
    "plot_roc_curve(knn, x2_test, y2_test)\n",
    "\n",
    "#cross valiation \n",
    "print('Cross-Validation Accuracy Scores', cross_val_score(knn, x2_over, y2_over, cv=5, scoring = 'accuracy'))\n",
    "\n",
    "cm1 = confusion_matrix(y2_test, y2_pred)\n",
    "#accuracy\n",
    "print('Test Accuracy Score', knn.score(x2_test, y2_test))\n",
    "#print precision \n",
    "from sklearn.metrics import precision_score\n",
    "print('precision = ', precision_score(y2_test,y2_pred))\n",
    "#recall/sensetivity\n",
    "sensitivity1 = cm1[0,0]/(cm1[0,0]+cm1[0,1])\n",
    "print('Sensitivity : ', sensitivity1 )\n",
    "#specificity\n",
    "specificity1 = cm1[1,1]/(cm1[1,0]+cm1[1,1])\n",
    "print('Specificity : ', specificity1)\n",
    "#print f1 score \n",
    "from sklearn.metrics import f1_score\n",
    "print('f1 score = ', f1_score(y2_test,y2_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy Score 0.625\n",
      "[[72 29]\n",
      " [52 63]]\n",
      "Cross-Validation Accuracy Scores [0.67938931 0.73282443 0.67938931 0.85496183 0.78461538]\n",
      "Test Accuracy Score 0.625\n",
      "precision =  0.6847826086956522\n",
      "Sensitivity :  0.7128712871287128\n",
      "Specificity :  0.5478260869565217\n",
      "f1 score =  0.608695652173913\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Random Forrest \n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rfc = RandomForestClassifier(max_depth = 10)\n",
    "rfc.fit(x1_train, y1_train) \n",
    "y1_pred = rfc.predict(x1_test)\n",
    "\n",
    "# accuracy \n",
    "print('Test Accuracy Score', rfc.score(x1_test,y1_test))\n",
    "\n",
    "#confusion matrix\n",
    "print(confusion_matrix(y1_test, y1_pred))\n",
    "\n",
    "#ROC curve \n",
    "plot_roc_curve(rfc, x1_test, y1_test)\n",
    "plt.savefig('RF_ROC.jpg')\n",
    "#cross valiation \n",
    "print('Cross-Validation Accuracy Scores', cross_val_score(rfc, x1_over, y1_over, cv=5))\n",
    "\n",
    "cm1 = confusion_matrix(y1_test, y1_pred)\n",
    "#accuracy\n",
    "print('Test Accuracy Score', rfc.score(x1_test, y1_test))\n",
    "#print precision \n",
    "from sklearn.metrics import precision_score\n",
    "print('precision = ', precision_score(y1_test,y1_pred))\n",
    "#recall/sensetivity\n",
    "sensitivity1 = cm1[0,0]/(cm1[0,0]+cm1[0,1])\n",
    "print('Sensitivity : ', sensitivity1 )\n",
    "#specificity\n",
    "specificity1 = cm1[1,1]/(cm1[1,0]+cm1[1,1])\n",
    "print('Specificity : ', specificity1)\n",
    "#print f1 score \n",
    "from sklearn.metrics import f1_score\n",
    "print('f1 score = ', f1_score(y1_test,y1_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy Score 0.6805555555555556\n",
      "[[89 27]\n",
      " [42 58]]\n",
      "Cross-Validation Accuracy Scores [0.63358779 0.64122137 0.65648855 0.75572519 0.68461538]\n",
      "Test Accuracy Score 0.6805555555555556\n",
      "precision =  0.6823529411764706\n",
      "Sensitivity :  0.7672413793103449\n",
      "Specificity :  0.58\n",
      "f1 score =  0.6270270270270271\n"
     ]
    },
    {
     "data": {
      "image/png": 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SaanqCEqAQ919u5k1AT4DDnL3f0Y4tyVZ59WW7wB+6+7bqs11UPUg93HAOICSkpLq58g7ygWISCalSgg2u/t2AHffZGZLIyYCEOQA9k9YbkfQ2ihRCTAlTAT2Bk42s63u/teI1xARkV2UKiHoZGYLwtcGHBguG+Dufmgtx74LFJlZR+AfwE+BMxN3cPeOFa/NbALwbKEmAhV1A5pnWEQyLVVC0HlnT+zuW81sOEFroAbAQ+6+yMwuDLfXWi9QaNRPQESyJdWgc7s00Jy7Pw88X21d0gTA3YftyrXygeoGRCQbog4xISIieSpqz2KJQbI+AyIimRY5R2BmTc3skDiDKTTqMyAi9UGkHIGZDSDoSNYY6GhmxcBIdx8YY2wFQfUCIpJtUXME1xMMGfEFgLuXAh3iCEhERDIrakKw1d3XxxqJiIhkRdTK4jIzOxNoYGZFwGXArPjCEhGRTImaEFwKXA18AzxG0EnshriCykeJLYQqqKWQiNQHUROCQ9z9aoLEQHZCsuEj1FJIROqDqAnBn8ysLfA4MMXdF8UYU95SCyERqY+izlDWF+gDrAHGmdlCM7smzsBERCQzIvcsDoefHmNmrwK/Aa5F9QRJqT5ARHJJpByBmXU2s+vNrAy4m6DFULtYI8thiT2GK6g+QETqq6g5gvHAZOAEd68+uYwkofoAEckVkRICdz8y7kBERCQ7ak0IzOx/3f0MM1tI1fmGo8xQJiIiOSBVjuCX4e9T4w5ERESyo9bKYndfHb682N1XJP4AF8cfnoiIxC3qoHP9kqzrn85AREQkO1LVEVxE8OR/gJktSNjUAngzzsDqs2T9BBKpz4CI5JJUdQSPAS8ANwNXJKzf4O6fxxZVPZds3KBE6jMgIrkkVULg7r7czC6pvsHM9irkxED9BEQkX0TJEZwKzCVoPmoJ2xw4IKa46h1NNC8i+arWhMDdTw1/d8xMOPVXYnGQin5EJJ9Enbz+aKDU3b80s7OBnsAd7r4y1ujqGRUHiUg+itp89M/AV2bWg2Dk0RXAI7FFJSIiGVOXyesdGATc6e53EjQhFRGRHBd19NENZnYlcA5wrJk1ABrFF5aIiGRK1BzBEIKJ638eTlCzH3BrbFGJiEjGRJ2q8p/AJKCVmZ0KbHL3h2ONTEREMiLqDGVnAO8ApwNnAG+b2U8iHHeSmX1gZsvM7Iok288yswXhz6ywMlpERDIoah3B1cD33P1TADPbB3gJeKKmA8J6hHsIBqwrB941s2nuvjhht4+B77v7OjPrD4wDetX9NkREZGdFrSPYrSIRCK2NcOwRwDJ3/8jdNwNTCFodVXL3We6+Llx8i3o4D/Jjb69kyH2zd5iDWEQkX0TNEUw3sxcJ5i2GoPL4+RTH7AesSlgup/an/f8iGOBuB2Z2AXABQPv27aPEmzaJPYrVm1hE8lHUOYtHmNlpwDEE4w2Nc/e/pDjMkqzzJOsws74ECcExNVx/HEGxESUlJUnPESf1KBaRfJZqPoIiYDRwILAQuNzdax6Iv6pyYP+E5XbAJ0mucSjwANDf3ddGPLeIiKRJqnL+h4BngR8TjEB6Vx3O/S5QZGYdzawx8FNgWuIOZtYeeAo4x92X1uHcIiKSJqmKhlq4+/3h6w/M7L2oJ3b3rWY2HHgRaAA85O6LzOzCcPtY4FqgNXCvmUEwlEVJXW9CRER2XqqEoImZHca35f1NE5fdvdaEwd2fp1qlcpgAVLw+Hzi/rkGLiEj6pEoIVgN/Slj+Z8KyAz+IIygREcmcVBPT9M1UICIikh1RO5SJiEieUkIgIlLglBCIiBS4qHMWG3AWcIC7jwzb//+Hu78Ta3QZ9NjbK3m6dMe+chXDS4iI5KuoOYJ7gd7A0HB5A8HIonmjYkyh6jTGkIjku6iDzvVy955mNg8gHDa6cYxxZYXGFBKRQhQ1R7AlnF/AoXI+gu2xRSUiIhkTNSEYA/wF2NfMbgTeAG6KLSoREcmYqMNQTzKzucDxBMNL/Mjdl8QamYiIZETUVkPtga+AZxLXufvKuAITEZHMiFpZ/BxB/YABTYCOwAdA15jiEhGRDIlaNNQ9cdnMegK/iCWiDKvoP6D+AiJSqHaqZ3E4/PT30hxLVmhOYhEpdFHrCH6dsLgb0BNYE0tEWaD+AyJSyKLWEbRIeL2VoM7gyfSHIyIimZYyIQg7kjV39xEZiEdERDKs1joCM2vo7tsIioJERCQPpcoRvEOQCJSa2TTgceDLio3u/lSMscUmcaRRtRYSkUIXtY5gL2AtwRzFFf0JHMjJhCCxpZBaC4lIoUuVEOwbthgq49sEoILHFlUGqKVQ/LZs2UJ5eTmbNm3KdigiBaNJkya0a9eORo0aRT4mVULQAGhO1QSgQk4nBBK/8vJyWrRoQYcOHQjmNhKROLk7a9eupby8nI4dO0Y+LlVCsNrdR+5aaFKoNm3apERAJIPMjNatW7NmTd26eaXqWaz/YNklSgREMmtn/udSJQTH71woIiKSK2pNCNz980wFIhKHBg0aUFxcTLdu3RgwYABffPFFWs47YcIEhg8fnpZzdejQge7du1NcXExxcTGzZs1Ky3mrKy0t5fnnn6+y7oUXXqCkpITOnTvTqVMnLr/8cgCuv/56Ro8enbZrH3XUUZWvR4wYQdeuXRkxYgRjx47l4Ycf3qVzz5s3j/PPP7/KukGDBtG7d9XGIMOGDeOJJ56osq558+aVr5cuXcrJJ5/MQQcdROfOnTnjjDP417/+tUuxff755/Tr14+ioiL69evHunXrdtjngw8+qPzsi4uLadmyJXfccQcQvFedOnXi0EMPZfDgwZV/vwsXLmTYsGG7FFuinRp0Lhc99vZKhtw3myH3zU46Sb3kp6ZNm1JaWkpZWRl77bUX99xzT7ZDSurVV1+ltLSU0tLSKl+atdm6dWudrlE9ISgrK2P48OE8+uijLFmyhLKyMg444IA6nTOqxMTtvvvu47333uPWW2/lwgsv5Nxzz418nmT3fNNNN3HppZdWLn/xxRe89957fPHFF3z88ceRzrtp0yZOOeUULrroIpYtW8aSJUu46KKL6lzWXt2oUaM4/vjj+fvf/87xxx/PqFGjdtjnkEMOqfzs586dS7NmzRg8eDAA/fr1o6ysjAULFnDwwQdz8803A9C9e3fKy8tZuTI9U8JE7UeQ89R3ILt+/8wiFn+S3gS4y3+25LoB0afE6N27NwsWLADgnXfe4Ve/+hVff/01TZs2Zfz48RxyyCFMmDCBadOm8dVXX/Hhhx8yePBg/vjHPwIwfvx4br75Ztq2bcvBBx/M7rvvDsCKFSv4+c9/zpo1a9hnn30YP3487du3Z9iwYTRt2pT333+fFStWMH78eCZOnMjs2bPp1asXEyZMqDHW2s651157MW/ePHr27MnFF1/MJZdcwpo1a2jWrBn3338/nTp14vHHH+f3v/89DRo0oFWrVrz00ktce+21fP3117zxxhtceeWVPPfcc1x99dV06tQJgIYNG3LxxRfvEMv999/PuHHj2Lx5MwcddBCPPPIIzZo12+EaM2fOZNGiRZx33nls3ryZ7du38+STT1JUVETz5s3ZuHEjAwcO5Msvv6RXr15ceeWVLFmyhObNm3P55Zfz4YcfJr2X6vd82223Vca2YcMGFixYQI8ePSrXPfnkkwwYMIA2bdowZcoUrrzyypR/G4899hi9e/dmwIABlev69u2b8rhUnn76aV577TUAfvazn9GnTx9uueWWGvd/+eWXOfDAA/nud78LwAknnFC57cgjj6ySoxkwYABTpkzhN7/5zS7HWTA5Avi278DUX/TmzF7tsx2OZNC2bdt4+eWXGThwIACdOnVi5syZzJs3j5EjR3LVVVdV7ltaWsrUqVNZuHAhU6dOZdWqVaxevZrrrruON998kxkzZrB48eLK/YcPH865557LggULOOuss7jssssqt61bt45XXnmF22+/nQEDBvDf//3fLFq0iIULF1JaWlq5X9++fSkuLqZXr14pz7l06VJeeuklbrvtNi644ALuuusu5s6dy+jRoyu/yEeOHMmLL77I/PnzmTZtGo0bN2bkyJEMGTKE0tJShgwZQllZGYcffnjK9+60007j3XffZf78+XTu3JkHH3ww6TUAxo4dyy9/+UtKS0uZM2cO7dq1q3KuadOmVebShgwZUmVbTfdS/Z4TzZkzh27dulVZN3nyZIYOHcrQoUOZPHlyyvsDIr8XGzZsqFKMk/iT+DdR4V//+hdt27YFoG3btnz66ae1nn/KlCkMHTo06baHHnqI/v37Vy6XlJTw+uuvp4w5ioLJEUh21eXJPZ2+/vpriouLWb58OYcffjj9+vUDYP369fzsZz/j73//O2bGli1bKo85/vjjadWqFQBdunRhxYoVfPbZZ/Tp04d99tkHgCFDhrB06VIAZs+ezVNPBZ3szznnnCpPaAMGDMDM6N69O23atKF792COp65du7J8+XKKi4uBoGho7733rjyutnOefvrpNGjQgI0bNzJr1ixOP/30ym3ffPMNAEcffTTDhg3jjDPO4LTTTtul97CsrIxrrrmGL774go0bN3LiiSfWeI3evXtz4403Ul5ezmmnnUZRUVGka9R2L4n3XN3q1asrPxMIvniXLVvGMcccg5nRsGFDysrK6NatW9LWNHVtYdOiRYsqCXg6bd68mWnTplUW/yS68cYbadiwIWeddVblun333ZdPPvkkLdeONUdgZieZ2QdmtszMrkiy3cxsTLh9QTjzmUjaVDx9rlixgs2bN1fWEfzud7+jb9++lJWV8cwzz1Tp/VxR5ANBZXNFuXTUL43E/SrOtdtuu1U572677VanMv7Ec+6xxx4AbN++nT333LOyfLm0tJQlS5YAwZP5DTfcwKpVqyguLmbt2rU7nLNr167MnTs35bWHDRvG3XffzcKFC7nuuusq36tk1zjzzDMrn/pPPPFEXnnllUj3V9u9JN5zdU2bNq3y2U2dOpV169bRsWNHOnTowPLly5kyZQoArVu3rlJZ+/nnn1cmvlHfi7rmCNq0acPq1auBINHad999azz3Cy+8QM+ePWnTpk2V9RMnTuTZZ59l0qRJVf4ONm3aRNOmTVPGHEVsCUE4fPU9QH+gCzDUzLpU260/UBT+XAD8Oa54pLC1atWKMWPGMHr0aLZs2cL69evZb7+gnqi2svoKvXr14rXXXmPt2rVs2bKFxx9/vHLbUUcdVfllM2nSJI455phdjjfKOVu2bEnHjh0rY3F35s+fD8CHH35Ir169GDlyJHvvvTerVq2iRYsWbNiwofL4ESNGcNNNN1XmbLZv386f/vSnHa6zYcMG2rZty5YtW5g0aVLl+mTX+OijjzjggAO47LLLGDhwYGWdTCq13UttOnfuzLJlyyqXJ0+ezPTp01m+fDnLly9n7ty5le9jnz59mDp1Kps3bwaCz72iHuDMM89k1qxZPPfcc5Xnmj59OgsXLqxyvYocQbKfLl2qf73BwIEDmThxIhB8oQ8aNKjGe6ko0ko0ffp0brnlFqZNm0azZs2qbFu6dOkOxWI7K84cwRHAMnf/yN03A1OA6u/CIOBhD7wF7GlmbWOMSQrYYYcdRo8ePSor2K688kqOPvpotm3blvLYtm3bcv3119O7d29++MMf0rPnt5nXMWPGMH78eA499FAeeeQR7rzzzl2ONeo5J02axIMPPkiPHj3o2rUrTz/9NBB8yXfv3p1u3bpx3HHH0aNHD/r27cvixYspLi5m6tSpHHroodxxxx0MHTqUzp07061bt8qn10R/+MMf6NWrF/369ausWK7pGlOnTqVbt24UFxfz/vvv16lFUE33UptOnTqxfv16NmzYwPLly1m5ciVHHnlk5faOHTvSsmVL3n77bU499VSOPfZYDj/8cIqLi3nzzTcrK26bNm3Ks88+y1133UVRURFdunRhwoQJtT7BR3HFFVcwY8YMioqKmDFjBldcERSMfPLJJ5x88smV+3311VfMmDFjh2K84cOHs2HDBvr160dxcTEXXnhh5bZXX32VU045ZZfiq2Du8QwZZGY/AU5y9/PD5XOAXu4+PGGfZ4FR7v5GuPwy8Ft3n1PtXBcQ5Bho37794StWrKhzPL9/ZhGQvbLqQrRkyRI6d+6c7TAkz91+++20aNFih74E+eybb77h+9//Pm+88QYNG+5Y1Zvsf8/M5rp7SbLzxZkjiDJQXaTB7Nx9nLuXuHtJYsVQXVw3oKsSAZE8dNFFF1WpfykEK1euZNSoUUkTgZ0RZ6uhcmD/hOV2QPUq7ij7iIjUqEmTJpxzzjnZDiOjioqKIrfIiiLOHMG7QJGZdTSzxsBPgWnV9pkGnBu2HjoSWO/uOxZSSs6Kq+hRRJLbmf+52HIE7r7VzIYDLxLMa/CQuy8yswvD7WOB54GTgWXAV8B5ccUjmdekSRPWrl1L69atNQqpSAZUzEfQpEmTOh0XW2VxXEpKSnzOnDmpd5Ss0wxlIplX0wxltVUWq2exxKZRo0Z1miVJRLKjoMYaEhGRHSkhEBEpcEoIREQKXM5VFpvZGqDuXYsDewOfpTGcXKB7Lgy658KwK/f8XXdP2iM35xKCXWFmc2qqNc9XuufCoHsuDHHds4qGREQKnBICEZECV2gJwbhsB5AFuufCoHsuDLHcc0HVEYiIyI4KLUcgIiLVKCEQESlweZkQmNlJZvaBmS0zsyuSbDczGxNuX2BmPZOdJ5dEuOezwntdYGazzKxHNuJMp1T3nLDf98xsWzhrXk6Lcs9m1sfMSs1skZn9LdMxpluEv+1WZvaMmc0P7zmnRzE2s4fM7FMzK6the/q/v9w9r34Ihrz+EDgAaAzMB7pU2+dk4AWCGdKOBN7OdtwZuOejgO+Er/sXwj0n7PcKwZDnP8l23Bn4nPcEFgPtw+V9sx13Bu75KuCW8PU+wOdA42zHvgv3fBzQEyirYXvav7/yMUdwBLDM3T9y983AFGBQtX0GAQ974C1gTzNrm+lA0yjlPbv7LHdfFy6+RTAbXC6L8jkDXAo8CXyayeBiEuWezwSecveVAO6e6/cd5Z4daGHBpBfNCRKCrZkNM33cfSbBPdQk7d9f+ZgQ7AesSlguD9fVdZ9cUtf7+S+CJ4pclvKezWw/YDAwNoNxxSnK53ww8B0ze83M5prZuRmLLh5R7vluoDPBNLcLgV+6+/bMhJcVaf/+ysf5CJJNhVW9jWyUfXJJ5Psxs74ECcExsUYUvyj3fAfwW3ffliczpEW554bA4cDxQFNgtpm95e5L4w4uJlHu+USgFPgBcCAww8xed/d/xxxbtqT9+ysfE4JyYP+E5XYETwp13SeXRLofMzsUeADo7+5rMxRbXKLccwkwJUwE9gZONrOt7v7XjESYflH/tj9z9y+BL81sJtADyNWEIMo9nweM8qAAfZmZfQx0At7JTIgZl/bvr3wsGnoXKDKzjmbWGPgpMK3aPtOAc8Pa9yOB9e6+OtOBplHKezaz9sBTwDk5/HSYKOU9u3tHd+/g7h2AJ4CLczgRgGh/208Dx5pZQzNrBvQClmQ4znSKcs8rCXJAmFkb4BDgo4xGmVlp//7KuxyBu281s+HAiwQtDh5y90VmdmG4fSxBC5KTgWXAVwRPFDkr4j1fC7QG7g2fkLd6Do/cGPGe80qUe3b3JWY2HVgAbAcecPekzRBzQcTP+Q/ABDNbSFBs8lt3z9nhqc1sMtAH2NvMyoHrgEYQ3/eXhpgQESlw+Vg0JCIidaCEQESkwCkhEBEpcEoIREQKnBICEZECp4SgAIQjb5Ym/HSoZd+NabjeBDP7OLzWe2bWeyfO8YCZdQlfX1Vt26xdjTE8T8X7UhaOXrlniv2LzezknbhOWzN7Nnzdx8zWm9k8M1tiZtftxPkGVozCaWY/qnifwuWRZvbDup4zyTUmWIrRWsNhLCI3QQ7v/dkI+yUdfdPMRpvZD6JeT6JTQlAYvnb34oSf5Rm45gh3LwauAO6r68Hufr67Lw4Xr6q27ahdDw/49n3pRjDI1yUp9i8maL9dV78G7k9Yft3dDyPo+Xy2mR1el5O5+zR3HxUu/gjokrDtWnd/aSdirE8mACclWX8Xwd+TpJkSggJkZs3N7OXwaX2hme0wamf4FDsz4Yn52HD9CWY2Ozz2cTNrnuJyM4GDwmN/HZ6rzMx+Fa7bw8yes2As+TIzGxKuf83MSsxsFNA0jGNSuG1j+Htq4hN6+BT7YzNrYGa3mtm7FozX/osIb8tswoG7zOwIC+ZsmBf+PiTs1ToSGBLGMiSM/aHwOvOSvY+hHwPTq68Mh4GYCxwY5jbeCuP9i5l9J4zlMjNbHK6fEq4bZmZ3m9lRwEDg1jCmAyue5M2sv5n9b8J708fMnglf1+kzNLNrw3ssM7NxZlUGbjo7fI/KzOyIcP+o70tSNY2+6e4rgNZm9h91OZ9EkKkxtvWTvR9gG8GgXKXAXwh6lLcMt+1N0EOxonPhxvD3/wBXh68bAC3CfWcCe4Trfwtcm+R6EwjH/gdOB94mGAhtIbAHwVDBi4DDCL4k7084tlX4+zWgJDGmhH0qYhwMTAxfNyYYkbEpcAFwTbh+d2AO0DFJnBsT7u9x4KRwuSXQMHz9Q+DJ8PUw4O6E428Czg5f70kwns8e1a7REZibsNwHeDZ83RpYDnQl6An8/XD9SOCO8PUnwO4V16geR+J7nbgcfsYrEz6rPwNn7+RnuFfC+keAAQmf0f3h6+MIx8+v6X2pdu8lBL2ea/qb7UCS8fgJclY/zvb/VL795N0QE5LU1x4U0wBgZo2Am8zsOIJhCPYD2gD/TDjmXeChcN+/unupmX2foBjizfChsDHBk3Qyt5rZNcAagtFOjwf+4sFTMGb2FHAswZPyaDO7heBL4vU63NcLwBgz252gKGGmu39tZicAhyaUcbcCioCPqx3f1MxKCb505gIzEvafaGZFBKM6Nqrh+icAA83s8nC5CdCeqmP7tA3fg0THmtk8gvd+FMEgYnu6e8VsYhMJEiYIEohJZvZX4K81xLEDD4ZmmA4MMLMngFOA3wB1+Qwr9DWz3wDNgL0IEvFnwm2Tw+vNNLOWFtSz1PS+JMY3Bzg/6v0k+BT4z504TmqhhKAwnUUwk9Ph7r7FzJYT/LNWCv+xjyP4AnnEzG4F1gEz3H1ohGuMcPcnKhashgpMd18alpGfDNxsZv/n7iOj3IS7bzKz1wiGIR5C+KVEMN7Mpe7+YopTfO3uxWbWCniWoI5gDMHYNa+6+2ALKtZfq+F4I3g6/aC2a1DtvSWoIzi18iTB9WtyCsHT9kDgd2bWtZZ9q5tKcE+fA++6+4awWCfqZ4iZNQHuJcidrTKz66l6P9XHqHFqeF8sGBBuVzUheE8ljVRHUJhaAZ+GiUBf4LvVdzCz74b73A88SDB13lvA0WZWUebfzMwOjnjNmcCPwmP2ICjWed3M/hP4yt0fBUaH16luS5gzSWYKwaBbxxIMTEb4+6KKY8zs4PCaSbn7euAy4PLwmFbAP8LNwxJ23UBQRFbhReDSijJzMzssyemXEuQ4ahRef52F9TDAOcDfzGw3YH93f5XgaX5PgmK1RNVjSvQawfv5/wgSBaj7Z1jxpf9ZWJdQvSVRRZ3OMQSjYK4n2vuysw4GcnYQvfpKCUFhmgSUmNkcgtzB+0n26QOUhkUYPwbudPc1BF+Mk81sAcGXSqcoF3T39wjKnd8hqDN4wN3nAd2Bd8IimquBG5IcPg5YYGFlcTX/R/DE/JIHUxlCMOfCYuA9C5og3keK3G8Yy3yCYY7/SJA7eZOg/qDCq0CXispigpxDozC2snC5+nm/BD6s+OKtxc8IitMWELROGhle+1ELRtWcB9zu7l9UO24KMCKslD2w2rW3EeR0+oe/qetnGF7vfoL6nb8SFBkmWmdBc96xBEWAEOF9saAhwAPJrmnB6JuzgUPMrNzM/itc34ig4cGcmuKVnaPRR0ViZmaDCYrhrsl2LLksfB97uvvvsh1LvlEdgUjM3P0vZtY623HkgYbAbdkOIh8pRyAiUuBURyAiUuCUEIiIFDglBCIiBU4JgYhIgVNCICJS4P4/MVJsN8SfgnwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Random Forrest \n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rfc = RandomForestClassifier(max_depth = 10)\n",
    "rfc.fit(x2_train, y2_train) \n",
    "y2_pred = rfc.predict(x2_test)\n",
    "\n",
    "# accuracy \n",
    "print('Test Accuracy Score', rfc.score(x2_test,y2_test))\n",
    "\n",
    "#confusion matrix\n",
    "print(confusion_matrix(y2_test, y2_pred))\n",
    "\n",
    "#ROC curve \n",
    "plot_roc_curve(rfc, x2_test, y2_test)\n",
    "\n",
    "#cross valiation \n",
    "print('Cross-Validation Accuracy Scores', cross_val_score(rfc, x2_over, y2_over, cv=5))\n",
    "\n",
    "\n",
    "cm1 = confusion_matrix(y2_test, y2_pred)\n",
    "#accuracy\n",
    "print('Test Accuracy Score', rfc.score(x2_test, y2_test))\n",
    "#print precision \n",
    "from sklearn.metrics import precision_score\n",
    "print('precision = ', precision_score(y2_test,y2_pred))\n",
    "#recall/sensetivity\n",
    "sensitivity1 = cm1[0,0]/(cm1[0,0]+cm1[0,1])\n",
    "print('Sensitivity : ', sensitivity1 )\n",
    "#specificity\n",
    "specificity1 = cm1[1,1]/(cm1[1,0]+cm1[1,1])\n",
    "print('Specificity : ', specificity1)\n",
    "#print f1 score \n",
    "from sklearn.metrics import f1_score\n",
    "print('f1 score = ', f1_score(y2_test,y2_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dimensionality Check (only peak amplitude, no latency)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "X3 = pd.DataFrame(list(zip(p300_amp_avg, p300_amp_var, n200_amp_avg, n200_amp_var)),\n",
    "               columns =['p300_amp_avg', 'p300_amp_var', 'n200_amp_avg', 'n200_amp_var'])\n",
    "Y = Y = pd.get_dummies(new_eeg_ndar[\"Phenotype/diagnosis for the subject\"])\n",
    "Y = Y.drop(['Control'], axis = 1)\n",
    "x3_train, x3_test, y3_train, y3_test = train_test_split(X3, Y, test_size=0.33)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy Score 0.5823529411764706\n",
      "[[20 35]\n",
      " [36 79]]\n",
      "Cross-Validation Accuracy Scores [0.55339806 0.62135922 0.59223301 0.55339806 0.54368932]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Random Forrest \n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rfc = RandomForestClassifier(max_depth = 10)\n",
    "rfc.fit(x3_train, y3_train) \n",
    "y3_pred = rfc.predict(x3_test)\n",
    "\n",
    "# accuracy \n",
    "print('Test Accuracy Score', rfc.score(x3_test,y3_test))\n",
    "\n",
    "#confusion matrix\n",
    "print(confusion_matrix(y3_test, y3_pred))\n",
    "\n",
    "#ROC curve \n",
    "plot_roc_curve(rfc, x3_test, y3_test)\n",
    "\n",
    "#cross valiation \n",
    "print('Cross-Validation Accuracy Scores', cross_val_score(rfc, X3, Y, cv=5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy Score 0.6588235294117647\n",
      "[[14 41]\n",
      " [17 98]]\n",
      "Cross-Validation Accuracy Scores [0.58252427 0.67961165 0.59223301 0.60194175 0.62135922]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#KNN\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier(n_neighbors=15)\n",
    "knn.fit(x3_train, y3_train) \n",
    "y3_pred = knn.predict(x3_test)\n",
    "\n",
    "# accuracy \n",
    "print('Test Accuracy Score', knn.score(x3_test,y3_test))\n",
    "\n",
    "#confusion matrix\n",
    "C = confusion_matrix(y3_test, y3_pred)\n",
    "print(C)\n",
    "\n",
    "#ROC curve \n",
    "plot_roc_curve(knn, x3_test, y3_test)\n",
    "\n",
    "#cross valiation \n",
    "print('Cross-Validation Accuracy Scores', cross_val_score(knn, X3, Y, cv=5, scoring = 'accuracy'))"
   ]
  }
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