Pierre Karashchuk
153 linhas
3.3 KiB
Plaintext
153 linhas
3.3 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 43,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"from scipy import signal, stats\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"from time import time, strftime, gmtime\n",
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"from pylsl import StreamInlet, resolve_byprop\n",
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"from matplotlib.pyplot import *\n",
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"\n",
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"from collect_tools import collect_eeg\n",
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"\n",
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"%matplotlib inline"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 44,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"looking for an EEG stream...\n",
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"Found stream!\n"
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]
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}
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],
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"source": [
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"print(\"looking for an EEG stream...\")\n",
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"streams = resolve_byprop('type', 'EEG', timeout=2)\n",
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"\n",
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"if len(streams) == 0:\n",
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" print('No streams found! Are you streaming data?')\n",
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"else:\n",
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" print('Found stream!')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 45,
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"metadata": {},
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"outputs": [],
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"source": [
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"inlet = StreamInlet(streams[0], max_chunklen=24)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 46,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"baseline: just stay still and don't smell too much\n",
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"\n",
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"Start recording at time t=1491953530.525\n",
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"Finished recording at time 1491953540.611 (10.086 seconds)\n"
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]
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}
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],
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"source": [
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"print(\"baseline: just stay still and don't smell too much\\n\")\n",
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"baseline = collect_eeg(inlet,duration=10, tag='baseline')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 47,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"smell: try to smell the object as much as possible\n",
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"\n",
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"Start recording at time t=1491953540.627\n",
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"Finished recording at time 1491953550.697 (10.069 seconds)\n"
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]
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}
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],
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"source": [
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"print(\"smell: try to smell the object as much as possible\\n\")\n",
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"smell = collect_eeg(inlet,duration=10, tag='smell')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 48,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"data saved in:\n",
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"data/smell_1491953550.csv\n"
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]
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}
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],
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"source": [
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"data = pd.concat([baseline, smell])\n",
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"last = np.array(data.timestamps)[-1]\n",
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"last = int(float(last))\n",
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"fname = 'data/smell_{}.csv'.format(last)\n",
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"data.to_csv(fname, index=False)\n",
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"print('data saved in:\\n{}'.format(fname))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.0"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 1
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}
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