{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/cluster/apps/python/3.6.1/x86_64/lib64/python3.6/site-packages/sklearn/ensemble/weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.\n",
" from numpy.core.umath_tests import inner1d\n"
]
}
],
"source": [
"import numpy as np\n",
"import os\n",
"import glob\n",
"import pandas as pd\n",
"from scipy.signal import resample, butter, lfilter\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from tqdm import tqdm\n",
"from itertools import islice\n",
"\n",
"from sklearn.decomposition import PCA\n",
"from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.ensemble import RandomForestClassifier, VotingClassifier, GradientBoostingClassifier, AdaBoostClassifier\n",
"from sklearn.metrics import precision_recall_fscore_support, roc_auc_score, confusion_matrix\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.preprocessing import StandardScaler\n",
"\n",
"from multiprocessing import Pool\n",
"from multiprocessing.pool import ThreadPool\n",
"\n",
"import time"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Chapter 9: Use case - prediction of arm movements\n",
"\n",
"<center>\n",
"<figure>\n",
" <img src=\"./images/eeg_cap.png\" title=\"made at imgflip.com\" width=35%/> \n",
" <img src=\"./images/arm_movement.png\" title=\"made at imgflip.com\" width=35%/>\n",
" <figcaption>Setup of an EEG-experiment.</figcaption>\n",
"</figure>\n",
"</center>\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This data contains EEG recordings of subjects performing **grasp-and-lift (GAL)** trials. \n",
"\n",
"There are **12 subjects** in total, **10 series** of trials for each subject, and approximately **30 trials** within each series. The number of trials varies for each series. The training set contains the first 8 series for each subject. The test set contains the 9th and 10th series.\n",
"\n",
"For each **GAL**, you are tasked to detect 6 events:\n",
"\n",
"- HandStart\n",
"- FirstDigitTouch\n",
"- BothStartLoadPhase\n",
"- LiftOff\n",
"- Replace\n",
"- BothReleased\n",
"\n",
"These events always occur in the same order. In the training set, there are two files for each subject + series combination:\n",
"\n",
"the *_data.csv files contain the raw 32 channels EEG data (sampling rate 500Hz)\n",
"the *_events.csv files contains the ground truth frame-wise labels for all events\n",
"\n",
"\n",
"Detailed information about the data can be found here:\n",
"Luciw MD, Jarocka E, Edin BB (2014) Multi-channel EEG recordings during 3,936 grasp and lift trials with varying weight and friction. Scientific Data 1:140047. www.nature.com/articles/sdata201447\n",
"\n",
"*Description from https://www.kaggle.com/c/grasp-and-lift-eeg-detection/data*"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Load data"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"train_path = '../ml-use-case-eeg/train/' #'../ml-use-case-eeg/train/' #'data/eeg_use_case/train/'\n",
"test_path = '../ml-use-case-eeg/test/' #'../ml-use-case-eeg/test/' #'data/eeg_use_case/test/'\n",
"all_files = os.listdir(train_path)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def filter_data(data, events, subj = None):\n",
" # filter data for specific subjects\n",
" if subj:\n",
" data_filt = list(filter(lambda x: subj + '_' in x, data))\n",
" else:\n",
" data_filt = data\n",
"\n",
" events_filt = []\n",
" for d in data_filt:\n",
" subj, series, end = d.split('_')\n",
" ix = np.where([subj + '_' in a and series in a for a in events])[0][0]\n",
" events_filt.append(events[ix])\n",
"\n",
" return data_filt, events_filt"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# all_data_files = glob.glob('data/eeg_use_case/train/*data.csv')\n",
"# all_event_files = glob.glob('data/eeg_use_case/train/*events.csv')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"all_data_files = list(filter(lambda x: '_data' in x, os.listdir(train_path)))\n",
"all_event_files = list(filter(lambda x: '_events' in x, os.listdir(train_path)))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"data_filt, events_filt = filter_data(all_data_files, all_event_files, subj='subj1')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"def load_data(file_names, path):\n",
" # read the csv file and drop the id column\n",
" dfs = []\n",
" for f in file_names:\n",
" df = pd.read_csv(path + f)\n",
" df = df.drop('id', axis = 1)\n",
" dfs.append(df)\n",
" #all_dfs = pd.concat(dfs)\n",
" all_dfs = dfs\n",
" return all_dfs"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"# get channel names\n",
"channels = pd.read_csv(train_path + all_data_files[0]).columns[1:]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"all_data = np.concatenate(load_data(data_filt, train_path))\n",
"all_events = np.concatenate(load_data(events_filt, train_path))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Visualization"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Feature extraction"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<center>\n",
"<figure>\n",
" <img src=\"./images/eeg_signal_preprocessing.png\" title=\"made at imgflip.com\" width=75%/> \n",
" <figcaption>Preprocessing steps for EEG-signals.</figcaption>\n",
"</figure>\n",
"</center>"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 697 ms, sys: 22.7 ms, total: 720 ms\n",
"Wall time: 718 ms\n"
]
}
],
"source": [
"%%time\n",
"win_size = 500\n",
"step_size = 2\n",
"num_feat = 3\n",
"num_win = int((all_data.shape[0] - (win_size * num_feat))/step_size)\n",
"ix_start = np.arange(0, num_win*step_size - win_size*num_feat, step_size)\n",
"ix_end = ix_start + 500\n",
"\n",
"#all_data_resh = np.array([all_data[start:end, :] for start, end in zip(ix_start, ix_end)])\n",
"\n",
"#all_data[:(num_win*win_size), :].reshape(num_win, win_size, 32)\n",
"\n",
"#ix_events = np.arange(500, num_win*win_size + 1, 500)\n",
"all_events_resh = np.array([all_events[end + 1501, :] for end in ix_end])\n",
"#all_events[ix_events, :]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 500, 502, 504, ..., 1419886, 1419888, 1419890])"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ix_end"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"# def sliding_window(seq, n):\n",
"# #returns an iterator for a sliding window\n",
"# it = iter(seq)\n",
"# result = tuple(islice(it, n))\n",
"# if len(result) == n:\n",
"# yield result\n",
"# for elem in it:\n",
"# result = result[1:] + (elem,)\n",
"# yield result"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Extract specific channels"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"#select channel\n",
"ix_c3 = np.where(channels == 'C3')[0][0]\n",
"ix_c4 = np.where(channels == 'C4')[0][0]"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"#red_data = all_data#[:, [ix_c3, ix_c4]]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Apply a high-pass filter "
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"def butter_bandpass(fs, lowcut, highcut, order = 5):\n",
" nyq = 0.5 * fs\n",
" low = lowcut / nyq\n",
" high = highcut / nyq\n",
" b, a = butter(order, [low, high], btype='band')\n",
" return b, a\n",
"\n",
"def mean_pow(y):\n",
" return np.mean(y**2, axis = 0)\n",
"\n",
"def butter_bandpass_filter(data):\n",
" b, a = butter_bandpass(fs = 500, lowcut = 0, highcut = 50)\n",
" y = lfilter(b, a, data, axis = 0)\n",
"\n",
" filt_mean_pow = mean_pow(y)\n",
" return filt_mean_pow"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"#win_size = 500\n",
"# iter_window = sliding_window(red_data, win_size)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
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"output_type": "stream",
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},
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]
},
{
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"output_type": "stream",
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]
},
{
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"output_type": "stream",
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]
},
{
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"output_type": "stream",
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]
},
{
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"output_type": "stream",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 2min 8s, sys: 621 ms, total: 2min 9s\n",
"Wall time: 2min 9s\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n",
"\n",
"\n",
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]
}
],
"source": [
"%%time\n",
"\n",
"# n_jobs = 1\n",
"# with Pool(n_jobs) as p:\n",
"# filt_data = p.map(butter_bandpass_filter, iter_window)\n",
"\n",
"pbar = tqdm(total = len(ix_start))\n",
"filt_data = []\n",
"#for window in all_data_resh:\n",
"for start, end in zip(ix_start, ix_end):\n",
" pow_1 = mean_pow(all_data[start:end, :])\n",
" pow_2 = mean_pow(all_data[start+500:end+500, :])\n",
" pow_3 = mean_pow(all_data[start+1000:end+1000, :])\n",
" filt_data.append(np.hstack([pow_1, pow_2, pow_3]))\n",
" \n",
"# window = all_data[start:end, :]\n",
"# filt_data.append(mean_pow(window))\n",
" \n",
" #window = np.vstack(window)\n",
" #filt_data.append(butter_bandpass_filter(window))\n",
" \n",
" pbar.update(1)\n",
" \n",
"filt_data = np.array(filt_data)\n",
"#scale = StandardScaler()\n",
"#filt_data = scale.fit_transform(filt_data)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"#filt_data = np.array(filt_data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Compute the mean power per time window"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
"# all_mean_power = []\n",
"\n",
"# for window in iter_window:\n",
"# window = np.vstack(window)\n",
"# all_mean_power.append(np.mean(window**2, axis = 0))\n",
"# all_mean_power = np.vstack(all_mean_power)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Analysis of features"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"pca = PCA(n_components=2)\n",
"filt_data_red = pca.fit_transform(filt_data)"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(709696, 96)"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"filt_data.shape"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x2b8adc2940f0>"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
},
{
"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": [
"# reshape events acc to sliding window\n",
"#all_events_resh = all_events[int(win_size/2) -1 : int(-win_size/2), :]\n",
"\n",
"plt.scatter(filt_data_red[all_events_resh[:,0] == 1, 0], filt_data_red[all_events_resh[:,0] == 1, 1])\n",
"plt.scatter(filt_data_red[all_events_resh[:,1] == 1, 0], filt_data_red[all_events_resh[:,1] == 1, 1])\n",
"plt.scatter(filt_data_red[all_events_resh[:,2] == 1, 0], filt_data_red[all_events_resh[:,2] == 1, 1])\n",
"plt.scatter(filt_data_red[all_events_resh[:,3] == 1, 0], filt_data_red[all_events_resh[:,3] == 1, 1])\n",
"plt.scatter(filt_data_red[all_events_resh[:,4] == 1, 0], filt_data_red[all_events_resh[:,4] == 1, 1])\n",
"plt.scatter(filt_data_red[all_events_resh[:,5] == 1, 0], filt_data_red[all_events_resh[:,5] == 1, 1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Modeling"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"training of the data\n"
]
}
],
"source": [
"# training, transition into binary classification problem\n",
"print('training of the data')\n",
"X_train, X_test, y_train, y_test = train_test_split(filt_data, all_events_resh,\\\n",
" test_size = 0.33, shuffle = True)"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [],
"source": [
"# %%time\n",
"# gbc = GradientBoostingClassifier()\n",
"\n",
"# all_pred = []\n",
"# all_labels = []\n",
"# for i in range(6):\n",
"\n",
"# gbc.fit(X_train, y_train[:,i])\n",
"# y_pred = gbc.predict(X_test)\n",
"\n",
"# all_pred.append(y_pred)\n",
"# all_labels.append(y_test[:,i])\n",
"# print(roc_auc_score(y_test[:,i], y_pred))\n",
"# print(confusion_matrix(y_test[:,i], y_pred))"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [],
"source": [
"# %%time\n",
"# abc = AdaBoostClassifier()\n",
"\n",
"# all_pred = []\n",
"# all_labels = []\n",
"# for i in range(6):\n",
"\n",
"# abc.fit(X_train, y_train[:,i])\n",
"# y_pred = abc.predict(X_test)\n",
"\n",
"# all_pred.append(y_pred)\n",
"# all_labels.append(y_test[:,i])\n",
"# print(roc_auc_score(y_test[:,i], y_pred))\n",
"# print(confusion_matrix(y_test[:,i], y_pred))"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.7463346727811783\n",
"[[161712 66093]\n",
" [ 1389 5006]]\n",
"0.7253640701194027\n",
"[[156626 71169]\n",
" [ 1517 4888]]\n",
"0.7273466702521886\n",
"[[159181 68665]\n",
" [ 1550 4804]]\n",
"0.7406915298355794\n",
"[[157298 70412]\n",
" [ 1359 5131]]\n",
"0.9017967429048931\n",
"[[199076 28703]\n",
" [ 452 5969]]\n",
"0.8739729959818299\n",
"[[192261 35503]\n",
" [ 619 5817]]\n",
"CPU times: user 9min 59s, sys: 967 µs, total: 9min 59s\n",
"Wall time: 9min 59s\n"
]
}
],
"source": [
"%%time\n",
"lr = LogisticRegression(class_weight='balanced')\n",
"\n",
"all_pred = []\n",
"all_labels = []\n",
"for i in range(6):\n",
"\n",
" lr.fit(X_train, y_train[:,i])\n",
" y_pred = lr.predict(X_test)\n",
"\n",
" all_pred.append(y_pred)\n",
" all_labels.append(y_test[:,i])\n",
" print(roc_auc_score(y_test[:,i], y_pred))\n",
" print(confusion_matrix(y_test[:,i], y_pred))"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/cluster/apps/python/3.6.1/x86_64/lib64/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
" if diff:\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.8700681448145394\n",
"[[227633 172]\n",
" [ 1657 4738]]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/cluster/apps/python/3.6.1/x86_64/lib64/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
" if diff:\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.8513977080512853\n",
"[[227596 199]\n",
" [ 1898 4507]]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/cluster/apps/python/3.6.1/x86_64/lib64/python3.6/site-packages/sklearn/linear_model/base.py:340: RuntimeWarning: overflow encountered in exp\n",
" np.exp(prob, prob)\n",
"/cluster/apps/python/3.6.1/x86_64/lib64/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
" if diff:\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.8528683729746489\n",
"[[227604 242]\n",
" [ 1863 4491]]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/cluster/apps/python/3.6.1/x86_64/lib64/python3.6/site-packages/sklearn/linear_model/base.py:340: RuntimeWarning: overflow encountered in exp\n",
" np.exp(prob, prob)\n",
"/cluster/apps/python/3.6.1/x86_64/lib64/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
" if diff:\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.8637231356700219\n",
"[[227539 171]\n",
" [ 1764 4726]]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/cluster/apps/python/3.6.1/x86_64/lib64/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
" if diff:\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.9430318406614016\n",
"[[224672 3107]\n",
" [ 644 5777]]\n",
"0.9318073940741972\n",
"[[225684 2080]\n",
" [ 819 5617]]\n",
"CPU times: user 15min 24s, sys: 3.72 s, total: 15min 28s\n",
"Wall time: 15min 28s\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/cluster/apps/python/3.6.1/x86_64/lib64/python3.6/site-packages/sklearn/linear_model/base.py:340: RuntimeWarning: overflow encountered in exp\n",
" np.exp(prob, prob)\n",
"/cluster/apps/python/3.6.1/x86_64/lib64/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
" if diff:\n"
]
}
],
"source": [
"%%time\n",
"lda = LDA()\n",
"rf = RandomForestClassifier(class_weight = 'balanced')\n",
"lr = LogisticRegression(class_weight = 'balanced')\n",
"\n",
"eclf = VotingClassifier(estimators=[('lda', lda), ('rf', rf), ('lr', lr)], voting = 'soft', weights=[1,1,1])\n",
"\n",
"all_pred = []\n",
"all_labels = []\n",
"for i in range(6):\n",
"\n",
" eclf.fit(X_train, y_train[:,i])\n",
" y_pred = eclf.predict(X_test)\n",
"\n",
" all_pred.append(y_pred)\n",
" all_labels.append(y_test[:,i])\n",
" print(roc_auc_score(y_test[:,i], y_pred))\n",
" print(confusion_matrix(y_test[:,i], y_pred))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pd.read_csv('../ml-use-case-eeg/train/subj10_series1_events.csv').loc[::100, :]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}