386 lines
25 KiB
Plaintext
386 lines
25 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "39df48da",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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" 59%|█████▉ | 453/768 [00:40<00:23, 13.66it/s]"
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]
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}
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],
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"source": [
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"import os\n",
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"from glob import glob\n",
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"import pandas as pd\n",
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"from tqdm import tqdm\n",
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"\n",
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"def load_data(user_filter=None):\n",
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" dic_data = []\n",
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" \n",
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" for p in tqdm(glob('/opt/iui-datarelease3-sose2021/*.csv')):\n",
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" path = p\n",
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" filename = path.split('/')[-1]\n",
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" user = int(filename.split('_')[0][1:])\n",
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" if (user_filter):\n",
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" if (user != user_filter):\n",
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" continue\n",
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" scenario = filename.split('_')[1][len('Scenario'):]\n",
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" heightnorm = filename.split('_')[2][len('HeightNormalization'):] == 'True'\n",
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" armnorm = filename.split('_')[3][len('ArmNormalization'):] == 'True'\n",
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" rep = int(filename.split('.')[0].split('_')[4][len('Repetition'):])\n",
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" session = filename.split('_')[5][len('Session'):]\n",
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" session = session.split('.')[0]\n",
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" \n",
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" data = pd.read_csv(path)\n",
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" dic_data.append(\n",
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" {\n",
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" 'filename': path,\n",
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" 'user': user,\n",
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" 'scenario': scenario,\n",
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" 'heightnorm': heightnorm,\n",
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" 'armnorm': armnorm,\n",
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" 'rep': rep,\n",
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" 'session': session,\n",
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" 'data': data \n",
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" \n",
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" }\n",
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" )\n",
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" return dic_data\n",
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"\n",
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"dic_data = load_data()"
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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": 11,
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"id": "855aa409",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"768"
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]
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},
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"execution_count": 11,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"len(dic_data)"
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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": 12,
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"id": "e1d660ea",
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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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"{'filename': '/opt/iui-datarelease3-sose2021/P2_ScenarioSorting_HeightNormalizationFalse_ArmNormalizationTrue_Repetition0_Session1.csv', 'user': 2, 'scenario': 'Sorting', 'heightnorm': False, 'armnorm': True, 'rep': 0, 'session': ['1', 'csv'], 'data': Unnamed: 0 FrameID participantID Scenario \\\n",
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"0 0 0 2 SortingBlocksScene \n",
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"1 1 1 2 SortingBlocksScene \n",
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"2 2 2 2 SortingBlocksScene \n",
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"3 3 3 2 SortingBlocksScene \n",
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"4 4 4 2 SortingBlocksScene \n",
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"... ... ... ... ... \n",
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"1734 1734 1734 2 SortingBlocksScene \n",
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"1735 1735 1735 2 SortingBlocksScene \n",
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"1736 1736 1736 2 SortingBlocksScene \n",
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"1737 1737 1737 2 SortingBlocksScene \n",
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"1738 1738 1738 2 SortingBlocksScene \n",
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"\n",
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" HeightNormalization ArmNormalization Repetition \\\n",
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"0 False True 0 \n",
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"1 False True 0 \n",
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"2 False True 0 \n",
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"3 False True 0 \n",
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"4 False True 0 \n",
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"... ... ... ... \n",
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"1734 False True 0 \n",
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"1735 False True 0 \n",
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"1736 False True 0 \n",
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"1737 False True 0 \n",
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"1738 False True 0 \n",
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"\n",
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" LeftHandTrackingAccuracy CenterEyeAnchor_pos_X CenterEyeAnchor_pos_Y \\\n",
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"0 High 0.092306 1.541967 \n",
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"1 High 0.092339 1.542134 \n",
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"2 High 0.092368 1.542324 \n",
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"3 High 0.092528 1.542059 \n",
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"4 High 0.092597 1.541883 \n",
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"... ... ... ... \n",
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"1734 Low -0.118714 1.209641 \n",
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"1735 Low -0.120236 1.208805 \n",
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"1736 Low -0.121484 1.208166 \n",
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"1737 Low -0.122358 1.207728 \n",
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"1738 Low -0.123330 1.207573 \n",
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"\n",
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" ... right_Hand_RingTip_euler_Y right_Hand_RingTip_euler_Z \\\n",
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"0 ... 22.05669 133.1912 \n",
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"1 ... 22.27071 132.8817 \n",
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"2 ... 22.46026 132.6752 \n",
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"3 ... 22.69390 132.5095 \n",
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"4 ... 22.71980 132.6141 \n",
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"... ... ... ... \n",
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"1734 ... 276.90730 109.5109 \n",
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"1735 ... 276.74050 110.6240 \n",
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"1736 ... 276.70190 111.3780 \n",
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"1737 ... 275.85200 112.9337 \n",
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"1738 ... 275.01000 114.0286 \n",
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"\n",
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" right_Hand_PinkyTip_pos_X right_Hand_PinkyTip_pos_Y \\\n",
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"0 0.153212 1.137668 \n",
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"1 0.144130 1.161759 \n",
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"2 0.144180 1.161877 \n",
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"3 0.144442 1.161859 \n",
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"4 0.144659 1.161498 \n",
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"... ... ... \n",
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"1734 0.079172 0.720395 \n",
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"1735 0.076673 0.720138 \n",
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"1736 0.075629 0.720296 \n",
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"1737 0.076548 0.716786 \n",
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"1738 0.078380 0.713724 \n",
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"\n",
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" right_Hand_PinkyTip_pos_Z right_Hand_PinkyTip_euler_X \\\n",
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"0 0.254763 319.9573 \n",
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"1 0.240258 320.2673 \n",
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"2 0.240361 320.5427 \n",
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"3 0.240253 320.9518 \n",
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"4 0.239949 321.0735 \n",
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"... ... ... \n",
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"1734 1.235342 350.7823 \n",
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"1735 1.245831 351.2207 \n",
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"1736 1.252426 351.6184 \n",
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"1737 1.262976 352.0619 \n",
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"1738 1.269815 352.2750 \n",
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"\n",
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" right_Hand_PinkyTip_euler_Y right_Hand_PinkyTip_euler_Z Session \\\n",
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"0 23.96579 143.5809 1 \n",
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"1 23.99540 143.5675 1 \n",
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"2 24.01744 143.6467 1 \n",
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"3 23.98431 144.0452 1 \n",
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"4 23.90498 144.4992 1 \n",
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"... ... ... ... \n",
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"1734 284.40790 113.3901 1 \n",
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"1735 284.28250 114.6937 1 \n",
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"1736 284.30020 115.6711 1 \n",
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"1737 283.35190 117.7055 1 \n",
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"1738 282.37350 119.1508 1 \n",
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"\n",
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" RightHandTrackingAccuracy \n",
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"0 High \n",
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"1 High \n",
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"2 High \n",
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"3 High \n",
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"4 High \n",
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"... ... \n",
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"1734 Low \n",
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"1735 Low \n",
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"1736 Low \n",
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"1737 Low \n",
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"1738 Low \n",
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"\n",
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"[1739 rows x 346 columns]}\n"
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]
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}
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],
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"source": [
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"print (dic_data[0])"
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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": 4,
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"id": "6284add1",
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"metadata": {},
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"outputs": [],
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"source": [
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"fil_dic_data = []\n",
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"for d in dic_data:\n",
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" if d['scenario'] == 'Sorting':\n",
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" if d['heightnorm'] == d['armnorm']:\n",
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" fil_dic_data.append(d)"
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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": 5,
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"id": "82167504",
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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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"/opt/iui-datarelease3-sose2021/P7_ScenarioSorting_HeightNormalizationFalse_ArmNormalizationFalse_Repetition0_Session1.csv\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"336"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"index = 1\n",
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"entry = fil_dic_data[index]['data']\n",
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"print(fil_dic_data[index]['filename'])\n",
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"col_of_interst = []\n",
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"for col in entry:\n",
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" if 'float' in str(entry[col].dtype):\n",
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" col_of_interst.append(col)\n",
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"len(col_of_interst)"
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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": 6,
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"id": "badc87d6",
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"metadata": {},
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"outputs": [],
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"source": [
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"len_list = []\n",
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"for i in dic_data:\n",
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" len_list.append(len(i['data']))"
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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": 7,
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"id": "97c17107",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"count 768.000000\n",
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"mean 2606.889323\n",
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"std 1941.835990\n",
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"min 407.000000\n",
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"50% 2102.000000\n",
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"90% 4427.900000\n",
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"91% 4606.940000\n",
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"92% 4788.760000\n",
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"93% 5131.990000\n",
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"94% 5815.100000\n",
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"95% 6182.450000\n",
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"96% 6400.920000\n",
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"97% 7223.400000\n",
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"98% 8273.100000\n",
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"99% 10162.200000\n",
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"max 21108.000000\n",
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"dtype: float64"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"len_series = pd.Series(len_list, dtype='int64')\n",
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"len_series.describe(percentiles=[x*0.01 for x in range(90,100)])"
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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": 8,
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"id": "ab1295b4",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[<matplotlib.lines.Line2D at 0x7fa8b8863850>]"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"image/png": 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\n",
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"<Figure size 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"from matplotlib import pyplot as plt\n",
|
|
"l = []\n",
|
|
"ptiles = [x*0.01 for x in range(100)]\n",
|
|
"for i in ptiles:\n",
|
|
" l.append(len_series.quantile(i))\n",
|
|
"\n",
|
|
"plt.plot(l, ptiles)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"id": "b530d28c",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"ename": "NameError",
|
|
"evalue": "name 'float64' is not defined",
|
|
"output_type": "error",
|
|
"traceback": [
|
|
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
|
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
|
|
"\u001b[0;32m<ipython-input-9-676ff4f042a6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdtype\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat64\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
|
|
"\u001b[0;31mNameError\u001b[0m: name 'float64' is not defined"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"\n",
|
|
"dtype: float64"
|
|
]
|
|
}
|
|
],
|
|
"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": "3.8.10"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|